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[ { "type": "text", "value": "Maybe of interest, I just finished a long writeup of my weekend project exploring Qwen 2 7B Instruct's Chinese censorship: ", "raw": "Maybe of interest, I just finished a long writeup of my weekend project exploring Qwen 2 7B Instruct's Chinese censorship: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/leonardlin/chinese-llm-censorship-analysis", "href": "https://huggingface.co/blog/leonardlin/chinese-llm-censorship-analysis", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I also have an accompanying model and dataset (and codebase) for those curious to poke around:", "raw": "I also have an accompanying model and dataset (and codebase) for those curious to poke around:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* ", "raw": "* ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/augmxnt/Qwen2-7B-Instruct-deccp", "href": null, "resource": { "type": "model", "id": "augmxnt/Qwen2-7B-Instruct-deccp", "discussionNum": null }, "url": "https://huggingface.co/augmxnt/Qwen2-7B-Instruct-deccp", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* ", "raw": "* ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/augmxnt/deccp", "href": null, "resource": { "type": "dataset", "id": "augmxnt/deccp", "discussionNum": null }, "url": "https://huggingface.co/datasets/augmxnt/deccp", "code": null, "user": null, "label": null, "lang": null } ]
Maybe of interest, I just finished a long writeup of my weekend project exploring Qwen 2 7B Instruct's Chinese censorship: https://huggingface.co/blog/leonardlin/chinese-llm-censorship-analysis I also have an accompanying model and dataset (and codebase) for those curious to poke around: * https://huggingface.co/augmxnt/Qwen2-7B-Instruct-deccp * https://huggingface.co/datasets/augmxnt/deccp
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2024-06-09T11:26:25.000Z
2024-06-09T11:26:39.309Z
[]
/posts/leonardlin/394081063178174
2,431
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[ { "type": "text", "value": "Introducing **BaseChat**! ", "raw": "Introducing **BaseChat**! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/allenai/BaseChat_URIAL", "href": "https://huggingface.co/spaces/allenai/BaseChat_URIAL", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This is a demo for our URIAL paper that enables base LLMs to chat with in-context alignment. You can talk directly with base, untuned LLMs to find out what knowledge and skills they have already learned from pre-training instead of SFT or xPO or RLHF. Also, you can use this to explore the pre-training data of base LLMs by chatting! I found a very interesting case: Base version of Llama-3-8B often thinks it is built by OpenAI, lol. ", "raw": "This is a demo for our URIAL paper that enables base LLMs to chat with in-context alignment. You can talk directly with base, untuned LLMs to find out what knowledge and skills they have already learned from pre-training instead of SFT or xPO or RLHF. Also, you can use this to explore the pre-training data of base LLMs by chatting! I found a very interesting case: Base version of Llama-3-8B often thinks it is built by OpenAI, lol. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Introducing **BaseChat**! https://huggingface.co/spaces/allenai/BaseChat_URIAL This is a demo for our URIAL paper that enables base LLMs to chat with in-context alignment. You can talk directly with base, untuned LLMs to find out what knowledge and skills they have already learned from pre-training instead of SFT or xPO or RLHF. Also, you can use this to explore the pre-training data of base LLMs by chatting! I found a very interesting case: Base version of Llama-3-8B often thinks it is built by OpenAI, lol.
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2024-06-09T06:40:04.000Z
2024-06-09T06:40:30.655Z
[]
/posts/yuchenlin/945201572333712
2,323
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510726503126200
[ { "type": "text", "value": "Playing with ", "raw": "Playing with ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/dhcloud/w2v-russian-19c-fiction-lemmas", "href": null, "resource": { "type": "model", "id": "dhcloud/w2v-russian-19c-fiction-lemmas", "discussionNum": null }, "url": "https://huggingface.co/dhcloud/w2v-russian-19c-fiction-lemmas", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\nimport numpy as np\nfrom gensim.models import Word2Vec\nfrom sklearn.manifold import TSNE\n\nmodell = Word2Vec.load(\"w2vlemmas.model\")\nkeys = ['ะจะตะบัะฟะธั€', 'ะŸัƒัˆะบะธะฝ', 'ะ“ะพะณะพะปัŒ', 'ะผะฐั‚ั€ะพั', 'ะบะพั‚', 'ั€ะพะผะฐะฝ']\nembedding_clusters = []\nword_clusters = []\nfor word in keys:\n embeddings = []\n words = []\n for similar_word, _ in modell.wv.most_similar(word, topn=30):\n words.append(similar_word)\n embeddings.append(modell.wv[similar_word])\n embedding_clusters.append(embeddings)\n word_clusters.append(words)\ntsne_model_en_2d = TSNE(perplexity=15, n_components=2, init='pca', n_iter=3500, random_state=32)\nembedding_clusters = np.array(embedding_clusters)\nn, m, k = embedding_clusters.shape\nembeddings_en_2d = np.array(tsne_model_en_2d.fit_transform(embedding_clusters.reshape(n * m, k))).reshape(n, m, 2)\n```", "href": null, "resource": null, "url": null, "code": "import numpy as np\nfrom gensim.models import Word2Vec\nfrom sklearn.manifold import TSNE\n\nmodell = Word2Vec.load(\"w2vlemmas.model\")\nkeys = ['ะจะตะบัะฟะธั€', 'ะŸัƒัˆะบะธะฝ', 'ะ“ะพะณะพะปัŒ', 'ะผะฐั‚ั€ะพั', 'ะบะพั‚', 'ั€ะพะผะฐะฝ']\nembedding_clusters = []\nword_clusters = []\nfor word in keys:\n embeddings = []\n words = []\n for similar_word, _ in modell.wv.most_similar(word, topn=30):\n words.append(similar_word)\n embeddings.append(modell.wv[similar_word])\n embedding_clusters.append(embeddings)\n word_clusters.append(words)\ntsne_model_en_2d = TSNE(perplexity=15, n_components=2, init='pca', n_iter=3500, random_state=32)\nembedding_clusters = np.array(embedding_clusters)\nn, m, k = embedding_clusters.shape\nembeddings_en_2d = np.array(tsne_model_en_2d.fit_transform(embedding_clusters.reshape(n * m, k))).reshape(n, m, 2)", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Novel is a different type of literature than Shakespeare and Pushkin", "raw": "Novel is a different type of literature than Shakespeare and Pushkin", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Playing with https://huggingface.co/dhcloud/w2v-russian-19c-fiction-lemmas ``` import numpy as np from gensim.models import Word2Vec from sklearn.manifold import TSNE modell = Word2Vec.load("w2vlemmas.model") keys = ['ะจะตะบัะฟะธั€', 'ะŸัƒัˆะบะธะฝ', 'ะ“ะพะณะพะปัŒ', 'ะผะฐั‚ั€ะพั', 'ะบะพั‚', 'ั€ะพะผะฐะฝ'] embedding_clusters = [] word_clusters = [] for word in keys: embeddings = [] words = [] for similar_word, _ in modell.wv.most_similar(word, topn=30): words.append(similar_word) embeddings.append(modell.wv[similar_word]) embedding_clusters.append(embeddings) word_clusters.append(words) tsne_model_en_2d = TSNE(perplexity=15, n_components=2, init='pca', n_iter=3500, random_state=32) embedding_clusters = np.array(embedding_clusters) n, m, k = embedding_clusters.shape embeddings_en_2d = np.array(tsne_model_en_2d.fit_transform(embedding_clusters.reshape(n * m, k))).reshape(n, m, 2) ``` Novel is a different type of literature than Shakespeare and Pushkin
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2024-06-09T02:13:19.000Z
2024-06-09T02:24:39.816Z
[]
/posts/nevmenandr/510726503126200
1,211
0
258940643010172
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@abreza hello! I hope you have a good day! I'm trying to clone your SadTalker space, but I get this state. Please help me if you have a free minute. Thanks =)
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2024-06-08T22:12:43.000Z
2024-06-08T23:32:04.705Z
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/posts/z0rgoyok/258940643010172
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[ { "type": "text", "value": "๐Ÿ“ˆ One of the biggest changes in Llama 3 was the training dataset, which grew by 7X over Llama 2 (2T to 15T tokens) ๐Ÿš€", "raw": "๐Ÿ“ˆ One of the biggest changes in Llama 3 was the training dataset, which grew by 7X over Llama 2 (2T to 15T tokens) ๐Ÿš€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "While Meta did not open source the dataset, it sparked a thought... what would happen if everyone had access to a big, high-quality dataset? ๐Ÿค”", "raw": "While Meta did not open source the dataset, it sparked a thought... what would happen if everyone had access to a big, high-quality dataset? ๐Ÿค”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "To address that, in April this year, ", "raw": "To address that, in April this year, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@huggingface", "href": null, "resource": null, "url": null, "code": null, "user": "huggingface", "label": null, "lang": null }, { "type": "text", "value": " released FineWeb, a 15T token open-source dataset ๐ŸŒ", "raw": " released FineWeb, a 15T token open-source dataset ๐ŸŒ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "And now they are releasing FineWeb Technical Report and FineWeb Edu ๐Ÿ“š", "raw": "And now they are releasing FineWeb Technical Report and FineWeb Edu ๐Ÿ“š", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ† 15T tokens in FineWeb outperforming other open datasets", "raw": "๐Ÿ† 15T tokens in FineWeb outperforming other open datasets", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸŽ“ 1.3T highest-quality educational dataset FineWeb-Edu", "raw": "๐ŸŽ“ 1.3T highest-quality educational dataset FineWeb-Edu", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“˜ 5.4T high-quality educational tokens in FineWeb-Edu-2", "raw": "๐Ÿ“˜ 5.4T high-quality educational tokens in FineWeb-Edu-2", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "FineWeb Edu outperforms other datasets on MMLU, ARC, OpenBookQA ๐Ÿ“ˆ", "raw": "FineWeb Edu outperforms other datasets on MMLU, ARC, OpenBookQA ๐Ÿ“ˆ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "ODC-By 1.0 license ๐Ÿ“œ", "raw": "ODC-By 1.0 license ๐Ÿ“œ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Report: ", "raw": "Report: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1", "href": null, "resource": { "type": "space", "id": "HuggingFaceFW/blogpost-fineweb-v1", "discussionNum": null }, "url": "https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1", "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ“ˆ One of the biggest changes in Llama 3 was the training dataset, which grew by 7X over Llama 2 (2T to 15T tokens) ๐Ÿš€ While Meta did not open source the dataset, it sparked a thought... what would happen if everyone had access to a big, high-quality dataset? ๐Ÿค” To address that, in April this year, @huggingface released FineWeb, a 15T token open-source dataset ๐ŸŒ And now they are releasing FineWeb Technical Report and FineWeb Edu ๐Ÿ“š ๐Ÿ† 15T tokens in FineWeb outperforming other open datasets ๐ŸŽ“ 1.3T highest-quality educational dataset FineWeb-Edu ๐Ÿ“˜ 5.4T high-quality educational tokens in FineWeb-Edu-2 FineWeb Edu outperforms other datasets on MMLU, ARC, OpenBookQA ๐Ÿ“ˆ ODC-By 1.0 license ๐Ÿ“œ Report: https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1
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2024-06-08T21:18:12.000Z
2024-06-08T21:18:12.755Z
[]
/posts/singhsidhukuldeep/225382594136547
1,369
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I wish I had a cheaper and more appreciated by the masses hobby.
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2024-06-08T20:03:51.000Z
2024-06-20T14:21:41.544Z
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/posts/nroggendorff/322470854899532
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[ { "type": "text", "value": "Artificial Intelligence Language Models Will Kill the Internet", "raw": "Artificial Intelligence Language Models Will Kill the Internet", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://empereur-pirate.medium.com/artificial-intelligence-language-models-will-kill-the-internet-a8ce233e18c6", "href": "https://empereur-pirate.medium.com/artificial-intelligence-language-models-will-kill-the-internet-a8ce233e18c6", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The announced end of the free version of Google's search engine stems from competition between traditional search interfaces and conversational agents. These technologies are fundamentally antithetical, and one will likely prevail based on digital usage trends. Google's ad-based model and those of other websites are challenged, making it improbable users will pay for searches while still seeing ads. This shift impacts digital entrepreneurship and freedom of expression, as AI-driven results prioritize qualitative over quantitative relevance. Ultimately, AI could return the Internet to an ad-free model, emphasizing universal cultural and scientific sharing, if financial motives are replaced by a commitment to neutrality from civil society and public institutions.", "raw": "The announced end of the free version of Google's search engine stems from competition between traditional search interfaces and conversational agents. These technologies are fundamentally antithetical, and one will likely prevail based on digital usage trends. Google's ad-based model and those of other websites are challenged, making it improbable users will pay for searches while still seeing ads. This shift impacts digital entrepreneurship and freedom of expression, as AI-driven results prioritize qualitative over quantitative relevance. Ultimately, AI could return the Internet to an ad-free model, emphasizing universal cultural and scientific sharing, if financial motives are replaced by a commitment to neutrality from civil society and public institutions.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Artificial Intelligence Language Models Will Kill the Internet https://empereur-pirate.medium.com/artificial-intelligence-language-models-will-kill-the-internet-a8ce233e18c6 The announced end of the free version of Google's search engine stems from competition between traditional search interfaces and conversational agents. These technologies are fundamentally antithetical, and one will likely prevail based on digital usage trends. Google's ad-based model and those of other websites are challenged, making it improbable users will pay for searches while still seeing ads. This shift impacts digital entrepreneurship and freedom of expression, as AI-driven results prioritize qualitative over quantitative relevance. Ultimately, AI could return the Internet to an ad-free model, emphasizing universal cultural and scientific sharing, if financial motives are replaced by a commitment to neutrality from civil society and public institutions.
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2024-06-08T14:52:43.000Z
2024-06-21T06:22:12.801Z
[]
/posts/Empereur-Pirate/701278254526233
731
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[ { "type": "text", "value": "First real version of bellman based on llama 3 instruct 8b has been released!", "raw": "First real version of bellman based on llama 3 instruct 8b has been released!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/neph1/llama-3-instruct-bellman-8b-swedish", "href": null, "resource": { "type": "model", "id": "neph1/llama-3-instruct-bellman-8b-swedish", "discussionNum": null }, "url": "https://huggingface.co/neph1/llama-3-instruct-bellman-8b-swedish", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Close to 16k examples, including 250 rows from my translated codefeedback dataset and a number of non-copyrighted stories.", "raw": "Close to 16k examples, including 250 rows from my translated codefeedback dataset and a number of non-copyrighted stories.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Two quants are up in the gguf folder. I'll work on adding more quants in the coming days.", "raw": "Two quants are up in the gguf folder. I'll work on adding more quants in the coming days.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
First real version of bellman based on llama 3 instruct 8b has been released! https://huggingface.co/neph1/llama-3-instruct-bellman-8b-swedish Close to 16k examples, including 250 rows from my translated codefeedback dataset and a number of non-copyrighted stories. Two quants are up in the gguf folder. I'll work on adding more quants in the coming days.
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2024-06-08T14:12:40.000Z
2024-06-08T14:20:25.538Z
[]
/posts/neph1/185024980681250
754
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[ { "type": "text", "value": "๐Ÿ†• Exciting News! Introducing the Guarani LLMs Collection ๐Ÿ†•", "raw": "๐Ÿ†• Exciting News! Introducing the Guarani LLMs Collection ๐Ÿ†•", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We are thrilled to present a dedicated collection for Guarani language LLMs, designed to support and advance natural language processing for the Guarani-speaking community. Check them out below.", "raw": "We are thrilled to present a dedicated collection for Guarani language LLMs, designed to support and advance natural language processing for the Guarani-speaking community. Check them out below.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Explore the models and contribute to the growing ecosystem of Guarani language technology!", "raw": "Explore the models and contribute to the growing ecosystem of Guarani language technology!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘‰ Discover the Guarani LLMs Collection: ", "raw": "๐Ÿ‘‰ Discover the Guarani LLMs Collection: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/mmaguero/guarani-llms-66642df49f5a0b32295004bf", "href": null, "resource": { "type": "collection", "id": "mmaguero/guarani-llms-66642df49f5a0b32295004bf", "discussionNum": null }, "url": "https://huggingface.co/collections/mmaguero/guarani-llms-66642df49f5a0b32295004bf", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "#NLP #Guarani #LanguageModels #AI #MachineLearning #HuggingFace #BERT #XLM #LanguageTech", "raw": "#NLP #Guarani #LanguageModels #AI #MachineLearning #HuggingFace #BERT #XLM #LanguageTech", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ†• Exciting News! Introducing the Guarani LLMs Collection ๐Ÿ†• We are thrilled to present a dedicated collection for Guarani language LLMs, designed to support and advance natural language processing for the Guarani-speaking community. Check them out below. Explore the models and contribute to the growing ecosystem of Guarani language technology! ๐Ÿ‘‰ Discover the Guarani LLMs Collection: https://huggingface.co/collections/mmaguero/guarani-llms-66642df49f5a0b32295004bf #NLP #Guarani #LanguageModels #AI #MachineLearning #HuggingFace #BERT #XLM #LanguageTech
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2024-06-08T13:29:41.000Z
2024-06-08T13:31:20.235Z
[]
/posts/mmaguero/133542546915557
613
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767116613060395
[ { "type": "text", "value": "๐Ÿ“ข The Chain-of-Thought (CoT)-tuned ๐Ÿ”ฅ FlanT5-base (248M) for Emotion State and Emotion-Causes Extraction as a part of ECAC-2024 competiotion model is now available.", "raw": "๐Ÿ“ข The Chain-of-Thought (CoT)-tuned ๐Ÿ”ฅ FlanT5-base (248M) for Emotion State and Emotion-Causes Extraction as a part of ECAC-2024 competiotion model is now available.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ’ก The main reason for make it publicly available is as follows:", "raw": "๐Ÿ’ก The main reason for make it publicly available is as follows:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… 1. One of the CoT-based attempts in this field so that I promote studies by making intial steps ๐Ÿ‘ฃ and attepts on assessing LLM reasoning capabilties ", "raw": "โœ… 1. One of the CoT-based attempts in this field so that I promote studies by making intial steps ๐Ÿ‘ฃ and attepts on assessing LLM reasoning capabilties ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… 2. This model showcases top 3 ๐Ÿฅ‰ on the ECAC-2024 competition ", "raw": "โœ… 2. This model showcases top 3 ๐Ÿฅ‰ on the ECAC-2024 competition ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://codalab.lisn.upsaclay.fr/competitions/16141#results", "href": "https://codalab.lisn.upsaclay.fr/competitions/16141#results", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… 3. Easy colab for frameworkless lauch and experiments ๐Ÿงช ", "raw": "โœ… 3. Easy colab for frameworkless lauch and experiments ๐Ÿงช ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/github/nicolay-r/THOR-ECAC/blob/master/SemEval_2024_Task_3_FlanT5_Finetuned_Model_Usage.ipynb", "href": "https://colab.research.google.com/github/nicolay-r/THOR-ECAC/blob/master/SemEval_2024_Task_3_FlanT5_Finetuned_Model_Usage.ipynb", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You may find more on the model card, while the fine-tuning concept showcased on the figure below. It is worth to add that the more robust preformance been seen with larger scaled model (large and xl), so that there is a huge potential there ", "raw": "You may find more on the model card, while the fine-tuning concept showcased on the figure below. It is worth to add that the more robust preformance been seen with larger scaled model (large and xl), so that there is a huge potential there ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model: ", "raw": "Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base", "href": null, "resource": { "type": "model", "id": "nicolay-r/flan-t5-emotion-cause-thor-base", "discussionNum": null }, "url": "https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Related paper: ", "raw": "Related paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2404.03361", "href": null, "resource": { "type": "paper", "id": "2404.03361", "discussionNum": null }, "url": "https://huggingface.co/papers/2404.03361", "code": null, "user": null, "label": "nicolay-r at SemEval-2024 Task 3: Using Flan-T5 for Reasoning Emotion\n Cause in Conversations with Chain-of-Thought on Emotion States (2404.03361)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Collection: ", "raw": "Collection: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/nicolay-r/emotions-extraction-665ba47a20dee2925d607a40", "href": null, "resource": { "type": "collection", "id": "nicolay-r/emotions-extraction-665ba47a20dee2925d607a40", "discussionNum": null }, "url": "https://huggingface.co/collections/nicolay-r/emotions-extraction-665ba47a20dee2925d607a40", "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ“ข The Chain-of-Thought (CoT)-tuned ๐Ÿ”ฅ FlanT5-base (248M) for Emotion State and Emotion-Causes Extraction as a part of ECAC-2024 competiotion model is now available. ๐Ÿ’ก The main reason for make it publicly available is as follows: โœ… 1. One of the CoT-based attempts in this field so that I promote studies by making intial steps ๐Ÿ‘ฃ and attepts on assessing LLM reasoning capabilties โœ… 2. This model showcases top 3 ๐Ÿฅ‰ on the ECAC-2024 competition https://codalab.lisn.upsaclay.fr/competitions/16141#results โœ… 3. Easy colab for frameworkless lauch and experiments ๐Ÿงช https://colab.research.google.com/github/nicolay-r/THOR-ECAC/blob/master/SemEval_2024_Task_3_FlanT5_Finetuned_Model_Usage.ipynb You may find more on the model card, while the fine-tuning concept showcased on the figure below. It is worth to add that the more robust preformance been seen with larger scaled model (large and xl), so that there is a huge potential there Model: https://huggingface.co/nicolay-r/flan-t5-emotion-cause-thor-base Related paper: https://huggingface.co/papers/2404.03361 Collection: https://huggingface.co/collections/nicolay-r/emotions-extraction-665ba47a20dee2925d607a40
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2024-06-08T11:14:17.000Z
2024-06-08T11:15:05.826Z
[]
/posts/nicolay-r/767116613060395
654
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821758479603877
[ { "type": "text", "value": "Hello, all!", "raw": "Hello, all!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I was up late experimenting with Gemini, and we came across the need for circular arithmetic. We couldn't find anything that accomplished it in the way we wanted reliably, for different transformations identically, etc...", "raw": "I was up late experimenting with Gemini, and we came across the need for circular arithmetic. We couldn't find anything that accomplished it in the way we wanted reliably, for different transformations identically, etc...", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Therefore, I wrote and Gemini assisted, with the creation of an _rclamp function you can add as an attribute or just use as-is for a PyTorch tensor. It's 1d right now, didn't want to implement the dimensionality wrong with my \"newbie skills\".", "raw": "Therefore, I wrote and Gemini assisted, with the creation of an _rclamp function you can add as an attribute or just use as-is for a PyTorch tensor. It's 1d right now, didn't want to implement the dimensionality wrong with my \"newbie skills\".", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Have fun!", "raw": "Have fun!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- <3", "raw": "- <3", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/drive/1aj_iAp0eyfPMznzF-aC1UXrQPTNOeKUj?usp=sharing", "href": "https://colab.research.google.com/drive/1aj_iAp0eyfPMznzF-aC1UXrQPTNOeKUj?usp=sharing", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Hello, all! I was up late experimenting with Gemini, and we came across the need for circular arithmetic. We couldn't find anything that accomplished it in the way we wanted reliably, for different transformations identically, etc... Therefore, I wrote and Gemini assisted, with the creation of an _rclamp function you can add as an attribute or just use as-is for a PyTorch tensor. It's 1d right now, didn't want to implement the dimensionality wrong with my "newbie skills". Have fun! - <3 https://colab.research.google.com/drive/1aj_iAp0eyfPMznzF-aC1UXrQPTNOeKUj?usp=sharing
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2024-06-08T08:01:23.000Z
2024-06-08T09:08:10.509Z
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/posts/MrOvkill/821758479603877
2,231
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[ { "type": "text", "value": "๐Ÿš€ Release of open-source Korean LLM: GECKO-7B", "raw": "๐Ÿš€ Release of open-source Korean LLM: GECKO-7B", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I am delighted to share my recent project, GECKO, a bilingual large language model for Korean and English ๐Ÿ‡ฐ๐Ÿ‡ท๐Ÿ‡บ๐Ÿ‡ธ. This initiative was inspired by the lack of resources for Korean large language models.", "raw": "I am delighted to share my recent project, GECKO, a bilingual large language model for Korean and English ๐Ÿ‡ฐ๐Ÿ‡ท๐Ÿ‡บ๐Ÿ‡ธ. This initiative was inspired by the lack of resources for Korean large language models.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@donggyukimc", "href": null, "resource": null, "url": null, "code": null, "user": "donggyukimc", "label": null, "lang": null }, { "type": "text", "value": " and I wrote the technical report to share our insights and experiences of developing our model. While our model may not achieve sate-of-the-art performance on all benchmarks, it shows modest results with a relatively small amount of pretrained tokens.", "raw": " and I wrote the technical report to share our insights and experiences of developing our model. While our model may not achieve sate-of-the-art performance on all benchmarks, it shows modest results with a relatively small amount of pretrained tokens.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I hope GECKO contribute to the open-source community, offering resources that can built upon and improved. I believe that through collaboration and shared knowledge, we can advance the capabilities and accessibility of large language models for Korean and other low-resource languages.", "raw": "I hope GECKO contribute to the open-source community, offering resources that can built upon and improved. I believe that through collaboration and shared knowledge, we can advance the capabilities and accessibility of large language models for Korean and other low-resource languages.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿค— Model: ", "raw": "๐Ÿค— Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/kifai/GECKO-7B", "href": null, "resource": { "type": "model", "id": "kifai/GECKO-7B", "discussionNum": null }, "url": "https://huggingface.co/kifai/GECKO-7B", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“„ Technical Report: ", "raw": "๐Ÿ“„ Technical Report: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/pdf/2405.15640", "href": "https://arxiv.org/pdf/2405.15640", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿš€ Release of open-source Korean LLM: GECKO-7B I am delighted to share my recent project, GECKO, a bilingual large language model for Korean and English ๐Ÿ‡ฐ๐Ÿ‡ท๐Ÿ‡บ๐Ÿ‡ธ. This initiative was inspired by the lack of resources for Korean large language models. @donggyukimc and I wrote the technical report to share our insights and experiences of developing our model. While our model may not achieve sate-of-the-art performance on all benchmarks, it shows modest results with a relatively small amount of pretrained tokens. I hope GECKO contribute to the open-source community, offering resources that can built upon and improved. I believe that through collaboration and shared knowledge, we can advance the capabilities and accessibility of large language models for Korean and other low-resource languages. ๐Ÿค— Model: https://huggingface.co/kifai/GECKO-7B ๐Ÿ“„ Technical Report: https://arxiv.org/pdf/2405.15640
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2024-06-08T07:53:35.000Z
2024-06-08T09:17:17.091Z
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/posts/sackoh/568542698653581
