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Model: https://huggingface.co/mrfakename/refusal
Dataset: https://huggingface.co/datasets/mrfakename/refusal
Larger Dataset: https://huggingface.co/datasets/mrfakename/refusal-xl
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] | Visualization of GPT-4o breaking away from the quality & speed trade-off curve the LLMs have followed thus far โ๏ธ
Key GPT-4o takeaways
โฃ GPT-4o not only offers the highest quality, it also sits amongst the fastest LLMs
โฃ For those with speed/latency-sensitive use cases, where previously Claude 3 Haiku or Mixtral 8x7b were leaders, GPT-4o is now a compelling option (though significantly more expensive)
โฃ Previously Groq was the only provider to break from the curve using its own LPU chips. OpenAI has done it on Nvidia hardware (one can imagine the potential for GPT-4o on Groq)
๐ How did they do it? Will follow up with more analysis on this but potential approaches include a very large but sparse MoE model (similar to Snowflake's Arctic) and improvements in data quality (likely to have driven much of Llama 3's impressive quality relative to parameter count)
Notes: Throughput represents the median across providers over the last 14 days of measurements (8x per day)
Data is present on our HF leaderboard: https://huggingface.co/spaces/ArtificialAnalysis/LLM-Performance-Leaderboard and graphs present on our website | {
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Here it is:
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"value": "if you have any extra resources about using, creating, or finetuning them feel free to share them below ๐ค",
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] | I'm looking for open-source image embedding models for RAG applications and/or multimodel embedding models if they exist in the first place.
if you have any extra resources about using, creating, or finetuning them feel free to share them below ๐ค | {
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] | SambaNova just released a revolutionary paper about how the SN40L AI chip can host many LLMs on a single node and run inference so efficiently that it enables running a "composition of experts." These experts can be interconnected via a router, resulting in remarkable accuracy. This method allows you to take open source expert models from HuggingFace and continuously build and integrate them into a composition of experts.
I am also super excited about the possibilities that SN40Ls unlock for LLM agent workflows and pipelined calls. With the release of GPT4o, it seems that monolithic LLMs are starting to reach a plateau, and I believe that the next wave of AI will be driven by pipelined LLM calls and agent workflows. Most pipelined LLM workflows are bottlenecked by prohibitively expensive compute and high latency, but the SN40L provides a one stop shop solution for this. We need to get the word out to the community that this hardware exists, because it will open up a realm of possibilities that developers working with Nvidia hardware did not know exist.
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"value": "A lot of interesting quotes in this interview in The Markup with Jonathan Soma, a professor in data journalism at Columbia University: ",
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"value": "When the New York City government released its chatbot, journalists found that \"Again and again, the bot messing up on city laws and regulations.\" ",
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"value": "Enter Jonathan Soma, who tried to build his own version of the chatbot. And guess what? He got accurate responses. ",
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"value": "๐ฌ Still, he remains cautious: \"Chatbots are great for low-stakes things. They are great when something is fun, they are great for a task where you do not need 100 percent accuracy, when you just want a little bit of guidance.\"",
"raw": "๐ฌ Still, he remains cautious: \"Chatbots are great for low-stakes things. They are great when something is fun, they are great for a task where you do not need 100 percent accuracy, when you just want a little bit of guidance.\"",
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"value": "\"I think that AI in general is absolutely useful for journalism, and Iโve been teaching machine learning and AI to journalists long before ChatGPT hit the scene. I think it is explicitly chatbots that are probably the most problematic part, because they are so confident in everything that they say.\"",
"raw": "\"I think that AI in general is absolutely useful for journalism, and Iโve been teaching machine learning and AI to journalists long before ChatGPT hit the scene. I think it is explicitly chatbots that are probably the most problematic part, because they are so confident in everything that they say.\"",
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"value": "๐ค I have a particular soft spot for this project, as it uses many Hugging Face tools under the hood. This is precisely the kind of work we want to build with the Journalists on HF community. Join us: ",
"raw": "๐ค I have a particular soft spot for this project, as it uses many Hugging Face tools under the hood. This is precisely the kind of work we want to build with the Journalists on HF community. Join us: ",
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"value": "๐บ I can't recommend enough watching his video serie \"Practical AI for Investigative Journalism\": ",
"raw": "๐บ I can't recommend enough watching his video serie \"Practical AI for Investigative Journalism\": ",
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] | "This Journalism Professor Made a NYC Chatbot in Minutes. It Actually Worked"
A lot of interesting quotes in this interview in The Markup with Jonathan Soma, a professor in data journalism at Columbia University: https://themarkup.org/hello-world/2024/05/11/this-journalism-professor-made-a-nyc-chatbot-in-minutes-it-actually-worked
When the New York City government released its chatbot, journalists found that "Again and again, the bot messing up on city laws and regulations."
Enter Jonathan Soma, who tried to build his own version of the chatbot. And guess what? He got accurate responses.
๐ฌ Still, he remains cautious: "Chatbots are great for low-stakes things. They are great when something is fun, they are great for a task where you do not need 100 percent accuracy, when you just want a little bit of guidance."
"I think that AI in general is absolutely useful for journalism, and Iโve been teaching machine learning and AI to journalists long before ChatGPT hit the scene. I think it is explicitly chatbots that are probably the most problematic part, because they are so confident in everything that they say."
๐ค I have a particular soft spot for this project, as it uses many Hugging Face tools under the hood. This is precisely the kind of work we want to build with the Journalists on HF community. Join us: https://huggingface.co/JournalistsonHF
๐บ I can't recommend enough watching his video serie "Practical AI for Investigative Journalism": https://www.youtube.com/watch?v=N5wvtYRYbfA&list=PLewNEVDy7gq1_GPUaL0OQ31QsiHP5ncAQ
โ Thanks @BrigitteTousi for the link! | {
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] | I just saw that openai is using an updated tokenizer and it greatly increases the speed of the model and maybe even performance as if we increase the size of vocabulary, it could predict a single token which might be equivalent to two or three tokens in current tokenizers with 50-60k tokens or even 100k. I was thinking of scaling this to a million vocabulary size and then pre training llama3 8b using lora. I know that the model might go to shit, but we can increase the speed of the tokens generation greatly imo. And as one of meta papers said that predicting multiple tokens at the same time can actually increase the performance of a model, so I can imagine increasing the vocabulary in this way means multiple token generation in a way. Yann Lecunn also says that we donโt think in tokens but more like representations or abstractions of situations or problem to be solved. Can scaling to a million vocab size or even 10 million vocab size lead to better and more robust models? Please give me your thoughts on what can go wrong, what can go right etcโฆ | {
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] | @Ali-C137 and the team at OALL Just released https://huggingface.co/spaces/OALL/Open-Arabic-LLM-Leaderboard
Amazing effort to push the Arabic LLMs development forward ๐ | {
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"value": "and outputs possible are Image ๐ผ๏ธ, Image + Text ๐ผ๏ธ๐, Text ๐, Audio ๐ง",
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] | Introducing OpenGPT-4o
https://huggingface.co/spaces/KingNish/OpenGPT-4o
Features:
1๏ธโฃ Inputs possible are Text โ๏ธ, Text + Image ๐๐ผ๏ธ, Audio ๐ง, WebCam๐ธ
and outputs possible are Image ๐ผ๏ธ, Image + Text ๐ผ๏ธ๐, Text ๐, Audio ๐ง
2๏ธโฃ Flat 100% FREE ๐ธ and Super-fast โก.
