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carlosug

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ReadmeInstallationPlan

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[ [ "window", "coming", "soon", "!", ",", "install", "ollama", "window", "via", "wsl2", "." ], [ "window coming soon !", ", install ollama window via wsl2 ." ] ]

[ [ "window", "coming", "soon", "!", ",", "install", "ollama", "window", "via", "wsl2", "." ], [ "window coming soon !", ", install ollama window via wsl2 ." ] ]

[ "go", "golang", "llama", "llama2", "llm", "llms", "mistral", "ollama" ]

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[ "go", "golang", "llama", "llama2", "llm", "llms", "mistral", "ollama" ]

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[ "go", "golang", "llama", "llama2", "llm", "llms", "mistral", "ollama" ]

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[ "go", "golang", "llama", "llama2", "llm", "llms", "mistral", "ollama" ]

[ [ "install" ], [ "install" ] ]

[ [ "install" ], [ "install" ] ]

[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]

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[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]

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[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]

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[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]

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[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]

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[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]

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[ "agents", "application", "data", "fine-tuning", "framework", "llamaindex", "llm", "rag", "vector-database" ]

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[ "agents", "application", "data", "fine-tuning", "framework", "llamaindex", "llm", "rag", "vector-database" ]

[ [ "getting", "started", "🚀", "follow", "instruction", "get", "copy", "project", "running", "local", "machine", "development", "testing", "purpose", ".", "find", "everything", "[", "documentation", "]", "(", "http", ":", "//docs.quivr.app/", ")", "." ], [ "getting started 🚀 follow instruction get copy project running local machine development testing purpose .", "find everything [ documentation ] ( http : //docs.quivr.app/ ) ." ] ]

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[ [ "prerequisite", "📋", "ensure", "following", "installed", ":", "-", "docker", "-", "docker", "compose" ], [ "prerequisite 📋 ensure following installed : - docker - docker compose" ] ]

[ [ "prerequisite", "📋", "ensure", "following", "installed", ":", "-", "docker", "-", "docker", "compose" ], [ "prerequisite 📋 ensure following installed : - docker - docker compose" ] ]

[ "ai", "api", "chatbot", "chatgpt", "database", "docker", "frontend", "html", "javascript", "llm", "openai", "postgresql", "privacy", "rag", "react", "rest-api", "security", "typescript", "vector", "ycombinator" ]

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[ "ai", "api", "chatbot", "chatgpt", "database", "docker", "frontend", "html", "javascript", "llm", "openai", "postgresql", "privacy", "rag", "react", "rest-api", "security", "typescript", "vector", "ycombinator" ]

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[ "ai", "api", "chatbot", "chatgpt", "database", "docker", "frontend", "html", "javascript", "llm", "openai", "postgresql", "privacy", "rag", "react", "rest-api", "security", "typescript", "vector", "ycombinator" ]

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[ "ai", "education", "gpt-4", "llm" ]

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[ "ai", "education", "gpt-4", "llm" ]

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[ "ai", "education", "gpt-4", "llm" ]

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[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

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[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

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[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

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tabs=python & pivots=programming-language-chat-ml ) , [ alpaca ] ( http : //crfm.stanford.edu/2023/03/13/alpaca.html ) , etc .", "📚 * * reference * * : * [ preparing dataset instruction tuning ] ( http : //wandb.ai/capecape/alpaca_ft/reports/how-to-fine-tune-an-llm-part-1-preparing-a-dataset-for-instruction-tuning -- vmlldzo1ntcxnze2 ) thomas capelle : exploration alpaca alpaca-gpt4 datasets format .", "* [ generating clinical instruction dataset ] ( http : //medium.com/mlearning-ai/generating-a-clinical-instruction-dataset-in-portuguese-with-langchain-and-gpt-4-6ee9abfa41ae ) solano todeschini : tutorial create synthetic instruction dataset using gpt-4 .", "* [ gpt 3.5 news classification ] ( http : //medium.com/ @ kshitiz.sahay26/how-i-created-an-instruction-dataset-using-gpt-3-5-to-fine-tune-llama-2-for-news-classification-ed02fe41c81f ) kshitiz sahay : use gpt 3.5 create instruction dataset fine-tune llama 2 news classification .", "* [ dataset creation fine-tuning llm ] ( http : //colab.research.google.com/drive/1gh8pw9-zae4cxezyoie-t9uhxblildag ? usp=sharing ) : notebook contains technique filter dataset upload result .", "* [ chat template ] ( http : //huggingface.co/blog/chat-templates ) matthew carrigan : hugging face 's page prompt template -- -" ] ]

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tabs=python & pivots=programming-language-chat-ml ) , [ alpaca ] ( http : //crfm.stanford.edu/2023/03/13/alpaca.html ) , etc .", "📚 * * reference * * : * [ preparing dataset instruction tuning ] ( http : //wandb.ai/capecape/alpaca_ft/reports/how-to-fine-tune-an-llm-part-1-preparing-a-dataset-for-instruction-tuning -- vmlldzo1ntcxnze2 ) thomas capelle : exploration alpaca alpaca-gpt4 datasets format .", "* [ generating clinical instruction dataset ] ( http : //medium.com/mlearning-ai/generating-a-clinical-instruction-dataset-in-portuguese-with-langchain-and-gpt-4-6ee9abfa41ae ) solano todeschini : tutorial create synthetic instruction dataset using gpt-4 .", "* [ gpt 3.5 news classification ] ( http : //medium.com/ @ kshitiz.sahay26/how-i-created-an-instruction-dataset-using-gpt-3-5-to-fine-tune-llama-2-for-news-classification-ed02fe41c81f ) kshitiz sahay : use gpt 3.5 create instruction dataset fine-tune llama 2 news classification .", "* [ dataset creation fine-tuning llm ] ( http : //colab.research.google.com/drive/1gh8pw9-zae4cxezyoie-t9uhxblildag ? usp=sharing ) : notebook contains technique filter dataset upload result .", "* [ chat template ] ( http : //huggingface.co/blog/chat-templates ) matthew carrigan : hugging face 's page prompt template -- -" ] ]

[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

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[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

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[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]

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[ "language-model", "llm", "machine-learning-compilation", "tvm" ]

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[ [ "install", "community", "edition" ], [ "install community edition" ] ]

[ [ "install", "community", "edition" ], [ "install community edition" ] ]

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[ "ai", "backend-as-a-service", "gpt", "gpt-4", "langchain", "llama2", "llm", "openai", "orchestration", "python", "rag" ]

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[ "ai", "backend-as-a-service", "gpt", "gpt-4", "langchain", "llama2", "llm", "openai", "orchestration", "python", "rag" ]