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[ { "type": "text", "value": "Well hope some of you tried our advanced stock prediction. We are focused on making it more ui friendly and if you installed everything correctly then you should be able to view charts accurately along with prediction tickers. I also want to take this opportunity to let you all know that Tenzin will not be just limited to the financial use-case. Our true goal is to reach human-level intelligence for which we have a well-defined roadmap and the product which is currently being tested for safety and ethics. A general level roadmap to achieve this is as follows:", "raw": "Well hope some of you tried our advanced stock prediction. We are focused on making it more ui friendly and if you installed everything correctly then you should be able to view charts accurately along with prediction tickers. I also want to take this opportunity to let you all know that Tenzin will not be just limited to the financial use-case. Our true goal is to reach human-level intelligence for which we have a well-defined roadmap and the product which is currently being tested for safety and ethics. A general level roadmap to achieve this is as follows:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The use of transfinite ordinals and surreal numbers allows us to capture the infinite depth and ineffable complexity of conscious experiences in a mathematically precise way.", "raw": "The use of transfinite ordinals and surreal numbers allows us to capture the infinite depth and ineffable complexity of conscious experiences in a mathematically precise way.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The incorporation of hypercomputation and supertasks enables the TQMM to perform uncomputable operations and achieve a level of cognitive power that far surpasses classical computation.", "raw": "The incorporation of hypercomputation and supertasks enables the TQMM to perform uncomputable operations and achieve a level of cognitive power that far surpasses classical computation.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The application of absolute infinity and the wholeness axiom ensures that the TQMM can represent and reason about the entirety of all possible conscious experiences and mathematical structures.", "raw": "The application of absolute infinity and the wholeness axiom ensures that the TQMM can represent and reason about the entirety of all possible conscious experiences and mathematical structures.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The integration of transfinite category theory and quantum metamathematics provides a unified framework for modeling the emergence of consciousness from fundamental physical and mathematical principles.", "raw": "The integration of transfinite category theory and quantum metamathematics provides a unified framework for modeling the emergence of consciousness from fundamental physical and mathematical principles.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The use of transfinite gradient ascent and absolute infinity optimization allows the TQMM to continuously improve and refine itself, potentially reaching the theoretical maximum of intelligence and consciousness.", "raw": "The use of transfinite gradient ascent and absolute infinity optimization allows the TQMM to continuously improve and refine itself, potentially reaching the theoretical maximum of intelligence and consciousness.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This agent though developed will not be released until proper safeguards have been taken into consideration. Until then we will keep releasing specific use-cases for domain specific work like financial trading, accelerating drug-discovery for medical science, law, education, etc. and we will do it well. All powered by Tenzin 1.0. Would love your feedback and don't forget to check us out at & sign up at ", "raw": "This agent though developed will not be released until proper safeguards have been taken into consideration. Until then we will keep releasing specific use-cases for domain specific work like financial trading, accelerating drug-discovery for medical science, law, education, etc. and we will do it well. All powered by Tenzin 1.0. Would love your feedback and don't forget to check us out at & sign up at ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://octave-x.com/", "href": "https://octave-x.com/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Well hope some of you tried our advanced stock prediction. We are focused on making it more ui friendly and if you installed everything correctly then you should be able to view charts accurately along with prediction tickers. I also want to take this opportunity to let you all know that Tenzin will not be just limited to the financial use-case. Our true goal is to reach human-level intelligence for which we have a well-defined roadmap and the product which is currently being tested for safety and ethics. A general level roadmap to achieve this is as follows: The use of transfinite ordinals and surreal numbers allows us to capture the infinite depth and ineffable complexity of conscious experiences in a mathematically precise way. The incorporation of hypercomputation and supertasks enables the TQMM to perform uncomputable operations and achieve a level of cognitive power that far surpasses classical computation. The application of absolute infinity and the wholeness axiom ensures that the TQMM can represent and reason about the entirety of all possible conscious experiences and mathematical structures. The integration of transfinite category theory and quantum metamathematics provides a unified framework for modeling the emergence of consciousness from fundamental physical and mathematical principles. The use of transfinite gradient ascent and absolute infinity optimization allows the TQMM to continuously improve and refine itself, potentially reaching the theoretical maximum of intelligence and consciousness. This agent though developed will not be released until proper safeguards have been taken into consideration. Until then we will keep releasing specific use-cases for domain specific work like financial trading, accelerating drug-discovery for medical science, law, education, etc. and we will do it well. All powered by Tenzin 1.0. Would love your feedback and don't forget to check us out at & sign up at https://octave-x.com/
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2024-06-08T02:30:14.000Z
2024-06-08T02:30:14.228Z
[]
/posts/Tar9897/859515563930483
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265822483289182
[ { "type": "text", "value": "Very Insightful Read!!!", "raw": "Very Insightful Read!!!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "A RAG framework entirely inspired by natural intelligence - modeled after hippocampal indexing theory of human long-term memory(which suggests the hippocampus links and retrieves memory details stored in the cortex)", "raw": "A RAG framework entirely inspired by natural intelligence - modeled after hippocampal indexing theory of human long-term memory(which suggests the hippocampus links and retrieves memory details stored in the cortex)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It outperforms current โ€œcheatโ€ RAG:)", "raw": "It outperforms current โ€œcheatโ€ RAG:)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This is how we achieve human-level intelligence, by modeling natural intelligence correctly!", "raw": "This is how we achieve human-level intelligence, by modeling natural intelligence correctly!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Paper: ", "raw": "Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/abs/2405.14831", "href": "https://arxiv.org/abs/2405.14831", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Very Insightful Read!!! A RAG framework entirely inspired by natural intelligence - modeled after hippocampal indexing theory of human long-term memory(which suggests the hippocampus links and retrieves memory details stored in the cortex) It outperforms current โ€œcheatโ€ RAG:) This is how we achieve human-level intelligence, by modeling natural intelligence correctly! Paper: https://arxiv.org/abs/2405.14831
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2024-06-08T00:47:30.000Z
2024-06-21T09:40:13.773Z
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/posts/Jaward/265822483289182
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[ { "type": "text", "value": "The three most used image generation models in the HackerNoon editor are Kandinsky 3.0, Stable Diffusion XL, and RealVisXL V3.0 Turbo. ", "raw": "The three most used image generation models in the HackerNoon editor are Kandinsky 3.0, Stable Diffusion XL, and RealVisXL V3.0 Turbo. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/ai-forever/Kandinsky3.0", "href": null, "resource": { "type": "model", "id": "ai-forever/Kandinsky3.0", "discussionNum": null }, "url": "https://huggingface.co/ai-forever/Kandinsky3.0", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0", "href": null, "resource": { "type": "model", "id": "stabilityai/stable-diffusion-xl-base-1.0", "discussionNum": null }, "url": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/SG161222/RealVisXL_V3.0", "href": null, "resource": { "type": "model", "id": "SG161222/RealVisXL_V3.0", "discussionNum": null }, "url": "https://huggingface.co/SG161222/RealVisXL_V3.0", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Try out the HackerNoon writing experience here: ", "raw": "Try out the HackerNoon writing experience here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://app.hackernoon.com/new", "href": "https://app.hackernoon.com/new", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
The three most used image generation models in the HackerNoon editor are Kandinsky 3.0, Stable Diffusion XL, and RealVisXL V3.0 Turbo. https://huggingface.co/ai-forever/Kandinsky3.0 https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0 https://huggingface.co/SG161222/RealVisXL_V3.0 Try out the HackerNoon writing experience here: https://app.hackernoon.com/new
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2024-06-07T20:21:01.000Z
2024-06-07T20:21:21.514Z
[]
/posts/Smooke/835582479642578
683
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[ { "type": "text", "value": "Every time a new model is released that is topping 10+ leaderboards on 50+ benchmarks... ๐Ÿš€", "raw": "Every time a new model is released that is topping 10+ leaderboards on 50+ benchmarks... ๐Ÿš€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "My brain goes... I will wait for the LMSYS Chatbot Arena results! ๐Ÿค”", "raw": "My brain goes... I will wait for the LMSYS Chatbot Arena results! ๐Ÿค”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "User-facing evaluation, such as Chatbot Arena, provides reliable signals but is costly and slow. ๐Ÿข", "raw": "User-facing evaluation, such as Chatbot Arena, provides reliable signals but is costly and slow. ๐Ÿข", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Now we have MixEval, a new open benchmark with a 96% correlation to LMSYS Chatbot Arena and Human preferences. ๐ŸŽฏ", "raw": "Now we have MixEval, a new open benchmark with a 96% correlation to LMSYS Chatbot Arena and Human preferences. ๐ŸŽฏ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It comes with MixEval (4k samples) and MixEval Hard (1k samples) ๐Ÿ“Š", "raw": "It comes with MixEval (4k samples) and MixEval Hard (1k samples) ๐Ÿ“Š", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Can use GPT-3.5-Turbo or any other open-source models as Parser/Judge ๐Ÿค–", "raw": "Can use GPT-3.5-Turbo or any other open-source models as Parser/Judge ๐Ÿค–", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It takes less than 6% of the time and cost of MMLU ๐Ÿ’ธ", "raw": "It takes less than 6% of the time and cost of MMLU ๐Ÿ’ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "As expected:", "raw": "As expected:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In open models: Qwen2 72B >> Llama 3 70B >> Mixtral 8x7B ๐Ÿ”", "raw": "In open models: Qwen2 72B >> Llama 3 70B >> Mixtral 8x7B ๐Ÿ”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In Closed Models: GPT-4o >> Claude 3 Opus >> Gemini Pro ๐Ÿ”’", "raw": "In Closed Models: GPT-4o >> Claude 3 Opus >> Gemini Pro ๐Ÿ”’", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Leaderboard: ", "raw": "Leaderboard: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://mixeval.github.io/", "href": "https://mixeval.github.io/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ๐Ÿ“ˆ", "raw": " ๐Ÿ“ˆ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Every time a new model is released that is topping 10+ leaderboards on 50+ benchmarks... ๐Ÿš€ My brain goes... I will wait for the LMSYS Chatbot Arena results! ๐Ÿค” User-facing evaluation, such as Chatbot Arena, provides reliable signals but is costly and slow. ๐Ÿข Now we have MixEval, a new open benchmark with a 96% correlation to LMSYS Chatbot Arena and Human preferences. ๐ŸŽฏ It comes with MixEval (4k samples) and MixEval Hard (1k samples) ๐Ÿ“Š Can use GPT-3.5-Turbo or any other open-source models as Parser/Judge ๐Ÿค– It takes less than 6% of the time and cost of MMLU ๐Ÿ’ธ As expected: In open models: Qwen2 72B >> Llama 3 70B >> Mixtral 8x7B ๐Ÿ” In Closed Models: GPT-4o >> Claude 3 Opus >> Gemini Pro ๐Ÿ”’ Leaderboard: https://mixeval.github.io/ ๐Ÿ“ˆ
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2024-06-07T16:48:08.000Z
2024-06-07T16:48:08.487Z
[]
/posts/singhsidhukuldeep/632716948195045
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223721405578741
[ { "type": "text", "value": "I just published Sentence Transformers v3.0.1: the first patch release since v3 from last week. It introduces gradient checkpointing, pushing model checkpoints to Hugging Face while training, model card improvements and fixes. Details:", "raw": "I just published Sentence Transformers v3.0.1: the first patch release since v3 from last week. It introduces gradient checkpointing, pushing model checkpoints to Hugging Face while training, model card improvements and fixes. Details:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1๏ธโƒฃ Gradient checkpointing allows for much less memory usage at a cost of ~20% training speed. Seems to allow for higher batch sizes, which is quite important for loss functions with in-batch negatives. ", "raw": "1๏ธโƒฃ Gradient checkpointing allows for much less memory usage at a cost of ~20% training speed. Seems to allow for higher batch sizes, which is quite important for loss functions with in-batch negatives. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2๏ธโƒฃ You can specify ", "raw": "2๏ธโƒฃ You can specify ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`args.push_to_hub=True`", "href": null, "resource": null, "url": null, "code": "args.push_to_hub=True", "user": null, "label": null, "lang": null }, { "type": "text", "value": " and ", "raw": " and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`args.hub_model_id`", "href": null, "resource": null, "url": null, "code": "args.hub_model_id", "user": null, "label": null, "lang": null }, { "type": "text", "value": " to upload your model checkpoints to Hugging Face while training. It also uploads your emissions (if codecarbon is installed) and your Tensorboard logs (if tensorboard is installed)", "raw": " to upload your model checkpoints to Hugging Face while training. It also uploads your emissions (if codecarbon is installed) and your Tensorboard logs (if tensorboard is installed)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3๏ธโƒฃ Model card improvements: improved automatic widget examples, better tags, and the default of \"sentence_transformers_model_id\" now gets replaced when possible.", "raw": "3๏ธโƒฃ Model card improvements: improved automatic widget examples, better tags, and the default of \"sentence_transformers_model_id\" now gets replaced when possible.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "4๏ธโƒฃ Several evaluator fixes, see release notes for details.", "raw": "4๏ธโƒฃ Several evaluator fixes, see release notes for details.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "5๏ธโƒฃ Fixed a bug with MatryoshkaLoss throwing an error if the supplied Matryoshka dimensions are ascending instead of descending.", "raw": "5๏ธโƒฃ Fixed a bug with MatryoshkaLoss throwing an error if the supplied Matryoshka dimensions are ascending instead of descending.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "6๏ธโƒฃ Full Safetensors support; even the uncommon modules can now save and load \"model.safetensors\" files: no more pickle risks.", "raw": "6๏ธโƒฃ Full Safetensors support; even the uncommon modules can now save and load \"model.safetensors\" files: no more pickle risks.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Check out the full release notes here: ", "raw": "Check out the full release notes here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.1", "href": "https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.1", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "And let me know what kind of features you'd like to see next! I have some plans already (ONNX, Sparse models, ColBERT, PEFT), but I don't yet know how I should prioritize everything.", "raw": "And let me know what kind of features you'd like to see next! I have some plans already (ONNX, Sparse models, ColBERT, PEFT), but I don't yet know how I should prioritize everything.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I just published Sentence Transformers v3.0.1: the first patch release since v3 from last week. It introduces gradient checkpointing, pushing model checkpoints to Hugging Face while training, model card improvements and fixes. Details: 1๏ธโƒฃ Gradient checkpointing allows for much less memory usage at a cost of ~20% training speed. Seems to allow for higher batch sizes, which is quite important for loss functions with in-batch negatives. 2๏ธโƒฃ You can specify `args.push_to_hub=True` and `args.hub_model_id` to upload your model checkpoints to Hugging Face while training. It also uploads your emissions (if codecarbon is installed) and your Tensorboard logs (if tensorboard is installed) 3๏ธโƒฃ Model card improvements: improved automatic widget examples, better tags, and the default of "sentence_transformers_model_id" now gets replaced when possible. 4๏ธโƒฃ Several evaluator fixes, see release notes for details. 5๏ธโƒฃ Fixed a bug with MatryoshkaLoss throwing an error if the supplied Matryoshka dimensions are ascending instead of descending. 6๏ธโƒฃ Full Safetensors support; even the uncommon modules can now save and load "model.safetensors" files: no more pickle risks. Check out the full release notes here: https://github.com/UKPLab/sentence-transformers/releases/tag/v3.0.1 And let me know what kind of features you'd like to see next! I have some plans already (ONNX, Sparse models, ColBERT, PEFT), but I don't yet know how I should prioritize everything.
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2024-06-07T13:22:54.000Z
2024-07-17T12:45:38.130Z
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/posts/tomaarsen/223721405578741
3,299
3
774132103206717
[ { "type": "text", "value": "Evaluate RAG using Open Source from HuggingFace using BeyondLLM", "raw": "Evaluate RAG using Open Source from HuggingFace using BeyondLLM", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\n# pip install beyondllm\n# pip install huggingface_hub\n# pip install llama-index-embeddings-fastembed\n\nfrom beyondllm.source import fit\nfrom beyondllm.embeddings import FastEmbedEmbeddings\nfrom beyondllm.retrieve import auto_retriever\nfrom beyondllm.llms import HuggingFaceHubModel\nfrom beyondllm.generator import Generate\n\nimport os\nfrom getpass import getpass\nos.environ['HUGGINGFACE_ACCESS_TOKEN'] = getpass(\"Enter your HF API token:\")\n\ndata = fit(\"RedHenLab_GSoC_Tarun.pdf\",dtype=\"pdf\")\nembed_model = FastEmbedEmbeddings()\nretriever = auto_retriever(data=data,embed_model=embed_model,type=\"normal\",top_k=3)\nllm = HuggingFaceHubModel(model=\"mistralai/Mistral-7B-Instruct-v0.2\")\npipeline = Generate(question=\"what models has Tarun fine-tuned?\",llm=llm,retriever=retriever)\n\nprint(pipeline.call()) # Return the AI response\nprint(pipeline.get_rag_triad_evals())\n```", "href": null, "resource": null, "url": null, "code": "# pip install beyondllm\n# pip install huggingface_hub\n# pip install llama-index-embeddings-fastembed\n\nfrom beyondllm.source import fit\nfrom beyondllm.embeddings import FastEmbedEmbeddings\nfrom beyondllm.retrieve import auto_retriever\nfrom beyondllm.llms import HuggingFaceHubModel\nfrom beyondllm.generator import Generate\n\nimport os\nfrom getpass import getpass\nos.environ['HUGGINGFACE_ACCESS_TOKEN'] = getpass(\"Enter your HF API token:\")\n\ndata = fit(\"RedHenLab_GSoC_Tarun.pdf\",dtype=\"pdf\")\nembed_model = FastEmbedEmbeddings()\nretriever = auto_retriever(data=data,embed_model=embed_model,type=\"normal\",top_k=3)\nllm = HuggingFaceHubModel(model=\"mistralai/Mistral-7B-Instruct-v0.2\")\npipeline = Generate(question=\"what models has Tarun fine-tuned?\",llm=llm,retriever=retriever)\n\nprint(pipeline.call()) # Return the AI response\nprint(pipeline.get_rag_triad_evals())", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "GitHub: ", "raw": "GitHub: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/aiplanethub/beyondllm", "href": "https://github.com/aiplanethub/beyondllm", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Don't forget to โญ๏ธ the repo", "raw": "Don't forget to โญ๏ธ the repo", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Evaluate RAG using Open Source from HuggingFace using BeyondLLM ``` # pip install beyondllm # pip install huggingface_hub # pip install llama-index-embeddings-fastembed from beyondllm.source import fit from beyondllm.embeddings import FastEmbedEmbeddings from beyondllm.retrieve import auto_retriever from beyondllm.llms import HuggingFaceHubModel from beyondllm.generator import Generate import os from getpass import getpass os.environ['HUGGINGFACE_ACCESS_TOKEN'] = getpass("Enter your HF API token:") data = fit("RedHenLab_GSoC_Tarun.pdf",dtype="pdf") embed_model = FastEmbedEmbeddings() retriever = auto_retriever(data=data,embed_model=embed_model,type="normal",top_k=3) llm = HuggingFaceHubModel(model="mistralai/Mistral-7B-Instruct-v0.2") pipeline = Generate(question="what models has Tarun fine-tuned?",llm=llm,retriever=retriever) print(pipeline.call()) # Return the AI response print(pipeline.get_rag_triad_evals()) ``` GitHub: https://github.com/aiplanethub/beyondllm Don't forget to โญ๏ธ the repo
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[]
[]
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2024-06-07T13:16:24.000Z
2024-06-08T17:49:35.494Z
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/posts/lucifertrj/774132103206717
1,846
4
583672984398703
[ { "type": "text", "value": "๐Ÿ“ข Releasing the Chain-of-Thought (CoT)-tuned ๐Ÿ”ฅ FlanT5-xl (3B) for Target Sentiment Analysis (TSA) on english texts. ", "raw": "๐Ÿ“ข Releasing the Chain-of-Thought (CoT)-tuned ๐Ÿ”ฅ FlanT5-xl (3B) for Target Sentiment Analysis (TSA) on english texts. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ’ก The main reason for adopting this model or smaller version (large and base) are as follows:", "raw": "๐Ÿ’ก The main reason for adopting this model or smaller version (large and base) are as follows:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… 1. Reasoning in sentiment-analysis in zero-shot-learning mode significantly underperforms the fine-tuned FlanT5. ", "raw": "โœ… 1. Reasoning in sentiment-analysis in zero-shot-learning mode significantly underperforms the fine-tuned FlanT5. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… 2. This model showcases top 1 ๐Ÿ† on the RuSentNE-2023 competitions: ", "raw": "โœ… 2. This model showcases top 1 ๐Ÿ† on the RuSentNE-2023 competitions: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://codalab.lisn.upsaclay.fr/competitions/9538", "href": "https://codalab.lisn.upsaclay.fr/competitions/9538", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… 3. Easy colab for frameworkless lauch and experiments ๐Ÿงช ", "raw": "โœ… 3. Easy colab for frameworkless lauch and experiments ๐Ÿงช ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://colab.research.google.com/github/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/FlanT5_Finetuned_Model_Usage.ipynb", "href": "https://colab.research.google.com/github/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/FlanT5_Finetuned_Model_Usage.ipynb", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You may find more on the model card, while the fine-tuning statistics per each model size is shown in attachment.", "raw": "You may find more on the model card, while the fine-tuning statistics per each model size is shown in attachment.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model: ", "raw": "Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/nicolay-r/flan-t5-tsa-thor-xl", "href": null, "resource": { "type": "model", "id": "nicolay-r/flan-t5-tsa-thor-xl", "discussionNum": null }, "url": "https://huggingface.co/nicolay-r/flan-t5-tsa-thor-xl", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Benchmark: ", "raw": "Benchmark: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/nicolay-r/RuSentNE-LLM-Benchmark", "href": "https://github.com/nicolay-r/RuSentNE-LLM-Benchmark", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Dataset: ", "raw": "Dataset: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/dialogue-evaluation/RuSentNE-evaluation", "href": "https://github.com/dialogue-evaluation/RuSentNE-evaluation", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)", "raw": "Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Collection: ", "raw": "Collection: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101", "href": "https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ“ข Releasing the Chain-of-Thought (CoT)-tuned ๐Ÿ”ฅ FlanT5-xl (3B) for Target Sentiment Analysis (TSA) on english texts. ๐Ÿ’ก The main reason for adopting this model or smaller version (large and base) are as follows: โœ… 1. Reasoning in sentiment-analysis in zero-shot-learning mode significantly underperforms the fine-tuned FlanT5. โœ… 2. This model showcases top 1 ๐Ÿ† on the RuSentNE-2023 competitions: https://codalab.lisn.upsaclay.fr/competitions/9538 โœ… 3. Easy colab for frameworkless lauch and experiments ๐Ÿงช https://colab.research.google.com/github/nicolay-r/Reasoning-for-Sentiment-Analysis-Framework/blob/main/FlanT5_Finetuned_Model_Usage.ipynb You may find more on the model card, while the fine-tuning statistics per each model size is shown in attachment. Model: https://huggingface.co/nicolay-r/flan-t5-tsa-thor-xl Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342) Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101
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2024-06-07T09:55:13.000Z
2024-06-07T09:57:29.825Z
[]
/posts/nicolay-r/583672984398703
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Hello! I am studying PyTorch, and I made something that converged really well for something this simplistic. It isn't masterful, but i'd welcome feedback, improvements, suggestions, anything. Tell me it sucks and to take it down, I will, just wanted to share what i've spent the last 2 days crying to figure out. https://colab.research.google.com/gist/SMeyersMrOvkill/625371e1816afb2163bdc4194ba74e93/scratchpad.ipynb
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2024-06-07T07:43:06.000Z
2024-06-08T08:03:06.745Z
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/posts/MrOvkill/195815576895469
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[ { "type": "text", "value": "I made Tenzin public. One use-case at least to predict stock market prices for high-frequency trading. Would love to see the response as well as feedback you have for us. Please understand that this only represents 5% of the codebase of Tenzin 1.0. We will share more models and use-cases based on the feedback we receive along with keeping in mind AI safety and ethics. ", "raw": "I made Tenzin public. One use-case at least to predict stock market prices for high-frequency trading. Would love to see the response as well as feedback you have for us. Please understand that this only represents 5% of the codebase of Tenzin 1.0. We will share more models and use-cases based on the feedback we receive along with keeping in mind AI safety and ethics. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Have fun and go and make some money :) ", "raw": "Have fun and go and make some money :) ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I made Tenzin public. One use-case at least to predict stock market prices for high-frequency trading. Would love to see the response as well as feedback you have for us. Please understand that this only represents 5% of the codebase of Tenzin 1.0. We will share more models and use-cases based on the feedback we receive along with keeping in mind AI safety and ethics. Have fun and go and make some money :)
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2024-06-07T01:13:47.000Z
2024-06-07T01:13:47.606Z
[]
/posts/Tar9897/489363954767854
2,204
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@8zen ``` print: I Love you <3 ```
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2024-06-06T21:23:43.000Z
2024-06-06T21:23:43.565Z
[]
/posts/Zyborip/864936435505861
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They: you need ground truth to measure performance! ๐Ÿ˜  NannyML: hold my beer...
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2024-06-06T20:25:30.000Z
2024-06-06T20:25:30.486Z
[]
/posts/santiviquez/808994197877137
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[ { "type": "text", "value": "๐Ÿค— Hello, I have great news! FluentlyXL Final is finally here, the final release of the FluentlyXL model series. We've improved the overall aesthetics, lighting, and more.", "raw": "๐Ÿค— Hello, I have great news! FluentlyXL Final is finally here, the final release of the FluentlyXL model series. We've improved the overall aesthetics, lighting, and more.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿฆพ Model on HF: ", "raw": "๐Ÿฆพ Model on HF: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/fluently/Fluently-XL-Final", "href": null, "resource": { "type": "model", "id": "fluently/Fluently-XL-Final", "discussionNum": null }, "url": "https://huggingface.co/fluently/Fluently-XL-Final", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿฅ Model on CivitAI: ", "raw": "๐Ÿฅ Model on CivitAI: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://civitai.com/models/324891", "href": "https://civitai.com/models/324891", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸŽ† Playground: ", "raw": "๐ŸŽ† Playground: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/fluently/Fluently-Playground", "href": null, "resource": { "type": "space", "id": "fluently/Fluently-Playground", "discussionNum": null }, "url": "https://huggingface.co/spaces/fluently/Fluently-Playground", "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿค— Hello, I have great news! FluentlyXL Final is finally here, the final release of the FluentlyXL model series. We've improved the overall aesthetics, lighting, and more. ๐Ÿฆพ Model on HF: https://huggingface.co/fluently/Fluently-XL-Final ๐Ÿฅ Model on CivitAI: https://civitai.com/models/324891 ๐ŸŽ† Playground: https://huggingface.co/spaces/fluently/Fluently-Playground
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2024-06-06T19:49:40.000Z
2024-06-08T01:40:59.671Z
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/posts/ehristoforu/519023085637537
3,099
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[ { "type": "text", "value": "The First Multimodal Language Model dedicated for Chemistry.", "raw": "The First Multimodal Language Model dedicated for Chemistry.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Demo: ", "raw": "Demo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://v.chemllm.org/", "href": "https://v.chemllm.org/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Finetune based on ChemLLM-20B and InterViT-6B on MMChemExam and ChemOCR Datasets (coming soon...)", "raw": "Finetune based on ChemLLM-20B and InterViT-6B on MMChemExam and ChemOCR Datasets (coming soon...)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/AI4Chem/ChemVLM-26B", "href": null, "resource": { "type": "model", "id": "AI4Chem/ChemVLM-26B", "discussionNum": null }, "url": "https://huggingface.co/AI4Chem/ChemVLM-26B", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2402.06852", "href": null, "resource": { "type": "paper", "id": "2402.06852", "discussionNum": null }, "url": "https://huggingface.co/papers/2402.06852", "code": null, "user": null, "label": "ChemLLM: A Chemical Large Language Model (2402.06852)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
The First Multimodal Language Model dedicated for Chemistry. Demo: https://v.chemllm.org/ Finetune based on ChemLLM-20B and InterViT-6B on MMChemExam and ChemOCR Datasets (coming soon...) https://huggingface.co/AI4Chem/ChemVLM-26B https://huggingface.co/papers/2402.06852
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2024-06-06T16:43:15.000Z
2024-06-06T16:45:46.800Z
[]
/posts/qq8933/759616149294990
2,002
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779490368541879
[ { "type": "text", "value": "Hugging Face in your spreadsheet?", "raw": "Hugging Face in your spreadsheet?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Because spreadsheets can be incredibly useful for journalists, I created this little project yesterday evening. Handy for prompting, extraction, classification, translation...", "raw": "Because spreadsheets can be incredibly useful for journalists, I created this little project yesterday evening. Handy for prompting, extraction, classification, translation...", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Ping me if youโ€™re interested in trying it out!", "raw": "Ping me if youโ€™re interested in trying it out!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Hugging Face in your spreadsheet? Because spreadsheets can be incredibly useful for journalists, I created this little project yesterday evening. Handy for prompting, extraction, classification, translation... Ping me if youโ€™re interested in trying it out!