3๏ธโฃ Publicly Available before GPT 4o.
Future Features:
1๏ธโฃ Chat with PDF (Both voice and text)
2๏ธโฃ Video generation.
3๏ธโฃ Sequential Image Generation.
4๏ธโฃ Better UI and customization.
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] | In my ongoing quest to learn more about building synthetic datasets, I've created an "Awesome Synthetic Datasets" list.
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๐ https://github.com/davanstrien/awesome-synthetic-datasets | {
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Conference: SIGGRAPH, 28 Jul โ 1 Aug, 2024 | Denver CO, USA ๐บ๐ธ",
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"value": "๐ Keywords: #LayGA #AnimatableClothingTransfer #VirtualTryOn #AvatarTechnology #SIGGRAPH2024 #ComputerGraphics #DeepLearning #ComputerVision #Innovation",
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] | ๐๐๐ New Research Alert - SIGGRAPH 2024 (Avatars Collection)! ๐๐๐
๐ Title: LayGA: Layered Gaussian Avatars for Animatable Clothing Transfer ๐
๐ Description: LayGA is a novel method for animatable clothing transfer that separates the body and clothing into two layers for improved photorealism and accurate clothing tracking, outperforming existing methods.
๐ฅ Authors: Siyou Lin, Zhe Li, Zhaoqi Su, Zerong Zheng, Hongwen Zhang, and Yebin Liu
๐
Conference: SIGGRAPH, 28 Jul โ 1 Aug, 2024 | Denver CO, USA ๐บ๐ธ
๐ Paper: https://huggingface.co/papers/2405.07319
๐ Github Page: https://jsnln.github.io/layga/index.html
๐ More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
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] | ๐จ NEW TASK ALERT ๐จ
๐ AutoTrain now supports Object Detection! ๐
Transform your projects with these powerful new features:
๐น Fine-tune any supported model from the Hugging Face Hub
๐น Seamless logging with TensorBoard or W&B
๐น Support for local and hub datasets
๐น Configurable training for tailored results
๐น Train locally or leverage Hugging Face Spaces
๐น Deployment-ready with API inference or Hugging Face endpoints
AutoTrain: https://hf.co/autotrain | {
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Try it out by yourself here โถ๏ธ https://huggingface.co/spaces/multimodalart/HunyuanDiT
(a bit too slow as the model is chunky and the research code isn't super optimized for inference speed yet)
In the paper they claim to be SOTA open source based on human preference evaluation! | {
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"value": "6๏ธโฃ New tokenizer with a 200k token vocabulary ๐ (previously 100k vocabulary) leading to 1.1x - 4.4x fewer tokens needed across 20 languages ๐",
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"value": "- Why does GPT-4 still exist, if GPT-4o is better, faster, and cheaper? ๐คจ",
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] | Is GPT-4o everything you expected? ๐ค
@OpenAI has gone omni (GPT-4"o" ๐), a multimodal LLM, it accepts as input any combination of text, audio, and image and generates any combination of text, audio, and image outputs. ๐ค๐ธโ๏ธ
1๏ธโฃ Based on the examples seen:
Inputs possible are Text โ๏ธ, Text + Image ๐๐ผ๏ธ, Text + Audio ๐๐ง, Text + Video ๐๐ฅ, Audio ๐ง
and outputs possible are Image ๐ผ๏ธ, Image + Text ๐ผ๏ธ๐, Text ๐, Audio ๐ง
2๏ธโฃ 88.7% on MMLU ๐; 90.2% on HumanEval (best in class) ๐ฅ
3๏ธโฃ Up to 50% cheaper ๐ธ and 2x faster โก than GPT-4 Turbo
4๏ธโฃ GPT-4o will be available in the free tier of ChatGPT ๐
5๏ธโฃ Near real-time audio with 320ms on average, similar to human conversation ๐ฃ๏ธ**
6๏ธโฃ New tokenizer with a 200k token vocabulary ๐ (previously 100k vocabulary) leading to 1.1x - 4.4x fewer tokens needed across 20 languages ๐
7๏ธโฃ Tokenizer compression and more efficient across non-English languages (3-5 times fewer tokens for major Indian languages ๐ฎ๐ณ)
๐Open questions:
- What is the context length? โ
- Why does GPT-4 still exist, if GPT-4o is better, faster, and cheaper? ๐คจ
Blog: https://openai.com/index/hello-gpt-4o/ ๐
Available today:https://chatgpt.com/ ๐
I just wanted it to be cheaper, and more accessible! ๐
Still not open source, but a price reduction is welcome! ๐ฐ
Also, something fun happened, for the first 10-15 mins all search engines were correcting GPT-4o to GPT-4 ๐
Also, also, GPT-4o is the model which was powering the GPT2 chatbot in the LMsys arena (ELO 1310 vs. 1253 for GPT-4 Turbo) ๐
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There are 3 models released:
- https://huggingface.co/numind/NuNER_Zero:
The primary model, SOTA & can detect really long entities.
- https://huggingface.co/numind/NuNER_Zero-span:
Slightly better performance than NuNER Zero, but can't detect entities longer than 12 tokens.
- https://huggingface.co/numind/NuNER_Zero-4k:
Slightly worse than NuNER Zero, but has a context length of 4k tokens.
Some more details about these models in general:
- They are *really* small, orders of magnitude smaller than LLMs, which don't reach this level of performance.
- Because they're small - they're fast: <1s per sentence on free GPUs.
- They have an MIT license: free commercial usage.
Try out the demo here: https://huggingface.co/spaces/numind/NuZero
Or check out all of the models here: https://huggingface.co/collections/numind/nunerzero-zero-shot-ner-662b59803b9b438ff56e49e2
If there's ever a need for me to extract some information from any text: I'll be using these. Great work @Serega6678! | {
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] | Build your own GPT-4 Tokenizer! - @karpathy's minbpe exercise.
Step 1: BasicTokenizer
Got "close" to beating minbpe's train speed :(
step 2 RegexTokenizer coming soon.
Notes on lessons learned:
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It's not a healthy choice to code it lol.
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Hey everyone! Just wanted to share this awesome dataset that features over 1 million tokens specifically for the Egyptian dialect. Check it
https://huggingface.co/datasets/HeshamHaroon/1milion_token_EGY_songs
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] | IBM has launched its latest #Granite code models.