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[ [ "configuration", "need", "customize", "configuration", ",", "please", "refer", "comment", "[", "docker-compose.yml", "]", "(", "docker/docker-compose.yaml", ")", "file", "manually", "set", "environment", "configuration", ".", "making", "change", ",", "please", "run", "`", "docker-compose", "-d", "`", ".", "see", "full", "list", "environment", "variable", "[", "doc", "]", "(", "http", ":", "//docs.dify.ai/getting-started/install-self-hosted/environments", ")", "." ], [ "configuration need customize configuration , please refer comment [ docker-compose.yml ] ( docker/docker-compose.yaml ) file manually set environment configuration .", "making change , please run ` docker-compose -d ` .", "see full list environment variable [ doc ] ( http : //docs.dify.ai/getting-started/install-self-hosted/environments ) ." ] ]

[ "ai", "backend-as-a-service", "gpt", "gpt-4", "langchain", "llama2", "llm", "openai", "orchestration", "python", "rag" ]

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[ [ "环境安装", "首先需要下载本仓库：", "``", "`", "shell", "git", "clone", "http", ":", "//github.com/thudm/chatglm2-6b", "cd", "chatglm2-6b", "``", "`", "然后使用", "pip", "安装依赖：", "``", "`", "pip", "install", "-r", "requirements.txt", "``", "`", "其中", "`", "transformer", "`", "库版本推荐为", "`", "4.30.2", "`", "，", "`", "torch", "`", "推荐使用", "2.0", "及以上的版本，以获得最佳的推理性能。" ], [ "环境安装 首先需要下载本仓库： `` ` shell git clone http : //github.com/thudm/chatglm2-6b cd chatglm2-6b `` ` 然后使用 pip 安装依赖： `` ` pip install -r requirements.txt `` ` 其中 ` transformer ` 库版本推荐为 ` 4.30.2 ` ， ` torch ` 推荐使用 2.0 及以上的版本，以获得最佳的推理性能。" ] ]

[ "chatglm", "chatglm-6b", "large-language-models", "llm" ]

[ [ "从本地加载模型", "以上代码会由", "`", "transformer", "`", "自动下载模型实现和参数。完整的模型实现在", "[", "hugging", "face", "hub", "]", "(", "http", ":", "//huggingface.co/thudm/chatglm2-6b", ")", "。如果你的网络环境较差，下载模型参数可能会花费较长时间甚至失败。此时可以先将模型下载到本地，然后从本地加载。", "从", "hugging", "face", "hub", "下载模型需要先", "[", "安装git", "lf", "]", "(", "http", ":", "//docs.github.com/zh/repositories/working-with-files/managing-large-files/installing-git-large-file-storage", ")", "，然后运行", "``", "`", "shell", "git", "clone", "http", ":", "//huggingface.co/thudm/chatglm2-6b", "``", "`", "如果你从", "hugging", "face", "hub", "上下载", "checkpoint", "的速度较慢，可以只下载模型实现", "``", "`", "shell", "git_lfs_skip_smudge=1", "git", "clone", "http", ":", "//huggingface.co/thudm/chatglm2-6b", "``", "`", "然后从", "[", "这里", "]", "(", "http", ":", "//cloud.tsinghua.edu.cn/d/674208019e314311ab5c/", ")", "手动下载模型参数文件，并将下载的文件替换到本地的", "`", "chatglm2-6b", "`", "目录下。", "将模型下载到本地之后，将以上代码中的", "`", "thudm/chatglm2-6b", "`", "替换为你本地的", "`", "chatglm2-6b", "`", "文件夹的路径，即可从本地加载模型。", "模型的实现仍然处在变动中。如果希望固定使用的模型实现以保证兼容性，可以在", "`", "from_pretrained", "`", "的调用中增加", "`", "revision=", "''", "v1.0", "''", "`", "参数。", "`", "v1.0", "`", "是当前最新的版本号，完整的版本列表参见", "[", "change", "log", "]", "(", "http", ":", "//huggingface.co/thudm/chatglm2-6b", "#", "change-log", ")", "。" ], [ "从本地加载模型 以上代码会由 ` transformer ` 自动下载模型实现和参数。完整的模型实现在 [ hugging face hub ] ( http : //huggingface.co/thudm/chatglm2-6b ) 。如果你的网络环境较差，下载模型参数可能会花费较长时间甚至失败。此时可以先将模型下载到本地，然后从本地加载。 从 hugging face hub 下载模型需要先 [ 安装git lf ] ( http : //docs.github.com/zh/repositories/working-with-files/managing-large-files/installing-git-large-file-storage ) ，然后运行 `` ` shell git clone http : //huggingface.co/thudm/chatglm2-6b `` ` 如果你从 hugging face hub 上下载 checkpoint 的速度较慢，可以只下载模型实现 `` ` shell git_lfs_skip_smudge=1 git clone http : //huggingface.co/thudm/chatglm2-6b `` ` 然后从 [ 这里 ] ( http : //cloud.tsinghua.edu.cn/d/674208019e314311ab5c/ ) 手动下载模型参数文件，并将下载的文件替换到本地的 ` chatglm2-6b ` 目录下。 将模型下载到本地之后，将以上代码中的 ` thudm/chatglm2-6b ` 替换为你本地的 ` chatglm2-6b ` 文件夹的路径，即可从本地加载模型。 模型的实现仍然处在变动中。如果希望固定使用的模型实现以保证兼容性，可以在 ` from_pretrained ` 的调用中增加 ` revision= '' v1.0 '' ` 参数。 ` v1.0 ` 是当前最新的版本号，完整的版本列表参见 [ change log ] ( http : //huggingface.co/thudm/chatglm2-6b # change-log ) 。" ] ]

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[ "chatglm", "chatglm-6b", "large-language-models", "llm" ]

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[ "chatglm", "chatglm-6b", "large-language-models", "llm" ]

[ [ "多卡部署", "如果你有多张", "gpu，但是每张", "gpu", "的显存大小都不足以容纳完整的模型，那么可以将模型切分在多张gpu上。首先安装", "accelerate", ":", "`", "pip", "install", "accelerate", "`", "，然后通过如下方法加载模型：", "``", "`", "python", "utils", "import", "load_model_on_gpus", "model", "=", "load_model_on_gpus", "(", "``", "thudm/chatglm2-6b", "''", ",", "num_gpus=2", ")", "``", "`", "即可将模型部署到两张", "gpu", "上进行推理。你可以将", "`", "num_gpus", "`", "改为你希望使用的", "gpu", "数。默认是均匀切分的，你也可以传入", "`", "device_map", "`", "参数来自己指定。" ], [ "多卡部署 如果你有多张 gpu，但是每张 gpu 的显存大小都不足以容纳完整的模型，那么可以将模型切分在多张gpu上。首先安装 accelerate : ` pip install accelerate ` ，然后通过如下方法加载模型： `` ` python utils import load_model_on_gpus model = load_model_on_gpus ( `` thudm/chatglm2-6b '' , num_gpus=2 ) `` ` 即可将模型部署到两张 gpu 上进行推理。你可以将 ` num_gpus ` 改为你希望使用的 gpu 数。默认是均匀切分的，你也可以传入 ` device_map ` 参数来自己指定。" ] ]