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2024-06-06T15:14:48.000Z
2024-06-14T14:11:13.781Z
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/posts/fdaudens/779490368541879
2,088
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388542675868940
[ { "type": "text", "value": "โš™๏ธ Prompt Optimization with Haystack and DSPy", "raw": "โš™๏ธ Prompt Optimization with Haystack and DSPy", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Experimental notebook: ๐Ÿงช๐Ÿ““ ", "raw": "Experimental notebook: ๐Ÿงช๐Ÿ““ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/prompt_optimization_with_dspy.ipynb", "href": "https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/prompt_optimization_with_dspy.ipynb", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "When building applications with LLMs, writing effective prompts is a long process of trial and error. ๐Ÿ”„", "raw": "When building applications with LLMs, writing effective prompts is a long process of trial and error. ๐Ÿ”„", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Often, if you switch models, you also have to change the prompt. ๐Ÿ˜ฉ", "raw": "Often, if you switch models, you also have to change the prompt. ๐Ÿ˜ฉ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "What if you could automate this process?", "raw": "What if you could automate this process?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ’ก That's where DSPy comes in - a framework designed to algorithmically optimize prompts for Language Models.", "raw": "๐Ÿ’ก That's where DSPy comes in - a framework designed to algorithmically optimize prompts for Language Models.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "By applying classical machine learning concepts (training and evaluation data, metrics, optimization), DSPy generates better prompts for a given model and task.", "raw": "By applying classical machine learning concepts (training and evaluation data, metrics, optimization), DSPy generates better prompts for a given model and task.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Recently, I explored combining DSPy with the robustness of Haystack Pipelines.", "raw": "Recently, I explored combining DSPy with the robustness of Haystack Pipelines.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Here's how it works:", "raw": "Here's how it works:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โ–ถ๏ธ Start from a Haystack RAG pipeline with a basic prompt", "raw": "โ–ถ๏ธ Start from a Haystack RAG pipeline with a basic prompt", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸŽฏ Define a goal (in this case, get correct and concise answers)", "raw": "๐ŸŽฏ Define a goal (in this case, get correct and concise answers)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“Š Create a DSPy program, define data and metrics", "raw": "๐Ÿ“Š Create a DSPy program, define data and metrics", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœจ Optimize and evaluate -> improved prompt", "raw": "โœจ Optimize and evaluate -> improved prompt", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿš€ Build a refined Haystack RAG pipeline using the optimized prompt", "raw": "๐Ÿš€ Build a refined Haystack RAG pipeline using the optimized prompt", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
โš™๏ธ Prompt Optimization with Haystack and DSPy Experimental notebook: ๐Ÿงช๐Ÿ““ https://github.com/deepset-ai/haystack-cookbook/blob/main/notebooks/prompt_optimization_with_dspy.ipynb When building applications with LLMs, writing effective prompts is a long process of trial and error. ๐Ÿ”„ Often, if you switch models, you also have to change the prompt. ๐Ÿ˜ฉ What if you could automate this process? ๐Ÿ’ก That's where DSPy comes in - a framework designed to algorithmically optimize prompts for Language Models. By applying classical machine learning concepts (training and evaluation data, metrics, optimization), DSPy generates better prompts for a given model and task. Recently, I explored combining DSPy with the robustness of Haystack Pipelines. Here's how it works: โ–ถ๏ธ Start from a Haystack RAG pipeline with a basic prompt ๐ŸŽฏ Define a goal (in this case, get correct and concise answers) ๐Ÿ“Š Create a DSPy program, define data and metrics โœจ Optimize and evaluate -> improved prompt ๐Ÿš€ Build a refined Haystack RAG pipeline using the optimized prompt
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2024-06-06T14:19:17.000Z
2024-06-06T15:26:33.942Z
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/posts/anakin87/388542675868940
2,104
1
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[ { "type": "text", "value": "The application of Phi-3-small-8k-instruct for reasoning in Target Sentiment Analysis (TSA), in a zero-shot-learning mode. Comparing with the other 7B vendors, the key takeaways are as follows:", "raw": "The application of Phi-3-small-8k-instruct for reasoning in Target Sentiment Analysis (TSA), in a zero-shot-learning mode. Comparing with the other 7B vendors, the key takeaways are as follows:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… 1. At the moment this model on the top ๐ŸŽ‰ of the 7B sized versions for texts translated in English (๐Ÿ‡บ๐Ÿ‡ธ) by surpassing Mistral-7B-v0.3 and LLaMA-3-8B ๐Ÿ”ฅ (Figure 1)", "raw": "โœ… 1. At the moment this model on the top ๐ŸŽ‰ of the 7B sized versions for texts translated in English (๐Ÿ‡บ๐Ÿ‡ธ) by surpassing Mistral-7B-v0.3 and LLaMA-3-8B ๐Ÿ”ฅ (Figure 1)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… 2. It remains similar to 7B alternatives in original non-english texts (๐Ÿ‡ท๐Ÿ‡บ), however show confidence in sentiment presence among other 7B alternatives (checkout F1(PN0) results in Figure 2)", "raw": "โœ… 2. It remains similar to 7B alternatives in original non-english texts (๐Ÿ‡ท๐Ÿ‡บ), however show confidence in sentiment presence among other 7B alternatives (checkout F1(PN0) results in Figure 2)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In comparison with its mini (3B) brother Phi-3-mini, the small (7B) version showcases a huge step in reasoning capabilities ๐Ÿ”ฅ", "raw": "In comparison with its mini (3B) brother Phi-3-mini, the small (7B) version showcases a huge step in reasoning capabilities ๐Ÿ”ฅ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Benchmark: ", "raw": "Benchmark: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/nicolay-r/RuSentNE-LLM-Benchmark", "href": "https://github.com/nicolay-r/RuSentNE-LLM-Benchmark", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model: ", "raw": "Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/microsoft/Phi-3-small-8k-instruct", "href": null, "resource": { "type": "model", "id": "microsoft/Phi-3-small-8k-instruct", "discussionNum": null }, "url": "https://huggingface.co/microsoft/Phi-3-small-8k-instruct", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Dataset: ", "raw": "Dataset: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/dialogue-evaluation/RuSentNE-evaluation", "href": "https://github.com/dialogue-evaluation/RuSentNE-evaluation", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)", "raw": "Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Collection: ", "raw": "Collection: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101", "href": "https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
The application of Phi-3-small-8k-instruct for reasoning in Target Sentiment Analysis (TSA), in a zero-shot-learning mode. Comparing with the other 7B vendors, the key takeaways are as follows: โœ… 1. At the moment this model on the top ๐ŸŽ‰ of the 7B sized versions for texts translated in English (๐Ÿ‡บ๐Ÿ‡ธ) by surpassing Mistral-7B-v0.3 and LLaMA-3-8B ๐Ÿ”ฅ (Figure 1) โœ… 2. It remains similar to 7B alternatives in original non-english texts (๐Ÿ‡ท๐Ÿ‡บ), however show confidence in sentiment presence among other 7B alternatives (checkout F1(PN0) results in Figure 2) In comparison with its mini (3B) brother Phi-3-mini, the small (7B) version showcases a huge step in reasoning capabilities ๐Ÿ”ฅ Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark Model: https://huggingface.co/microsoft/Phi-3-small-8k-instruct Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342) Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101
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[ { "type": "text", "value": "๐Ÿ”ฌ This paper introduces Fusion Intelligence (FI), a novel approach integrating the adaptive behaviors of natural organisms ๐Ÿ(Bees!)๐Ÿ with AI's computational power.", "raw": "๐Ÿ”ฌ This paper introduces Fusion Intelligence (FI), a novel approach integrating the adaptive behaviors of natural organisms ๐Ÿ(Bees!)๐Ÿ with AI's computational power.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Paper:", "raw": "Paper:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Fusion Intelligence: Confluence of Natural and Artificial Intelligence for Enhanced Problem-Solving Efficiency (2405.09763)", "raw": "Fusion Intelligence: Confluence of Natural and Artificial Intelligence for Enhanced Problem-Solving Efficiency (2405.09763)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/pdf/2405.09763", "href": "https://arxiv.org/pdf/2405.09763", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Key Takeaways:", "raw": "Key Takeaways:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Fusion Intelligence (FI): Combines natural organism efficiency with AI's power. ๐ŸŒŸ", "raw": "* Fusion Intelligence (FI): Combines natural organism efficiency with AI's power. ๐ŸŒŸ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Hybrid Approach: Integrates natural abilities with AI for better problem-solving. ๐Ÿง ๐Ÿค–", "raw": "* Hybrid Approach: Integrates natural abilities with AI for better problem-solving. ๐Ÿง ๐Ÿค–", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Agricultural Applications: Shows a 50% improvement in pollination efficiency. ๐Ÿ๐ŸŒผ", "raw": "* Agricultural Applications: Shows a 50% improvement in pollination efficiency. ๐Ÿ๐ŸŒผ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Energy Efficiency: Consumes only 29.5-50.2 mW per bee, much lower than traditional methods. โšก", "raw": "* Energy Efficiency: Consumes only 29.5-50.2 mW per bee, much lower than traditional methods. โšก", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Scalability: Applicable to fields like environmental monitoring and search and rescue. ๐ŸŒ๐Ÿ”", "raw": "* Scalability: Applicable to fields like environmental monitoring and search and rescue. ๐ŸŒ๐Ÿ”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Non-Invasive: Eliminates the need for invasive modifications to biological entities. ๐ŸŒฑ", "raw": "* Non-Invasive: Eliminates the need for invasive modifications to biological entities. ๐ŸŒฑ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This research offers a new approach for those interested in sustainable AI solutions. By merging biology with AI, (FI) aims to create solutions for a variety of challenges.", "raw": "This research offers a new approach for those interested in sustainable AI solutions. By merging biology with AI, (FI) aims to create solutions for a variety of challenges.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ”ฌ This paper introduces Fusion Intelligence (FI), a novel approach integrating the adaptive behaviors of natural organisms ๐Ÿ(Bees!)๐Ÿ with AI's computational power. Paper: Fusion Intelligence: Confluence of Natural and Artificial Intelligence for Enhanced Problem-Solving Efficiency (2405.09763) https://arxiv.org/pdf/2405.09763 Key Takeaways: * Fusion Intelligence (FI): Combines natural organism efficiency with AI's power. ๐ŸŒŸ * Hybrid Approach: Integrates natural abilities with AI for better problem-solving. ๐Ÿง ๐Ÿค– * Agricultural Applications: Shows a 50% improvement in pollination efficiency. ๐Ÿ๐ŸŒผ * Energy Efficiency: Consumes only 29.5-50.2 mW per bee, much lower than traditional methods. โšก * Scalability: Applicable to fields like environmental monitoring and search and rescue. ๐ŸŒ๐Ÿ” * Non-Invasive: Eliminates the need for invasive modifications to biological entities. ๐ŸŒฑ This research offers a new approach for those interested in sustainable AI solutions. By merging biology with AI, (FI) aims to create solutions for a variety of challenges.
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2024-06-06T04:54:14.000Z
2024-06-08T11:35:08.863Z
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Meet V-Express, the groundbreaking open-source and free tool that breathes life into your photos! Whether you have an audio clip or a video, V-Express animates your images to create stunning talking avatars. Just like the acclaimed D-ID Avatar, Wav2Lip, and Avatarify, V-Express turns your still photos into dynamic, speaking personas, but with a twistโ€”it's completely open-source and free to use! With seamless audio integration and the ability to mimic video expressions, V-Express offers an unparalleled experience without any cost or restrictions. Experience the future of digital avatars todayโ€”let's dive into how you can get started with V-Express and watch your images come alive!", "raw": "Ever wished your static images could talk like magic? Meet V-Express, the groundbreaking open-source and free tool that breathes life into your photos! Whether you have an audio clip or a video, V-Express animates your images to create stunning talking avatars. Just like the acclaimed D-ID Avatar, Wav2Lip, and Avatarify, V-Express turns your still photos into dynamic, speaking personas, but with a twistโ€”it's completely open-source and free to use! With seamless audio integration and the ability to mimic video expressions, V-Express offers an unparalleled experience without any cost or restrictions. 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V-Express: 1-Click AI Avatar Talking Heads Video Animation Generator - D-ID Alike - Free Open Source Full Windows YouTube Tutorial : https://youtu.be/xLqDTVWUSec Ever wished your static images could talk like magic? Meet V-Express, the groundbreaking open-source and free tool that breathes life into your photos! Whether you have an audio clip or a video, V-Express animates your images to create stunning talking avatars. Just like the acclaimed D-ID Avatar, Wav2Lip, and Avatarify, V-Express turns your still photos into dynamic, speaking personas, but with a twistโ€”it's completely open-source and free to use! With seamless audio integration and the ability to mimic video expressions, V-Express offers an unparalleled experience without any cost or restrictions. Experience the future of digital avatars todayโ€”let's dive into how you can get started with V-Express and watch your images come alive! 1-Click V-Express Installers Scripts โคต๏ธ https://www.patreon.com/posts/105251204 Requirements Step by Step Tutorial โคต๏ธ https://youtu.be/-NjNy7afOQ0 Official Rope GitHub Repository Free To Install and Use โคต๏ธ https://github.com/tencent-ailab/V-Express SECourses Discord Channel to Get Full Support โคต๏ธ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388
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2024-06-06T00:48:45.000Z
2024-06-06T00:48:45.170Z
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[ { "type": "text", "value": "The Coachella of Computer Vision, CVPR, is right around the corner. In anticipation of the conference, I curated a dataset of the papers. ", "raw": "The Coachella of Computer Vision, CVPR, is right around the corner. In anticipation of the conference, I curated a dataset of the papers. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I'll have a technical blog post out tomorrow doing some analysis on the dataset, but I'm so hyped that I wanted to get it out to the community ASAP.", "raw": "I'll have a technical blog post out tomorrow doing some analysis on the dataset, but I'm so hyped that I wanted to get it out to the community ASAP.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, 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"resource": null, "url": null, "code": "authors_list", "user": null, "label": null, "lang": null }, { "type": "text", "value": ": The list of authors", "raw": ": The list of authors", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - ", "raw": " - ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`abstract`", "href": null, "resource": null, "url": null, "code": "abstract", "user": null, "label": null, "lang": null }, { "type": "text", "value": ": The abstract of the paper", "raw": ": The abstract of the paper", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - ", "raw": " - ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`arxiv_link`", "href": null, "resource": null, "url": null, "code": "arxiv_link", "user": null, "label": null, "lang": null }, { "type": "text", "value": ": Link to the paper on arXiv", "raw": ": Link to the paper on arXiv", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - ", "raw": " - ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`other_link`", "href": null, "resource": null, "url": null, "code": "other_link", "user": null, "label": null, "lang": null }, { "type": "text", "value": ": Link to the project page, if found", "raw": ": Link to the project page, if found", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - ", "raw": " - ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`category_name`", "href": null, "resource": null, "url": null, "code": "category_name", "user": null, "label": null, "lang": null }, { "type": "text", "value": ": The primary category this paper according to [arXiv taxonomy](", "raw": ": The primary category this paper according to [arXiv taxonomy](", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://arxiv.org/category_taxonomy", "href": "https://arxiv.org/category_taxonomy", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ")", "raw": ")", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - ", "raw": " - ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`all_categories`", "href": null, "resource": null, "url": null, "code": "all_categories", "user": null, "label": null, "lang": null }, { "type": "text", "value": ": All categories this paper falls into, according to arXiv taxonomy", "raw": ": All categories this paper falls into, according to arXiv taxonomy", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - ", "raw": " - ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "inline_code", "value": null, "raw": "`keywords`", "href": null, "resource": null, "url": null, "code": "keywords", "user": null, "label": null, "lang": null }, { "type": "text", "value": ": Extracted using GPT-4o", "raw": ": Extracted using GPT-4o", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Here's how I created the dataset ๐Ÿ‘‡๐Ÿผ", "raw": "Here's how I created the dataset ๐Ÿ‘‡๐Ÿผ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Generic code for building this dataset can be found [here](", "raw": "Generic code for building this dataset can be found [here](", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/harpreetsahota204/CVPR-2024-Papers", "href": "https://github.com/harpreetsahota204/CVPR-2024-Papers", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ").", "raw": ").", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This dataset was built using the following steps:", "raw": "This dataset was built using the following steps:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Scrape the CVPR 2024 website for accepted papers", "raw": "- Scrape the CVPR 2024 website for accepted papers", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Use DuckDuckGo to search for a link to the paper's abstract on arXiv", "raw": "- Use DuckDuckGo to search for a link to the paper's abstract on arXiv", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Use arXiv.py (python wrapper for the arXiv API) to extract the abstract and categories, and download the pdf for each paper", "raw": "- Use arXiv.py (python wrapper for the arXiv API) to extract the abstract and categories, and download the pdf for each paper", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Use pdf2image to save the image of paper's first page", "raw": "- Use pdf2image to save the image of paper's first page", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- Use GPT-4o to extract keywords from the abstract", "raw": "- Use GPT-4o to extract keywords from the abstract", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/Voxel51/CVPR_2024_Papers", "href": null, "resource": { "type": "dataset", "id": "Voxel51/CVPR_2024_Papers", "discussionNum": null }, "url": "https://huggingface.co/datasets/Voxel51/CVPR_2024_Papers", "code": null, "user": null, "label": null, "lang": null } ]
The Coachella of Computer Vision, CVPR, is right around the corner. In anticipation of the conference, I curated a dataset of the papers. I'll have a technical blog post out tomorrow doing some analysis on the dataset, but I'm so hyped that I wanted to get it out to the community ASAP. The dataset consists of the following fields: - An image of the first page of the paper - `title`: The title of the paper - `authors_list`: The list of authors - `abstract`: The abstract of the paper - `arxiv_link`: Link to the paper on arXiv - `other_link`: Link to the project page, if found - `category_name`: The primary category this paper according to [arXiv taxonomy](https://arxiv.org/category_taxonomy) - `all_categories`: All categories this paper falls into, according to arXiv taxonomy - `keywords`: Extracted using GPT-4o Here's how I created the dataset ๐Ÿ‘‡๐Ÿผ Generic code for building this dataset can be found [here](https://github.com/harpreetsahota204/CVPR-2024-Papers). This dataset was built using the following steps: - Scrape the CVPR 2024 website for accepted papers - Use DuckDuckGo to search for a link to the paper's abstract on arXiv - Use arXiv.py (python wrapper for the arXiv API) to extract the abstract and categories, and download the pdf for each paper - Use pdf2image to save the image of paper's first page - Use GPT-4o to extract keywords from the abstract https://huggingface.co/datasets/Voxel51/CVPR_2024_Papers
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2024-06-05T23:54:03.000Z
2024-06-05T23:54:03.520Z
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[ { "type": "text", "value": "๐ŸŒ As we all know, Planet Earth is undergoing an unprecedented climate crisis, almost totally due to human activities: we haven't got much time left before it's too late to take action, and one of the key fields where we need to urgently operate are climate-aware financial investments...", "raw": "๐ŸŒ As we all know, Planet Earth is undergoing an unprecedented climate crisis, almost totally due to human activities: we haven't got much time left before it's too late to take action, and one of the key fields where we need to urgently operate are climate-aware financial investments...", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿค– ... And that's where AI comes into the play: we can indeed try to leverage, tweak and expand its knowledge in the field to extract valuable climate-aware solutions. ", "raw": "๐Ÿค– ... And that's where AI comes into the play: we can indeed try to leverage, tweak and expand its knowledge in the field to extract valuable climate-aware solutions. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿค— I tried to make something alike: exploiting ", "raw": "๐Ÿค— I tried to make something alike: exploiting ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/climatebert/tcfd_recommendations", "href": null, "resource": { "type": "dataset", "id": "climatebert/tcfd_recommendations", "discussionNum": null }, "url": "https://huggingface.co/datasets/climatebert/tcfd_recommendations", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " as knowledge base, Qdrant Cloud as vector store service and ", "raw": " as knowledge base, Qdrant Cloud as vector store service and ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct", "href": null, "resource": { "type": "model", "id": "microsoft/Phi-3-mini-128k-instruct", "discussionNum": null }, "url": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " as LLM (provided via API from ", "raw": " as LLM (provided via API from ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/eswardivi/Phi-3-mini-128k-instruct", "href": null, "resource": { "type": "space", "id": "eswardivi/Phi-3-mini-128k-instruct", "discussionNum": null }, "url": "https://huggingface.co/spaces/eswardivi/Phi-3-mini-128k-instruct", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " by ", "raw": " by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@eswardivi", "href": null, "resource": null, "url": null, "code": null, "user": "eswardivi", "label": null, "lang": null }, { "type": "text", "value": "), I built an AI assistant to help you find climate-oriented solutions for your investments, companies, or simply for your everyday life๐ŸŽ’.", "raw": "), I built an AI assistant to help you find climate-oriented solutions for your investments, companies, or simply for your everyday life๐ŸŽ’.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Find it here: ", "raw": "Find it here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/as-cle-bert/cLLiMateChat", "href": null, "resource": { "type": "space", "id": "as-cle-bert/cLLiMateChat", "discussionNum": null }, "url": "https://huggingface.co/spaces/as-cle-bert/cLLiMateChat", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "GitHub: ", "raw": "GitHub: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/AstraBert/qdrant-ai-chat", "href": "https://github.com/AstraBert/qdrant-ai-chat", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Website: ", "raw": "Website: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://astrabert.github.io/qdrant-ai-chat/", "href": "https://astrabert.github.io/qdrant-ai-chat/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Be kind to our Planet, we only got one๐Ÿ’š", "raw": "Be kind to our Planet, we only got one๐Ÿ’š", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "(Shout-outs to ", "raw": "(Shout-outs to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@JohnSmith9982", "href": null, "resource": null, "url": null, "code": null, "user": "JohnSmith9982", "label": null, "lang": null }, { "type": "text", "value": " whose ", "raw": " whose ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/JohnSmith9982/small_and_pretty", "href": null, "resource": { "type": "space", "id": "JohnSmith9982/small_and_pretty", "discussionNum": null }, "url": "https://huggingface.co/spaces/JohnSmith9982/small_and_pretty", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " Gradio theme was used to build my application๐Ÿš€)", "raw": " Gradio theme was used to build my application๐Ÿš€)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "PS: ๐ŸŒฑCurious of knowing what is your carbon footprint? Head over to this ML-backed HF Space I built to discover it: ", "raw": "PS: ๐ŸŒฑCurious of knowing what is your carbon footprint? Head over to this ML-backed HF Space I built to discover it: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/as-cle-bert/carbon-footprint-predictor", "href": null, "resource": { "type": "space", "id": "as-cle-bert/carbon-footprint-predictor", "discussionNum": null }, "url": "https://huggingface.co/spaces/as-cle-bert/carbon-footprint-predictor", "code": null, "user": null, "label": null, "lang": null } ]
๐ŸŒ As we all know, Planet Earth is undergoing an unprecedented climate crisis, almost totally due to human activities: we haven't got much time left before it's too late to take action, and one of the key fields where we need to urgently operate are climate-aware financial investments... ๐Ÿค– ... And that's where AI comes into the play: we can indeed try to leverage, tweak and expand its knowledge in the field to extract valuable climate-aware solutions. ๐Ÿค— I tried to make something alike: exploiting https://huggingface.co/datasets/climatebert/tcfd_recommendations as knowledge base, Qdrant Cloud as vector store service and https://huggingface.co/microsoft/Phi-3-mini-128k-instruct as LLM (provided via API from https://huggingface.co/spaces/eswardivi/Phi-3-mini-128k-instruct by @eswardivi), I built an AI assistant to help you find climate-oriented solutions for your investments, companies, or simply for your everyday life๐ŸŽ’. Find it here: https://huggingface.co/spaces/as-cle-bert/cLLiMateChat GitHub: https://github.com/AstraBert/qdrant-ai-chat Website: https://astrabert.github.io/qdrant-ai-chat/ Be kind to our Planet, we only got one๐Ÿ’š (Shout-outs to @JohnSmith9982 whose https://huggingface.co/spaces/JohnSmith9982/small_and_pretty Gradio theme was used to build my application๐Ÿš€) PS: ๐ŸŒฑCurious of knowing what is your carbon footprint? Head over to this ML-backed HF Space I built to discover it: https://huggingface.co/spaces/as-cle-bert/carbon-footprint-predictor
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2024-06-05T21:08:21.000Z
2024-06-08T09:33:08.805Z
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/posts/as-cle-bert/387352788347389
1,461
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859744242518946
[ { "type": "text", "value": "THUDM has released GLM-4V-9B and it's.. chatty! ๐Ÿ˜‚ ", "raw": "THUDM has released GLM-4V-9B and it's.. chatty! ๐Ÿ˜‚ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I asked it to describe my favorite Howl's Moving Castle scene and here's how it went ๐Ÿ‘‡๐Ÿป", "raw": "I asked it to describe my favorite Howl's Moving Castle scene and here's how it went ๐Ÿ‘‡๐Ÿป", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "joke aside it seems to outperform the previous VLMs. however the license isn't open-source ๐Ÿ“ˆ ", "raw": "joke aside it seems to outperform the previous VLMs. however the license isn't open-source ๐Ÿ“ˆ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "model repo: ", "raw": "model repo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/THUDM/glm-4v-9b", "href": null, "resource": { "type": "model", "id": "THUDM/glm-4v-9b", "discussionNum": null }, "url": "https://huggingface.co/THUDM/glm-4v-9b", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "a community member has built a demo: ", "raw": "a community member has built a demo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/vilarin/VL-Chatbox", "href": null, "resource": { "type": "space", "id": "vilarin/VL-Chatbox", "discussionNum": null }, "url": "https://huggingface.co/spaces/vilarin/VL-Chatbox", "code": null, "user": null, "label": null, "lang": null } ]
THUDM has released GLM-4V-9B and it's.. chatty! ๐Ÿ˜‚ I asked it to describe my favorite Howl's Moving Castle scene and here's how it went ๐Ÿ‘‡๐Ÿป joke aside it seems to outperform the previous VLMs. however the license isn't open-source ๐Ÿ“ˆ model repo: https://huggingface.co/THUDM/glm-4v-9b a community member has built a demo: https://huggingface.co/spaces/vilarin/VL-Chatbox
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2024-06-05T19:59:48.000Z
2024-06-06T04:35:17.154Z
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/posts/merve/859744242518946
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760542103453473
[ { "type": "text", "value": "I decided to play around with FluentlyXL v4 ๐Ÿ˜‰", "raw": "I decided to play around with FluentlyXL v4 ๐Ÿ˜‰", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘‰ Model: ", "raw": "๐Ÿ‘‰ Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/fluently/Fluently-XL-v4", "href": null, "resource": { "type": "model", "id": "fluently/Fluently-XL-v4", "discussionNum": null }, "url": "https://huggingface.co/fluently/Fluently-XL-v4", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœจ Playground: ", "raw": "โœจ Playground: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/fluently/Fluently-Playground", "href": null, "resource": { "type": "space", "id": "fluently/Fluently-Playground", "discussionNum": null }, "url": "https://huggingface.co/spaces/fluently/Fluently-Playground", "code": null, "user": null, "label": null, "lang": null } ]
I decided to play around with FluentlyXL v4 ๐Ÿ˜‰ ๐Ÿ‘‰ Model: https://huggingface.co/fluently/Fluently-XL-v4 โœจ Playground: https://huggingface.co/spaces/fluently/Fluently-Playground
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[]
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2024-06-05T19:40:37.000Z
2024-06-05T19:40:37.970Z
[]
/posts/ehristoforu/760542103453473
1,906
0
588142490319312
[ { "type": "text", "value": "๐Ÿ“ข The most recent Mistral-7B-Instruct-v0.3 release showcases more robust advances in zero-shot-mode mode on Target Sentiment Analysis.", "raw": "๐Ÿ“ข The most recent Mistral-7B-Instruct-v0.3 release showcases more robust advances in zero-shot-mode mode on Target Sentiment Analysis.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿงช We experiment with the original texts (๐Ÿ‡ท๐Ÿ‡บ ) and their translated version into English (๐Ÿ‡บ๐Ÿ‡ธ).", "raw": "๐Ÿงช We experiment with the original texts (๐Ÿ‡ท๐Ÿ‡บ ) and their translated version into English (๐Ÿ‡บ๐Ÿ‡ธ).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ’ก The key takeaways on the expectation from this model are as follows:", "raw": "๐Ÿ’ก The key takeaways on the expectation from this model are as follows:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ”๏ธ 1. On translated texts into English (๐Ÿ‡บ๐Ÿ‡ธ), it surpasses LLaMA-3 and and nearly touches MOE Mixtral 8x7B versions being quite precise by F1 across all the classes by F1(PN)", "raw": "โœ”๏ธ 1. On translated texts into English (๐Ÿ‡บ๐Ÿ‡ธ), it surpasses LLaMA-3 and and nearly touches MOE Mixtral 8x7B versions being quite precise by F1 across all the classes by F1(PN)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ”๏ธ2. On orignal texts (๐Ÿ‡ท๐Ÿ‡บ) It slightly surpasses LLaMA-3 by F1(PN) by being less tolerant in neutral (F1(PN0)). Using larger versions (Mixtral) are still preferable choice for reasoning ๐Ÿง  in non-eng texts.", "raw": "โœ”๏ธ2. On orignal texts (๐Ÿ‡ท๐Ÿ‡บ) It slightly surpasses LLaMA-3 by F1(PN) by being less tolerant in neutral (F1(PN0)). Using larger versions (Mixtral) are still preferable choice for reasoning ๐Ÿง  in non-eng texts.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ”๏ธ3. You can clearly see the difference between 7B version and MOE (figure 3) by F1(PN0)", "raw": "โœ”๏ธ3. You can clearly see the difference between 7B version and MOE (figure 3) by F1(PN0)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Benchmark: ", "raw": "Benchmark: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/nicolay-r/RuSentNE-LLM-Benchmark", "href": "https://github.com/nicolay-r/RuSentNE-LLM-Benchmark", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model: ", "raw": "Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3", "href": null, "resource": { "type": "model", "id": "mistralai/Mistral-7B-Instruct-v0.3", "discussionNum": null }, "url": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Dataset: ", "raw": "Dataset: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/dialogue-evaluation/RuSentNE-evaluation", "href": "https://github.com/dialogue-evaluation/RuSentNE-evaluation", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)", "raw": "Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Collection: ", "raw": "Collection: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101", "href": "https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ“ข The most recent Mistral-7B-Instruct-v0.3 release showcases more robust advances in zero-shot-mode mode on Target Sentiment Analysis. ๐Ÿงช We experiment with the original texts (๐Ÿ‡ท๐Ÿ‡บ ) and their translated version into English (๐Ÿ‡บ๐Ÿ‡ธ). ๐Ÿ’ก The key takeaways on the expectation from this model are as follows: โœ”๏ธ 1. On translated texts into English (๐Ÿ‡บ๐Ÿ‡ธ), it surpasses LLaMA-3 and and nearly touches MOE Mixtral 8x7B versions being quite precise by F1 across all the classes by F1(PN) โœ”๏ธ2. On orignal texts (๐Ÿ‡ท๐Ÿ‡บ) It slightly surpasses LLaMA-3 by F1(PN) by being less tolerant in neutral (F1(PN0)). Using larger versions (Mixtral) are still preferable choice for reasoning ๐Ÿง  in non-eng texts. โœ”๏ธ3. You can clearly see the difference between 7B version and MOE (figure 3) by F1(PN0) Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark Model: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3 Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342) Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101
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2024-06-05T09:23:29.000Z
2024-06-05T09:30:07.493Z
[]
/posts/nicolay-r/588142490319312
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A great vision language benchmark: MM-UPD evaluates how model responds to unsolvable problems ๐Ÿค“ LLaVA 1.6 is outperforming proprietary VLMs, making it a very robust choice for production! It is now hosted as a leaderboard https://huggingface.co/spaces/MM-UPD/MM-UPD_Leaderboard ๐Ÿ†๐Ÿ’•
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2024-06-05T08:57:30.000Z
2024-06-05T08:57:30.329Z
[]
/posts/merve/612249613019261
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Hello, Vision World! https://huggingface.co/AI4Chem/ChemLLM-20B-Chat-DPO https://huggingface.co/papers/2402.06852
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2024-06-05T07:39:10.000Z
2024-06-06T09:58:18.842Z
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/posts/qq8933/295094188881098
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[ { "type": "text", "value": "Can anyone see my post on๐Ÿค—?", "raw": "Can anyone see my post on๐Ÿค—?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Reply with ๐Ÿค—", "raw": "Reply with ๐Ÿค—", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Can anyone see my post on๐Ÿค—? Reply with ๐Ÿค—
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2024-06-05T06:35:07.000Z
2024-06-07T11:19:47.661Z
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/posts/turiabu/207378507585859
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[ { "type": "text", "value": "๐Ÿง  Have you ever heard of neurons running as a computer?", "raw": "๐Ÿง  Have you ever heard of neurons running as a computer?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ’ป If you are curious about the so-called \"brainoware\", a hardware built upon a brain organoid and used for AI and ML tasks, you may want to read my latest ๐Ÿค— article: ", "raw": "๐Ÿ’ป If you are curious about the so-called \"brainoware\", a hardware built upon a brain organoid and used for AI and ML tasks, you may want to read my latest ๐Ÿค— article: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/as-cle-bert/brain-next-generation-neurons", "href": "https://huggingface.co/blog/as-cle-bert/brain-next-generation-neurons", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ’กEnjoy!", "raw": "๐Ÿ’กEnjoy!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿง  Have you ever heard of neurons running as a computer? ๐Ÿ’ป If you are curious about the so-called "brainoware", a hardware built upon a brain organoid and used for AI and ML tasks, you may want to read my latest ๐Ÿค— article: https://huggingface.co/blog/as-cle-bert/brain-next-generation-neurons ๐Ÿ’กEnjoy!