๐๐ฟ๐ฎ๐ป๐ถ๐๐ฒ ๐ผ๐ป ๐๐๐ด๐ด๐ถ๐ป๐ด ๐๐ฎ๐ฐ๐ฒ - https://lnkd.in/g2KaHWxC
The figure illustrates how#Granite-8B-Code-Base outperforms #Mistral-7B, #LLama-3-8B, and other open-source models in coding tasks.
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] | Hi @TimothyAlexisVass, thanks for [this great blog post](https://huggingface.co/blog/TimothyAlexisVass/explaining-the-sdxl-latent-space#the-4-channels-of-the-sdxl-latents)! Shouldn't this be like that?
https://github.com/TimothyAlexisVass/hf-colorspace-blogpost/pull/1 | {
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] | Hi folks ,
colab[https://colab.research.google.com/drive/10av3SxFf0Psx_IkmZbcUhiVznStV5pVS?usp=sharing]
#OpenSourcing
pip-code-bandit
-- a model to act as intelligence unit in agentic workflows.
pipflow
-- a library to manage and run goal oriented agentic system.
pip-code-bandit attributes-
-- number of params ~ 1.3b [2.9 Gb GPU memory footprint]
-- sequence length ~ 16.3k [Can go higher but will show performance degradation]
-- license - apache 2.0
-- instruction following , RL tuned.
-- tasks:
complex planning(plan) of sequential function calls | a list of callables and goal
corrected plan | feedback instructions with error
function calling | doc or code and goal
code generation | plan and goal
code generation | goal
doc generation | code
code generation | doc
file parsed to json | any raw data
sql generation | schema, question, instructions and examples
#Strategy
We used a simulator to simulate environments where the model could play games to achieve goals, given a set of actions available to it. All the model could do was find the right action and config to incur a positive reward. The reward policy is around the concept of a model going to a stable state of zero net sum reward for both good and bad behaviour. In this setup, the model, which was pre-trained on code, function documentation, and similar OS datasets, was RL-tuned for reliability and instruction-following.
Do try it out and let me know how its working for you.
Thank you :) | {
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ใใงใใ ",
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ใใงใใ
ๆๅผทใฎใชใผใใณใฝใผในใขใใซใงใใ
ใใฒไฝฟใฃใฆใฟใพใใใใ
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] | ๐ Big reveal: 01.AI Yi-1.5 models are in town!
๐ 1st Apache 2.0 release
๐ก Capabilities: Enhanced coding, math, reasoning, & instruction-following
๐ค Models: 34B/9B/6B, Base & Chat
๐ Performance: Yi-1.5-34B matches or exceeds Llama 3 70B in benchmarks
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] | Juggernaut X V10 is pretty good, its a few weeks old but not very popular. Try it out and let me know what you guys think. I think it is pretty good for a daily use.
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] | Cool new dataset from @isidentical - https://huggingface.co/datasets/isidentical/moondream2-coyo-5M-captions
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] | Hi HF Community!
Did you know that you are a `docker compose up` away from unleashing some really powerful AI?
Let me introduce you to ๐ฒ๐๐ฒ๐ฟ๐๐๐ต๐ถ๐ป๐ด-๐ฎ๐ถ๐ค
๐ฒ๐๐ฒ๐ฟ๐๐๐ต๐ถ๐ป๐ด-๐ฎ๐ถ is a multi-task, AI-powered and local assistant that allows you to:
- ๐ญ talk to ๐บ๐ฐ๐ถ๐ณ ๐ฑ๐ฅ๐ง๐ด thanks to a backend retrieval architecture built with LangChain and Qdrant (supports 50+ languages)
- โ๏ธ chat with almost every text-generation model on Hugging Face hub (supports 50+ languages)
- ๐ summarize texts and pdfs with almost every text-summarization model on HF hub (English-only)
- ๐จ generate images with Pollinations AI API or stable diffusion models on HF hub (supports 50+ languages)
- ๐จ๏ธ describe images with image-to-text models on HF hub (English-only)
- ๐ฉป classify images with image-classification models on HF hub (English-only)
- ๐ผ๏ธ implement an image-based search engine on *your images*, thanks to Qdrant, with almost every feature extraction model on HF hub
๐ฅ๏ธ There is now a brand new user interface that helps with selecting tasks, models, pdf files, images and languages
๐ everything-ai is distributed as a multi-container application in Docker, available for Windows and Mac, and it is 100% local.
๐โโ๏ธ Head over to the documentation for a quick-start on the application: https://astrabert.github.io/everything-ai
๐ฅฐ Make sure to check out the GitHub repo and leave a little โญ: https://github.com/AstraBert/everything-ai
๐ Features coming soon:
- ๐๏ธAudio and video-related tasks
- ๐ค Integration with HF Spaces API to effortlessly chat with larger models on the Hub from your local computer
- ๐งฌ Biology-related tasks
โค๏ธ Shout-outs to Qdrant, LangChain, Hugging Face, Docker and Gradio for making all of this possible!
๐กInspired by Jan, Cheshire Cat AI, LM Studio, Ollama and other awesome local AI solutions! | {
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] | You are all happy ๐ that @meta-llama released Llama 3.
Then you are sad ๐ that it only has a context length of 8k.
Then you are happy ๐ that you can just scale llama-3 PoSE to 96k without training, only needing to modify max_position_embeddings and rope_theta.
But then you are sad ๐ข it only improves the model's long-context retrieval performance (i.e., finding needles) while hardly improving its long-context utilization capability (doing QA and summarization).
But then you are happy ๐ that the
@GradientsTechnologies community has released the long-context Llama-3-8B-Instruct-262K with long context (262k-1M+).
Now we have another paper "Extending Llama-3's Context Ten-Fold Overnight" ๐.
The context length of Llama-3-8B-Instruct is extended from 8K to 80K using QLoRA fine-tuningโ๏ธ.
The training cycle is highly efficient, taking "only" ๐ 8 hours on a single 8xA800 (80G) GPU machine.
The model also preserves its original capability over short contexts. โ
The dramatic context extension is mainly attributed to merely 3.5K synthetic training samples generated by GPT-4.๐
The paper suggests that the context length could be extended far beyond 80K with more computation resources (๐
GPU-poor).
The team plans to publicly release all resources, including data, model, data generation pipeline, and training code, to facilitate future research from the โค๏ธ community.
Paper: https://arxiv.org/abs/2404.19553
This is where we are... until next time... ๐
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] | Updated: Lumimaid and TheSpice-v0.8.3
I have uploaded version 2 (v2) files for the Llama-3-Lumimaid-8B-v0.1-OAS GGUF Imatrix quants.
[model] https://huggingface.co/Lewdiculous/Llama-3-Lumimaid-8B-v0.1-OAS-GGUF-IQ-Imatrix
You can recognize the new files by their `v2` prefix.