[ [ "多卡部署", "如果你有多张", "gpu，但是每张", "gpu", "的显存大小都不足以容纳完整的模型，那么可以将模型切分在多张gpu上。首先安装", "accelerate", ":", "`", "pip", "install", "accelerate", "`", "，然后通过如下方法加载模型：", "``", "`", "python", "utils", "import", "load_model_on_gpus", "model", "=", "load_model_on_gpus", "(", "``", "thudm/chatglm2-6b", "''", ",", "num_gpus=2", ")", "``", "`", "即可将模型部署到两张", "gpu", "上进行推理。你可以将", "`", "num_gpus", "`", "改为你希望使用的", "gpu", "数。默认是均匀切分的，你也可以传入", "`", "device_map", "`", "参数来自己指定。" ], [ "多卡部署 如果你有多张 gpu，但是每张 gpu 的显存大小都不足以容纳完整的模型，那么可以将模型切分在多张gpu上。首先安装 accelerate : ` pip install accelerate ` ，然后通过如下方法加载模型： `` ` python utils import load_model_on_gpus model = load_model_on_gpus ( `` thudm/chatglm2-6b '' , num_gpus=2 ) `` ` 即可将模型部署到两张 gpu 上进行推理。你可以将 ` num_gpus ` 改为你希望使用的 gpu 数。默认是均匀切分的，你也可以传入 ` device_map ` 参数来自己指定。" ] ]

[ "chatglm", "chatglm-6b", "large-language-models", "llm" ]

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state-of-the-art serving throughput - efficient management attention key value memory * * pagedattention * * - continuous batching incoming request - fast model execution cuda/hip graph - quantization : [ gptq ] ( http : //arxiv.org/abs/2210.17323 ) , [ awq ] ( http : //arxiv.org/abs/2306.00978 ) , [ squeezellm ] ( http : //arxiv.org/abs/2306.07629 ) , fp8 kv cache - optimized cuda kernel vllm flexible easy use : - seamless integration popular hugging face model - high-throughput serving various decoding algorithm , including * parallel sampling * , * beam search * , - tensor parallelism support distributed inference - streaming output - openai-compatible api server - support nvidia gpus amd gpus - ( experimental ) prefix caching support - ( experimental ) multi-lora support vllm seamlessly support many hugging face model , including following architecture : - aquila & aquila2 ( ` baai/aquilachat2-7b ` , ` baai/aquilachat2-34b ` , ` baai/aquila-7b ` , ` baai/aquilachat-7b ` , etc . )", "- baichuan & baichuan2 ( ` baichuan-inc/baichuan2-13b-chat ` , ` baichuan-inc/baichuan-7b ` , etc . )", "- bloom ( ` bigscience/bloom ` , ` bigscience/bloomz ` , etc . )", "- chatglm ( ` thudm/chatglm2-6b ` , ` thudm/chatglm3-6b ` , etc . )", "- decilm ( ` deci/decilm-7b ` , ` deci/decilm-7b-instruct ` , etc . )", "- falcon ( ` tiiuae/falcon-7b ` , ` tiiuae/falcon-40b ` , ` tiiuae/falcon-rw-7b ` , etc . )", "- gpt-2 ( ` gpt2 ` , ` gpt2-xl ` , etc . )", "- gpt bigcode ( ` bigcode/starcoder ` , ` bigcode/gpt_bigcode-santacoder ` , etc . )", "- gpt-j ( ` eleutherai/gpt-j-6b ` , ` nomic-ai/gpt4all-j ` , etc . )", "- gpt-neox ( ` eleutherai/gpt-neox-20b ` , ` databricks/dolly-v2-12b ` , ` stabilityai/stablelm-tuned-alpha-7b ` , etc . )", "- internlm ( ` internlm/internlm-7b ` , ` internlm/internlm-chat-7b ` , etc . )", "- internlm2 ( ` internlm/internlm2-7b ` , ` internlm/internlm2-chat-7b ` , etc . )", "- llama & llama-2 ( ` meta-llama/llama-2-70b-hf ` , ` lmsys/vicuna-13b-v1.3 ` , ` young-geng/koala ` , ` openlm-research/open_llama_13b ` , etc . )", "- mistral ( ` mistralai/mistral-7b-v0.1 ` , ` mistralai/mistral-7b-instruct-v0.1 ` , etc . )", "- mixtral ( ` mistralai/mixtral-8x7b-v0.1 ` , ` mistralai/mixtral-8x7b-instruct-v0.1 ` , etc . )", "- mpt ( ` mosaicml/mpt-7b ` , ` mosaicml/mpt-30b ` , etc . )", "- opt ( ` facebook/opt-66b ` , ` facebook/opt-iml-max-30b ` , etc . )", "- phi ( ` microsoft/phi-1_5 ` , ` microsoft/phi-2 ` , etc . )", "- qwen ( ` qwen/qwen-7b ` , ` qwen/qwen-7b-chat ` , etc . )", "- qwen2 ( ` qwen/qwen2-7b-beta ` , ` qwen/qwen-7b-chat-beta ` , etc . )", "- stablelm ( ` stabilityai/stablelm-3b-4e1t ` , ` stabilityai/stablelm-base-alpha-7b-v2 ` , etc . )", "- yi ( ` 01-ai/yi-6b ` , ` 01-ai/yi-34b ` , etc . )", "install vllm pip [ source ] ( http : //vllm.readthedocs.io/en/latest/getting_started/installation.html # build-from-source ) : `` ` bash pip install vllm `` `" ] ]