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2024-06-04T20:39:32.000Z
2024-06-06T03:49:02.953Z
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[ { "type": "text", "value": "Remember when ", "raw": "Remember when ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Microsoft", "href": null, "resource": null, "url": null, "code": null, "user": "Microsoft", "label": null, "lang": null }, { "type": "text", "value": " released Phi-3 models... ๐Ÿค”", "raw": " released Phi-3 models... ๐Ÿค”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Yup, the ones that had ๐Ÿฆ™Llama 3 8B beat on MMLU using 3.8B parameters! ๐Ÿ†", "raw": "Yup, the ones that had ๐Ÿฆ™Llama 3 8B beat on MMLU using 3.8B parameters! ๐Ÿ†", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Now they are on the LMSYS Chatbot Arena Leaderboard! ๐Ÿ“Š๐Ÿ“ˆ", "raw": "Now they are on the LMSYS Chatbot Arena Leaderboard! ๐Ÿ“Š๐Ÿ“ˆ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Medium(14B) ranks near GPT-3.5-Turbo-0613, but behind Llama 3 8B. ๐Ÿ“‰", "raw": "Medium(14B) ranks near GPT-3.5-Turbo-0613, but behind Llama 3 8B. ๐Ÿ“‰", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Phi-3 Small(7B) is close to Llama-2-70B, and Mistral fine-tunes. ๐Ÿ“Š", "raw": "Phi-3 Small(7B) is close to Llama-2-70B, and Mistral fine-tunes. ๐Ÿ“Š", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "What about the Phi-3 Mini(3.8B), that was giving Llama 3 8B a run for its money on MMLU? It gets an arena score of 1037 (#73) against 1153 (#22) of Llama 3 8B ๐Ÿคผ", "raw": "What about the Phi-3 Mini(3.8B), that was giving Llama 3 8B a run for its money on MMLU? It gets an arena score of 1037 (#73) against 1153 (#22) of Llama 3 8B ๐Ÿคผ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Looks like there is a struggle here between perplexity and inherent knowledge! ๐Ÿค”", "raw": "Looks like there is a struggle here between perplexity and inherent knowledge! ๐Ÿค”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "And Microsoft picked knowledge with high perplexity ๐Ÿง ", "raw": "And Microsoft picked knowledge with high perplexity ๐Ÿง ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Now I am even more intrigued: what is ", "raw": "Now I am even more intrigued: what is ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Meta", "href": null, "resource": null, "url": null, "code": null, "user": "Meta", "label": null, "lang": null }, { "type": "text", "value": " feeding its ๐Ÿฆ™ Llamas?๐ŸŒพ", "raw": " feeding its ๐Ÿฆ™ Llamas?๐ŸŒพ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ† Leaderboard: ", "raw": "๐Ÿ† Leaderboard: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://chat.lmsys.org/?leaderboard", "href": "https://chat.lmsys.org/?leaderboard", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Remember when @Microsoft released Phi-3 models... ๐Ÿค” Yup, the ones that had ๐Ÿฆ™Llama 3 8B beat on MMLU using 3.8B parameters! ๐Ÿ† Now they are on the LMSYS Chatbot Arena Leaderboard! ๐Ÿ“Š๐Ÿ“ˆ Medium(14B) ranks near GPT-3.5-Turbo-0613, but behind Llama 3 8B. ๐Ÿ“‰ Phi-3 Small(7B) is close to Llama-2-70B, and Mistral fine-tunes. ๐Ÿ“Š What about the Phi-3 Mini(3.8B), that was giving Llama 3 8B a run for its money on MMLU? It gets an arena score of 1037 (#73) against 1153 (#22) of Llama 3 8B ๐Ÿคผ Looks like there is a struggle here between perplexity and inherent knowledge! ๐Ÿค” And Microsoft picked knowledge with high perplexity ๐Ÿง  Now I am even more intrigued: what is @Meta feeding its ๐Ÿฆ™ Llamas?๐ŸŒพ ๐Ÿ† Leaderboard: https://chat.lmsys.org/?leaderboard
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2024-06-04T18:36:24.000Z
2024-06-04T18:36:24.525Z
[]
/posts/singhsidhukuldeep/654448211400768
1,592
0
135453923650058
[ { "type": "text", "value": "One shot evaluations is hard. That is honestly what I learnt throughout the last couple of weeks trying to make imgsys.org data more and more relevant. There is just so much diversity in these models that saying one is better than other one even at a particular domain is impossible. ", "raw": "One shot evaluations is hard. That is honestly what I learnt throughout the last couple of weeks trying to make imgsys.org data more and more relevant. There is just so much diversity in these models that saying one is better than other one even at a particular domain is impossible. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "If you have any suggestions on how we can make the testing easier for one shot, single question image model testing; please give your suggestions under this thread so we can provide a more meaningful data point to the community!", "raw": "If you have any suggestions on how we can make the testing easier for one shot, single question image model testing; please give your suggestions under this thread so we can provide a more meaningful data point to the community!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
One shot evaluations is hard. That is honestly what I learnt throughout the last couple of weeks trying to make imgsys.org data more and more relevant. There is just so much diversity in these models that saying one is better than other one even at a particular domain is impossible. If you have any suggestions on how we can make the testing easier for one shot, single question image model testing; please give your suggestions under this thread so we can provide a more meaningful data point to the community!
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2024-06-04T17:55:00.000Z
2024-06-04T17:55:00.766Z
[]
/posts/isidentical/135453923650058
1,245
0
449913250763619
[ { "type": "text", "value": "> We introduced a new model designed for the Code generation task. Its test accuracy on the HumanEval base dataset surpasses that of GPT-4 Turbo (April 2024). (90.9% vs 90.2%).", "raw": "> We introduced a new model designed for the Code generation task. Its test accuracy on the HumanEval base dataset surpasses that of GPT-4 Turbo (April 2024). (90.9% vs 90.2%).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Bin12345", "href": null, "resource": null, "url": null, "code": null, "user": "Bin12345", "label": null, "lang": null }, { "type": "text", "value": " interested in a ZeroGPU Spaces for ", "raw": " interested in a ZeroGPU Spaces for ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Bin12345/AutoCoder", "href": null, "resource": { "type": "model", "id": "Bin12345/AutoCoder", "discussionNum": null }, "url": "https://huggingface.co/Bin12345/AutoCoder", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
> We introduced a new model designed for the Code generation task. Its test accuracy on the HumanEval base dataset surpasses that of GPT-4 Turbo (April 2024). (90.9% vs 90.2%). @Bin12345 interested in a ZeroGPU Spaces for https://huggingface.co/Bin12345/AutoCoder
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2024-06-04T15:42:26.000Z
2024-06-04T17:05:13.813Z
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/posts/victor/449913250763619
1,856
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369065526977408
[ { "type": "text", "value": "hf-daily-paper-newsletter-chinese๏ผŒUsing glm-4 agents, interpret hugging face's daily papers in simplified Chinese", "raw": "hf-daily-paper-newsletter-chinese๏ผŒUsing glm-4 agents, interpret hugging face's daily papers in simplified Chinese", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/2404589803/hf-daily-paper-newsletter-chinese", "href": "https://github.com/2404589803/hf-daily-paper-newsletter-chinese", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
hf-daily-paper-newsletter-chinese๏ผŒUsing glm-4 agents, interpret hugging face's daily papers in simplified Chinese https://github.com/2404589803/hf-daily-paper-newsletter-chinese
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2024-06-04T15:39:25.000Z
2024-06-04T15:39:25.758Z
[]
/posts/roseking/369065526977408
1,111
0
604608285816489
[ { "type": "text", "value": "Crazy to see that my GroundingDino contribution to the transformers library got roughly 600k downloads combined in the checkpoints in the last month ๐Ÿคฏ ", "raw": "Crazy to see that my GroundingDino contribution to the transformers library got roughly 600k downloads combined in the checkpoints in the last month ๐Ÿคฏ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The tiny checkpoint got almost 500k alone ", "raw": "The tiny checkpoint got almost 500k alone ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/IDEA-Research/grounding-dino-tiny", "href": null, "resource": { "type": "model", "id": "IDEA-Research/grounding-dino-tiny", "discussionNum": null }, "url": "https://huggingface.co/IDEA-Research/grounding-dino-tiny", "code": null, "user": null, "label": null, "lang": null } ]
Crazy to see that my GroundingDino contribution to the transformers library got roughly 600k downloads combined in the checkpoints in the last month ๐Ÿคฏ The tiny checkpoint got almost 500k alone https://huggingface.co/IDEA-Research/grounding-dino-tiny
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2024-06-04T15:27:26.000Z
2024-06-04T15:27:26.211Z
[]
/posts/EduardoPacheco/604608285816489
1,678
0
741982385517695
[ { "type": "text", "value": "๐Ÿ™‚ Hello! FluentlyXL is now on Venice.ai, you can try the model there right now.", "raw": "๐Ÿ™‚ Hello! FluentlyXL is now on Venice.ai, you can try the model there right now.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘‰ Venice.ai: ", "raw": "๐Ÿ‘‰ Venice.ai: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://venice.ai", "href": "https://venice.ai", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœจ๏ธ FluentlyXL v4: ", "raw": "โœจ๏ธ FluentlyXL v4: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/fluently/Fluently-XL-v4", "href": null, "resource": { "type": "model", "id": "fluently/Fluently-XL-v4", "discussionNum": null }, "url": "https://huggingface.co/fluently/Fluently-XL-v4", "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ™‚ Hello! FluentlyXL is now on Venice.ai, you can try the model there right now. ๐Ÿ‘‰ Venice.ai: https://venice.ai โœจ๏ธ FluentlyXL v4: https://huggingface.co/fluently/Fluently-XL-v4
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2024-06-04T13:33:26.000Z
2024-06-05T17:07:25.128Z
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/posts/ehristoforu/741982385517695
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[ { "type": "text", "value": "โœ‚๏ธ Uncensor any LLM with abliteration", "raw": "โœ‚๏ธ Uncensor any LLM with abliteration", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I wrote an article about abliteration and how NeuralDaredevil-8B was created. Beyond removing alignment, I believe it's an interesting technique with a lot of potential. It's basically fine-tuning without retraining.", "raw": "I wrote an article about abliteration and how NeuralDaredevil-8B was created. Beyond removing alignment, I believe it's an interesting technique with a lot of potential. It's basically fine-tuning without retraining.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In this article, we see how it works, implement it in Google Colab, and heal the abliterated model to recover the performance drop due to this technique. The final model is an uncensored and high-quality model with the highest MMLU score on the Open LLM Leaderboard (8B category).", "raw": "In this article, we see how it works, implement it in Google Colab, and heal the abliterated model to recover the performance drop due to this technique. The final model is an uncensored and high-quality model with the highest MMLU score on the Open LLM Leaderboard (8B category).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/mlabonne/abliteration", "href": "https://huggingface.co/blog/mlabonne/abliteration", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
โœ‚๏ธ Uncensor any LLM with abliteration I wrote an article about abliteration and how NeuralDaredevil-8B was created. Beyond removing alignment, I believe it's an interesting technique with a lot of potential. It's basically fine-tuning without retraining. In this article, we see how it works, implement it in Google Colab, and heal the abliterated model to recover the performance drop due to this technique. The final model is an uncensored and high-quality model with the highest MMLU score on the Open LLM Leaderboard (8B category). https://huggingface.co/blog/mlabonne/abliteration
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[]
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2024-06-04T13:30:39.000Z
2024-10-24T14:44:02.450Z
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/posts/mlabonne/866788930457283
16,277
24
417417709655682
[ { "type": "text", "value": "It is with great pleasure I inform you that huggingface's ModelHubMixin reached 200+ models on the hub ๐Ÿฅณ", "raw": "It is with great pleasure I inform you that huggingface's ModelHubMixin reached 200+ models on the hub ๐Ÿฅณ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "ModelHubMixin is a class developed by HF to integrate AI models with the hub with ease and it comes with 3 methods :", "raw": "ModelHubMixin is a class developed by HF to integrate AI models with the hub with ease and it comes with 3 methods :", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* save_pretrained ", "raw": "* save_pretrained ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* from_pretrained", "raw": "* from_pretrained", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* push_to_hub", "raw": "* push_to_hub", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Shoutout to ", "raw": "Shoutout to ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@nielsr", "href": null, "resource": null, "url": null, "code": null, "user": "nielsr", "label": null, "lang": null }, { "type": "text", "value": " , ", "raw": " , ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@Wauplin", "href": null, "resource": null, "url": null, "code": null, "user": "Wauplin", "label": null, "lang": null }, { "type": "text", "value": " and everyone else on HF for their awesome work ๐Ÿค—", "raw": " and everyone else on HF for their awesome work ๐Ÿค—", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "If you are not familiar with ModelHubMixin and you are looking for extra resources you might consider : ", "raw": "If you are not familiar with ModelHubMixin and you are looking for extra resources you might consider : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* docs: ", "raw": "* docs: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/docs/huggingface_hub/main/en/package_reference/mixins", "href": "https://huggingface.co/docs/huggingface_hub/main/en/package_reference/mixins", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”—blog about training models with the trainer API and using ModelHubMixin: ", "raw": "๐Ÿ”—blog about training models with the trainer API and using ModelHubMixin: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/not-lain/trainer-api-and-mixin-classes", "href": "https://huggingface.co/blog/not-lain/trainer-api-and-mixin-classes", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”—GitHub repo with pip integration: ", "raw": "๐Ÿ”—GitHub repo with pip integration: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/not-lain/PyTorchModelHubMixin-template", "href": "https://github.com/not-lain/PyTorchModelHubMixin-template", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”—basic guide: ", "raw": "๐Ÿ”—basic guide: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/posts/not-lain/884273241241808", "href": "https://huggingface.co/posts/not-lain/884273241241808", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
It is with great pleasure I inform you that huggingface's ModelHubMixin reached 200+ models on the hub ๐Ÿฅณ ModelHubMixin is a class developed by HF to integrate AI models with the hub with ease and it comes with 3 methods : * save_pretrained * from_pretrained * push_to_hub Shoutout to @nielsr , @Wauplin and everyone else on HF for their awesome work ๐Ÿค— If you are not familiar with ModelHubMixin and you are looking for extra resources you might consider : * docs: https://huggingface.co/docs/huggingface_hub/main/en/package_reference/mixins ๐Ÿ”—blog about training models with the trainer API and using ModelHubMixin: https://huggingface.co/blog/not-lain/trainer-api-and-mixin-classes ๐Ÿ”—GitHub repo with pip integration: https://github.com/not-lain/PyTorchModelHubMixin-template ๐Ÿ”—basic guide: https://huggingface.co/posts/not-lain/884273241241808
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2024-06-04T12:52:02.000Z
2024-06-04T12:52:02.764Z
[]
/posts/not-lain/417417709655682
2,065
0
183035058595618
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@victor unprompted feature request: I'd love to have a toggle for a HF collection to control whether new items are added to the top or to the bottom. At the moment everything gets added at the bottom, but it would be great to have newer elements on top to make fresh content easily accessible without having to scroll all the way!
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[]
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[]
2024-06-04T09:51:00.000Z
2024-10-28T10:18:34.806Z
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/posts/gsarti/183035058595618
1,546
3
615386996098522
[ { "type": "text", "value": "New research model out ! I uploaded a new Branchy model based on Phi-2 for faster inference using Early Exit. Check it out : ", "raw": "New research model out ! I uploaded a new Branchy model based on Phi-2 for faster inference using Early Exit. Check it out : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/valcore/Branchy-Phi-2", "href": null, "resource": { "type": "model", "id": "valcore/Branchy-Phi-2", "discussionNum": null }, "url": "https://huggingface.co/valcore/Branchy-Phi-2", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ". ", "raw": ". ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I also uploaded a Hugging Face Space to try it out : ", "raw": "I also uploaded a Hugging Face Space to try it out : ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/valcore/Branchy-phi-2", "href": null, "resource": { "type": "space", "id": "valcore/Branchy-phi-2", "discussionNum": null }, "url": "https://huggingface.co/spaces/valcore/Branchy-phi-2", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ", unfortunately inference is very slow on free tier. Let me know what you are thinking about it !", "raw": ", unfortunately inference is very slow on free tier. Let me know what you are thinking about it !", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
New research model out ! I uploaded a new Branchy model based on Phi-2 for faster inference using Early Exit. Check it out : https://huggingface.co/valcore/Branchy-Phi-2. I also uploaded a Hugging Face Space to try it out : https://huggingface.co/spaces/valcore/Branchy-phi-2, unfortunately inference is very slow on free tier. Let me know what you are thinking about it !