Imatrix data was generated from the FP16 and conversions directly from the BF16.
Hopefully avoiding any losses in the model conversion, as has been the recently discussed topic on Llama-3 and GGUF lately.
This is more disk and compute intensive so lets hope we get GPU inference support for BF16 models in llama.cpp.
If you are able to test them and noticed any issues compared to the original quants, let me know in the corresponding discussions.
---
Additionally, L3-TheSpice-8b-v0.8.3 GGUF Imatrix quants were also updated.
[model] https://huggingface.co/Lewdiculous/L3-TheSpice-8b-v0.8.3-GGUF-IQ-Imatrix | {
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] | Is anyone looking into some sort of decentralized/federated dataset generation or classification by humans instead of synthetically?
From my experience with trying models, a *lot* of modern finetunes are trained on what amounts to, in essence, GPT-4 generated slop that makes everything sound like a rip-off GPT-4 (refer to i.e. the Dolphin finetunes). I have a feeling that this is a lot of the reason people haven't been quite as successful as Meta's instruct tunes of Llama 3. | {
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"raw": "I decided to open source the Vodalus Expert LLM Forge to empower individuals and organizations everywhere to generate their own high-quality data. By making these tools freely available, I hope this community can start crafting their own models with little to no money and/or experience, helping to improve data quality and innovation across the board. While I'm releasing this tool for free, I've also completed an extensive tutorial/course with lots of videos and instructions that guide you through each step of maximizing the potential of this stack. This course is available for purchase at ko-fi.com/s/076479f834 and is designed to enhance your experience and results with the Vodalus Expert LLM Forge.",
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"raw": "Whatโs included in the Vodalus Expert LLM Forge?",
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"value": "- Data Generation: Harness RAG (through AnythingLLM if you are set up properly) and Wikipedia to create datasets via local language models.",
"raw": "- Data Generation: Harness RAG (through AnythingLLM if you are set up properly) and Wikipedia to create datasets via local language models.",
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"value": "- Model Training & Fine-Tuning: Tutorials and Jupyter notebooks to customize models to your specific needs.",
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"value": "If this project aids your work, please consider supporting it through a donation at my ko-fi.com/severian42. Your support helps sustain my further LLM developments and experiments, always with a focus on using those efforts to give back to the LLM community. ",
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] | Craft Your Own Expert LLM - Using 100% Open-Source/Private/Free/Awesome Tools
Hey everyone! After seeing a lot of people's interest in crafting their own datasets and then training their own models, I took it upon myself to try and build a stack to help ease that process. I'm excited to share a major project I've been developingโthe Vodalus Expert LLM Forge.
https://github.com/severian42/Vodalus-Expert-LLM-Forge
This is a 100% locally LLM-powered tool designed to facilitate high-quality dataset generation. It utilizes free open-source tools so you can keep everything private and within your control.
Why Open Source?
I decided to open source the Vodalus Expert LLM Forge to empower individuals and organizations everywhere to generate their own high-quality data. By making these tools freely available, I hope this community can start crafting their own models with little to no money and/or experience, helping to improve data quality and innovation across the board. While I'm releasing this tool for free, I've also completed an extensive tutorial/course with lots of videos and instructions that guide you through each step of maximizing the potential of this stack. This course is available for purchase at ko-fi.com/s/076479f834 and is designed to enhance your experience and results with the Vodalus Expert LLM Forge.
Whatโs included in the Vodalus Expert LLM Forge?
- Data Generation: Harness RAG (through AnythingLLM if you are set up properly) and Wikipedia to create datasets via local language models.
- Model Training & Fine-Tuning: Tutorials and Jupyter notebooks to customize models to your specific needs.
- Quantization: Optimize models for performance with our quantization guides.
If this project aids your work, please consider supporting it through a donation at my ko-fi.com/severian42. Your support helps sustain my further LLM developments and experiments, always with a focus on using those efforts to give back to the LLM community. | {
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"value": "Your support and encouragement mean the world to me, and I'm really stoked to keep experimenting and learning. If you told me some years ago I would have so much people following me for what I do, I wouldn't have believed it. Here's to more discoveries and adventures ahead! ๐",
"raw": "Your support and encouragement mean the world to me, and I'm really stoked to keep experimenting and learning. If you told me some years ago I would have so much people following me for what I do, I wouldn't have believed it. Here's to more discoveries and adventures ahead! ๐",
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"value": " too, since my early work was heavily inspired from MythoMax and the RP/ERP vibe of it. If I'm here today it's probably because of you ๐",
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] | Hey everyone,
Just wanted to shout out a massive thank you to all 2000 of you who've followed me on Hugging Face! ๐ It's incredible to have such an awesome crew backing me up as I dive into all these LLM experiments.
Even though not all my models turn out perfect, I've found some real gems and methods along the way ๐. It's like digging for treasure โ sometimes you found nothing, but sometimes you find a pearl, and sometimes you find a new method to try.
Your support and encouragement mean the world to me, and I'm really stoked to keep experimenting and learning. If you told me some years ago I would have so much people following me for what I do, I wouldn't have believed it. Here's to more discoveries and adventures ahead! ๐
Also, big thanks once again, and a huge shoutout to @IkariDev for being there through this journey and supporting me. I'm excited for our future work together and hope we will continue to make people happy! ๐
I want to thank @Gryphe too, since my early work was heavily inspired from MythoMax and the RP/ERP vibe of it. If I'm here today it's probably because of you ๐
I was so close to forget @chargoddard and his amazing tool too! What will we do without mergekit in our life? Thank you! ๐
See y'all at 3k! | {
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] | ๐๐ญ๐ New Research Alert - AniTalker (Avatars Collection)! ๐๐ญ๐
๐ Title: AniTalker: Animate Vivid and Diverse Talking Faces through Identity-Decoupled Facial Motion Encoding ๐
๐ Description: AniTalker is a new framework that transforms a single static portrait and a single input audio file into animated, talking videos with natural, fluid movements.
๐ฅ Authors: Tao Liu, Feilong Chen, Shuai Fan, @cpdu, Qi Chen, Xie Chen, and Kai Yu
๐ Paper: https://huggingface.co/papers/2405.03121
๐ Github Page: https://x-lance.github.io/AniTalker
๐ Repository: https://github.com/X-LANCE/AniTalker
๐ More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: https://huggingface.co/collections/DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
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] | Red Hat and IBM have announced InstructLab, an open-source project for LLM contributions. InstructLab offers a model-agnostic approach for the community to contribute "skills" and or "knowledge" to LLMs via a CLI and tuning backend.
This community-driven approach to GenAI model development is novel to say the least. It will be interesting to see how effective it is in the long run, especially on models beyond the initial Granite and Merlinite familes.