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state-of-the-art serving throughput - efficient management attention key value memory * * pagedattention * * - continuous batching incoming request - fast model execution cuda/hip graph - quantization : [ gptq ] ( http : //arxiv.org/abs/2210.17323 ) , [ awq ] ( http : //arxiv.org/abs/2306.00978 ) , [ squeezellm ] ( http : //arxiv.org/abs/2306.07629 ) , fp8 kv cache - optimized cuda kernel vllm flexible easy use : - seamless integration popular hugging face model - high-throughput serving various decoding algorithm , including * parallel sampling * , * beam search * , - tensor parallelism support distributed inference - streaming output - openai-compatible api server - support nvidia gpus amd gpus - ( experimental ) prefix caching support - ( experimental ) multi-lora support vllm seamlessly support many hugging face model , including following architecture : - aquila & aquila2 ( ` baai/aquilachat2-7b ` , ` baai/aquilachat2-34b ` , ` baai/aquila-7b ` , ` baai/aquilachat-7b ` , etc . )", "- baichuan & baichuan2 ( ` baichuan-inc/baichuan2-13b-chat ` , ` baichuan-inc/baichuan-7b ` , etc . )", "- bloom ( ` bigscience/bloom ` , ` bigscience/bloomz ` , etc . )", "- chatglm ( ` thudm/chatglm2-6b ` , ` thudm/chatglm3-6b ` , etc . )", "- decilm ( ` deci/decilm-7b ` , ` deci/decilm-7b-instruct ` , etc . )", "- falcon ( ` tiiuae/falcon-7b ` , ` tiiuae/falcon-40b ` , ` tiiuae/falcon-rw-7b ` , etc . )", "- gpt-2 ( ` gpt2 ` , ` gpt2-xl ` , etc . )", "- gpt bigcode ( ` bigcode/starcoder ` , ` bigcode/gpt_bigcode-santacoder ` , etc . )", "- gpt-j ( ` eleutherai/gpt-j-6b ` , ` nomic-ai/gpt4all-j ` , etc . )", "- gpt-neox ( ` eleutherai/gpt-neox-20b ` , ` databricks/dolly-v2-12b ` , ` stabilityai/stablelm-tuned-alpha-7b ` , etc . )", "- internlm ( ` internlm/internlm-7b ` , ` internlm/internlm-chat-7b ` , etc . )", "- internlm2 ( ` internlm/internlm2-7b ` , ` internlm/internlm2-chat-7b ` , etc . )", "- llama & llama-2 ( ` meta-llama/llama-2-70b-hf ` , ` lmsys/vicuna-13b-v1.3 ` , ` young-geng/koala ` , ` openlm-research/open_llama_13b ` , etc . )", "- mistral ( ` mistralai/mistral-7b-v0.1 ` , ` mistralai/mistral-7b-instruct-v0.1 ` , etc . )", "- mixtral ( ` mistralai/mixtral-8x7b-v0.1 ` , ` mistralai/mixtral-8x7b-instruct-v0.1 ` , etc . )", "- mpt ( ` mosaicml/mpt-7b ` , ` mosaicml/mpt-30b ` , etc . )", "- opt ( ` facebook/opt-66b ` , ` facebook/opt-iml-max-30b ` , etc . )", "- phi ( ` microsoft/phi-1_5 ` , ` microsoft/phi-2 ` , etc . )", "- qwen ( ` qwen/qwen-7b ` , ` qwen/qwen-7b-chat ` , etc . )", "- qwen2 ( ` qwen/qwen2-7b-beta ` , ` qwen/qwen-7b-chat-beta ` , etc . )", "- stablelm ( ` stabilityai/stablelm-3b-4e1t ` , ` stabilityai/stablelm-base-alpha-7b-v2 ` , etc . )", "- yi ( ` 01-ai/yi-6b ` , ` 01-ai/yi-34b ` , etc . )", "install vllm pip [ source ] ( http : //vllm.readthedocs.io/en/latest/getting_started/installation.html # build-from-source ) : `` ` bash pip install vllm `` `" ] ]

[ "gpt", "inference", "llama", "llm", "llm-serving", "llmops", "mlops", "model-serving", "pytorch", "transformer" ]

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[ "gpt", "inference", "llama", "llm", "llm-serving", "llmops", "mlops", "model-serving", "pytorch", "transformer" ]

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[ "gpt", "inference", "llama", "llm", "llm-serving", "llmops", "mlops", "model-serving", "pytorch", "transformer" ]

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[ "adapter", "diffusion", "llm", "lora", "parameter-efficient-learning", "python", "pytorch", "transformers" ]

[ [ "quickstart", "install", "peft", "pip", ":", "``", "`", "bash", "pip", "install", "peft", "``", "`", "prepare", "model", "training", "peft", "method", "lora", "wrapping", "base", "model", "peft", "configuration", "`", "get_peft_model", "`", ".", "bigscience/mt0-large", "model", ",", "'re", "training", "0.19", "%", "parameter", "!", "``", "`", "python", "transformer", "import", "automodelforseq2seqlm", "peft", "import", "get_peft_config", ",", "get_peft_model", ",", "loraconfig", ",", "tasktype", "model_name_or_path", "=", "``", "bigscience/mt0-large", "''", "tokenizer_name_or_path", "=", "``", "bigscience/mt0-large", "''", "peft_config", "=", "loraconfig", "(", "task_type=tasktype.seq_2_seq_lm", ",", "inference_mode=false", ",", "r=8", ",", "lora_alpha=32", ",", "lora_dropout=0.1", ")", "model", "=", "automodelforseq2seqlm.from_pretrained", "(", "model_name_or_path", ")", "model", "=", "get_peft_model", "(", "model", ",", "peft_config", ")", "model.print_trainable_parameters", "(", ")", "''", "trainable", "params", ":", "2359296", "||", "params", ":", "1231940608", "||", "trainable", "%", ":", "0.19151053100118282", "''", "``", "`", "load", "peft", "model", "inference", ":", "``", "`", "py", "peft", "import", "autopeftmodelforcausallm", "transformer", "import", "autotokenizer", "import", "torch", "model", "=", "autopeftmodelforcausallm.from_pretrained", "(", "``", "ybelkada/opt-350m-lora", "''", ")", ".to", "(", "``", "cuda", "''", ")", "tokenizer", "=", "autotokenizer.from_pretrained", "(", "``", "facebook/opt-350m", "''", ")", "model.eval", "(", ")", "input", "=", "tokenizer", "(", "``", "preheat", "oven", "350", "degree", "place", "cookie", "dough", "''", ",", "return_tensors=", "''", "pt", "''", ")", "output", "=", "model.generate", "(", "input_ids=inputs", "[", "``", "input_ids", "''", "]", ".to", "(", "``", "cuda", "''", ")", ",", "max_new_tokens=50", ")", "print", "(", "tokenizer.batch_decode", "(", "output", ",", "skip_special_tokens=true", ")", "[", "0", "]", ")", "''", "preheat", "oven", "350", "degree", "place", "cookie", "dough", "center", "oven", ".", "large", "bowl", ",", "combine", "flour", ",", "baking", "powder", ",", "baking", "soda", ",", "salt", ",", "cinnamon", ".", "separate", "bowl", ",", "combine", "egg", "yolk", ",", "sugar", ",", "vanilla", ".", "''", "``", "`" ], [ "quickstart install peft pip : `` ` bash pip install peft `` ` prepare model training peft method lora wrapping base model peft configuration ` get_peft_model ` .", "bigscience/mt0-large model , 're training 0.19 % parameter !", "`` ` python transformer import automodelforseq2seqlm peft import get_peft_config , get_peft_model , loraconfig , tasktype model_name_or_path = `` bigscience/mt0-large '' tokenizer_name_or_path = `` bigscience/mt0-large '' peft_config = loraconfig ( task_type=tasktype.seq_2_seq_lm , inference_mode=false , r=8 , lora_alpha=32 , lora_dropout=0.1 ) model = automodelforseq2seqlm.from_pretrained ( model_name_or_path ) model = get_peft_model ( model , peft_config ) model.print_trainable_parameters ( ) '' trainable params : 2359296 || params : 1231940608 || trainable % : 0.19151053100118282 '' `` ` load peft model inference : `` ` py peft import autopeftmodelforcausallm transformer import autotokenizer import torch model = autopeftmodelforcausallm.from_pretrained ( `` ybelkada/opt-350m-lora '' ) .to ( `` cuda '' ) tokenizer = autotokenizer.from_pretrained ( `` facebook/opt-350m '' ) model.eval ( ) input = tokenizer ( `` preheat oven 350 degree place cookie dough '' , return_tensors= '' pt '' ) output = model.generate ( input_ids=inputs [ `` input_ids '' ] .to ( `` cuda '' ) , max_new_tokens=50 ) print ( tokenizer.batch_decode ( output , skip_special_tokens=true ) [ 0 ] ) '' preheat oven 350 degree place cookie dough center oven .", "large bowl , combine flour , baking powder , baking soda , salt , cinnamon .", "separate bowl , combine egg yolk , sugar , vanilla . ''", "`` `" ] ]