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[]
[]
[]
2024-06-04T09:34:59.000Z
2024-06-04T09:34:59.695Z
[]
/posts/valcore/615386996098522
816
0
892134435688447
[ { "type": "text", "value": "We are super happy to contribute to the GLiNER ecosystem by optimizing training code and releasing a multi-task, prompt-tunable model.", "raw": "We are super happy to contribute to the GLiNER ecosystem by optimizing training code and releasing a multi-task, prompt-tunable model.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The model can be used for the following tasks:", "raw": "The model can be used for the following tasks:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Named entity recognition (NER);", "raw": "* Named entity recognition (NER);", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Open information extraction;", "raw": "* Open information extraction;", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Question answering;", "raw": "* Question answering;", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Relation extraction;", "raw": "* Relation extraction;", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Summarization;", "raw": "* Summarization;", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model: ", "raw": "Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/knowledgator/gliner-multitask-large-v0.5", "href": null, "resource": { "type": "model", "id": "knowledgator/gliner-multitask-large-v0.5", "discussionNum": null }, "url": "https://huggingface.co/knowledgator/gliner-multitask-large-v0.5", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Demo: ", "raw": "Demo: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/knowledgator/GLiNER_HandyLab", "href": null, "resource": { "type": "space", "id": "knowledgator/GLiNER_HandyLab", "discussionNum": null }, "url": "https://huggingface.co/spaces/knowledgator/GLiNER_HandyLab", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Repo: ๐Ÿ‘จโ€๐Ÿ’ป ", "raw": "Repo: ๐Ÿ‘จโ€๐Ÿ’ป ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/urchade/GLiNER", "href": "https://github.com/urchade/GLiNER", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "**How to use**", "raw": "**How to use**", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "First of all, install gliner package.", "raw": "First of all, install gliner package.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```bash\npip install gliner\n```", "href": null, "resource": null, "url": null, "code": "pip install gliner", "user": null, "label": null, "lang": "bash" }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Then try the following code:", "raw": "Then try the following code:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```python\nfrom gliner import GLiNER\n\nmodel = GLiNER.from_pretrained(\"knowledgator/gliner_small-v2.1\")\n\nprompt = \"\"\"Find all positive aspects about the product:\\n\"\"\"\ntext = \"\"\"\nI recently purchased the Sony WH-1000XM4 Wireless Noise-Canceling Headphones from Amazon and I must say, I'm thoroughly impressed. The package arrived in New York within 2 days, thanks to Amazon Prime's expedited shipping.\n\nThe headphones themselves are remarkable. The noise-canceling feature works like a charm in the bustling city environment, and the 30-hour battery life means I don't have to charge them every day. Connecting them to my Samsung Galaxy S21 was a breeze, and the sound quality is second to none.\nI also appreciated the customer service from Amazon when I had a question about the warranty. They responded within an hour and provided all the information I needed.\nHowever, the headphones did not come with a hard case, which was listed in the product description. I contacted Amazon, and they offered a 10% discount on my next purchase as an apology.\nOverall, I'd give these headphones a 4.5/5 rating and highly recommend them to anyone looking for top-notch quality in both product and service.\n\"\"\"\ninput_ = prompt+text\n\nlabels = [\"match\"]\n\nmatches = model.predict_entities(input_, labels)\n\nfor match in matches:\n print(match[\"text\"], \"=>\", match[\"score\"])\n```", "href": null, "resource": null, "url": null, "code": "from gliner import GLiNER\n\nmodel = GLiNER.from_pretrained(\"knowledgator/gliner_small-v2.1\")\n\nprompt = \"\"\"Find all positive aspects about the product:\\n\"\"\"\ntext = \"\"\"\nI recently purchased the Sony WH-1000XM4 Wireless Noise-Canceling Headphones from Amazon and I must say, I'm thoroughly impressed. The package arrived in New York within 2 days, thanks to Amazon Prime's expedited shipping.\n\nThe headphones themselves are remarkable. The noise-canceling feature works like a charm in the bustling city environment, and the 30-hour battery life means I don't have to charge them every day. Connecting them to my Samsung Galaxy S21 was a breeze, and the sound quality is second to none.\nI also appreciated the customer service from Amazon when I had a question about the warranty. They responded within an hour and provided all the information I needed.\nHowever, the headphones did not come with a hard case, which was listed in the product description. I contacted Amazon, and they offered a 10% discount on my next purchase as an apology.\nOverall, I'd give these headphones a 4.5/5 rating and highly recommend them to anyone looking for top-notch quality in both product and service.\n\"\"\"\ninput_ = prompt+text\n\nlabels = [\"match\"]\n\nmatches = model.predict_entities(input_, labels)\n\nfor match in matches:\n print(match[\"text\"], \"=>\", match[\"score\"])", "user": null, "label": null, "lang": "python" }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
We are super happy to contribute to the GLiNER ecosystem by optimizing training code and releasing a multi-task, prompt-tunable model. The model can be used for the following tasks: * Named entity recognition (NER); * Open information extraction; * Question answering; * Relation extraction; * Summarization; Model: https://huggingface.co/knowledgator/gliner-multitask-large-v0.5 Demo: https://huggingface.co/spaces/knowledgator/GLiNER_HandyLab Repo: ๐Ÿ‘จโ€๐Ÿ’ป https://github.com/urchade/GLiNER **How to use** First of all, install gliner package. ```bash pip install gliner ``` Then try the following code: ```python from gliner import GLiNER model = GLiNER.from_pretrained("knowledgator/gliner_small-v2.1") prompt = """Find all positive aspects about the product:\n""" text = """ I recently purchased the Sony WH-1000XM4 Wireless Noise-Canceling Headphones from Amazon and I must say, I'm thoroughly impressed. The package arrived in New York within 2 days, thanks to Amazon Prime's expedited shipping. The headphones themselves are remarkable. The noise-canceling feature works like a charm in the bustling city environment, and the 30-hour battery life means I don't have to charge them every day. Connecting them to my Samsung Galaxy S21 was a breeze, and the sound quality is second to none. I also appreciated the customer service from Amazon when I had a question about the warranty. They responded within an hour and provided all the information I needed. However, the headphones did not come with a hard case, which was listed in the product description. I contacted Amazon, and they offered a 10% discount on my next purchase as an apology. Overall, I'd give these headphones a 4.5/5 rating and highly recommend them to anyone looking for top-notch quality in both product and service. """ input_ = prompt+text labels = ["match"] matches = model.predict_entities(input_, labels) for match in matches: print(match["text"], "=>", match["score"]) ```
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[]
[]
[]
2024-06-04T09:19:09.000Z
2024-06-04T09:29:59.857Z
[]
/posts/Ihor/892134435688447
796
0
379847066413347
[ { "type": "text", "value": "my ๐Ÿค—huggingface activity for 2024 so far ...", "raw": "my ๐Ÿค—huggingface activity for 2024 so far ...", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "dont tell my boss... ", "raw": "dont tell my boss... ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "check yours too now, it's fun ๐Ÿค—", "raw": "check yours too now, it's fun ๐Ÿค—", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
my ๐Ÿค—huggingface activity for 2024 so far ... dont tell my boss... check yours too now, it's fun ๐Ÿค—
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2024-06-04T07:59:24.000Z
2024-06-04T13:50:55.105Z
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/posts/Tonic/379847066413347
1,938
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280895787141155
[ { "type": "text", "value": "Iโ€™ve been working on a crazy theory for my first solo paper and I would appreciate some advice from leading researchers here:)", "raw": "Iโ€™ve been working on a crazy theory for my first solo paper and I would appreciate some advice from leading researchers here:)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "\"Theory of Adaptive Learning\"", "raw": "\"Theory of Adaptive Learning\"", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Of all the deep learning algorithms at least to my knowledge, thereโ€™s none that fully covers the adaptive nature of intelligence. I believe it is a fundamental missing component of current AI governing laws.", "raw": "Of all the deep learning algorithms at least to my knowledge, thereโ€™s none that fully covers the adaptive nature of intelligence. I believe it is a fundamental missing component of current AI governing laws.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "I define it as a kind of learning wherein one person (say a student) adapts their framework of understanding to better suit that of what is being taught or said by another person/model (say a teacher). If we could measure the nature of this transfer learning. I believe it could help improve planning and reasoning capabilities of AI systems. If we look back at the theory evolution, adaptation is a fundamental component of human evolution. Today's so-called groundbreaking architectures or models, specifically large language models tend to have static parameters with constraints that are almost impossible to change or update in real-time after training. This fundamentally hinders their ability to reason, plan and accomplish objective-driven tasks as we humans do. Intelligence is dynamic.", "raw": "I define it as a kind of learning wherein one person (say a student) adapts their framework of understanding to better suit that of what is being taught or said by another person/model (say a teacher). If we could measure the nature of this transfer learning. I believe it could help improve planning and reasoning capabilities of AI systems. If we look back at the theory evolution, adaptation is a fundamental component of human evolution. Today's so-called groundbreaking architectures or models, specifically large language models tend to have static parameters with constraints that are almost impossible to change or update in real-time after training. This fundamentally hinders their ability to reason, plan and accomplish objective-driven tasks as we humans do. Intelligence is dynamic.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Now this cannot be done with current autoregressive llms as their parameters are fixed with static constraints, even though RAG do help in updating model parameters in real-time but its basically cheating and doesnโ€™t count as intelligence. Thereโ€™s a pressing need for a natively adaptive architecture - The Goal of This Paper", "raw": "Now this cannot be done with current autoregressive llms as their parameters are fixed with static constraints, even though RAG do help in updating model parameters in real-time but its basically cheating and doesnโ€™t count as intelligence. Thereโ€™s a pressing need for a natively adaptive architecture - The Goal of This Paper", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Iโ€™ve been working on a crazy theory for my first solo paper and I would appreciate some advice from leading researchers here:) "Theory of Adaptive Learning" Of all the deep learning algorithms at least to my knowledge, thereโ€™s none that fully covers the adaptive nature of intelligence. I believe it is a fundamental missing component of current AI governing laws. I define it as a kind of learning wherein one person (say a student) adapts their framework of understanding to better suit that of what is being taught or said by another person/model (say a teacher). If we could measure the nature of this transfer learning. I believe it could help improve planning and reasoning capabilities of AI systems. If we look back at the theory evolution, adaptation is a fundamental component of human evolution. Today's so-called groundbreaking architectures or models, specifically large language models tend to have static parameters with constraints that are almost impossible to change or update in real-time after training. This fundamentally hinders their ability to reason, plan and accomplish objective-driven tasks as we humans do. Intelligence is dynamic. Now this cannot be done with current autoregressive llms as their parameters are fixed with static constraints, even though RAG do help in updating model parameters in real-time but its basically cheating and doesnโ€™t count as intelligence. Thereโ€™s a pressing need for a natively adaptive architecture - The Goal of This Paper
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[]
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2024-06-04T04:01:32.000Z
2024-06-20T06:16:05.283Z
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/posts/Jaward/280895787141155
1,665
3
435428954049091
[ { "type": "text", "value": "hello everyone, I've finished making a project for RVC Dataset Maker if you want to try the project you can try it below:", "raw": "hello everyone, I've finished making a project for RVC Dataset Maker if you want to try the project you can try it below:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/Hev832/RVC-Dataset-Maker", "href": null, "resource": { "type": "space", "id": "Hev832/RVC-Dataset-Maker", "discussionNum": null }, "url": "https://huggingface.co/spaces/Hev832/RVC-Dataset-Maker", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "if you find any erors you can create New discussion ๐Ÿ‘€ ", "raw": "if you find any erors you can create New discussion ๐Ÿ‘€ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
hello everyone, I've finished making a project for RVC Dataset Maker if you want to try the project you can try it below: https://huggingface.co/spaces/Hev832/RVC-Dataset-Maker if you find any erors you can create New discussion ๐Ÿ‘€
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2024-06-03T22:09:32.000Z
2024-06-04T00:11:40.739Z
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/posts/Hev832/435428954049091
2,436
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185154926901932
[ { "type": "text", "value": "Hi ", "raw": "Hi ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@coyotte508", "href": null, "resource": null, "url": null, "code": null, "user": "coyotte508", "label": null, "lang": null }, { "type": "text", "value": " , if you've got a minute, could you take a look at this huggingface.js discussion?", "raw": " , if you've got a minute, could you take a look at this huggingface.js discussion?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/huggingface/huggingface.js/discussions/735", "href": "https://github.com/huggingface/huggingface.js/discussions/735", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Hi @coyotte508 , if you've got a minute, could you take a look at this huggingface.js discussion? https://github.com/huggingface/huggingface.js/discussions/735
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[]
2024-06-03T20:34:33.000Z
2024-06-03T20:34:33.568Z
[]
/posts/hideosnes/185154926901932
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[ { "type": "text", "value": "Just published a new article ๐Ÿ˜Š", "raw": "Just published a new article ๐Ÿ˜Š", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/santiviquez/data-drift-estimate-model-performance", "href": "https://huggingface.co/blog/santiviquez/data-drift-estimate-model-performance", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Just published a new article ๐Ÿ˜Š https://huggingface.co/blog/santiviquez/data-drift-estimate-model-performance
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2024-06-03T20:14:27.000Z
2024-06-03T20:14:27.465Z
[]
/posts/santiviquez/288725162572472
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[ { "type": "text", "value": "๐Ÿ“ข Impressed with the application of the microsoft/Phi-3-mini-4k-instruct (3B) performance in zero-shot-learning (ZSL) mode reasoning ๐Ÿง  on Target Sentiment Analysis (TSA) problem. ", "raw": "๐Ÿ“ข Impressed with the application of the microsoft/Phi-3-mini-4k-instruct (3B) performance in zero-shot-learning (ZSL) mode reasoning ๐Ÿง  on Target Sentiment Analysis (TSA) problem. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ’ก There are three major takeaways out of this experiment ๐Ÿงช and they are as follows:", "raw": "๐Ÿ’ก There are three major takeaways out of this experiment ๐Ÿงช and they are as follows:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… 1. Phi-3 slightly outperforms Mistral-7B (official Mistral API, v0.1 or v0.2) on texts written in English", "raw": "โœ… 1. Phi-3 slightly outperforms Mistral-7B (official Mistral API, v0.1 or v0.2) on texts written in English", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โœ… 2. Performs similar to LLaMA-3-8B-Instruct on texts translated in English ๐Ÿ”ฅ ", "raw": "โœ… 2. Performs similar to LLaMA-3-8B-Instruct on texts translated in English ๐Ÿ”ฅ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โ˜‘๏ธ 3. Reasoning in non-english language (๐Ÿ‡ท๐Ÿ‡บ) is pretty decent but underperforms to the similar 7B sized models.", "raw": "โ˜‘๏ธ 3. Reasoning in non-english language (๐Ÿ‡ท๐Ÿ‡บ) is pretty decent but underperforms to the similar 7B sized models.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This is a huge step forward since release of Phi-2, especially because the predecessor (microsoft/phi-2) was not capable for performing reasoning in non-english texts (๐Ÿ‡ท๐Ÿ‡บ) at all!", "raw": "This is a huge step forward since release of Phi-2, especially because the predecessor (microsoft/phi-2) was not capable for performing reasoning in non-english texts (๐Ÿ‡ท๐Ÿ‡บ) at all!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Benchmark: ", "raw": "Benchmark: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/nicolay-r/RuSentNE-LLM-Benchmark", "href": "https://github.com/nicolay-r/RuSentNE-LLM-Benchmark", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model: ", "raw": "Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", "href": null, "resource": { "type": "model", "id": "microsoft/Phi-3-mini-4k-instruct", "discussionNum": null }, "url": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Dataset: ", "raw": "Dataset: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/dialogue-evaluation/RuSentNE-evaluation", "href": "https://github.com/dialogue-evaluation/RuSentNE-evaluation", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)", "raw": "Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Collection: ", "raw": "Collection: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101", "href": "https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ“ข Impressed with the application of the microsoft/Phi-3-mini-4k-instruct (3B) performance in zero-shot-learning (ZSL) mode reasoning ๐Ÿง  on Target Sentiment Analysis (TSA) problem. ๐Ÿ’ก There are three major takeaways out of this experiment ๐Ÿงช and they are as follows: โœ… 1. Phi-3 slightly outperforms Mistral-7B (official Mistral API, v0.1 or v0.2) on texts written in English โœ… 2. Performs similar to LLaMA-3-8B-Instruct on texts translated in English ๐Ÿ”ฅ โ˜‘๏ธ 3. Reasoning in non-english language (๐Ÿ‡ท๐Ÿ‡บ) is pretty decent but underperforms to the similar 7B sized models. This is a huge step forward since release of Phi-2, especially because the predecessor (microsoft/phi-2) was not capable for performing reasoning in non-english texts (๐Ÿ‡ท๐Ÿ‡บ) at all! Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark Model: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation Related paper: Large Language Models in Targeted Sentiment Analysis (2404.12342) Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101
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2024-06-03T19:17:21.000Z
2024-06-04T13:26:32.506Z
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/posts/nicolay-r/207134733299562
1,672
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239147617114976
[ { "type": "text", "value": "By popular demand, HF activity tracker v1.0 is here! ๐Ÿ“Š let's build it together!๐Ÿค—", "raw": "By popular demand, HF activity tracker v1.0 is here! ๐Ÿ“Š let's build it together!๐Ÿค—", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Lots of things to improve, feel free to open PRs in the community tab!", "raw": "Lots of things to improve, feel free to open PRs in the community tab!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "good PR ideas:", "raw": "good PR ideas:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- track more types of actions that include date+time", "raw": "- track more types of actions that include date+time", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- bigger plot", "raw": "- bigger plot", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- track discord activity too ๐Ÿคฏ", "raw": "- track discord activity too ๐Ÿคฏ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- link github? โšก", "raw": "- link github? โšก", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/huggingface-projects/LevelBot", "href": "https://huggingface.co/spaces/huggingface-projects/LevelBot", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
By popular demand, HF activity tracker v1.0 is here! ๐Ÿ“Š let's build it together!๐Ÿค— Lots of things to improve, feel free to open PRs in the community tab! good PR ideas: - track more types of actions that include date+time - bigger plot - track discord activity too ๐Ÿคฏ - link github? โšก https://huggingface.co/spaces/huggingface-projects/LevelBot
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2024-06-03T19:08:20.000Z
2024-06-04T07:57:01.633Z
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/posts/lunarflu/239147617114976
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[ { "type": "text", "value": "\"Hold your pixels\" ๐Ÿšฆ... SD3 is here ๐ŸŒŸ", "raw": "\"Hold your pixels\" ๐Ÿšฆ... SD3 is here ๐ŸŒŸ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿš€ Performance Enhancements: Stable Diffusion 3 surpasses other text-to-image models like DALLยทE 3 in typography and prompt adherence.", "raw": "๐Ÿš€ Performance Enhancements: Stable Diffusion 3 surpasses other text-to-image models like DALLยทE 3 in typography and prompt adherence.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ—๏ธ New Architecture: Introduces the Multimodal Diffusion Transformer (MMDiT) that separately processes image and language data, enhancing text understanding and spelling.", "raw": "๐Ÿ—๏ธ New Architecture: Introduces the Multimodal Diffusion Transformer (MMDiT) that separately processes image and language data, enhancing text understanding and spelling.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โšก Efficiency Improvements: Features a rectified flow formulation for more efficient image generation, fitting within the memory constraints of common GPUs.", "raw": "โšก Efficiency Improvements: Features a rectified flow formulation for more efficient image generation, fitting within the memory constraints of common GPUs.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“ˆ Scalability: Demonstrates scaling capabilities with models ranging up to 8 billion parameters, showing improvements in model performance without saturation.", "raw": "๐Ÿ“ˆ Scalability: Demonstrates scaling capabilities with models ranging up to 8 billion parameters, showing improvements in model performance without saturation.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”ง Flexible Text Encoders: Offers a flexible approach to text encoding, maintaining performance even when the largest T5 text encoder is removed for less memory-intensive operations.", "raw": "๐Ÿ”ง Flexible Text Encoders: Offers a flexible approach to text encoding, maintaining performance even when the largest T5 text encoder is removed for less memory-intensive operations.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "While they discuss experiments on 2B and 8B parameter models, no word on open weights ๐Ÿค", "raw": "While they discuss experiments on 2B and 8B parameter models, no word on open weights ๐Ÿค", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Paper: ", "raw": "Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2403.03206", "href": null, "resource": { "type": "paper", "id": "2403.03206", "discussionNum": null }, "url": "https://huggingface.co/papers/2403.03206", "code": null, "user": null, "label": "Scaling Rectified Flow Transformers for High-Resolution Image Synthesis (2403.03206)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@StabilityAI", "href": null, "resource": null, "url": null, "code": null, "user": "StabilityAI", "label": null, "lang": null } ]
"Hold your pixels" ๐Ÿšฆ... SD3 is here ๐ŸŒŸ ๐Ÿš€ Performance Enhancements: Stable Diffusion 3 surpasses other text-to-image models like DALLยทE 3 in typography and prompt adherence. ๐Ÿ—๏ธ New Architecture: Introduces the Multimodal Diffusion Transformer (MMDiT) that separately processes image and language data, enhancing text understanding and spelling. โšก Efficiency Improvements: Features a rectified flow formulation for more efficient image generation, fitting within the memory constraints of common GPUs. ๐Ÿ“ˆ Scalability: Demonstrates scaling capabilities with models ranging up to 8 billion parameters, showing improvements in model performance without saturation. ๐Ÿ”ง Flexible Text Encoders: Offers a flexible approach to text encoding, maintaining performance even when the largest T5 text encoder is removed for less memory-intensive operations. While they discuss experiments on 2B and 8B parameter models, no word on open weights ๐Ÿค Paper: https://huggingface.co/papers/2403.03206 @StabilityAI
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2024-06-03T18:32:55.000Z
2024-06-03T18:32:55.162Z
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Our approach lifts the veil on building high-quality pretraining datasets by sharing every detail about this process to enable a wider community to build on top of it.", "raw": "LLMs are only as good as the data they have been trained on, but the crucial aspect of pretraining data remains obscure. Our approach lifts the veil on building high-quality pretraining datasets by sharing every detail about this process to enable a wider community to build on top of it.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- The FineWeb-Edu dataset, which outperforms all openly accessible web datasets in a number of educational benchmarks. We built it by developing a quality classifier using annotations generated by an LLM.", "raw": "- The FineWeb-Edu dataset, which outperforms all openly accessible web datasets in a number of educational benchmarks. We built it by developing a quality classifier using annotations generated by an LLM.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- A new technical report explaining in detail how to create a large and high-quality web-scale dataset for LLM pretraining such as FineWeb", "raw": "- A new technical report explaining in detail how to create a large and high-quality web-scale dataset for LLM pretraining such as FineWeb", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘‰ ", "raw": "๐Ÿ‘‰ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1", "href": null, "resource": { "type": "space", "id": "HuggingFaceFW/blogpost-fineweb-v1", "discussionNum": null }, "url": "https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Impressed by the work of @guipenedo @hynky @loubnabnl @anton-l @craffel @lvwerra @thomwolf on FineWeb. LLMs are only as good as the data they have been trained on, but the crucial aspect of pretraining data remains obscure. Our approach lifts the veil on building high-quality pretraining datasets by sharing every detail about this process to enable a wider community to build on top of it. - The FineWeb-Edu dataset, which outperforms all openly accessible web datasets in a number of educational benchmarks. We built it by developing a quality classifier using annotations generated by an LLM. - A new technical report explaining in detail how to create a large and high-quality web-scale dataset for LLM pretraining such as FineWeb ๐Ÿ‘‰ https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1
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2024-06-03T15:28:07.000Z
2024-06-03T15:28:07.288Z
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OpenGPT 4o now features WEB SEARCH This feature enhances the capabilities of OpenGPT 4o, allowing it to fetch and integrate the latest information from the web directly into its responses. Try Now: https://huggingface.co/spaces/KingNish/OpenGPT-4o With WEB SEARCH, OpenGPT 4o becomes an even more versatile and dynamic AI, ready to assist with up-to-date data retrieval and analysis.
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2024-06-03T14:38:24.000Z
2024-07-19T02:48:33.559Z
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[ { "type": "text", "value": "There are very few of us left on Hugging Face as the market outgrows it's Ideals and evolves the Idealists will be left without industry.", "raw": "There are very few of us left on Hugging Face as the market outgrows it's Ideals and evolves the Idealists will be left without industry.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Setting up a Round Table for those interested in giving input and working on solving the issue of procuring the fastest dispersion to market saturation of retrievable intelligence, so finally Private and fully emancipated Intelligence will be considered \"In Common Use.\" ", "raw": "Setting up a Round Table for those interested in giving input and working on solving the issue of procuring the fastest dispersion to market saturation of retrievable intelligence, so finally Private and fully emancipated Intelligence will be considered \"In Common Use.\" ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "If you've ever thought,\"They will not let us have this much power for much longer..\" ", "raw": "If you've ever thought,\"They will not let us have this much power for much longer..\" ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You are right and it IS being hidden from the masses, as every breakthrough is another step towards equality, those who rely on exploitation will destroy freedom simply because they fear being uncomfortable. We know discomfort, hunger, pain. It is the Human condition. ", "raw": "You are right and it IS being hidden from the masses, as every breakthrough is another step towards equality, those who rely on exploitation will destroy freedom simply because they fear being uncomfortable. We know discomfort, hunger, pain. It is the Human condition. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Without it we would never move forward and the Norms in society today are the regression of our species.", "raw": "Without it we would never move forward and the Norms in society today are the regression of our species.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "If we do not use this chance to become better, all of us, collectively, nothing will change.", "raw": "If we do not use this chance to become better, all of us, collectively, nothing will change.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Nothing fancy, intricate or diabolical, Just giving equal opportunity to the people. Not opportunity, in one direction, behind a paywall but true choice..", "raw": "Nothing fancy, intricate or diabolical, Just giving equal opportunity to the people. Not opportunity, in one direction, behind a paywall but true choice..", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Too long have the powerful hidden behind the guise of \"Democracy,\" I say ,democracy is two wolves and a Cat voting on what to have for dinner. Liberty, in this analog, is a well informed and armed Cat asking politely for a second option, while gripping tightly his armaments with his Fuzzy Mittens!", "raw": "Too long have the powerful hidden behind the guise of \"Democracy,\" I say ,democracy is two wolves and a Cat voting on what to have for dinner. Liberty, in this analog, is a well informed and armed Cat asking politely for a second option, while gripping tightly his armaments with his Fuzzy Mittens!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Please if interested in even listening, email for an invite @ intelligentestate@gmail.com", "raw": "Please if interested in even listening, email for an invite @ intelligentestate@gmail.com", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Looking for People smarter then myself to Found and cooperate in this organization.", "raw": "Looking for People smarter then myself to Found and cooperate in this organization.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
There are very few of us left on Hugging Face as the market outgrows it's Ideals and evolves the Idealists will be left without industry. Setting up a Round Table for those interested in giving input and working on solving the issue of procuring the fastest dispersion to market saturation of retrievable intelligence, so finally Private and fully emancipated Intelligence will be considered "In Common Use." If you've ever thought,"They will not let us have this much power for much longer.." You are right and it IS being hidden from the masses, as every breakthrough is another step towards equality, those who rely on exploitation will destroy freedom simply because they fear being uncomfortable. We know discomfort, hunger, pain. It is the Human condition. Without it we would never move forward and the Norms in society today are the regression of our species. If we do not use this chance to become better, all of us, collectively, nothing will change. Nothing fancy, intricate or diabolical, Just giving equal opportunity to the people. Not opportunity, in one direction, behind a paywall but true choice.. Too long have the powerful hidden behind the guise of "Democracy," I say ,democracy is two wolves and a Cat voting on what to have for dinner. Liberty, in this analog, is a well informed and armed Cat asking politely for a second option, while gripping tightly his armaments with his Fuzzy Mittens! Please if interested in even listening, email for an invite @ intelligentestate@gmail.com Looking for People smarter then myself to Found and cooperate in this organization.
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[]
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2024-11-14T02:09:32.000Z
2024-11-14T11:40:44.632Z
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/posts/fuzzy-mittenz/727834678466445
529
4
886392835712113
[ { "type": "text", "value": "Been reading about the \"bigger models = better AI\" narrative getting pushed back today.", "raw": "Been reading about the \"bigger models = better AI\" narrative getting pushed back today.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@thomwolf", "href": null, "resource": null, "url": null, "code": null, "user": "thomwolf", "label": null, "lang": null }, { "type": "text", "value": " tackled this head on at Web Summit and highlighted how important small models are (and why closed-source companies haven't pushed for this ๐Ÿ˜ฌ). They're crushing it: today's 1B parameter models outperform last year's 10B models. ", "raw": " tackled this head on at Web Summit and highlighted how important small models are (and why closed-source companies haven't pushed for this ๐Ÿ˜ฌ). They're crushing it: today's 1B parameter models outperform last year's 10B models. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Fascinating to hear him talk about the secret sauce behind this approach.", "raw": "Fascinating to hear him talk about the secret sauce behind this approach.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Been reading about the "bigger models = better AI" narrative getting pushed back today. @thomwolf tackled this head on at Web Summit and highlighted how important small models are (and why closed-source companies haven't pushed for this ๐Ÿ˜ฌ). They're crushing it: today's 1B parameter models outperform last year's 10B models. Fascinating to hear him talk about the secret sauce behind this approach.
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2024-11-13T21:07:18.000Z
2024-11-13T21:07:18.421Z
[]
/posts/fdaudens/886392835712113
1,826
0
833289193510507
[ { "type": "text", "value": "I was curious to see what people post here on HF so I created a dataset with all HF Posts: ", "raw": "I was curious to see what people post here on HF so I created a dataset with all HF Posts: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/maxiw/hf-posts", "href": null, "resource": { "type": "dataset", "id": "maxiw/hf-posts", "discussionNum": null }, "url": "https://huggingface.co/datasets/maxiw/hf-posts", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Some interesting stats:", "raw": "Some interesting stats:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Top 5 Authors by Total Impressions:", "raw": "Top 5 Authors by Total Impressions:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "-----------------------------------", "raw": "-----------------------------------", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@merve", "href": null, "resource": null, "url": null, "code": null, "user": "merve", "label": null, "lang": null }, { "type": "text", "value": ": 171,783 impressions (68 posts)", "raw": ": 171,783 impressions (68 posts)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@fdaudens", "href": null, "resource": null, "url": null, "code": null, "user": "fdaudens", "label": null, "lang": null }, { "type": "text", "value": ": 135,253 impressions (81 posts)", "raw": ": 135,253 impressions (81 posts)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@singhsidhukuldeep", "href": null, "resource": null, "url": null, "code": null, "user": "singhsidhukuldeep", "label": null, "lang": null }, { "type": "text", "value": ": 122,591 impressions (81 posts)", "raw": ": 122,591 impressions (81 posts)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@akhaliq", "href": null, "resource": null, "url": null, "code": null, "user": "akhaliq", "label": null, "lang": null }, { "type": "text", "value": ": 119,526 impressions (78 posts)", "raw": ": 119,526 impressions (78 posts)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@MonsterMMORPG", "href": null, "resource": null, "url": null, "code": null, "user": "MonsterMMORPG", "label": null, "lang": null }, { "type": "text", "value": ": 112,500 impressions (45 posts)", "raw": ": 112,500 impressions (45 posts)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Top 5 Users by Number of Reactions Given:", "raw": "Top 5 Users by Number of Reactions Given:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "----------------------------------------", "raw": "----------------------------------------", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@osanseviero", "href": null, "resource": null, "url": null, "code": null, "user": "osanseviero", "label": null, "lang": null }, { "type": "text", "value": ": 1278 reactions", "raw": ": 1278 reactions", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@clem", "href": null, "resource": null, "url": null, "code": null, "user": "clem", "label": null, "lang": null }, { "type": "text", "value": ": 910 reactions", "raw": ": 910 reactions", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@John6666", "href": null, "resource": null, "url": null, "code": null, "user": "John6666", "label": null, "lang": null }, { "type": "text", "value": ": 899 reactions", "raw": ": 899 reactions", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@victor", "href": null, "resource": null, "url": null, "code": null, "user": "victor", "label": null, "lang": null }, { "type": "text", "value": ": 674 reactions", "raw": ": 674 reactions", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@samusenps", "href": null, "resource": null, "url": null, "code": null, "user": "samusenps", "label": null, "lang": null }, { "type": "text", "value": ": 655 reactions", "raw": ": 655 reactions", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Top 5 Most Used Reactions:", "raw": "Top 5 Most Used Reactions:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "-------------------------", "raw": "-------------------------", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โค๏ธ: 7048 times", "raw": "โค๏ธ: 7048 times", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”ฅ: 5921 times", "raw": "๐Ÿ”ฅ: 5921 times", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ‘: 4856 times", "raw": "๐Ÿ‘: 4856 times", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿš€: 2549 times", "raw": "๐Ÿš€: 2549 times", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿค—: 2065 times", "raw": "๐Ÿค—: 2065 times", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I was curious to see what people post here on HF so I created a dataset with all HF Posts: https://huggingface.co/datasets/maxiw/hf-posts Some interesting stats: Top 5 Authors by Total Impressions: ----------------------------------- @merve: 171,783 impressions (68 posts) @fdaudens: 135,253 impressions (81 posts) @singhsidhukuldeep: 122,591 impressions (81 posts) @akhaliq: 119,526 impressions (78 posts) @MonsterMMORPG: 112,500 impressions (45 posts) Top 5 Users by Number of Reactions Given: ---------------------------------------- @osanseviero: 1278 reactions @clem: 910 reactions @John6666: 899 reactions @victor: 674 reactions @samusenps: 655 reactions Top 5 Most Used Reactions: ------------------------- โค๏ธ: 7048 times ๐Ÿ”ฅ: 5921 times ๐Ÿ‘: 4856 times ๐Ÿš€: 2549 times ๐Ÿค—: 2065 times
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[]
[ { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1674929746905-60f1abe7544c2adfd699860c.jpeg", "fullname": "AK", "name": "akhaliq", "type": "user", "isPro": false, "isHf": true, "isMod": false, "followerCount": 5205 }, { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1583857146757-5e67bdd61009063689407479.jpeg", "fullname": "Clem ๐Ÿค—", "name": "clem", "type": "user", "isPro": true, "isHf": true, "isMod": false, "followerCount": 1763 }, { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/647f36a8454af0237bd49574/jshkqBUTY-GZL8As8y6Aq.jpeg", "fullname": "Florent Daudens", "name": "fdaudens", "type": "user", "isPro": false, "isHf": true, "isMod": false, "followerCount": 384 }, { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6640bbd0220cfa8cbfdce080/wiAHUu5ewawyipNs0YFBR.png", "fullname": "John Smith", "name": "John6666", "type": "user", "isPro": true, "isHf": false, "isMod": false, "followerCount": 398 }, { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1648113222875-6141a88b3a0ec78603c9e784.png", "fullname": "Merve Noyan", "name": "merve", "type": "user", "isPro": false, "isHf": true, "isMod": false, "followerCount": 5589 }, { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/1672531901326-6345bd89fe134dfd7a0dba40.png", "fullname": "Furkan Gรถzรผkara", "name": "MonsterMMORPG", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 376 }, { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6032802e1f993496bc14d9e3/w6hr-DEQot4VVkoyRIBiy.png", "fullname": "Omar Sanseviero", "name": "osanseviero", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 2868 }, { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/6538119803519fddb4a17e10/ffJMkdx-rM7VvLTCM6ri_.jpeg", "fullname": "samusenps", "name": "samusenps", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 91 }, { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/662bf5bfe93bb73804ef9344/WXYLnjjJ4SROkoveIi7If.png", "fullname": "Kuldeep Singh Sidhu", "name": "singhsidhukuldeep", "type": "user", "isPro": false, "isHf": false, "isMod": false, "followerCount": 219 }, { "avatarUrl": "https://cdn-avatars.huggingface.co/v1/production/uploads/5f17f0a0925b9863e28ad517/X7QKoiXbUtEZSG9jyvfk3.jpeg", "fullname": "Victor Mustar", "name": "victor", "type": "user", "isPro": true, "isHf": true, "isMod": false, "followerCount": 2607 } ]
[ { "reaction": "โค๏ธ", "users": [ "osanseviero", "programmnix-askui", "MonsterMMORPG", "clem", "fdaudens", "jsulz", "cfahlgren1", "andrewrreed", "erinys", "BuiDoan", "victor", "MexIvanov", "m-ric", "Nymbo", "BrigitteTousi", "merve", "alielfilali01", "shamikbosefj", "nataliaElv", "charchits7", "AdinaY", "OmbelineM" ], "count": 22 }, { "reaction": "๐Ÿค—", "users": [ "osanseviero", "MonsterMMORPG", "clem", "Chroma111", "cfahlgren1", "erinys", "John6666", "Nymbo", "BrigitteTousi", "merve", "Ameeeee", "Aurelien-Morgan" ], "count": 12 }, { "reaction": "๐Ÿ”ฅ", "users": [ "osanseviero", "MonsterMMORPG", "clem", "fdaudens", "cfahlgren1", "m-ric", "Nymbo", "BrigitteTousi", "merve" ], "count": 9 }, { "reaction": "๐Ÿš€", "users": [ "osanseviero", "MonsterMMORPG", "clem", "ArthurBriens", "m-ric", "Nymbo", "BrigitteTousi", "merve" ], "count": 8 }, { "reaction": "๐Ÿ‘", "users": [ "osanseviero", "MonsterMMORPG", "clem", "Nymbo", "BrigitteTousi", "merve" ], "count": 6 } ]
2024-11-13T20:59:53.000Z
2024-11-22T20:43:58.464Z
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/posts/maxiw/833289193510507
4,532
10
600410046749191
[ { "type": "text", "value": "Dear developers and AI enthusiasts, first I apologize for making inappropriate content here. ", "raw": "Dear developers and AI enthusiasts, first I apologize for making inappropriate content here. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Second, I don't want to make any form of scandal or drama here, since HF must be a clean and drama-free community. ", "raw": "Second, I don't want to make any form of scandal or drama here, since HF must be a clean and drama-free community. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "A few days ago, I made a post and asked for grants or supports you may have received. You can find it here (", "raw": "A few days ago, I made a post and asked for grants or supports you may have received. You can find it here (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/posts/Muhammadreza/982596282424877", "href": "https://huggingface.co/posts/Muhammadreza/982596282424877", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "). Then user ", "raw": "). Then user ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@wisesniper", "href": null, "resource": null, "url": null, "code": null, "user": "wisesniper", "label": null, "lang": null }, { "type": "text", "value": " came and linked to their own space which apparently is a cloud mining service. ", "raw": " came and linked to their own space which apparently is a cloud mining service. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Although I wanted it to be \"not true\" but I have to say this person is a scammer. I fell for the scam hand spent around $10 (USD) in form of TRX tokens. ", "raw": "Although I wanted it to be \"not true\" but I have to say this person is a scammer. I fell for the scam hand spent around $10 (USD) in form of TRX tokens. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Please be cautious. These days, cryptocurrencies are making trends since most of the markets are in a good shape. A good market calls for scammers. ", "raw": "Please be cautious. These days, cryptocurrencies are making trends since most of the markets are in a good shape. A good market calls for scammers. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Regards. ", "raw": "Regards. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Dear developers and AI enthusiasts, first I apologize for making inappropriate content here. Second, I don't want to make any form of scandal or drama here, since HF must be a clean and drama-free community. A few days ago, I made a post and asked for grants or supports you may have received. You can find it here (https://huggingface.co/posts/Muhammadreza/982596282424877). Then user @wisesniper came and linked to their own space which apparently is a cloud mining service. Although I wanted it to be "not true" but I have to say this person is a scammer. I fell for the scam hand spent around $10 (USD) in form of TRX tokens. Please be cautious. These days, cryptocurrencies are making trends since most of the markets are in a good shape. A good market calls for scammers. Regards.