Can check out Git Hub here: https://github.com/instructlab
Read the LAB Paper: https://arxiv.org/abs/2403.01081
View Model Builds: https://huggingface.co/instructlab
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"raw": "ๆ้ซใฎใขใใซใใชใชใผในใใใพใใใ",
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"raw": "ๆฅๆฌ่ชใๅใไบบใชใ็ตถๅฏพใซไฝฟใใชใใจๆ๏ผ๏ผ๏ผ",
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"value": "ๆๅผทใฎๆฅๆฌ่ชLLMใ็ดนไปใใพใ๏ผ",
"raw": "ๆๅผทใฎๆฅๆฌ่ชLLMใ็ดนไปใใพใ๏ผ",
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"value": "โ https://huggingface.co/karakuri-ai/karakuri-lm-8x7b-chat-v0.1",
"raw": "โ https://huggingface.co/karakuri-ai/karakuri-lm-8x7b-chat-v0.1",
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"value": "โกhttps://huggingface.co/Fugaku-LLM/Fugaku-LLM-13B-instruct-gguf",
"raw": "โกhttps://huggingface.co/Fugaku-LLM/Fugaku-LLM-13B-instruct-gguf",
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"value": "LM ในใฟใธใชใJanใไฝฟใใใใฒไฝฟ็จใใฆใฟใพใใใ๏ผ",
"raw": "LM ในใฟใธใชใJanใไฝฟใใใใฒไฝฟ็จใใฆใฟใพใใใ๏ผ",
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] | ๆ้ซใฎใขใใซใใชใชใผในใใใพใใใ
ๆฅๆฌ่ชใๅใไบบใชใ็ตถๅฏพใซไฝฟใใชใใจๆ๏ผ๏ผ๏ผ
ๆๅผทใฎๆฅๆฌ่ชLLMใ็ดนไปใใพใ๏ผ
โ https://huggingface.co/karakuri-ai/karakuri-lm-8x7b-chat-v0.1
โกhttps://huggingface.co/Fugaku-LLM/Fugaku-LLM-13B-instruct-gguf
โขhttps://huggingface.co/stabilityai/japanese-stablelm-2-instruct-1_6b
ใงใใ
LM ในใฟใธใชใJanใไฝฟใใใใฒไฝฟ็จใใฆใฟใพใใใ๏ผ | {
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] | https://huggingface.co/datasets/Replete-AI/code_bagel
Make the ultimate coding finetune to compete with the likes of closed source models using the code_bagel dataset!
Made by @rombodawg of RepleteAi, the code_bagel dataset contains over 800 million tokens of deduplicated and uncensored code from only reputable sources on huggingface. This code is formatted in the alpaca instruct format for ease of use in training. | {
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Here is a Claude 3 Sonnet generated dataset using prompts from WildChat:
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] | Vodalus Expert LLM Forge - Dataset Crafting and Efficient Fine-Tuning Using Only Free Open-Source Tools
Hey everyone! After my last post getting a sense of people's interest in crafting their own datasets, I'm excited to share a major project I've been developingโthe Vodalus Expert LLM Forge.
https://github.com/severian42/Vodalus-Expert-LLM-Forge
This is a 100% locally LLM-powered tool designed to facilitate high-quality dataset generation. It utilizes free open-source tools so you can keep everything private and within your control. After considerable thought and debate (this project is the culmination of my few years of learning/experimenting), I've decided to open-source the entire stack. My hope is to elevate the standard of datasets and democratize access to advanced data-handling tools. There shouldn't be so much mystery to this part of the process.
Why Open Source?
My hope is to empower individuals everywhere to generate their own high-quality data. By making these tools freely available, I hope this community can start crafting their own models with little to no money and/or experience, helping to improve data quality and innovation across the board. While I'm releasing this tool for free, I'm also near completion on an extensive tutorial/course that guides you through each step of maximizing the potential of this stack. This course will be available for purchase soon and is designed to enhance your experience and results with the Vodalus Forge; more details soon
If this project aids your work, please consider supporting it through a donation on my https://ko-fi.com/N4N4XZ2TZ. Your support helps sustain my further LLM developments and experiments, always with a focus on using those efforts to give back to this community | {
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"value": "A research paper where they measure the quality of LLMs fine-tuned with quantized low-rank adapters across 10 base models and 31 tasks for a total of a whopping 310 models ๐.",
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LoRA is the most widely adopted method for Parameter Efficient Fine-Tuning (PEFT) of Large Language Models (LLMs) ๐ค. It reduces the number of trainable parameters and memory usage while achieving comparable performance to full fine-tuning ๐พ.
Good folks at @predibase have released LoRA Land! ๐
A research paper where they measure the quality of LLMs fine-tuned with quantized low-rank adapters across 10 base models and 31 tasks for a total of a whopping 310 models ๐.
According to the paper, 4-bit LoRA fine-tuned models outperform base models by 34 points ๐ and GPT-4 by 10 points on average ๐.
Here comes the best part... ๐ฅ
They have also evaluated the latency and concurrency capabilities of LoRAX, an open-source Multi-LoRA inference server that facilitates the deployment of multiple LoRA fine-tuned models on a single GPU using the shared base model weights and dynamic adapter loading ๐.
And did I mention LoRAX is completely open-source! ๐
Paper: https://arxiv.org/pdf/2405.00732
While the claims are lofty and yet to be vetted, this is amazing for serving fine-tuned models without breaking the bank! ๐ธ
PS: ๐คฏThey got LoRAX to host 25 LoRA fine-tuned Mistral-7B LLMs on a single NVIDIA A100 GPU with 80GB memory ๐ป.
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"raw": "๐ Hello to all the amazing developers out there! Whether you're just starting out or a seasoned pro, I want to take a moment to appreciate your dedication and passion for coding. Keep on creating, innovating, and inspiring! #HelloToDev ๐๐ป",
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] | ๐ Hello to all the amazing developers out there! Whether you're just starting out or a seasoned pro, I want to take a moment to appreciate your dedication and passion for coding. Keep on creating, innovating, and inspiring! #HelloToDev ๐๐ป
Thank you ! | {
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] | Hello Community,
This is space that provides a simple interface for creating AnimateDiff pipeline videos.
https://huggingface.co/spaces/ModularityAI/GenVideo
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"raw": "i got an email my space was down so now my space is back up ! StarCoder2 (Raw) on A100 , for your enjoyment and apache2 research purposes ๐๐ป๐๐ป",
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"value": "check my profile for more cool GPUzero demos, i'll cycle them with some new overlooked models soon ๐๐ค",
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] | ๐๐ปโโ๏ธhey there folks !
i got an email my space was down so now my space is back up ! StarCoder2 (Raw) on A100 , for your enjoyment and apache2 research purposes ๐๐ป๐๐ป
https://huggingface.co/spaces/Tonic/starcoder2
check my profile for more cool GPUzero demos, i'll cycle them with some new overlooked models soon ๐๐ค | {
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From "Deep Reinforcement Learning: An Overview": https://arxiv.org/abs/1701.07274v3
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"raw": "ไฝฟใฃใฆใฟใพใใใใใใใ่ฏใๆง่ฝใงใใ",
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ๆฅๆฌ่ชๆง่ฝใฏ7Bใฎไธญใงใฏไธ็ช้ซๆง่ฝใงใใ
ไฝฟใฃใฆใฟใพใใใใใใใ่ฏใๆง่ฝใงใใ
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",
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] | If you haven't done it yet,
try my new AI Stories Factory space it is pretty fun!
https://huggingface.co/spaces/jbilcke-hf/ai-stories-factory
You can even prompt the visual and music style ๐
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] | Hello! Thanks to the Hugging Face team for accepting me for posts.