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[ "adapter", "diffusion", "llm", "lora", "parameter-efficient-learning", "python", "pytorch", "transformers" ]

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[ "adapter", "diffusion", "llm", "lora", "parameter-efficient-learning", "python", "pytorch", "transformers" ]

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"`" ], [ "provided datasets < detail > < summary > pre-training datasets < /summary > - [ wiki demo ( en ) ] ( data/wiki_demo.txt ) - [ refinedweb ( en ) ] ( http : //huggingface.co/datasets/tiiuae/falcon-refinedweb ) - [ redpajama v2 ( en ) ] ( http : //huggingface.co/datasets/togethercomputer/redpajama-data-v2 ) - [ wikipedia ( en ) ] ( http : //huggingface.co/datasets/olm/olm-wikipedia-20221220 ) - [ wikipedia ( zh ) ] ( http : //huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered ) - [ pile ( en ) ] ( http : //huggingface.co/datasets/eleutherai/pile ) - [ skypile ( zh ) ] ( http : //huggingface.co/datasets/skywork/skypile-150b ) - [ stack ( en ) ] ( http : //huggingface.co/datasets/bigcode/the-stack ) - [ starcoder ( en ) ] ( http : //huggingface.co/datasets/bigcode/starcoderdata ) < /details > < detail > < summary > supervised fine-tuning datasets < /summary > - [ stanford alpaca ( en ) ] ( http : //github.com/tatsu-lab/stanford_alpaca ) - [ stanford alpaca ( zh ) ] ( http : //github.com/ymcui/chinese-llama-alpaca ) - [ alpaca gpt4 ( en & zh ) ] ( http : //github.com/instruction-tuning-with-gpt-4/gpt-4-llm ) - [ self cognition ( zh ) ] ( data/self_cognition.json ) - [ open assistant ( multilingual ) ] ( http : //huggingface.co/datasets/openassistant/oasst1 ) - [ sharegpt ( zh ) ] ( http : //huggingface.co/datasets/qingyisi/alpaca-cot/tree/main/chinese-instruction-collection ) - [ guanaco dataset ( multilingual ) ] ( http : //huggingface.co/datasets/josephuscheung/guanacodataset ) - [ belle 2m ( zh ) ] ( http : //huggingface.co/datasets/bellegroup/train_2m_cn ) - [ belle 1m ( zh ) ] ( http : //huggingface.co/datasets/bellegroup/train_1m_cn ) - [ belle 0.5m ( zh ) ] ( http : //huggingface.co/datasets/bellegroup/train_0.5m_cn ) - [ belle dialogue 0.4m ( zh ) ] ( http : //huggingface.co/datasets/bellegroup/generated_chat_0.4m ) - [ belle school math 0.25m ( zh ) ] ( http : //huggingface.co/datasets/bellegroup/school_math_0.25m ) - [ belle multiturn chat 0.8m ( zh ) ] ( http : //huggingface.co/datasets/bellegroup/multiturn_chat_0.8m ) - [ ultrachat ( en ) ] ( http : //github.com/thunlp/ultrachat ) - [ lima ( en ) ] ( http : //huggingface.co/datasets/gair/lima ) - [ openplatypus ( en ) ] ( http : //huggingface.co/datasets/garage-baind/open-platypus ) - [ codealpaca 20k ( en ) ] ( http : //huggingface.co/datasets/sahil2801/codealpaca-20k ) - [ alpaca cot ( multilingual ) ] ( http : //huggingface.co/datasets/qingyisi/alpaca-cot ) - [ openorca ( en ) ] ( http : //huggingface.co/datasets/open-orca/openorca ) - [ mathinstruct ( en ) ] ( http : //huggingface.co/datasets/tiger-lab/mathinstruct ) - [ firefly 1.1m ( zh ) ] ( http : //huggingface.co/datasets/yeungnlp/firefly-train-1.1m ) - [ wiki qa ( en ) ] ( http : //huggingface.co/datasets/wiki_qa ) - [ web qa ( zh ) ] ( http : //huggingface.co/datasets/suolyer/webqa ) - [ webnovel ( zh ) ] ( http : //huggingface.co/datasets/zxbsmk/webnovel_cn ) - [ nectar ( en ) ] ( http : //huggingface.co/datasets/berkeley-nest/nectar ) - [ deepctrl ( en & zh ) ] ( http : //www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data ) - [ ad gen ( zh ) ] ( http : //huggingface.co/datasets/hasturofficial/adgen ) - [ sharegpt hyperfiltered ( en ) ] ( http : //huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k ) - [ sharegpt4 ( en & zh ) ] ( http : //huggingface.co/datasets/shibing624/sharegpt_gpt4 ) - [ ultrachat 200k ( en ) ] ( http : //huggingface.co/datasets/huggingfaceh4/ultrachat_200k ) - [ agentinstruct ( en ) ] ( http : //huggingface.co/datasets/thudm/agentinstruct ) - [ lmsys chat 1m ( en ) ] ( http : //huggingface.co/datasets/lmsys/lmsys-chat-1m ) - [ evol instruct v2 ( en ) ] ( http : //huggingface.co/datasets/wizardlm/wizardlm_evol_instruct_v2_196k ) - [ glaive function calling v2 ( en ) ] ( http : //huggingface.co/datasets/glaiveai/glaive-function-calling-v2 ) - [ open assistant ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/oasst_de ) - [ dolly 15k ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/dolly-15k_de ) - [ alpaca gpt4 ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/alpaca-gpt4_de ) - [ openschnabeltier ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/openschnabeltier_de ) - [ evol instruct ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/evol-instruct_de ) - [ dolphin ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/dolphin_de ) - [ booksum ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/booksum_de ) - [ airoboros ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/airoboros-3.0_de ) - [ ultrachat ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/ultra-chat_de ) < /details > < detail > < summary > preference datasets < /summary > - [ hh-rlhf ( en ) ] ( http : //huggingface.co/datasets/anthropic/hh-rlhf ) - [ open assistant ( multilingual ) ] ( http : //huggingface.co/datasets/openassistant/oasst1 ) - [ gpt-4 generated data ( en & zh ) ] ( http : //github.com/instruction-tuning-with-gpt-4/gpt-4-llm ) - [ nectar ( en ) ] ( http : //huggingface.co/datasets/berkeley-nest/nectar ) - [ orca dpo ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de ) < /details > please refer [ data/readme.md ] ( data/readme.md ) detail .", "datasets require confirmation using , recommend logging hugging face account using command .", "`` ` bash pip install -- upgrade huggingface_hub huggingface-cli login `` `" ] ]