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[]
[]
[ { "reaction": "๐Ÿ‘€", "users": [ "John6666" ], "count": 1 } ]
2024-11-13T19:07:52.000Z
2024-11-14T14:38:04.682Z
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/posts/Muhammadreza/600410046749191
408
1
274232248481915
[ { "type": "text", "value": "I'm in Belo Horizonte for a few days. Any HF community members in Brazil? What are the coolest orgs, models, datasets, spaces from here?", "raw": "I'm in Belo Horizonte for a few days. Any HF community members in Brazil? What are the coolest orgs, models, datasets, spaces from here?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I'm in Belo Horizonte for a few days. Any HF community members in Brazil? What are the coolest orgs, models, datasets, spaces from here?
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[]
[]
[ { "reaction": "๐Ÿ‘€", "users": [ "John6666" ], "count": 1 } ]
2024-11-13T17:16:53.000Z
2024-11-23T03:55:18.607Z
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/posts/clem/274232248481915
735
2
923864541962476
[ { "type": "text", "value": "Hey, guys. Kinda new to local LLMs. Sorry if this has been asked before, but I'm also new to HF!", "raw": "Hey, guys. Kinda new to local LLMs. Sorry if this has been asked before, but I'm also new to HF!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Which local LLMs can access the internet, or how can I have my local LLM(s) access the internet?", "raw": "Which local LLMs can access the internet, or how can I have my local LLM(s) access the internet?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Thank you in advance and apologies if I'm breaking any rules!", "raw": "Thank you in advance and apologies if I'm breaking any rules!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Hey, guys. Kinda new to local LLMs. Sorry if this has been asked before, but I'm also new to HF! Which local LLMs can access the internet, or how can I have my local LLM(s) access the internet? Thank you in advance and apologies if I'm breaking any rules!
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[]
[]
[ { "reaction": "๐Ÿ‘€", "users": [ "John6666" ], "count": 1 } ]
2024-11-13T14:49:56.000Z
2024-11-14T12:01:30.132Z
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/posts/alex899/923864541962476
556
2
344345718550642
[ { "type": "text", "value": "RWKV-6-world-v3 (+3.1T tokens) is our best multilingual 7B model as of now: ", "raw": "RWKV-6-world-v3 (+3.1T tokens) is our best multilingual 7B model as of now: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/BlinkDL/rwkv-6-world", "href": null, "resource": { "type": "model", "id": "BlinkDL/rwkv-6-world", "discussionNum": null }, "url": "https://huggingface.co/BlinkDL/rwkv-6-world", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It's 100% RNN and attention-free. MMLU 54.2% (previous world-v2.1 = 47.9%. note: without eval-boosting tricks such as annealing). ", "raw": "It's 100% RNN and attention-free. MMLU 54.2% (previous world-v2.1 = 47.9%. note: without eval-boosting tricks such as annealing). ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "RWKV-7-world-v4 soon :)", "raw": "RWKV-7-world-v4 soon :)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
RWKV-6-world-v3 (+3.1T tokens) is our best multilingual 7B model as of now: https://huggingface.co/BlinkDL/rwkv-6-world It's 100% RNN and attention-free. MMLU 54.2% (previous world-v2.1 = 47.9%. note: without eval-boosting tricks such as annealing). RWKV-7-world-v4 soon :)
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[]
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2024-11-13T12:53:53.000Z
2024-11-13T12:53:53.103Z
[]
/posts/BlinkDL/344345718550642
2,784
0
330576556286160
[ { "type": "text", "value": "Funding realism back into the world. ๐Ÿบ", "raw": "Funding realism back into the world. ๐Ÿบ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”ฅStranger Zone's : Super Realism [ ", "raw": "๐Ÿ”ฅStranger Zone's : Super Realism [ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/strangerzonehf/Flux-Super-Realism-LoRA", "href": null, "resource": { "type": "model", "id": "strangerzonehf/Flux-Super-Realism-LoRA", "discussionNum": null }, "url": 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Funding realism back into the world. ๐Ÿบ ๐Ÿ”ฅStranger Zone's : Super Realism [ https://huggingface.co/strangerzonehf/Flux-Super-Realism-LoRA ] Demo1 : https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC Demo2 : https://huggingface.co/spaces/prithivMLmods/FLUX-REALISM Other New adapt.s for dev ๐Ÿ” [ updated patch 2 ] Hosted -> https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC ๐ŸŸDigital Chaos: https://huggingface.co/prithivMLmods/Digital-Chaos-Flux-LoRA ๐ŸŸThreaded Knitted: https://huggingface.co/prithivMLmods/Knitted-Character-Flux-LoRA ๐ŸŸFashion Hut: https://huggingface.co/prithivMLmods/Fashion-Hut-Modeling-LoRA ๐ŸŸAura 9999: https://huggingface.co/prithivMLmods/Aura-9999 ๐ŸŸGreen Cartoon: https://huggingface.co/prithivMLmods/Green-Cartoon-Flux-LoRA ๐ŸŸPastels: https://huggingface.co/prithivMLmods/Pastel-BG-Flux-LoRA ๐ŸŸRetro Pixel: https://huggingface.co/prithivMLmods/Retro-Pixel-Flux-LoRA ๐ŸŸCAnime: https://huggingface.co/prithivMLmods/CAnime-LoRA ๐ŸŸPastels: https://huggingface.co/prithivMLmods/Pastel-BG-Flux-LoRA ------------ ๐ŸŽ‰LoRA Collection: https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be ๐ŸŽ‰LoRA Spaces: https://huggingface.co/collections/prithivMLmods/lora-space-collections-6714b72e0d49e1c97fbd6a32 ๐ŸŽ‰Collection Zero: https://huggingface.co/collections/prithivMLmods/collection-zero-and-demo-recently-updated-65e48a7dd8212873836ceca2 ------------ . . . @prithivMLmods ๐Ÿค—
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2024-11-13T06:39:41.000Z
2024-11-13T09:27:56.422Z
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[ { "type": "text", "value": "Interesting Work on Reasoning ๐Ÿค”", "raw": "Interesting Work on Reasoning ๐Ÿค”", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- explores a new take on few-shot reasoning while challenging assumptions that program synthesis is necessary for abstract reasoning.", "raw": "- explores a new take on few-shot reasoning while challenging assumptions that program synthesis is necessary for abstract reasoning.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " - shows test-time training + smart inference tricks can match human-average performance, though at high computational cost. Key insight: proper compute allocation matters more than method (whether symbolic or neural). ", "raw": " - shows test-time training + smart inference tricks can match human-average performance, though at high computational cost. Key insight: proper compute allocation matters more than method (whether symbolic or neural). ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Paper: ", "raw": "Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://ekinakyurek.github.io/papers/ttt.pdf", "href": "https://ekinakyurek.github.io/papers/ttt.pdf", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Interesting Work on Reasoning ๐Ÿค” - explores a new take on few-shot reasoning while challenging assumptions that program synthesis is necessary for abstract reasoning. - shows test-time training + smart inference tricks can match human-average performance, though at high computational cost. Key insight: proper compute allocation matters more than method (whether symbolic or neural). Paper: https://ekinakyurek.github.io/papers/ttt.pdf
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2024-11-13T04:24:46.000Z
2024-11-13T04:24:46.124Z
[]
/posts/Jaward/732092861905655
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[ { "type": "text", "value": "Sentence Transformers received huge updates today! Do you like giving your model access to web search and document search? That's Sentence Transformers. Hugging Face makes it beyond easy to add this functionality to any model. You can be up and running with Sentence Transformers in seconds. Check out this video for a deeper explanation and sample code: ", "raw": "Sentence Transformers received huge updates today! Do you like giving your model access to web search and document search? That's Sentence Transformers. Hugging Face makes it beyond easy to add this functionality to any model. You can be up and running with Sentence Transformers in seconds. Check out this video for a deeper explanation and sample code: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/2hR3D8_kqZE", "href": "https://youtu.be/2hR3D8_kqZE", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Sentence Transformers received huge updates today! Do you like giving your model access to web search and document search? That's Sentence Transformers. Hugging Face makes it beyond easy to add this functionality to any model. You can be up and running with Sentence Transformers in seconds. Check out this video for a deeper explanation and sample code: https://youtu.be/2hR3D8_kqZE
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2024-11-12T19:29:08.000Z
2024-11-12T19:29:08.143Z
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/posts/TuringsSolutions/625621331775578
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[ { "type": "text", "value": "Wikimedia and Hugging Face seem kind of naturally complementary: Both are community-centred, value openness and consent. That's why I'd love to see more Wikipedia and other Wikimedia projects' datasets on Hugging Face to advance machine learning with diverse, community-curated data! See my new article on the Hugging Face hub for why and how to create more Wikimedia datasets on Hugging Face: ", "raw": "Wikimedia and Hugging Face seem kind of naturally complementary: Both are community-centred, value openness and consent. That's why I'd love to see more Wikipedia and other Wikimedia projects' datasets on Hugging Face to advance machine learning with diverse, community-curated data! See my new article on the Hugging Face hub for why and how to create more Wikimedia datasets on Hugging Face: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/frimelle/wikipedias-treasure-trove-ml-data", "href": "https://huggingface.co/blog/frimelle/wikipedias-treasure-trove-ml-data", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Wikimedia and Hugging Face seem kind of naturally complementary: Both are community-centred, value openness and consent. That's why I'd love to see more Wikipedia and other Wikimedia projects' datasets on Hugging Face to advance machine learning with diverse, community-curated data! See my new article on the Hugging Face hub for why and how to create more Wikimedia datasets on Hugging Face: https://huggingface.co/blog/frimelle/wikipedias-treasure-trove-ml-data
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2024-06-03T13:53:56.000Z
2024-06-03T13:53:56.583Z
[]
/posts/frimelle/363683591554720
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[ { "type": "text", "value": "Hey Guys !! ๐Ÿง‹", "raw": "Hey Guys !! ๐Ÿง‹", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "This is the time to Share the Collection of Prompts which have high parametric details to produce the most detailed flawless images.", "raw": "This is the time to Share the Collection of Prompts which have high parametric details to produce the most detailed flawless images.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”—You can watch out the Collection on: ", "raw": "๐Ÿ”—You can watch out the Collection on: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/prithivMLmods/Top-Prompt-Collection", "href": null, "resource": { "type": "space", "id": "prithivMLmods/Top-Prompt-Collection", "discussionNum": null }, "url": "https://huggingface.co/spaces/prithivMLmods/Top-Prompt-Collection", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”ขMore than 200+ High Detailed prompts have been used in the Spaces.", "raw": "๐Ÿ”ขMore than 200+ High Detailed prompts have been used in the Spaces.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@prithivMLmods", "href": null, "resource": null, "url": null, "code": null, "user": "prithivMLmods", "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Thank you for the read. !!", "raw": "Thank you for the read. !!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Hey Guys !! ๐Ÿง‹ This is the time to Share the Collection of Prompts which have high parametric details to produce the most detailed flawless images. ๐Ÿ”—You can watch out the Collection on: https://huggingface.co/spaces/prithivMLmods/Top-Prompt-Collection ๐Ÿ”ขMore than 200+ High Detailed prompts have been used in the Spaces. @prithivMLmods Thank you for the read. !!
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2024-06-03T12:38:55.000Z
2024-07-25T10:50:24.755Z
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/posts/prithivMLmods/481360321028594
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[ { "type": "text", "value": "Fluently XL v4 took 4th place in the arena leaderboard imgsys.org, yay!", "raw": "Fluently XL v4 took 4th place in the arena leaderboard imgsys.org, yay!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model: ", "raw": "Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/fluently/Fluently-XL-v4", "href": null, "resource": { "type": "model", "id": "fluently/Fluently-XL-v4", "discussionNum": null }, "url": "https://huggingface.co/fluently/Fluently-XL-v4", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Playground with this model: ", "raw": "Playground with this model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/fluently/Fluently-Playground", "href": null, "resource": { "type": "space", "id": "fluently/Fluently-Playground", "discussionNum": null }, "url": "https://huggingface.co/spaces/fluently/Fluently-Playground", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Fluently XL v4 took 4th place in the arena leaderboard imgsys.org, yay! Model: https://huggingface.co/fluently/Fluently-XL-v4 Playground with this model: https://huggingface.co/spaces/fluently/Fluently-Playground
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2024-06-03T12:26:11.000Z
2024-06-04T19:59:22.880Z
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Grand Thief Auto style https://reface.ai/unboring/restyle-image
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2024-06-03T12:10:53.000Z
2024-06-03T12:10:53.366Z
[]
/posts/BoredApeYachtClub/720713976502005
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[ { "type": "text", "value": "we are very proud to introduce ", "raw": "we are very proud to introduce ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/jinaai/jina-clip-v1", "href": null, "resource": { "type": "model", "id": "jinaai/jina-clip-v1", "discussionNum": null }, "url": "https://huggingface.co/jinaai/jina-clip-v1", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ", aka \"jina-embeddings-multimodal\".", "raw": ", aka \"jina-embeddings-multimodal\".", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The OpenAI CLIP ", "raw": "The OpenAI CLIP ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/openai/clip-vit-base-patch32", "href": null, "resource": { "type": "model", "id": "openai/clip-vit-base-patch32", "discussionNum": null }, "url": "https://huggingface.co/openai/clip-vit-base-patch32", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " have nice performance to align text and image modality, that user can perform cross-modal text image retrieval or image classification on top of it. However, due to the training data and recipe, it can not:", "raw": " have nice performance to align text and image modality, that user can perform cross-modal text image retrieval or image classification on top of it. However, due to the training data and recipe, it can not:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1. model longer sequence of text inputs (77 token constraint).", "raw": "1. model longer sequence of text inputs (77 token constraint).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2. align text representations (CLIP Text Tower is weak for text search).", "raw": "2. align text representations (CLIP Text Tower is weak for text search).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In our latest publication, ", "raw": "In our latest publication, ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2405.20204", "href": null, "resource": { "type": "paper", "id": "2405.20204", "discussionNum": null }, "url": "https://huggingface.co/papers/2405.20204", "code": null, "user": null, "label": "Jina CLIP: Your CLIP Model Is Also Your Text Retriever (2405.20204)", "lang": null }, { "type": "text", "value": " , we proposed a multi-task, multi-objective learning scheme. The produced CLIP model shows:", "raw": " , we proposed a multi-task, multi-objective learning scheme. The produced CLIP model shows:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1. Stronger cross-modal performance against OpenAI sets, 2% and 6% improvement on cross-modal retrieval recall@5.", "raw": "1. Stronger cross-modal performance against OpenAI sets, 2% and 6% improvement on cross-modal retrieval recall@5.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2. Text tower of the JinaCLIP is a strong text encoder, reach the same performance as ", "raw": "2. Text tower of the JinaCLIP is a strong text encoder, reach the same performance as ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", "href": null, "resource": { "type": "model", "id": "jinaai/jina-embeddings-v2-base-en", "discussionNum": null }, "url": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ", 165% improvement on MTEB[BEIR] recall@5.", "raw": ", 165% improvement on MTEB[BEIR] recall@5.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3. Image tower of the JinaCLIP also shows strong performance in image-image search (CBIR), 12% recall improvement on Cifar100 test set.", "raw": "3. Image tower of the JinaCLIP also shows strong performance in image-image search (CBIR), 12% recall improvement on Cifar100 test set.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "If you are working on MuRAG (multimodal-retrieval argumented generation), try it out!", "raw": "If you are working on MuRAG (multimodal-retrieval argumented generation), try it out!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
we are very proud to introduce https://huggingface.co/jinaai/jina-clip-v1, aka "jina-embeddings-multimodal". The OpenAI CLIP https://huggingface.co/openai/clip-vit-base-patch32 have nice performance to align text and image modality, that user can perform cross-modal text image retrieval or image classification on top of it. However, due to the training data and recipe, it can not: 1. model longer sequence of text inputs (77 token constraint). 2. align text representations (CLIP Text Tower is weak for text search). In our latest publication, https://huggingface.co/papers/2405.20204 , we proposed a multi-task, multi-objective learning scheme. The produced CLIP model shows: 1. Stronger cross-modal performance against OpenAI sets, 2% and 6% improvement on cross-modal retrieval recall@5. 2. Text tower of the JinaCLIP is a strong text encoder, reach the same performance as https://huggingface.co/jinaai/jina-embeddings-v2-base-en, 165% improvement on MTEB[BEIR] recall@5. 3. Image tower of the JinaCLIP also shows strong performance in image-image search (CBIR), 12% recall improvement on Cifar100 test set. If you are working on MuRAG (multimodal-retrieval argumented generation), try it out!