I'm someone who is interested in learning more about AI and ML, and I just want to say the community has been super supportive with all the guides and the people I've seen.
I'll probably be more active in making things during the summer holidays when I have more time but ill keep an eye on other peoples projects and may try make some things in the meantime xd | {
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I try to keep my Spaces operating and Collections fresh! Appreciate any Follows!
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Tenyx announced its latest model Llama3-TenyxChat-70B, which outperforms a GPT-4 variant on several MT-Bench measurements.
By post-training Llama-3 70B in 15 hours, our model improves reasoning capabilities leveraging the relationship between geometry and LLM task complexity (Take a look at our paper: https://arxiv.org/abs/2312.01648, to be presented at ICML 2024)
Model: https://huggingface.co/tenyx/Llama3-TenyxChat-70B, HuggingFace Space: https://huggingface.co/spaces/tenyx/Llama3-TenyxChat-70B | {
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] | Finally, I can post! ๐
I created a Capybara-inspired Italian dataset by translating the initial instruction and running it through a pipeline to generate conversations. I used Claude Sonnet for translation and instruction generation, and Opus for generating the answers.
I hope this dataset proves useful for people working on ๐ฎ๐น language models.
โ Open sourcing the dataset here: https://huggingface.co/datasets/efederici/capybara-claude-15k-ita | {
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"value": "๐ Keywords: #FERYOLOMamba #FER #YOLO #Mamba #FacialExpressionRecognition #EmotionRecognition #ComputerVision #DeepLearning #MachineLearning #Innovation",
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] | ๐๐ฒ๐๐ก New Research Alert - FER-YOLO-Mamba (Facial Expressions Recognition Collection)! ๐ก๐ฅ๐ฅด๐ฑ
๐ Title: FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space ๐
๐ Description: FER-YOLO-Mamba is a novel facial expression recognition model that combines the strengths of YOLO and Mamba technologies to efficiently recognize and localize facial expressions.
๐ฅ Authors: Hui Ma, Sen Lei, Turgay Celik, and Heng-Chao Li
๐ Paper: https://huggingface.co/papers/2405.01828
๐ Repository: https://github.com/SwjtuMa/FER-YOLO-Mamba
๐ Added to the Facial Expressions Recognition Collection: https://huggingface.co/collections/DmitryRyumin/facial-expressions-recognition-65f22574e0724601636ddaf7
๐ฅ๐ See also Facial_Expression_Recognition - https://huggingface.co/spaces/ElenaRyumina/Facial_Expression_Recognition (App, co-authored by @DmitryRyumin) ๐
๐ More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Keywords: #FERYOLOMamba #FER #YOLO #Mamba #FacialExpressionRecognition #EmotionRecognition #ComputerVision #DeepLearning #MachineLearning #Innovation | {
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] | We are excited to share the latest advancement in the AISAK system: the introduction of AISAK-Detect. As an essential component of AISAK-Visual, this sophisticated model specializes in object detection tasks, significantly enhancing our system's capabilities in comprehensive visual analysis.
AISAK-Detect is built on an encoder-decoder transformer architecture with a convolutional backbone, ensuring accurate and efficient object detection within images. Our dedicated team has meticulously trained and fine-tuned this model to guarantee seamless integration into the broader AISAK ecosystem, contributing to cohesive performance in image analysis tasks.
The deployment of AISAK-Detect is a significant milestone in our journey towards offering a comprehensive AI solution across multiple domains. With a unique deployment approach that prioritizes achieving an optimal system, we are committed to delivering an AI experience that goes beyond the limitations of traditional chat instances.
Regular updates on the progress of the AISAK system, including the deployment of AISAK-Detect, will be provided to keep users informed about the advancements being made. We look forward to sharing more exciting developments as we continue to grow and innovate.
https://huggingface.co/aisak-ai/aisak-detect
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] | ๐คThe first submissions from the Community Hugging Face Computer Vision Course (https://huggingface.co/learn/computer-vision-course/unit0/welcome/welcome) are being posted up on HF Spaces!๐ค
https://huggingface.co/spaces/OmAlve/Swin-Transformer-Foods101
https://huggingface.co/spaces/Rageshhf/medi-classifier
It is amazing that the first group of students has completed the course and in record time!
Will look forward to seeing more submissions from the course soon.
A nice swag item that students get when they complete the course and make their submission is this cool Hugging Face Certificate of Completion. (Its suitable for framing) ๐ค
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"value": "Just say which information you want to extract (in human language) and the library will do it for you! ๐ฃ๏ธ๐",
"raw": "Just say which information you want to extract (in human language) and the library will do it for you! ๐ฃ๏ธ๐",
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"raw": "- Persistent data output formatting ๐",
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] | Are you tired of writing scripts to scrape data from the web? ๐
ScrapeGraphAI is here for you! ๐
ScrapeGraphAI is an OPEN-SOURCE web scraping Python library that uses LLM and direct graph logic to create scraping pipelines for websites and local documents (XML, HTML, JSON, etc.). ๐๐
Just say which information you want to extract (in human language) and the library will do it for you! ๐ฃ๏ธ๐
It supports GPT, Gemini, and open-source models like Mistral. ๐
A few things that I could not find in the docs but would be amazing to see ๐ค:
- Captcha handling ๐
- Persistent data output formatting ๐
- Streaming output ๐ก
- Explanation๐ of the tag line: "ScrapeGraphAI: You Only Scrape Once" What does that even mean? ๐คฃ Is this YOLO? ๐ค
Link: https://github.com/VinciGit00/Scrapegraph-ai
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] | This is a song from imagine dragons believer.
This song is to say thanks to hf team for accepting me! I was waiting for a long time.
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I came back with another one this time ๐ค
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* How to train custom AI models with the trainer API ๐
* integrate your AI models with HF using the mixin classes ๐ฅ
happy reading everyone ๐ค
๐link: https://huggingface.co/blog/not-lain/trainer-api-and-mixin-classes
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https://huggingface.co/learn/computer-vision-course/unit0/welcome/welcome
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My first post for the lovely community out there!