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"`" ], [ "provided datasets < detail > < summary > pre-training datasets < /summary > - [ wiki demo ( en ) ] ( data/wiki_demo.txt ) - [ refinedweb ( en ) ] ( http : //huggingface.co/datasets/tiiuae/falcon-refinedweb ) - [ redpajama v2 ( en ) ] ( http : //huggingface.co/datasets/togethercomputer/redpajama-data-v2 ) - [ wikipedia ( en ) ] ( http : //huggingface.co/datasets/olm/olm-wikipedia-20221220 ) - [ wikipedia ( zh ) ] ( http : //huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered ) - [ pile ( en ) ] ( http : //huggingface.co/datasets/eleutherai/pile ) - [ skypile ( zh ) ] ( http : //huggingface.co/datasets/skywork/skypile-150b ) - [ stack ( en ) ] ( http : //huggingface.co/datasets/bigcode/the-stack ) - [ starcoder ( en ) ] ( http : //huggingface.co/datasets/bigcode/starcoderdata ) < /details > < detail > < summary > supervised fine-tuning datasets < /summary > - [ stanford alpaca ( en ) ] ( http : //github.com/tatsu-lab/stanford_alpaca ) - [ stanford alpaca ( zh ) ] ( http : //github.com/ymcui/chinese-llama-alpaca ) - [ alpaca gpt4 ( en & zh ) ] ( http : //github.com/instruction-tuning-with-gpt-4/gpt-4-llm ) - [ self cognition ( zh ) ] ( data/self_cognition.json ) - [ open assistant ( multilingual ) ] ( http : //huggingface.co/datasets/openassistant/oasst1 ) - [ sharegpt ( zh ) ] ( http : //huggingface.co/datasets/qingyisi/alpaca-cot/tree/main/chinese-instruction-collection ) - [ guanaco dataset ( multilingual ) ] ( http : //huggingface.co/datasets/josephuscheung/guanacodataset ) - [ belle 2m ( zh ) ] ( http : //huggingface.co/datasets/bellegroup/train_2m_cn ) - [ belle 1m ( zh ) ] ( http : //huggingface.co/datasets/bellegroup/train_1m_cn ) - [ belle 0.5m ( zh ) ] ( http : //huggingface.co/datasets/bellegroup/train_0.5m_cn ) - [ belle dialogue 0.4m ( zh ) ] ( http : //huggingface.co/datasets/bellegroup/generated_chat_0.4m ) - [ belle school math 0.25m ( zh ) ] ( http : //huggingface.co/datasets/bellegroup/school_math_0.25m ) - [ belle multiturn chat 0.8m ( zh ) ] ( http : //huggingface.co/datasets/bellegroup/multiturn_chat_0.8m ) - [ ultrachat ( en ) ] ( http : //github.com/thunlp/ultrachat ) - [ lima ( en ) ] ( http : //huggingface.co/datasets/gair/lima ) - [ openplatypus ( en ) ] ( http : //huggingface.co/datasets/garage-baind/open-platypus ) - [ codealpaca 20k ( en ) ] ( http : //huggingface.co/datasets/sahil2801/codealpaca-20k ) - [ alpaca cot ( multilingual ) ] ( http : //huggingface.co/datasets/qingyisi/alpaca-cot ) - [ openorca ( en ) ] ( http : //huggingface.co/datasets/open-orca/openorca ) - [ mathinstruct ( en ) ] ( http : //huggingface.co/datasets/tiger-lab/mathinstruct ) - [ firefly 1.1m ( zh ) ] ( http : //huggingface.co/datasets/yeungnlp/firefly-train-1.1m ) - [ wiki qa ( en ) ] ( http : //huggingface.co/datasets/wiki_qa ) - [ web qa ( zh ) ] ( http : //huggingface.co/datasets/suolyer/webqa ) - [ webnovel ( zh ) ] ( http : //huggingface.co/datasets/zxbsmk/webnovel_cn ) - [ nectar ( en ) ] ( http : //huggingface.co/datasets/berkeley-nest/nectar ) - [ deepctrl ( en & zh ) ] ( http : //www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data ) - [ ad gen ( zh ) ] ( http : //huggingface.co/datasets/hasturofficial/adgen ) - [ sharegpt hyperfiltered ( en ) ] ( http : //huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k ) - [ sharegpt4 ( en & zh ) ] ( http : //huggingface.co/datasets/shibing624/sharegpt_gpt4 ) - [ ultrachat 200k ( en ) ] ( http : //huggingface.co/datasets/huggingfaceh4/ultrachat_200k ) - [ agentinstruct ( en ) ] ( http : //huggingface.co/datasets/thudm/agentinstruct ) - [ lmsys chat 1m ( en ) ] ( http : //huggingface.co/datasets/lmsys/lmsys-chat-1m ) - [ evol instruct v2 ( en ) ] ( http : //huggingface.co/datasets/wizardlm/wizardlm_evol_instruct_v2_196k ) - [ glaive function calling v2 ( en ) ] ( http : //huggingface.co/datasets/glaiveai/glaive-function-calling-v2 ) - [ open assistant ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/oasst_de ) - [ dolly 15k ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/dolly-15k_de ) - [ alpaca gpt4 ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/alpaca-gpt4_de ) - [ openschnabeltier ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/openschnabeltier_de ) - [ evol instruct ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/evol-instruct_de ) - [ dolphin ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/dolphin_de ) - [ booksum ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/booksum_de ) - [ airoboros ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/airoboros-3.0_de ) - [ ultrachat ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/ultra-chat_de ) < /details > < detail > < summary > preference datasets < /summary > - [ hh-rlhf ( en ) ] ( http : //huggingface.co/datasets/anthropic/hh-rlhf ) - [ open assistant ( multilingual ) ] ( http : //huggingface.co/datasets/openassistant/oasst1 ) - [ gpt-4 generated data ( en & zh ) ] ( http : //github.com/instruction-tuning-with-gpt-4/gpt-4-llm ) - [ nectar ( en ) ] ( http : //huggingface.co/datasets/berkeley-nest/nectar ) - [ orca dpo ( de ) ] ( http : //huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de ) < /details > please refer [ data/readme.md ] ( data/readme.md ) detail .", "datasets require confirmation using , recommend logging hugging face account using command .", "`` ` bash pip install -- upgrade huggingface_hub huggingface-cli login `` `" ] ]