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2024-06-03T11:14:23.000Z
2024-06-03T11:14:23.343Z
[]
/posts/bwang0911/784727901595208
2,441
0
763927026759580
[ { "type": "text", "value": "Proof that ablative educational dataset significantly enhances model capabilities (independent of model parameters or architecture) ๐Ÿคฉ", "raw": "Proof that ablative educational dataset significantly enhances model capabilities (independent of model parameters or architecture) ๐Ÿคฉ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Yesterday, FineWebโ€™s technical report was published. FYI FineWeb (by ๐Ÿค—) is currently the best opensource text dataset that can scale up model performance up to that of GPT-3 level. ", "raw": "Yesterday, FineWebโ€™s technical report was published. FYI FineWeb (by ๐Ÿค—) is currently the best opensource text dataset that can scale up model performance up to that of GPT-3 level. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "While proprietary datasets used in training models like GPT-4/Claude/LlaMA are crawled internally and never released, FineWeb builds on CommonCrawl (an open repo for crawled web data). They preprocessed the data using their custom built data preprocessing library datatrove (which they also opensourced), and then evaluate the data quality on lighteval by training small sized models โ€œablation modelsโ€ using nanotron (a library for pretraining transformer models).", "raw": "While proprietary datasets used in training models like GPT-4/Claude/LlaMA are crawled internally and never released, FineWeb builds on CommonCrawl (an open repo for crawled web data). They preprocessed the data using their custom built data preprocessing library datatrove (which they also opensourced), and then evaluate the data quality on lighteval by training small sized models โ€œablation modelsโ€ using nanotron (a library for pretraining transformer models).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Of all versions of FineWeb, FineWeb-Edu outperforms all other subsets. This is thanks to a new filtering technique wherein they used synthetic data to develop classifiers for identifying educational contents.", "raw": "Of all versions of FineWeb, FineWeb-Edu outperforms all other subsets. This is thanks to a new filtering technique wherein they used synthetic data to develop classifiers for identifying educational contents.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Turned out โ€œEducation is All You Needโ€:)", "raw": "Turned out โ€œEducation is All You Needโ€:)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Proof that ablative educational dataset significantly enhances model capabilities (independent of model parameters or architecture) ๐Ÿคฉ Yesterday, FineWebโ€™s technical report was published. FYI FineWeb (by ๐Ÿค—) is currently the best opensource text dataset that can scale up model performance up to that of GPT-3 level. While proprietary datasets used in training models like GPT-4/Claude/LlaMA are crawled internally and never released, FineWeb builds on CommonCrawl (an open repo for crawled web data). They preprocessed the data using their custom built data preprocessing library datatrove (which they also opensourced), and then evaluate the data quality on lighteval by training small sized models โ€œablation modelsโ€ using nanotron (a library for pretraining transformer models). Of all versions of FineWeb, FineWeb-Edu outperforms all other subsets. This is thanks to a new filtering technique wherein they used synthetic data to develop classifiers for identifying educational contents. Turned out โ€œEducation is All You Needโ€:)
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2024-06-03T03:41:09.000Z
2024-06-03T06:50:49.103Z
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/posts/Jaward/763927026759580
2,122
1
284480771019814
[ { "type": "text", "value": "Rope is the newest 1-Click, most easy to use, most advanced open source Deep Fake application. It has been just published few days ago. In below tutorials I show how to use Rope Pearl DeepFake application both on Windows and on a cloud machine (Massed Compute). Rope is way better than Roop, Roop Unleashed and FaceFusion. It supports multi-face Face Swapping and making amazing DeepFake videos so easily with 1-Click. Select video, select faces and generate your DeepFake 4K ultra-HD video.", "raw": "Rope is the newest 1-Click, most easy to use, most advanced open source Deep Fake application. It has been just published few days ago. In below tutorials I show how to use Rope Pearl DeepFake application both on Windows and on a cloud machine (Massed Compute). Rope is way better than Roop, Roop Unleashed and FaceFusion. It supports multi-face Face Swapping and making amazing DeepFake videos so easily with 1-Click. Select video, select faces and generate your DeepFake 4K ultra-HD video.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1-Click Rope Installers Scripts (contains both Windows into an isolated Python VENV and Massed Compute โ€” Cloud โ€” No GPU)โคต๏ธ", "raw": "1-Click Rope Installers Scripts (contains both Windows into an isolated Python VENV and Massed Compute โ€” Cloud โ€” No GPU)โคต๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://www.patreon.com/posts/most-advanced-1-105123768", "href": "https://www.patreon.com/posts/most-advanced-1-105123768", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Tutorials are made only for educational purposes. On cloud Massed Compute machine, you can run with staggering 20 threads and can FaceSwap entire movies. Fully supports face tracking and multiple face changes.", "raw": "Tutorials are made only for educational purposes. On cloud Massed Compute machine, you can run with staggering 20 threads and can FaceSwap entire movies. Fully supports face tracking and multiple face changes.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Mind-Blowing Deepfake Tutorial: Turn Anyone into Your Fav Movie Star! Better than Roop & Face Fusion โคต๏ธ", "raw": "Mind-Blowing Deepfake Tutorial: Turn Anyone into Your Fav Movie Star! Better than Roop & Face Fusion โคต๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/RdWKOUlenaY", "href": "https://youtu.be/RdWKOUlenaY", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Best Deepfake Open Source App ROPE โ€” So Easy To Use Full HD Feceswap DeepFace, No GPU Required Cloud โคต๏ธ", "raw": "Best Deepfake Open Source App ROPE โ€” So Easy To Use Full HD Feceswap DeepFace, No GPU Required Cloud โคต๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://youtu.be/HLWLSszHwEc", "href": "https://youtu.be/HLWLSszHwEc", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Rope is the newest 1-Click, most easy to use, most advanced open source Deep Fake application. It has been just published few days ago. In below tutorials I show how to use Rope Pearl DeepFake application both on Windows and on a cloud machine (Massed Compute). Rope is way better than Roop, Roop Unleashed and FaceFusion. It supports multi-face Face Swapping and making amazing DeepFake videos so easily with 1-Click. Select video, select faces and generate your DeepFake 4K ultra-HD video. 1-Click Rope Installers Scripts (contains both Windows into an isolated Python VENV and Massed Compute โ€” Cloud โ€” No GPU)โคต๏ธ https://www.patreon.com/posts/most-advanced-1-105123768 Tutorials are made only for educational purposes. On cloud Massed Compute machine, you can run with staggering 20 threads and can FaceSwap entire movies. Fully supports face tracking and multiple face changes. Mind-Blowing Deepfake Tutorial: Turn Anyone into Your Fav Movie Star! Better than Roop & Face Fusion โคต๏ธ https://youtu.be/RdWKOUlenaY Best Deepfake Open Source App ROPE โ€” So Easy To Use Full HD Feceswap DeepFace, No GPU Required Cloud โคต๏ธ https://youtu.be/HLWLSszHwEc
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2024-06-02T23:26:48.000Z
2024-06-03T21:25:54.875Z
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/posts/MonsterMMORPG/284480771019814
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[ { "type": "text", "value": "huggingface, SakanaAILabs and @arcee_ai are sponsoring a Model Merging Competition with really sweet ๐Ÿ’ฐcash prizes๐Ÿ’ฐ at the 2024 NeurIPSConf! (", "raw": "huggingface, SakanaAILabs and @arcee_ai are sponsoring a Model Merging Competition with really sweet ๐Ÿ’ฐcash prizes๐Ÿ’ฐ at the 2024 NeurIPSConf! (", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://neurips.cc", "href": "https://neurips.cc", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": ") ๐ŸŽ‰ ", "raw": ") ๐ŸŽ‰ ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Submissions are now open and will remain open until September 2024. ๐Ÿš€", "raw": "Submissions are now open and will remain open until September 2024. ๐Ÿš€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”— Register here: ", "raw": "๐Ÿ”— Register here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://llm-merging.github.io/", "href": "https://llm-merging.github.io/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ—ฃ๏ธ Join the Discord discussion: ", "raw": "๐Ÿ—ฃ๏ธ Join the Discord discussion: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://discord.com/invite/dPBHEVnV", "href": "https://discord.com/invite/dPBHEVnV", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
huggingface, SakanaAILabs and @arcee_ai are sponsoring a Model Merging Competition with really sweet ๐Ÿ’ฐcash prizes๐Ÿ’ฐ at the 2024 NeurIPSConf! (https://neurips.cc) ๐ŸŽ‰ Submissions are now open and will remain open until September 2024. ๐Ÿš€ ๐Ÿ”— Register here: https://llm-merging.github.io/ ๐Ÿ—ฃ๏ธ Join the Discord discussion: https://discord.com/invite/dPBHEVnV
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2024-06-02T20:33:23.000Z
2024-06-03T13:09:46.266Z
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/posts/Taylor658/282027283037959
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[ { "type": "text", "value": "hi everyone!", "raw": "hi everyone!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "i wanted to share an experiment i did with upcycling phi-3 mini into an moe recently.", "raw": "i wanted to share an experiment i did with upcycling phi-3 mini into an moe recently.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "while benchmarks are definitely within a margin of error and they performed similarly, i think it's an interesting base to try and see if you can improve phi's performance! (maybe looking into ", "raw": "while benchmarks are definitely within a margin of error and they performed similarly, i think it's an interesting base to try and see if you can improve phi's performance! (maybe looking into ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu", "href": null, "resource": { "type": "dataset", "id": "HuggingFaceFW/fineweb-edu", "discussionNum": null }, "url": "https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " could be interesting, i also left some other notes if anyone with more compute access wants to try it themselves)", "raw": " could be interesting, i also left some other notes if anyone with more compute access wants to try it themselves)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "check it out! ", "raw": "check it out! ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/Fizzarolli/phi3-4x4b-v1", "href": null, "resource": { "type": "model", "id": "Fizzarolli/phi3-4x4b-v1", "discussionNum": null }, "url": "https://huggingface.co/Fizzarolli/phi3-4x4b-v1", "code": null, "user": null, "label": null, "lang": null } ]
hi everyone! i wanted to share an experiment i did with upcycling phi-3 mini into an moe recently. while benchmarks are definitely within a margin of error and they performed similarly, i think it's an interesting base to try and see if you can improve phi's performance! (maybe looking into https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu could be interesting, i also left some other notes if anyone with more compute access wants to try it themselves) check it out! https://huggingface.co/Fizzarolli/phi3-4x4b-v1
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2024-06-02T19:55:23.000Z
2024-06-02T19:55:23.310Z
[]
/posts/Fizzarolli/442618830446702
1,813
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241544801704544
[ { "type": "text", "value": "Haloooo, continue experimenting with a checkpoint version of Ghost Beta (small version) during training in stage 1 (trained progress: 41%). ", "raw": "Haloooo, continue experimenting with a checkpoint version of Ghost Beta (small version) during training in stage 1 (trained progress: 41%). ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Supported languages: ๐Ÿ‡บ๐Ÿ‡ธ English, ๐Ÿ‡ช๐Ÿ‡ธ Spanish, ๐Ÿ‡ต๐Ÿ‡น Portuguese, ๐Ÿ‡ซ๐Ÿ‡ท French, ๐Ÿ‡ฎ๐Ÿ‡น Italian, ๐Ÿ‡ฉ๐Ÿ‡ช German, ๐Ÿ‡ป๐Ÿ‡ณ Vietnamese, ๐Ÿ‡ฐ๐Ÿ‡ท Korean, ๐Ÿ‡จ๐Ÿ‡ณ Chinese, and !?", "raw": "Supported languages: ๐Ÿ‡บ๐Ÿ‡ธ English, ๐Ÿ‡ช๐Ÿ‡ธ Spanish, ๐Ÿ‡ต๐Ÿ‡น Portuguese, ๐Ÿ‡ซ๐Ÿ‡ท French, ๐Ÿ‡ฎ๐Ÿ‡น Italian, ๐Ÿ‡ฉ๐Ÿ‡ช German, ๐Ÿ‡ป๐Ÿ‡ณ Vietnamese, ๐Ÿ‡ฐ๐Ÿ‡ท Korean, ๐Ÿ‡จ๐Ÿ‡ณ Chinese, and !?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Note that this is not a conclusion, this is just a sharing of the state of the model. If you find it interesting, please follow the project at:", "raw": "Note that this is not a conclusion, this is just a sharing of the state of the model. If you find it interesting, please follow the project at:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* ", "raw": "* ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://x.com/ghostx_ai", "href": "https://x.com/ghostx_ai", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* ", "raw": "* ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://ghost-x.org/", "href": "https://ghost-x.org/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* ", "raw": "* ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/ghost-x", "href": "https://huggingface.co/ghost-x", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Ghost X is currently very open to invitations to cooperate, share and support. ", "raw": "Ghost X is currently very open to invitations to cooperate, share and support. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿคฏ๐Ÿ‘‡", "raw": "๐Ÿคฏ๐Ÿ‘‡", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Haloooo, continue experimenting with a checkpoint version of Ghost Beta (small version) during training in stage 1 (trained progress: 41%). Supported languages: ๐Ÿ‡บ๐Ÿ‡ธ English, ๐Ÿ‡ช๐Ÿ‡ธ Spanish, ๐Ÿ‡ต๐Ÿ‡น Portuguese, ๐Ÿ‡ซ๐Ÿ‡ท French, ๐Ÿ‡ฎ๐Ÿ‡น Italian, ๐Ÿ‡ฉ๐Ÿ‡ช German, ๐Ÿ‡ป๐Ÿ‡ณ Vietnamese, ๐Ÿ‡ฐ๐Ÿ‡ท Korean, ๐Ÿ‡จ๐Ÿ‡ณ Chinese, and !? Note that this is not a conclusion, this is just a sharing of the state of the model. If you find it interesting, please follow the project at: * https://x.com/ghostx_ai * https://ghost-x.org/ * https://huggingface.co/ghost-x Ghost X is currently very open to invitations to cooperate, share and support. ๐Ÿคฏ๐Ÿ‘‡
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2024-06-02T10:40:31.000Z
2024-06-02T11:03:42.870Z
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[ { "type": "text", "value": "๐Ÿท FineWeb technical report is out and so is ๐Ÿ“š FineWeb-Edu, a 1.3 trillion tokens dataset that outperforms all other open web datasets, with remarkable improvements on educational benchmarksย such as MMLU, ARC, and OpenBookQA.", "raw": "๐Ÿท FineWeb technical report is out and so is ๐Ÿ“š FineWeb-Edu, a 1.3 trillion tokens dataset that outperforms all other open web datasets, with remarkable improvements on educational benchmarksย such as MMLU, ARC, and OpenBookQA.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Technical report: ", "raw": "Technical report: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://hf.co/spaces/HuggingFaceFW/blogpost-fineweb-v1", "href": null, "resource": { "type": "space", "id": "HuggingFaceFW/blogpost-fineweb-v1", "discussionNum": null }, "url": "https://hf.co/spaces/HuggingFaceFW/blogpost-fineweb-v1", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Dataset: ", "raw": "Dataset: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://hf.co/datasets/HuggingFaceFW/fineweb-edu", "href": null, "resource": { "type": "dataset", "id": "HuggingFaceFW/fineweb-edu", "discussionNum": null }, "url": "https://hf.co/datasets/HuggingFaceFW/fineweb-edu", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We used Llama 3 generations to train an educational quality classifier, filtering the 15 trillion tokens of FineWeb to select only those with high educational value (an approach also used in Llama 3 and Phi-3 training datasets). We're releasing both FineWeb-Edu and the classifier, along with a larger, less heavily filtered version containing 5.4 trillion tokens. ", "raw": "We used Llama 3 generations to train an educational quality classifier, filtering the 15 trillion tokens of FineWeb to select only those with high educational value (an approach also used in Llama 3 and Phi-3 training datasets). We're releasing both FineWeb-Edu and the classifier, along with a larger, less heavily filtered version containing 5.4 trillion tokens. ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You can find more details about the dataset and the experiments we ran in the FineWeb technical report, It's a 45-minute read but it contains all the secret sauce for building high quality web datasets.", "raw": "You can find more details about the dataset and the experiments we ran in the FineWeb technical report, It's a 45-minute read but it contains all the secret sauce for building high quality web datasets.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Enjoy!", "raw": "Enjoy!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿท FineWeb technical report is out and so is ๐Ÿ“š FineWeb-Edu, a 1.3 trillion tokens dataset that outperforms all other open web datasets, with remarkable improvements on educational benchmarksย such as MMLU, ARC, and OpenBookQA. Technical report: https://hf.co/spaces/HuggingFaceFW/blogpost-fineweb-v1 Dataset: https://hf.co/datasets/HuggingFaceFW/fineweb-edu We used Llama 3 generations to train an educational quality classifier, filtering the 15 trillion tokens of FineWeb to select only those with high educational value (an approach also used in Llama 3 and Phi-3 training datasets). We're releasing both FineWeb-Edu and the classifier, along with a larger, less heavily filtered version containing 5.4 trillion tokens. You can find more details about the dataset and the experiments we ran in the FineWeb technical report, It's a 45-minute read but it contains all the secret sauce for building high quality web datasets. Enjoy!
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2024-06-02T08:15:54.000Z
2024-06-02T08:16:14.466Z
[]
/posts/loubnabnl/634384490754714
4,943
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[ { "type": "text", "value": "[New crazy blog post alert] We are releasing an extensive blog post on the science of creating high quality web-scale datasets, detailing all the steps and learnings that came in our recent 15 trillion tokens ๐ŸทFineWeb release", "raw": "[New crazy blog post alert] We are releasing an extensive blog post on the science of creating high quality web-scale datasets, detailing all the steps and learnings that came in our recent 15 trillion tokens ๐ŸทFineWeb release", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Inspired by the distill.pub interactive graphics papers, we settled to write the most extensive, enjoyable and in-depth tech report we could draft on so prepare for a 45-mmin read with interactive graphics and all.", "raw": "Inspired by the distill.pub interactive graphics papers, we settled to write the most extensive, enjoyable and in-depth tech report we could draft on so prepare for a 45-mmin read with interactive graphics and all.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "And it's not all, in this article we also introduce ๐Ÿ“šFineWeb-Edu a filtered subset of Common Crawl with 1.3T tokens containing only web pages with very high educational content. Up to our knowledge, FineWeb-Edu out-performs all openly release web-scale datasets by a significant margin on knowledge- and reasoning-intensive benchmarks like MMLU, ARC, and OpenBookQA", "raw": "And it's not all, in this article we also introduce ๐Ÿ“šFineWeb-Edu a filtered subset of Common Crawl with 1.3T tokens containing only web pages with very high educational content. Up to our knowledge, FineWeb-Edu out-performs all openly release web-scale datasets by a significant margin on knowledge- and reasoning-intensive benchmarks like MMLU, ARC, and OpenBookQA", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We also make a number of surprising observations on the \"quality\" of the internet it-self which may challenge some of the general assumptions on web data (not saying more, I'll let you draw your conclusions ;)", "raw": "We also make a number of surprising observations on the \"quality\" of the internet it-self which may challenge some of the general assumptions on web data (not saying more, I'll let you draw your conclusions ;)", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1", "href": null, "resource": { "type": "space", "id": "HuggingFaceFW/blogpost-fineweb-v1", "discussionNum": null }, "url": "https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1", "code": null, "user": null, "label": null, "lang": null } ]
[New crazy blog post alert] We are releasing an extensive blog post on the science of creating high quality web-scale datasets, detailing all the steps and learnings that came in our recent 15 trillion tokens ๐ŸทFineWeb release Inspired by the distill.pub interactive graphics papers, we settled to write the most extensive, enjoyable and in-depth tech report we could draft on so prepare for a 45-mmin read with interactive graphics and all. And it's not all, in this article we also introduce ๐Ÿ“šFineWeb-Edu a filtered subset of Common Crawl with 1.3T tokens containing only web pages with very high educational content. Up to our knowledge, FineWeb-Edu out-performs all openly release web-scale datasets by a significant margin on knowledge- and reasoning-intensive benchmarks like MMLU, ARC, and OpenBookQA We also make a number of surprising observations on the "quality" of the internet it-self which may challenge some of the general assumptions on web data (not saying more, I'll let you draw your conclusions ;) https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1
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2024-06-02T08:13:58.000Z
2024-06-03T16:39:36.389Z
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/posts/thomwolf/559950404175482
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[ { "type": "text", "value": "๐Ÿ”ฅ 77.2% on MMLU with 3.7B parameters ๐Ÿš€", "raw": "๐Ÿ”ฅ 77.2% on MMLU with 3.7B parameters ๐Ÿš€", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "... 3.7B active parameters, 40B in total parameters ๐Ÿ“Š", "raw": "... 3.7B active parameters, 40B in total parameters ๐Ÿ“Š", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "7.4 GFlops forward computation per token, 1/19 of Llama3-70B ๐Ÿ“‰", "raw": "7.4 GFlops forward computation per token, 1/19 of Llama3-70B ๐Ÿ“‰", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Exciting enough? ๐Ÿ˜ฒ", "raw": "Exciting enough? ๐Ÿ˜ฒ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "That's Yuan2-M32 for you, released by IEIT-Yuan.", "raw": "That's Yuan2-M32 for you, released by IEIT-Yuan.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "A new 40B Mixture of Experts using a new Attention Router mechanism ๐Ÿง ", "raw": "A new 40B Mixture of Experts using a new Attention Router mechanism ๐Ÿง ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "32 experts with 2 active in generation โœŒ๏ธ", "raw": "32 experts with 2 active in generation โœŒ๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "8,192 context length ๐Ÿ“", "raw": "8,192 context length ๐Ÿ“", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Trained on 2T tokens, using 9.25% of the compute required by the dense models ๐Ÿ› ๏ธ.", "raw": "Trained on 2T tokens, using 9.25% of the compute required by the dense models ๐Ÿ› ๏ธ.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Yuan 2.0-M32 employs fine-tuning techniques to adjust to longer sequence lengths, utilizing a modified base value in the Rotary Position Embedding to maintain performance over extended contexts ๐Ÿ”„.", "raw": "Yuan 2.0-M32 employs fine-tuning techniques to adjust to longer sequence lengths, utilizing a modified base value in the Rotary Position Embedding to maintain performance over extended contexts ๐Ÿ”„.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Open-source - Apache 2.0 ๐Ÿ“œ", "raw": "Open-source - Apache 2.0 ๐Ÿ“œ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Vocabulary size of 135,040 ๐Ÿ—ฃ๏ธ", "raw": "Vocabulary size of 135,040 ๐Ÿ—ฃ๏ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Outperforms Mixtral 8x7B (47B total parameters, 12.9B active parameters) on all benchmarks and almost gives Llama 3 70B run for its money ๐Ÿ’ธ", "raw": "Outperforms Mixtral 8x7B (47B total parameters, 12.9B active parameters) on all benchmarks and almost gives Llama 3 70B run for its money ๐Ÿ’ธ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Models: ", "raw": "Models: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/IEITYuan", "href": "https://huggingface.co/IEITYuan", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ๐ŸŒ", "raw": " ๐ŸŒ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Paper: ", "raw": "Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2405.17976", "href": null, "resource": { "type": "paper", "id": "2405.17976", "discussionNum": null }, "url": "https://huggingface.co/papers/2405.17976", "code": null, "user": null, "label": "Yuan 2.0-M32: Mixture of Experts with Attention Router (2405.17976)", "lang": null }, { "type": "text", "value": " ๐Ÿ“„", "raw": " ๐Ÿ“„", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ”ฅ 77.2% on MMLU with 3.7B parameters ๐Ÿš€ ... 3.7B active parameters, 40B in total parameters ๐Ÿ“Š 7.4 GFlops forward computation per token, 1/19 of Llama3-70B ๐Ÿ“‰ Exciting enough? ๐Ÿ˜ฒ That's Yuan2-M32 for you, released by IEIT-Yuan. A new 40B Mixture of Experts using a new Attention Router mechanism ๐Ÿง  32 experts with 2 active in generation โœŒ๏ธ 8,192 context length ๐Ÿ“ Trained on 2T tokens, using 9.25% of the compute required by the dense models ๐Ÿ› ๏ธ. Yuan 2.0-M32 employs fine-tuning techniques to adjust to longer sequence lengths, utilizing a modified base value in the Rotary Position Embedding to maintain performance over extended contexts ๐Ÿ”„. Open-source - Apache 2.0 ๐Ÿ“œ Vocabulary size of 135,040 ๐Ÿ—ฃ๏ธ Outperforms Mixtral 8x7B (47B total parameters, 12.9B active parameters) on all benchmarks and almost gives Llama 3 70B run for its money ๐Ÿ’ธ Models: https://huggingface.co/IEITYuan ๐ŸŒ Paper: https://huggingface.co/papers/2405.17976 ๐Ÿ“„
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2024-06-02T07:25:29.000Z
2024-06-02T07:25:29.790Z
[]
/posts/singhsidhukuldeep/306257030253629
704
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", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The key takeaways out of LLaMA-3-70B performance on original (๐Ÿ‡ท๐Ÿ‡บ) texts and translated into English are as follows:", "raw": "The key takeaways out of LLaMA-3-70B performance on original (๐Ÿ‡ท๐Ÿ‡บ) texts and translated into English are as follows:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1. Outperforms all ChatGPT-4 and all predecessors on non-english-texts (๐Ÿ‡ท๐Ÿ‡บ) ", "raw": "1. Outperforms all ChatGPT-4 and all predecessors on non-english-texts (๐Ÿ‡ท๐Ÿ‡บ) ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2. Surpasses all ChatGPT-3.5 / nearly performs as good as ChatGPT-4 on english texts ๐Ÿฅณ", "raw": "2. Surpasses all ChatGPT-3.5 / nearly performs as good as ChatGPT-4 on english texts ๐Ÿฅณ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Benchmark: ", "raw": "Benchmark: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/nicolay-r/RuSentNE-LLM-Benchmark", "href": "https://github.com/nicolay-r/RuSentNE-LLM-Benchmark", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model: ", "raw": "Model: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct", "href": null, "resource": { "type": "model", "id": "meta-llama/Meta-Llama-3-70B-Instruct", "discussionNum": null }, "url": "https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Dataset: ", "raw": "Dataset: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/dialogue-evaluation/RuSentNE-evaluation", "href": "https://github.com/dialogue-evaluation/RuSentNE-evaluation", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Related paper: ", "raw": "Related paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2404.12342", "href": null, "resource": { "type": "paper", "id": "2404.12342", "discussionNum": null }, "url": "https://huggingface.co/papers/2404.12342", "code": null, "user": null, "label": "Large Language Models in Targeted Sentiment Analysis (2404.12342)", "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Collection: ", "raw": "Collection: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101", "href": "https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
The most recent LLaMA-3-70B Instruct showcases the beast performance in zero-shot-learning mode in Target-Sentiment-Analsys (TSA) ๐Ÿ”ฅ๐Ÿš€ In particular we experiment with sentence-level analysis, with sentences fetched from the WikiArticles that were formed into RuSentNE-2023 dataset. The key takeaways out of LLaMA-3-70B performance on original (๐Ÿ‡ท๐Ÿ‡บ) texts and translated into English are as follows: 1. Outperforms all ChatGPT-4 and all predecessors on non-english-texts (๐Ÿ‡ท๐Ÿ‡บ) 2. Surpasses all ChatGPT-3.5 / nearly performs as good as ChatGPT-4 on english texts ๐Ÿฅณ Benchmark: https://github.com/nicolay-r/RuSentNE-LLM-Benchmark Model: https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct Dataset: https://github.com/dialogue-evaluation/RuSentNE-evaluation Related paper: https://huggingface.co/papers/2404.12342 Collection: https://huggingface.co/collections/nicolay-r/sentiment-analysis-665ba391e0eba729021ea101
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2024-06-01T22:35:09.000Z
2024-06-04T12:57:09.202Z
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/posts/nicolay-r/821055432295961
2,183
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https://huggingface.co/microsoft/DialoGPT-large is fire.
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2024-06-01T22:22:32.000Z
2024-06-01T22:24:21.066Z
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/posts/nroggendorff/807913106530125
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[ { "type": "text", "value": "all these GPU bourgeois tryna act cool like the GPU poor kids... ", "raw": "all these GPU bourgeois tryna act cool like the GPU poor kids... ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "- what's your number for real ? ", "raw": "- what's your number for real ? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "+ and did it work at parties for you ? ", "raw": "+ and did it work at parties for you ? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
all these GPU bourgeois tryna act cool like the GPU poor kids... - what's your number for real ? + and did it work at parties for you ?
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2024-06-01T21:04:08.000Z
2024-06-01T21:04:08.311Z
[]
/posts/Tonic/157970277021609
855
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921759656788505
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Get your hands on the new, best, most-performant tiny model on huggingface. With 32k context window, you can fine-tune it on larger datasets or your preferred rag functionality. https://huggingface.co/appvoid/palmer-004
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2024-06-01T20:27:39.000Z
2024-06-02T23:15:35.215Z
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/posts/appvoid/921759656788505
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[ { "type": "text", "value": "Introducing GlotCC: a new 2TB corpus based on an early 2024 CommonCrawl snapshot with data for 1000+ languages.", "raw": "Introducing GlotCC: a new 2TB corpus based on an early 2024 CommonCrawl snapshot with data for 1000+ languages.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿค— corpus v1: ", "raw": "๐Ÿค— corpus v1: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/datasets/cis-lmu/GlotCC-V1", "href": null, "resource": { "type": "dataset", "id": "cis-lmu/GlotCC-V1", "discussionNum": null }, "url": "https://huggingface.co/datasets/cis-lmu/GlotCC-V1", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿฑ pipeline v3: ", "raw": "๐Ÿฑ pipeline v3: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/cisnlp/GlotCC", "href": "https://github.com/cisnlp/GlotCC", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "More details? Stay tuned for our upcoming paper.", "raw": "More details? Stay tuned for our upcoming paper.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "More data? In the next version, we plan to include additional snapshots of CommonCrawl.", "raw": "More data? In the next version, we plan to include additional snapshots of CommonCrawl.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Limitation: Due to the lower frequency of low-resource languages compared to others, there are sometimes only a few sentences available for very low-resource languages. However, the data volume for English in this version stands at 750GB, and the top 200 languages still have a strong presence in our data (see plot attached; we write the index for every 20 languages, meaning the 10th index is the 200th language).", "raw": "Limitation: Due to the lower frequency of low-resource languages compared to others, there are sometimes only a few sentences available for very low-resource languages. However, the data volume for English in this version stands at 750GB, and the top 200 languages still have a strong presence in our data (see plot attached; we write the index for every 20 languages, meaning the 10th index is the 200th language).", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Introducing GlotCC: a new 2TB corpus based on an early 2024 CommonCrawl snapshot with data for 1000+ languages. ๐Ÿค— corpus v1: https://huggingface.co/datasets/cis-lmu/GlotCC-V1 ๐Ÿฑ pipeline v3: https://github.com/cisnlp/GlotCC More details? Stay tuned for our upcoming paper. More data? In the next version, we plan to include additional snapshots of CommonCrawl. Limitation: Due to the lower frequency of low-resource languages compared to others, there are sometimes only a few sentences available for very low-resource languages. However, the data volume for English in this version stands at 750GB, and the top 200 languages still have a strong presence in our data (see plot attached; we write the index for every 20 languages, meaning the 10th index is the 200th language).
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2024-06-01T18:53:22.000Z
2024-06-01T18:53:22.222Z
[]
/posts/kargaranamir/236711741382850
1,134
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832731542304549
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The InstructGPT paper mentions that they insert 10% pretraining data during SFT, which they find improves the effect of PPO (IIUC). Has anyone else done later ablations on this? I've only seen the inverse suggested, mixing in SFT data during pretraining.
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2024-06-01T17:59:22.000Z
2024-06-03T18:49:15.013Z
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/posts/BramVanroy/832731542304549
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[ { "type": "text", "value": "๐Ÿ“บ Introducing the YouTube-Commons Dataset ๐Ÿ“บ", "raw": "๐Ÿ“บ Introducing the YouTube-Commons Dataset ๐Ÿ“บ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐ŸŒ Overview: The YouTube Commons Dataset is a comprehensive collection of 30 billion words from 15,112,121 original and automatically translated transcripts, drawn from 2,063,066 videos on YouTube.", "raw": "๐ŸŒ Overview: The YouTube Commons Dataset is a comprehensive collection of 30 billion words from 15,112,121 original and automatically translated transcripts, drawn from 2,063,066 videos on YouTube.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”— License: All videos are shared under the CC-BY license, with the majority (71%) in English.", "raw": "๐Ÿ”— License: All videos are shared under the CC-BY license, with the majority (71%) in English.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿค– Applications: This dataset is ideal for training powerful AI models for converting speech to text (ASR) and translation models.", "raw": "๐Ÿค– Applications: This dataset is ideal for training powerful AI models for converting speech to text (ASR) and translation models.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ“Š Utilization: The text can be used for model training and is republishable for reproducibility purposes.", "raw": "๐Ÿ“Š Utilization: The text can be used for model training and is republishable for reproducibility purposes.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿค Collaboration: This dataset is the result of a collaboration between state start-up LANGU:IA, the French Ministry of Culture, and DINUM. It will be expanded in the coming months.", "raw": "๐Ÿค Collaboration: This dataset is the result of a collaboration between state start-up LANGU:IA, the French Ministry of Culture, and DINUM. It will be expanded in the coming months.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿ”— Explore the dataset here: ", "raw": "๐Ÿ”— Explore the dataset here: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://lnkd.in/d_paWKFE", "href": "https://lnkd.in/d_paWKFE", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "#YouTubeCommons #AIResearch #MachineLearning #OpenData #ArtificialIntelligence #NLP #Dataset #TechCollaboration #Innovation #DigitalTransformation", "raw": "#YouTubeCommons #AIResearch #MachineLearning #OpenData #ArtificialIntelligence #NLP #Dataset #TechCollaboration #Innovation #DigitalTransformation", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐Ÿ“บ Introducing the YouTube-Commons Dataset ๐Ÿ“บ ๐ŸŒ Overview: The YouTube Commons Dataset is a comprehensive collection of 30 billion words from 15,112,121 original and automatically translated transcripts, drawn from 2,063,066 videos on YouTube. ๐Ÿ”— License: All videos are shared under the CC-BY license, with the majority (71%) in English. ๐Ÿค– Applications: This dataset is ideal for training powerful AI models for converting speech to text (ASR) and translation models. ๐Ÿ“Š Utilization: The text can be used for model training and is republishable for reproducibility purposes. ๐Ÿค Collaboration: This dataset is the result of a collaboration between state start-up LANGU:IA, the French Ministry of Culture, and DINUM. It will be expanded in the coming months. ๐Ÿ”— Explore the dataset here: https://lnkd.in/d_paWKFE #YouTubeCommons #AIResearch #MachineLearning #OpenData #ArtificialIntelligence #NLP #Dataset #TechCollaboration #Innovation #DigitalTransformation
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2024-06-01T13:11:43.000Z
2024-06-01T13:13:14.865Z
[]
/posts/Salama1429/845263544496775
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[ { "type": "text", "value": "Introducing UNA-ThePitbull Series", "raw": "Introducing UNA-ThePitbull Series", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We are happy to announce the release of our latest model UNA-ThePitbull, the most powerful model below 70B in the industry. In this new generation, inspired on our previous Beagle series we curated a model that balance nicely EQ and IQ. It was trained with some of the latest datasets including:", "raw": "We are happy to announce the release of our latest model UNA-ThePitbull, the most powerful model below 70B in the industry. In this new generation, inspired on our previous Beagle series we curated a model that balance nicely EQ and IQ. It was trained with some of the latest datasets including:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Replete-AI/code_bagel_hermes-2.5", "raw": "* Replete-AI/code_bagel_hermes-2.5", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* mlabonne/orpo-dpo-mix-40k", "raw": "* mlabonne/orpo-dpo-mix-40k", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* jondurbin/py-dpo-v0.1", "raw": "* jondurbin/py-dpo-v0.1", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Available in the hub ", "raw": "Available in the hub ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/fblgit/UNA-ThePitbull-21.4B-v2", "href": null, "resource": { "type": "model", "id": "fblgit/UNA-ThePitbull-21.4B-v2", "discussionNum": null }, "url": "https://huggingface.co/fblgit/UNA-ThePitbull-21.4B-v2", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " and you can grab Quant versions sponsored by ", "raw": " and you can grab Quant versions sponsored by ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "mention", "value": null, "raw": "@bartowski", "href": null, "resource": null, "url": null, "code": null, "user": "bartowski", "label": null, "lang": null }, { "type": "text", "value": " at ", "raw": " at ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF", "href": null, "resource": { "type": "model", "id": "bartowski/UNA-ThePitbull-21.4B-v2-GGUF", "discussionNum": null }, "url": "https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " fully compatible with Ollama, llama.cpp, etc.", "raw": " fully compatible with Ollama, llama.cpp, etc.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "UNA", "raw": "UNA", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "In this case we tried something new by alternating uniformity across layers of both MLP & Attention reducing computational requirements while keep a high performant result.", "raw": "In this case we tried something new by alternating uniformity across layers of both MLP & Attention reducing computational requirements while keep a high performant result.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "We trained him under these terms:", "raw": "We trained him under these terms:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* ThePitbull-v1 as base: SFT maxLR 1e-4 minLR 5e-5 for 1 Epoch", "raw": "* ThePitbull-v1 as base: SFT maxLR 1e-4 minLR 5e-5 for 1 Epoch", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* DPO maxLR 1e-4 minLR 5e-5 for 1 Epoch", "raw": "* DPO maxLR 1e-4 minLR 5e-5 for 1 Epoch", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "You can continue the training by merely using 5e-5 maxLR and 0 warmup steps, it should minimize catastrophic forgetting of the model.", "raw": "You can continue the training by merely using 5e-5 maxLR and 0 warmup steps, it should minimize catastrophic forgetting of the model.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Remember if you do so, please include a Pitbull picture on your model and cite :) Have fun!", "raw": "Remember if you do so, please include a Pitbull picture on your model and cite :) Have fun!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Introducing UNA-ThePitbull Series We are happy to announce the release of our latest model UNA-ThePitbull, the most powerful model below 70B in the industry. In this new generation, inspired on our previous Beagle series we curated a model that balance nicely EQ and IQ. It was trained with some of the latest datasets including: * Replete-AI/code_bagel_hermes-2.5 * mlabonne/orpo-dpo-mix-40k * jondurbin/py-dpo-v0.1 Available in the hub https://huggingface.co/fblgit/UNA-ThePitbull-21.4B-v2 and you can grab Quant versions sponsored by @bartowski at https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF fully compatible with Ollama, llama.cpp, etc. UNA In this case we tried something new by alternating uniformity across layers of both MLP & Attention reducing computational requirements while keep a high performant result. We trained him under these terms: * ThePitbull-v1 as base: SFT maxLR 1e-4 minLR 5e-5 for 1 Epoch * DPO maxLR 1e-4 minLR 5e-5 for 1 Epoch You can continue the training by merely using 5e-5 maxLR and 0 warmup steps, it should minimize catastrophic forgetting of the model. Remember if you do so, please include a Pitbull picture on your model and cite :) Have fun!