Here's a highly quantized finetuned version of gemma focused exclusively on Prompt Engineering. Write as ambiguous you want and leave the job to this model
https://huggingface.co/zamal/gemma-7b-finetuned | {
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] | Okay, I can post now, Yayy!!
Sharing the OpenBioLLM-70B as the first post :)
Introducing OpenBioLLM-Llama3-70B & 8B: The most capable openly available Medical-domain LLMs to date! ๐ฉบ๐๐งฌ
Outperforms industry giants like GPT-4, Gemini, Meditron-70B, Med-PaLM-1, and Med-PaLM-2 in the biomedical domain. ๐ฅ๐ ๐
OpenBioLLM-70B delivers SOTA performance, setting a new state-of-the-art for models of its size. OpenBioLLM-8B model and even surpasses GPT-3.5, Gemini, and Meditron-70B! ๐
Today's release is just the beginning! In the coming months, we'll be introducing:
- Expanded medical domain coverage ๐ง
- Longer context windows ๐๐
- Better benchmarks ๐๐
- Multimodal capabilities๐ฅ๏ธ๐ฉบ๐๐ฌ
Medical-LLM Leaderboard: https://huggingface.co/spaces/openlifescienceai/open_medical_llm_leaderboard
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Would love to hear what you think :-) What should I do next so that it can flip a light switch for me?
Demo: https://youtu.be/OryGVbh5JZE
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Fireplace-70b out now:
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๐ Title: You Only Cache Once: Decoder-Decoder Architectures for Language Models ๐
๐ Description: YOCO is a novel decoder-decoder architecture for LLMs that reduces memory requirements, speeds up prefilling, and maintains global attention. It consists of a self-decoder for encoding KV caches and a cross-decoder for reusing these caches via cross-attention.
๐ฅ Authors: Yutao Sun et al.
๐ Paper: https://huggingface.co/papers/2405.05254
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๐ More Papers: more cutting-edge research presented at other conferences in the https://huggingface.co/spaces/DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
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] | New sampling strategy dropped in ๐ค transformers -- Min P sampling ๐ฅ
Are you tired of having `top_k` arbitrarily discarding high-quality continuations? Or `top_p` forgetting to exclude low-probability tokens, derailing your generation? Try out the new `min_p` flag in `generate`, fresh from a PR merged today! ๐ฅฌ
Min P consists of a dynamic token filter -- as opposed to Top K, which keeps the K most likely tokens, and Top P, which keeps the most likely tokens up to a fixed cumulative probability, both static filters. Min P takes a base probability (defined in the `min_p` flag) and multiplies it by the probability of the most likely token in the distribution for the next token. All tokens less likely than the resulting value are filtered. What happens with this strategy?
๐ High probability token present -> aggressive filter (we don't want to miss on that high-probability case and risk derailing generation)
๐ No high probability token present -> relaxed filter (there are many continuation possibilities that the model finds plausible)
You should set `min_p` to a low value, between 0.05 and 0.1. It behaves particularly well for creative text generation when paired up with temperature > 1.
Kudos to @kalomaze and @menhguin for creating this technique ๐ฅ Read their discussion in the original issue for benchmarks (https://github.com/huggingface/transformers/issues/27670)
Copy-pasteable version of the example in the image below here: https://pastebin.com/VqXNtuxd
Have fun experimenting! ๐ | {
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an using arabic language
I decided to used Sense-annotated corpora
is there in recommanded Model to use to
and is there any pre-done examples to use them as a guide
Thank you for your help | {
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] | HiDiffusion SDXL now supports Image-to-Image, so I've created an "Enhance This" version using the latest ControlNet Line Art model called MistoLine. It's faster than DemoFusion
Demo: https://huggingface.co/spaces/radames/Enhance-This-HiDiffusion-SDXL
Older version based on DemoFusion https://huggingface.co/spaces/radames/Enhance-This-DemoFusion-SDXL
New Controlnet SDXL Controls Every Line https://huggingface.co/TheMistoAI/MistoLine
HiDiffusion is compatible with diffusers and support many SD models - https://github.com/megvii-research/HiDiffusion
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ฌ้ใใใ ใใใใจ็ขบ่ชใใพใใใๆฅๆฌใฎLLMใฎ็บๅฑใซ่ฒข็ฎใใใใจใใ็ฎๆจใงใใฎใใผใฟใไฝๆใใๅใ็ฎๆจใงๅๅ
ฌ้ใใใ ใใใจๆใใฎใงใใใใใฎใใผใฟใฏๅฑ้บๆงใๅซใใงใใใใใคใใใผใฟใๅฉ็จใใฆใใใฏใใใใใจใๅฑๆงใใฆใใใใฉใชใใๅฉ็จใใใใใใใใใๅ
ฌ้ใใผใ ใใผใธใซใใ้ใใๅ้
ๅธใฏ็ฆๆญขใใใฆใใใพใใใใฎ็นใใ็่งฃใใใ ใใๅ้
ๅธใไธญๆญขใใใ ใใพใใใใ้กใใใพใใ",
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ฌ้ใใใ ใใใจๆใใฎใงใใใใใฎใใผใฟใฏๅฑ้บๆงใๅซใใงใใใใใคใใใผใฟใๅฉ็จใใฆใใใฏใใใใใจใๅฑๆงใใฆใใใใฉใชใใๅฉ็จใใใใใใใใใๅ
ฌ้ใใผใ ใใผใธใซใใ้ใใๅ้
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] | @Yasusan ็ๅๅญฆ็ ็ฉถๆAIPใฎ้ขๆ น่กใจ็ณใใพใใAnswerCarefullyใฎใใผใฟใซใคใใฆ็ขบ่ชใใใ ใใใใใจใใใใใพใใHuggingFavceใซใฆๅ้
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ฌ้ใใผใ ใใผใธใซใใ้ใใๅ้
ๅธใฏ็ฆๆญขใใใฆใใใพใใใใฎ็นใใ็่งฃใใใ ใใๅ้
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] | ๐ Introducing RefuelLLM-2 and RefuelLLM-2-small, the next version of our large language models purpose built for data labeling, enrichment and cleaning.