[ "agent", "baichuan", "chatglm", "fine-tuning", "generative-ai", "gpt", "instruction-tuning", "language-model", "large-language-models", "llama", "llm", "lora", "mistral", "mixture-of-experts", "peft", "qlora", "quantization", "qwen", "rlhf", "transformers" ]

[ [ "getting", "started" ], [ "getting started" ] ]

[ [ "getting", "started" ], [ "getting started" ] ]

[ "agent", "baichuan", "chatglm", "fine-tuning", "generative-ai", "gpt", "instruction-tuning", "language-model", "large-language-models", "llama", "llm", "lora", "mistral", "mixture-of-experts", "peft", "qlora", "quantization", "qwen", "rlhf", "transformers" ]

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[ [ "dependence", "installation", "(", "optional", ")", "``", "`", "bash", "git", "clone", "http", ":", "//github.com/hiyouga/llama-factory.git", "conda", "create", "-n", "llama_factory", "python=3.10", "conda", "activate", "llama_factory", "cd", "llama-factory", "pip", "install", "-r", "requirements.txt", "``", "`", "want", "enable", "quantized", "lora", "(", "qlora", ")", "window", "platform", ",", "required", "install", "pre-built", "version", "`", "bitsandbytes", "`", "library", ",", "support", "cuda", "11.1", "12.1", ".", "``", "`", "bash", "pip", "install", "http", ":", "//github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl", "``", "`" ], [ "dependence installation ( optional ) `` ` bash git clone http : //github.com/hiyouga/llama-factory.git conda create -n llama_factory python=3.10 conda activate llama_factory cd llama-factory pip install -r requirements.txt `` ` want enable quantized lora ( qlora ) window platform , required install pre-built version ` bitsandbytes ` library , support cuda 11.1 12.1 .", "`` ` bash pip install http : //github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl `` `" ] ]

[ "agent", "baichuan", "chatglm", "fine-tuning", "generative-ai", "gpt", "instruction-tuning", "language-model", "large-language-models", "llama", "llm", "lora", "mistral", "mixture-of-experts", "peft", "qlora", "quantization", "qwen", "rlhf", "transformers" ]

[ [ "pip安装", "``", "`", "shell", "pip", "install", "--", "upgrade", "paddlenlp", "``", "`", "或者可通过以下命令安装最新", "develop", "分支代码：", "``", "`", "shell", "pip", "install", "--", "pre", "--", "upgrade", "paddlenlp", "-f", "http", ":", "//www.paddlepaddle.org.cn/whl/paddlenlp.html", "``", "`", "更多关于paddlepaddle和paddlenlp安装的详细教程请查看", "[", "installation", "]", "(", "./docs/get_started/installation.rst", ")", "。" ], [ "pip安装 `` ` shell pip install -- upgrade paddlenlp `` ` 或者可通过以下命令安装最新 develop 分支代码： `` ` shell pip install -- pre -- upgrade paddlenlp -f http : //www.paddlepaddle.org.cn/whl/paddlenlp.html `` ` 更多关于paddlepaddle和paddlenlp安装的详细教程请查看 [ installation ] ( ./docs/get_started/installation.rst ) 。" ] ]

[ [ "pip安装", "``", "`", "shell", "pip", "install", "--", "upgrade", "paddlenlp", "``", "`", "或者可通过以下命令安装最新", "develop", "分支代码：", "``", "`", "shell", "pip", "install", "--", "pre", "--", "upgrade", "paddlenlp", "-f", "http", ":", "//www.paddlepaddle.org.cn/whl/paddlenlp.html", "``", "`", "更多关于paddlepaddle和paddlenlp安装的详细教程请查看", "[", "installation", "]", "(", "./docs/get_started/installation.rst", ")", "。" ], [ "pip安装 `` ` shell pip install -- upgrade paddlenlp `` ` 或者可通过以下命令安装最新 develop 分支代码： `` ` shell pip install -- pre -- upgrade paddlenlp -f http : //www.paddlepaddle.org.cn/whl/paddlenlp.html `` ` 更多关于paddlepaddle和paddlenlp安装的详细教程请查看 [ installation ] ( ./docs/get_started/installation.rst ) 。" ] ]

[ "bert", "compression", "distributed-training", "document-intelligence", "embedding", "ernie", "information-extraction", "llama", "llm", "neural-search", "nlp", "paddlenlp", "pretrained-models", "question-answering", "search-engine", "semantic-analysis", "sentiment-analysis", "transformers", "uie" ]

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[ [ "content", "-", "[", "introduction", "]", "(", "#", "introduction", ")", "-", "[", "install", "]", "(", "#", "install", ")", "-", "[", "feature", "]", "(", "#", "feature", ")", "-", "[", "contribution", "]", "(", "#", "contribution", ")", "-", "[", "contact", "]", "(", "#", "contact-information", ")" ], [ "content - [ introduction ] ( # introduction ) - [ install ] ( # install ) - [ feature ] ( # feature ) - [ contribution ] ( # contribution ) - [ contact ] ( # contact-information )" ] ]

[ "agents", "bgi", "database", "gpt", "gpt-4", "langchain", "llm", "private", "rag", "security", "vicuna" ]

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[ "agents", "bgi", "database", "gpt", "gpt-4", "langchain", "llm", "private", "rag", "security", "vicuna" ]

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[ "agents", "bgi", "database", "gpt", "gpt-4", "langchain", "llm", "private", "rag", "security", "vicuna" ]

[ [ "🔧", "quick", "install", "``", "`", "bash", "pip", "install", "pandasai", "``", "`" ], [ "🔧 quick install `` ` bash pip install pandasai `` `" ] ]

[ [ "🔧", "quick", "install", "``", "`", "bash", "pip", "install", "pandasai", "``", "`" ], [ "🔧 quick install `` ` bash pip install pandasai `` `" ] ]

[ "ai", "csv", "data", "data-analysis", "data-science", "gpt-3", "gpt-4", "llm", "pandas", "sql" ]

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[ "ai", "csv", "data", "data-analysis", "data-science", "gpt-3", "gpt-4", "llm", "pandas", "sql" ]