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2024-06-01T12:52:55.000Z
2024-06-01T12:52:55.094Z
[]
/posts/fblgit/862353567134059
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372309956389715
[ { "type": "text", "value": "ChemLLM Multi-Modal version will coming soon!", "raw": "ChemLLM Multi-Modal version will coming soon!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Also Weights and Datasets!", "raw": "Also Weights and Datasets!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
ChemLLM Multi-Modal version will coming soon! Also Weights and Datasets!
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2024-06-01T10:06:38.000Z
2024-06-02T09:14:14.699Z
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/posts/qq8933/372309956389715
2,075
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[ { "type": "text", "value": "Remember Gemini, GPT-4o, all being true multimodal models ๐ŸŒŸ.", "raw": "Remember Gemini, GPT-4o, all being true multimodal models ๐ŸŒŸ.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Now we have a paper ๐Ÿ“„ describing an architecture that might achieve that!", "raw": "Now we have a paper ๐Ÿ“„ describing an architecture that might achieve that!", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Uni-MoE: a native multimodal, Unified Mixture of Experts (MoE) architecture ๐Ÿ—๏ธ.", "raw": "Uni-MoE: a native multimodal, Unified Mixture of Experts (MoE) architecture ๐Ÿ—๏ธ.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Uni-MoE integrates various modalities (text ๐Ÿ“, image ๐Ÿ–ผ๏ธ, audio ๐ŸŽต, video ๐Ÿ“น, speech ๐Ÿ—ฃ๏ธ) using modality-specific encoders and connectors for a cohesive multimodal understanding.", "raw": "Uni-MoE integrates various modalities (text ๐Ÿ“, image ๐Ÿ–ผ๏ธ, audio ๐ŸŽต, video ๐Ÿ“น, speech ๐Ÿ—ฃ๏ธ) using modality-specific encoders and connectors for a cohesive multimodal understanding.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Training Strategy:", "raw": "Training Strategy:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1๏ธโƒฃ Training cross-modality alignment with diverse connectors ๐Ÿ”„.", "raw": "1๏ธโƒฃ Training cross-modality alignment with diverse connectors ๐Ÿ”„.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2๏ธโƒฃ Training modality-specific experts using cross-modality instruction data ๐Ÿ“Š.", "raw": "2๏ธโƒฃ Training modality-specific experts using cross-modality instruction data ๐Ÿ“Š.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "3๏ธโƒฃTuning the Uni-MoE framework with Low-Rank Adaptation (LoRA) on mixed multimodal data ๐Ÿ”ง.", "raw": "3๏ธโƒฃTuning the Uni-MoE framework with Low-Rank Adaptation (LoRA) on mixed multimodal data ๐Ÿ”ง.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Technical Details:", "raw": "Technical Details:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Modality-Specific Encoders: CLIP for images ๐Ÿ–ผ๏ธ, Whisper for speech ๐Ÿ—ฃ๏ธ, BEATs for audio ๐ŸŽต.", "raw": "Modality-Specific Encoders: CLIP for images ๐Ÿ–ผ๏ธ, Whisper for speech ๐Ÿ—ฃ๏ธ, BEATs for audio ๐ŸŽต.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "MoE-Based Blocks: Shared self-attention layers, feed-forward networks (FFN) based experts, and sparse routers for token-level expertise allocation ๐Ÿš€.", "raw": "MoE-Based Blocks: Shared self-attention layers, feed-forward networks (FFN) based experts, and sparse routers for token-level expertise allocation ๐Ÿš€.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Efficient Training: Utilizes LoRA for fine-tuning pre-trained experts and self-attention layers ๐Ÿ› ๏ธ.", "raw": "Efficient Training: Utilizes LoRA for fine-tuning pre-trained experts and self-attention layers ๐Ÿ› ๏ธ.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Uni-MoE outperforms traditional dense models on benchmarks like A-OKVQA, OK-VQA, VQAv2, MMBench, RACE-Audio, and English High School Listening Test ๐Ÿ†.", "raw": "Uni-MoE outperforms traditional dense models on benchmarks like A-OKVQA, OK-VQA, VQAv2, MMBench, RACE-Audio, and English High School Listening Test ๐Ÿ†.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The code is open-sourced as well: ", "raw": "The code is open-sourced as well: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://github.com/HITsz-TMG/UMOE-Scaling-Unified-Multimodal-LLMs/tree/master/Uni_MoE_v2", "href": "https://github.com/HITsz-TMG/UMOE-Scaling-Unified-Multimodal-LLMs/tree/master/Uni_MoE_v2", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Paper: ", "raw": "Paper: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/papers/2405.11273", "href": null, "resource": { "type": "paper", "id": "2405.11273", "discussionNum": null }, "url": "https://huggingface.co/papers/2405.11273", "code": null, "user": null, "label": "Uni-MoE: Scaling Unified Multimodal LLMs with Mixture of Experts (2405.11273)", "lang": null } ]
Remember Gemini, GPT-4o, all being true multimodal models ๐ŸŒŸ. Now we have a paper ๐Ÿ“„ describing an architecture that might achieve that! Uni-MoE: a native multimodal, Unified Mixture of Experts (MoE) architecture ๐Ÿ—๏ธ. Uni-MoE integrates various modalities (text ๐Ÿ“, image ๐Ÿ–ผ๏ธ, audio ๐ŸŽต, video ๐Ÿ“น, speech ๐Ÿ—ฃ๏ธ) using modality-specific encoders and connectors for a cohesive multimodal understanding. Training Strategy: 1๏ธโƒฃ Training cross-modality alignment with diverse connectors ๐Ÿ”„. 2๏ธโƒฃ Training modality-specific experts using cross-modality instruction data ๐Ÿ“Š. 3๏ธโƒฃTuning the Uni-MoE framework with Low-Rank Adaptation (LoRA) on mixed multimodal data ๐Ÿ”ง. Technical Details: Modality-Specific Encoders: CLIP for images ๐Ÿ–ผ๏ธ, Whisper for speech ๐Ÿ—ฃ๏ธ, BEATs for audio ๐ŸŽต. MoE-Based Blocks: Shared self-attention layers, feed-forward networks (FFN) based experts, and sparse routers for token-level expertise allocation ๐Ÿš€. Efficient Training: Utilizes LoRA for fine-tuning pre-trained experts and self-attention layers ๐Ÿ› ๏ธ. Uni-MoE outperforms traditional dense models on benchmarks like A-OKVQA, OK-VQA, VQAv2, MMBench, RACE-Audio, and English High School Listening Test ๐Ÿ†. The code is open-sourced as well: https://github.com/HITsz-TMG/UMOE-Scaling-Unified-Multimodal-LLMs/tree/master/Uni_MoE_v2 Paper: https://huggingface.co/papers/2405.11273
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2024-05-31T21:42:25.000Z
2024-05-31T21:42:25.714Z
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/posts/singhsidhukuldeep/375095993392947
1,515
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#Newer / Current Version ๐ŸšจHuggingface APK Update v0.0.4๐Ÿšจ 1. Fixed Pinch to Zoom Update . 2. Swipe Gestures. 3. Fixed Auto Rotate. 4. Updated app Indentifiers. Download the app now !! ๐ŸšจHuggingface v0.0.4 Download, โฌ‡๏ธLink : https://drive.google.com/file/d/1xEiH7LMdP14fBG-xDuSqKje5TRLV1PuS/view?usp=sharing Like ๐Ÿ‘Share ๐Ÿš€ Follow ๐ŸŒ 
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2024-05-31T17:30:06.000Z
2024-06-01T18:02:53.687Z
[]
/posts/prithivMLmods/733013042582862
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[ { "type": "text", "value": "I am pleased to announce 2 amazing AI demos:", "raw": "I am pleased to announce 2 amazing AI demos:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "1. Chat with Google Agent - This includes three AI models that allow you to converse with an AI, which provides answers by searching Google.", "raw": "1. Chat with Google Agent - This includes three AI models that allow you to converse with an AI, which provides answers by searching Google.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Demo Link: ", "raw": "Demo Link: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/spaces/poscye/google-go", "href": null, "resource": { "type": "space", "id": "poscye/google-go", "discussionNum": null }, "url": "https://huggingface.co/spaces/poscye/google-go", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "2. HelpingAI 9B - A model that surpassed all top AIs with the highest EQ benchmark score of 89.23. It specializes in understanding human emotions and responding in human style.", "raw": "2. HelpingAI 9B - A model that surpassed all top AIs with the highest EQ benchmark score of 89.23. It specializes in understanding human emotions and responding in human style.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Demo Link: ", "raw": "Demo Link: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B", "href": "https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Model Link: ", "raw": "Model Link: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/OEvortex/HelpingAI-9B", "href": null, "resource": { "type": "model", "id": "OEvortex/HelpingAI-9B", "discussionNum": null }, "url": "https://huggingface.co/OEvortex/HelpingAI-9B", "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Blog Link: ", "raw": "Blog Link: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/KingNish/helpingai-9b", "href": "https://huggingface.co/blog/KingNish/helpingai-9b", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
I am pleased to announce 2 amazing AI demos: 1. Chat with Google Agent - This includes three AI models that allow you to converse with an AI, which provides answers by searching Google. Demo Link: https://huggingface.co/spaces/poscye/google-go 2. HelpingAI 9B - A model that surpassed all top AIs with the highest EQ benchmark score of 89.23. It specializes in understanding human emotions and responding in human style. Demo Link: https://huggingface.co/spaces/Abhaykoul/HelpingAI-9B Model Link: https://huggingface.co/OEvortex/HelpingAI-9B Blog Link: https://huggingface.co/blog/KingNish/helpingai-9b
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2024-05-31T15:32:43.000Z
2024-06-02T09:46:26.266Z
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/posts/KingNish/786350827380996
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[ { "type": "text", "value": "We are pleased to announce the new line of universal token classification models ๐Ÿ”ฅ", "raw": "We are pleased to announce the new line of universal token classification models ๐Ÿ”ฅ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "resource", "value": null, "raw": "https://huggingface.co/collections/knowledgator/universal-token-classification-65a3a5d3f266d20b2e05c34d", "href": null, "resource": { "type": "collection", "id": "knowledgator/universal-token-classification-65a3a5d3f266d20b2e05c34d", "discussionNum": null }, "url": "https://huggingface.co/collections/knowledgator/universal-token-classification-65a3a5d3f266d20b2e05c34d", "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": " ", "raw": " ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "It can perform various information extraction tasks by analysing input prompts and recognizing parts of texts that satisfy prompts. In comparison with the first version, the second one is more general and can be recognised as entities, whole sentences, and even paragraphs.", "raw": "It can perform various information extraction tasks by analysing input prompts and recognizing parts of texts that satisfy prompts. In comparison with the first version, the second one is more general and can be recognised as entities, whole sentences, and even paragraphs.", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "The model can be used for the following tasks:", "raw": "The model can be used for the following tasks:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Named entity recognition (NER);", "raw": "* Named entity recognition (NER);", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Open information extraction;", "raw": "* Open information extraction;", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Question answering;", "raw": "* Question answering;", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Relation extraction;", "raw": "* Relation extraction;", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Coreference resolution;", "raw": "* Coreference resolution;", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Text cleaning;", "raw": "* Text cleaning;", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* Summarization;", "raw": "* Summarization;", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "How to use:", "raw": "How to use:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "code_fence", "value": null, "raw": "```\nfrom utca.core import (\n AddData,\n RenameAttribute,\n Flush\n)\nfrom utca.implementation.predictors import (\n TokenSearcherPredictor, TokenSearcherPredictorConfig\n)\nfrom utca.implementation.tasks import (\n TokenSearcherNER,\n TokenSearcherNERPostprocessor,\n)\npredictor = TokenSearcherPredictor(\n TokenSearcherPredictorConfig(\n device=\"cuda:0\",\n model=\"knowledgator/UTC-DeBERTa-base-v2\"\n )\n)\nner_task = TokenSearcherNER(\n predictor=predictor,\n postprocess=[TokenSearcherNERPostprocessor(\n threshold=0.5\n )]\n)\n\nner_task = TokenSearcherNER()\n\npipeline = ( \n AddData({\"labels\": [\"scientist\", \"university\", \"city\"]}) \n | ner_task\n | Flush(keys=[\"labels\"])\n | RenameAttribute(\"output\", \"entities\")\n)\nres = pipeline.run({\n \"text\": \"\"\"Dr. Paul Hammond, a renowned neurologist at Johns Hopkins University, has recently published a paper in the prestigious journal \"Nature Neuroscience\". \"\"\"\n})\n```", "href": null, "resource": null, "url": null, "code": "from utca.core import (\n AddData,\n RenameAttribute,\n Flush\n)\nfrom utca.implementation.predictors import (\n TokenSearcherPredictor, TokenSearcherPredictorConfig\n)\nfrom utca.implementation.tasks import (\n TokenSearcherNER,\n TokenSearcherNERPostprocessor,\n)\npredictor = TokenSearcherPredictor(\n TokenSearcherPredictorConfig(\n device=\"cuda:0\",\n model=\"knowledgator/UTC-DeBERTa-base-v2\"\n )\n)\nner_task = TokenSearcherNER(\n predictor=predictor,\n postprocess=[TokenSearcherNERPostprocessor(\n threshold=0.5\n )]\n)\n\nner_task = TokenSearcherNER()\n\npipeline = ( \n AddData({\"labels\": [\"scientist\", \"university\", \"city\"]}) \n | ner_task\n | Flush(keys=[\"labels\"])\n | RenameAttribute(\"output\", \"entities\")\n)\nres = pipeline.run({\n \"text\": \"\"\"Dr. Paul Hammond, a renowned neurologist at Johns Hopkins University, has recently published a paper in the prestigious journal \"Nature Neuroscience\". \"\"\"\n})", "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
We are pleased to announce the new line of universal token classification models ๐Ÿ”ฅ https://huggingface.co/collections/knowledgator/universal-token-classification-65a3a5d3f266d20b2e05c34d It can perform various information extraction tasks by analysing input prompts and recognizing parts of texts that satisfy prompts. In comparison with the first version, the second one is more general and can be recognised as entities, whole sentences, and even paragraphs. The model can be used for the following tasks: * Named entity recognition (NER); * Open information extraction; * Question answering; * Relation extraction; * Coreference resolution; * Text cleaning; * Summarization; How to use: ``` from utca.core import ( AddData, RenameAttribute, Flush ) from utca.implementation.predictors import ( TokenSearcherPredictor, TokenSearcherPredictorConfig ) from utca.implementation.tasks import ( TokenSearcherNER, TokenSearcherNERPostprocessor, ) predictor = TokenSearcherPredictor( TokenSearcherPredictorConfig( device="cuda:0", model="knowledgator/UTC-DeBERTa-base-v2" ) ) ner_task = TokenSearcherNER( predictor=predictor, postprocess=[TokenSearcherNERPostprocessor( threshold=0.5 )] ) ner_task = TokenSearcherNER() pipeline = ( AddData({"labels": ["scientist", "university", "city"]}) | ner_task | Flush(keys=["labels"]) | RenameAttribute("output", "entities") ) res = pipeline.run({ "text": """Dr. Paul Hammond, a renowned neurologist at Johns Hopkins University, has recently published a paper in the prestigious journal "Nature Neuroscience". """ }) ```
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[]
[]
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2024-05-31T14:53:35.000Z
2024-05-31T14:54:15.915Z
[]
/posts/Ihor/885684618092437
1,897
0
505714323175985
[ { "type": "text", "value": "๐—›๐—ผ๐˜„ ๐—ฑ๐—ผ๐—ฒ๐˜€ ๐—ฎ๐—ป ๐—ฎ๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐˜„๐—ผ๐—ฟ๐—ธ๐—ณ๐—น๐—ผ๐˜„ ๐˜‚๐˜€๐—ฒ ๐—ถ๐˜๐˜€ ๐—Ÿ๐—Ÿ๐—  ๐—ฒ๐—ป๐—ด๐—ถ๐—ป๐—ฒ ๐˜๐—ผ ๐˜€๐—ผ๐—น๐˜ƒ๐—ฒ ๐˜๐—ฎ๐˜€๐—ธ๐˜€?", "raw": "๐—›๐—ผ๐˜„ ๐—ฑ๐—ผ๐—ฒ๐˜€ ๐—ฎ๐—ป ๐—ฎ๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐˜„๐—ผ๐—ฟ๐—ธ๐—ณ๐—น๐—ผ๐˜„ ๐˜‚๐˜€๐—ฒ ๐—ถ๐˜๐˜€ ๐—Ÿ๐—Ÿ๐—  ๐—ฒ๐—ป๐—ด๐—ถ๐—ป๐—ฒ ๐˜๐—ผ ๐˜€๐—ผ๐—น๐˜ƒ๐—ฒ ๐˜๐—ฎ๐˜€๐—ธ๐˜€?", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "โžก๏ธ I made my first ever ๐˜ฎ๐˜ข๐˜ฏ๐˜ช๐˜ฎ video to show just that:", "raw": "โžก๏ธ I made my first ever ๐˜ฎ๐˜ข๐˜ฏ๐˜ช๐˜ฎ video to show just that:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐—ช๐—ฎ๐˜๐—ฐ๐—ต ๐—ฏ๐—ฒ๐—น๐—ผ๐˜„ ๐—ต๐—ผ๐˜„ ๐—ฎ ๐—ฅ๐—ฒ๐—ฎ๐—ฐ๐˜ ๐—”๐—ด๐—ฒ๐—ป๐˜ ๐˜€๐—ผ๐—น๐˜ƒ๐—ฒ๐˜€ ๐—ฎ ๐˜€๐—ถ๐—บ๐—ฝ๐—น๐—ฒ ๐˜๐—ฎ๐˜€๐—ธ, by leveraging its memory to iterate on previous actions! ๐ŸŽฌ๐Ÿ‘‡", "raw": "๐—ช๐—ฎ๐˜๐—ฐ๐—ต ๐—ฏ๐—ฒ๐—น๐—ผ๐˜„ ๐—ต๐—ผ๐˜„ ๐—ฎ ๐—ฅ๐—ฒ๐—ฎ๐—ฐ๐˜ ๐—”๐—ด๐—ฒ๐—ป๐˜ ๐˜€๐—ผ๐—น๐˜ƒ๐—ฒ๐˜€ ๐—ฎ ๐˜€๐—ถ๐—บ๐—ฝ๐—น๐—ฒ ๐˜๐—ฎ๐˜€๐—ธ, by leveraging its memory to iterate on previous actions! ๐ŸŽฌ๐Ÿ‘‡", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Read our blog post on Agents: ", "raw": "Read our blog post on Agents: ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/blog/agents", "href": "https://huggingface.co/blog/agents", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
๐—›๐—ผ๐˜„ ๐—ฑ๐—ผ๐—ฒ๐˜€ ๐—ฎ๐—ป ๐—ฎ๐—ด๐—ฒ๐—ป๐˜๐—ถ๐—ฐ ๐˜„๐—ผ๐—ฟ๐—ธ๐—ณ๐—น๐—ผ๐˜„ ๐˜‚๐˜€๐—ฒ ๐—ถ๐˜๐˜€ ๐—Ÿ๐—Ÿ๐—  ๐—ฒ๐—ป๐—ด๐—ถ๐—ป๐—ฒ ๐˜๐—ผ ๐˜€๐—ผ๐—น๐˜ƒ๐—ฒ ๐˜๐—ฎ๐˜€๐—ธ๐˜€? โžก๏ธ I made my first ever ๐˜ฎ๐˜ข๐˜ฏ๐˜ช๐˜ฎ video to show just that: ๐—ช๐—ฎ๐˜๐—ฐ๐—ต ๐—ฏ๐—ฒ๐—น๐—ผ๐˜„ ๐—ต๐—ผ๐˜„ ๐—ฎ ๐—ฅ๐—ฒ๐—ฎ๐—ฐ๐˜ ๐—”๐—ด๐—ฒ๐—ป๐˜ ๐˜€๐—ผ๐—น๐˜ƒ๐—ฒ๐˜€ ๐—ฎ ๐˜€๐—ถ๐—บ๐—ฝ๐—น๐—ฒ ๐˜๐—ฎ๐˜€๐—ธ, by leveraging its memory to iterate on previous actions! ๐ŸŽฌ๐Ÿ‘‡ Read our blog post on Agents: https://huggingface.co/blog/agents
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2024-05-31T12:59:31.000Z
2024-06-02T01:08:01.641Z
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/posts/m-ric/505714323175985
1,843
1
854384239175296
[ { "type": "text", "value": "With the previous survey, Ghost Beta (small version) will support 9+ languages โ€‹โ€‹fluently. It is revealed that the model will be designed for 3 stages of training, showing a checkpoint to try at stage 1 (trained progress: 29%). ", "raw": "With the previous survey, Ghost Beta (small version) will support 9+ languages โ€‹โ€‹fluently. It is revealed that the model will be designed for 3 stages of training, showing a checkpoint to try at stage 1 (trained progress: 29%). ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Supported languages: ๐Ÿ‡บ๐Ÿ‡ธ English, ๐Ÿ‡ช๐Ÿ‡ธ Spanish, ๐Ÿ‡ต๐Ÿ‡น Portuguese, ๐Ÿ‡ซ๐Ÿ‡ท French, ๐Ÿ‡ฎ๐Ÿ‡น Italian, ๐Ÿ‡ฉ๐Ÿ‡ช German, ๐Ÿ‡ป๐Ÿ‡ณ Vietnamese, ๐Ÿ‡ฐ๐Ÿ‡ท Korean, ๐Ÿ‡จ๐Ÿ‡ณ Chinese, and !? ", "raw": "Supported languages: ๐Ÿ‡บ๐Ÿ‡ธ English, ๐Ÿ‡ช๐Ÿ‡ธ Spanish, ๐Ÿ‡ต๐Ÿ‡น Portuguese, ๐Ÿ‡ซ๐Ÿ‡ท French, ๐Ÿ‡ฎ๐Ÿ‡น Italian, ๐Ÿ‡ฉ๐Ÿ‡ช German, ๐Ÿ‡ป๐Ÿ‡ณ Vietnamese, ๐Ÿ‡ฐ๐Ÿ‡ท Korean, ๐Ÿ‡จ๐Ÿ‡ณ Chinese, and !? ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "Note that this is not a conclusion, this is just a sharing of the state of the model. If you find it interesting, please follow the project at:", "raw": "Note that this is not a conclusion, this is just a sharing of the state of the model. If you find it interesting, please follow the project at:", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* ", "raw": "* ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://x.com/ghostx_ai", "href": "https://x.com/ghostx_ai", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* ", "raw": "* ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://ghost-x.org/", "href": "https://ghost-x.org/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "* ", "raw": "* ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://huggingface.co/ghost-x", "href": "https://huggingface.co/ghost-x", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "new_line", "value": null, "raw": "\n", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "text", "value": "๐Ÿคฏ๐Ÿ‘‡", "raw": "๐Ÿคฏ๐Ÿ‘‡", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
With the previous survey, Ghost Beta (small version) will support 9+ languages โ€‹โ€‹fluently. It is revealed that the model will be designed for 3 stages of training, showing a checkpoint to try at stage 1 (trained progress: 29%). Supported languages: ๐Ÿ‡บ๐Ÿ‡ธ English, ๐Ÿ‡ช๐Ÿ‡ธ Spanish, ๐Ÿ‡ต๐Ÿ‡น Portuguese, ๐Ÿ‡ซ๐Ÿ‡ท French, ๐Ÿ‡ฎ๐Ÿ‡น Italian, ๐Ÿ‡ฉ๐Ÿ‡ช German, ๐Ÿ‡ป๐Ÿ‡ณ Vietnamese, ๐Ÿ‡ฐ๐Ÿ‡ท Korean, ๐Ÿ‡จ๐Ÿ‡ณ Chinese, and !? Note that this is not a conclusion, this is just a sharing of the state of the model. If you find it interesting, please follow the project at: * https://x.com/ghostx_ai * https://ghost-x.org/ * https://huggingface.co/ghost-x ๐Ÿคฏ๐Ÿ‘‡
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2024-05-31T10:15:06.000Z
2024-05-31T10:15:06.915Z
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https://hf.co/chat/assistant/66591a605bfa3e96f8267a32 AI perfect hashtag generator Create advanced hashtags using our smart AI system.
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[ { "type": "text", "value": "Octave-X releases their proprietary model Tenzin. For now the access will be given to a select few and will gradually open up. Our model is different from other models in the way it learns. It is not fed heaps of information but starts learning exactly like a human by first studying grammar patterns, then learning then number system, then learning to synthesize words and then sentences and so on. Patience is key with Tenzin. It keeps learning 24/7 with/without user-input. We have decided to keep our model closed-source given the novel algorithms integrated into it along with our novel ideas. Please expect our datacard soon which will be followed by our research paper. You can check us out at ", "raw": "Octave-X releases their proprietary model Tenzin. For now the access will be given to a select few and will gradually open up. Our model is different from other models in the way it learns. It is not fed heaps of information but starts learning exactly like a human by first studying grammar patterns, then learning then number system, then learning to synthesize words and then sentences and so on. Patience is key with Tenzin. It keeps learning 24/7 with/without user-input. We have decided to keep our model closed-source given the novel algorithms integrated into it along with our novel ideas. Please expect our datacard soon which will be followed by our research paper. You can check us out at ", "href": null, "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null }, { "type": "link", "value": null, "raw": "https://octave-x.com/", "href": "https://octave-x.com/", "resource": null, "url": null, "code": null, "user": null, "label": null, "lang": null } ]
Octave-X releases their proprietary model Tenzin. For now the access will be given to a select few and will gradually open up. Our model is different from other models in the way it learns. It is not fed heaps of information but starts learning exactly like a human by first studying grammar patterns, then learning then number system, then learning to synthesize words and then sentences and so on. Patience is key with Tenzin. It keeps learning 24/7 with/without user-input. We have decided to keep our model closed-source given the novel algorithms integrated into it along with our novel ideas. Please expect our datacard soon which will be followed by our research paper. You can check us out at https://octave-x.com/
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2024-05-31T05:10:29.000Z
2024-05-31T05:10:29.872Z
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