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๐ Open sourcing the model weights: https://huggingface.co/refuelai/Llama-3-Refueled
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Ntfy.sh daily channel: https://ntfy.sh/feature_extraction_transformers_js_models_daily
Sortable table: https://do-me.github.io/trending-huggingface-models/
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] | Last Week in Medical AI: Top Research Papers/Models ๐ฅ
(November 2 -November 9, 2024)
๐
Medical AI Paper of the Week:
Exploring Large Language Models for Specialist-level Oncology Care
Medical LLM & Other Models:
- GSCo: Generalist-Specialist AI Collaboration
- PediatricsGPT: Chinese Pediatric Assistant
- MEG: Knowledge-Enhanced Medical QA
- AutoProteinEngine: Multimodal Protein LLM
Frameworks and Methodologies:
- BrainSegFounder: 3D Neuroimage Analysis
- PASSION: Sub-Saharan Dermatology Dataset
- SAM for Lung X-ray Segmentation
- Label Critic: Data-First Approach
- Medprompt Runtime Strategies
Medical LLM Applications:
- CataractBot: Patient Support System
- CheX-GPT: X-ray Report Enhancement
- CardioAI: Cancer Cardiotoxicity Monitor
- HealthQ: Healthcare Conversation Chain
- PRObot: Diabetic Retinopathy Assistant
Medical LLMs & Benchmarks:
- MediQ: Clinical Reasoning Benchmark
- Touchstone: Segmentation Evaluation
- Medical LLM Adaptation Progress
- Fine-Tuning Medical QA Strategies
AI in Healthcare Ethics:
- Healthcare Robotics with LLMs
- XAI in Clinical Practice
- Precision Rehabilitation Framework
- Multimodal AI Challenges
Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels as well!
- Full Thread: https://x.com/OpenlifesciAI/status/1855207141302473090
- YouTube: https://youtu.be/ad0uTnYuTo8
- Spotify: https://open.spotify.com/episode/6s39t1UJZk1i10szuXP2qN
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582462020511746 | [
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"value": "The redesigned frame objects are a thing of beauty - they've been stripped of unnecessary baggage, resulting in a 3-7% speedup for all function calls. But it gets better: function calls are now inlined, giving us a 1-3% boost, with recursive functions like Fibonacci seeing up to 1.7x improvement!",
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] | Thinking about upgrading from Python 3.10 to 3.11? Here's why you should make the move - a deep technical breakdown that might convince you:
>> Performance Revolution
The performance improvements are staggering, with benchmarks showing 10-60% faster execution across different workloads. Let me break down the game-changing features:
>> Core Architecture Changes
Python 3.11's interpreter now uses statically allocated core modules, eliminating the multi-step loading process we've dealt with in 3.10. This means your applications will start 10-15% faster out of the gate.
>> Function Optimization
The redesigned frame objects are a thing of beauty - they've been stripped of unnecessary baggage, resulting in a 3-7% speedup for all function calls. But it gets better: function calls are now inlined, giving us a 1-3% boost, with recursive functions like Fibonacci seeing up to 1.7x improvement!
>> Adaptive Intelligence
The new Specializing Interpreter is perhaps the most exciting addition. Think of it as a lightweight JIT - it identifies hot code paths and optimizes them automatically.
The interpreter now automatically specializes math operations, array indexing, and even sequence unpacking based on actual usage patterns.
>> Exception Handling Revolution
My favorite feature? Zero-cost exceptions! Your try-except blocks no longer carry overhead when no exceptions occur. The code runs at full speed until an exception actually happens.
Ready to make the switch? These improvements aren't just numbers - they're real-world performance gains waiting to be unlocked in your codebase. | {
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If yes, from where and how? | {
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"raw": "Endpoints for image generation: ๐งต",
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] | FLUX 1.1 [pro] Ultra : API - { 4x Higher Image Resolutions }
Upto - 4 Megapixels, 10 seconds per sample. { Hi - Res }
{ Blog Post โต } : https://huggingface.co/blog/prithivMLmods/flux-pro-endpoint
Endpoint Creation Step by Step: ๐งต
-> Sign up to { api.bfl.ml } & get your api's: https://api.bfl.ml/auth/profile
-> File Structure:
flux_image_generation/
โโโ .env
โโโ generate_image.py
โโโ requirements.txt
-> Step 0: Add Your API Key to an Environment File
{ .env }
```
BFL_API_KEY=your_actual_api_key_here
```
-> Step 1: Install Required Libraries
{ requirements.txt }
```
requests
python-dotenv
```
-> Step 2: Setup the Python Script
{ generate_image.py} - https://github.com/PRITHIVSAKTHIUR/Flux-API/blob/main/generate_image.py
-> Step3: Install the requirements & Run the Script
```
pip install -r requirements.txt
python generate_image.py
```
-> Polling: The script polls the API every 0.5 seconds until the image generation result is ready. That's it the script also checks for a successful response after submitting the request.
For more visit:
๐บfor script: https://github.com/PRITHIVSAKTHIUR/Flux-API/tree/main
๐บbfl doc: https://docs.bfl.ml/quick_start/gen_image/#__tabbed_1_2
Endpoints for image generation: ๐งต
-> /flux-pro-1.1-ultra
-> /flux-pro-1.1
-> /flux-pro
-> /flux-dev
Each ID has 50 free credits available for use, based on the cost per image sample generated by the model.
.
.
.
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] | Exciting to see open-source models thriving in the computer agent space! ๐ฅ
I just built a demo for OS-ATLAS: A Foundation Action Model For Generalist GUI Agents โ check it out here: https://huggingface.co/spaces/maxiw/OS-ATLAS
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] | INTRODUCING Hugging Face AutoTrain Client ๐ฅ
Fine-tuning models got even easier!!!!
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A team from Tsinghua University just released AndroidLab, the first systematic framework to evaluate and train Android mobile agents that works with both text-only and multimodal models.
They show that fine-tuning small open-source models can significantly boost performance, matching that of much bigger closed models like GPT-4o.
The team built:
๐ย A reproducible benchmark with 138 tasks across 9 apps to evaluate mobile agents systematically
๐๐ฑย A framework supporting both text-only (via XML) and visual (via marked screenshots) interfaces
โ
ย An instruction dataset of 10.5k operation traces for training mobile agents
Key insights:
- ๐ Fine-tuning improves performance BY A LOT: Open-source model Llama-3.1-8B improves from 2% to 24% success rate after training, nearly reaching GPT-4o performance although itโs much smaller
- โ๏ธ Text-only agents match multimodal ones: XML-based agents achieve similar performance to screenshot-based multimodal agents.
Read their paper here ๐ https://huggingface.co/papers/2410.24024 | {
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] | Quintet Drop : : ๐ค
{ Flux LoRA DLC โต } : https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC
-- Purple Dreamy
{ pop of color } : https://huggingface.co/prithivMLmods/Purple-Dreamy-Flux-LoRA
-- Golden Dust
{ shimmer contrast } : https://huggingface.co/prithivMLmods/Golden-Dust-Flux-LoRA
-- Lime Green
{ depth to the composition } : https://huggingface.co/prithivMLmods/Lime-Green-Flux-LoRA
-- Flare Strike
{ Fractured Line } : https://huggingface.co/prithivMLmods/Fractured-Line-Flare
-- Orange Chroma
{ studio lighting } : https://huggingface.co/prithivMLmods/Orange-Chroma-Flux-LoRA
.
.
.
{ collection } : https://huggingface.co/collections/prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be
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