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* * persistent * * database ( chroma , weaviate , in-memory faiss ) using accurate embeddings ( instructor-large , all-minilm-l6-v2 , etc . )", "- * * efficient * * use context using instruct-tuned llm ( need langchain 's few-shot approach ) - * * parallel * * summarization extraction , reaching output 80 token per second 13b llama2 model - * * hyde * * ( hypothetical document embeddings ) enhanced retrieval based upon llm response - * * variety * * model supported ( llama2 , mistral , falcon , vicuna , wizardlm .", "autogptq , 4-bit/8-bit , lora , etc . )", "- * * gpu * * support hf llama.cpp ggml model , * * cpu * * support using hf , llama.cpp , gpt4all model - * * attention sink * * [ arbitrarily long ] ( http : //github.com/tomaarsen/attention_sinks ) generation ( llama-2 , mistral , mpt , pythia , falcon , etc . )", "- * * ui * * cli streaming model - * * upload * * * * view * * document ui ( control multiple collaborative personal collection ) - * * vision llava * * model * * stable diffusion * * image generation - * * voice stt * * using whisper streaming audio conversion - * * voice tt * * using mit-licensed microsoft speech t5 multiple voice streaming audio conversion - * * voice tt * * using mpl2-licensed tt including voice cloning streaming audio conversion - * * ai assistant voice control mode * * hands-free control h2ogpt chat - * * bake-off * * ui mode many model time - * * easy download * * model artifact control model like llama.cpp ui - * * authentication * * ui user/password - * * state preservation * * ui user/password - * * linux , docker , macos , window * * support - [ * * easy window installer * * ] ( # windows-1011-64-bit-with-full-document-qa-capability ) window 10 64-bit ( cpu/cuda ) - [ * * easy macos installer * * ] ( # macos-cpum1m2-with-full-document-qa-capability ) macos ( cpu/m1/m2 ) - * * inference server * * support ( hf tgi server , vllm , gradio , exllama , replicate , openai , azure openai , anthropic ) - * * openai-compliant * * - server proxy api ( h2ogpt act drop-in-replacement openai server ) - python client api ( talk gradio server ) - * * web-search * * integration chat document q/a - * * agent * * search , document q/a , python code , csv frame ( experimental , best openai currently ) - * * evaluate * * performance using reward model - * * quality * * maintained 1000 unit integration test taking 4 gpu-hours" ] ]

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* * persistent * * database ( chroma , weaviate , in-memory faiss ) using accurate embeddings ( instructor-large , all-minilm-l6-v2 , etc . )", "- * * efficient * * use context using instruct-tuned llm ( need langchain 's few-shot approach ) - * * parallel * * summarization extraction , reaching output 80 token per second 13b llama2 model - * * hyde * * ( hypothetical document embeddings ) enhanced retrieval based upon llm response - * * variety * * model supported ( llama2 , mistral , falcon , vicuna , wizardlm .", "autogptq , 4-bit/8-bit , lora , etc . )", "- * * gpu * * support hf llama.cpp ggml model , * * cpu * * support using hf , llama.cpp , gpt4all model - * * attention sink * * [ arbitrarily long ] ( http : //github.com/tomaarsen/attention_sinks ) generation ( llama-2 , mistral , mpt , pythia , falcon , etc . )", "- * * ui * * cli streaming model - * * upload * * * * view * * document ui ( control multiple collaborative personal collection ) - * * vision llava * * model * * stable diffusion * * image generation - * * voice stt * * using whisper streaming audio conversion - * * voice tt * * using mit-licensed microsoft speech t5 multiple voice streaming audio conversion - * * voice tt * * using mpl2-licensed tt including voice cloning streaming audio conversion - * * ai assistant voice control mode * * hands-free control h2ogpt chat - * * bake-off * * ui mode many model time - * * easy download * * model artifact control model like llama.cpp ui - * * authentication * * ui user/password - * * state preservation * * ui user/password - * * linux , docker , macos , window * * support - [ * * easy window installer * * ] ( # windows-1011-64-bit-with-full-document-qa-capability ) window 10 64-bit ( cpu/cuda ) - [ * * easy macos installer * * ] ( # macos-cpum1m2-with-full-document-qa-capability ) macos ( cpu/m1/m2 ) - * * inference server * * support ( hf tgi server , vllm , gradio , exllama , replicate , openai , azure openai , anthropic ) - * * openai-compliant * * - server proxy api ( h2ogpt act drop-in-replacement openai server ) - python client api ( talk gradio server ) - * * web-search * * integration chat document q/a - * * agent * * search , document q/a , python code , csv frame ( experimental , best openai currently ) - * * evaluate * * performance using reward model - * * quality * * maintained 1000 unit integration test taking 4 gpu-hours" ] ]

[ "ai", "chatgpt", "embeddings", "generative", "gpt", "gpt4all", "llama2", "llm", "mixtral", "pdf", "private", "privategpt", "vectorstore" ]

[ [ "cu118", "use", "export", "pip_extra_index_url=", "''", "http", ":", "//download.pytorch.org/whl/cu118", "http", ":", "//huggingface.github.io/autogptq-index/whl/cu118", "''", "``", "`", "run", "following", "command", "system", ":", "``", "`", "bash", "git", "clone", "http", ":", "//github.com/h2oai/h2ogpt.git", "cd", "h2ogpt", "pip", "install", "-r", "requirements.txt", "pip", "install", "-r", "reqs_optional/requirements_optional_langchain.txt" ], [ "cu118 use export pip_extra_index_url= '' http : //download.pytorch.org/whl/cu118 http : //huggingface.github.io/autogptq-index/whl/cu118 '' `` ` run following command system : `` ` bash git clone http : //github.com/h2oai/h2ogpt.git cd h2ogpt pip install -r requirements.txt pip install -r reqs_optional/requirements_optional_langchain.txt" ] ]

[ [ "cu118", "use", "export", "pip_extra_index_url=", "''", "http", ":", "//download.pytorch.org/whl/cu118", "http", ":", "//huggingface.github.io/autogptq-index/whl/cu118", "''", "``", "`", "run", "following", "command", "system", ":", "``", "`", "bash", "git", "clone", "http", ":", "//github.com/h2oai/h2ogpt.git", "cd", "h2ogpt", "pip", "install", "-r", "requirements.txt", "pip", "install", "-r", "reqs_optional/requirements_optional_langchain.txt" ], [ "cu118 use export pip_extra_index_url= '' http : //download.pytorch.org/whl/cu118 http : //huggingface.github.io/autogptq-index/whl/cu118 '' `` ` run following command system : `` ` bash git clone http : //github.com/h2oai/h2ogpt.git cd h2ogpt pip install -r requirements.txt pip install -r reqs_optional/requirements_optional_langchain.txt" ] ]

[ "ai", "chatgpt", "embeddings", "generative", "gpt", "gpt4all", "llama2", "llm", "mixtral", "pdf", "private", "privategpt", "vectorstore" ]

[ [ "amd", "roc", ",", "comment-out", "except", "correct", "roc", "wheel", "pip", "install", "-r", "reqs_optional/requirements_optional_gpt4all.txt", "pip", "install", "-r", "reqs_optional/requirements_optional_langchain.urls.txt" ], [ "amd roc , comment-out except correct roc wheel pip install -r reqs_optional/requirements_optional_gpt4all.txt pip install -r reqs_optional/requirements_optional_langchain.urls.txt" ] ]

[ [ "amd", "roc", ",", "comment-out", "except", "correct", "roc", "wheel", "pip", "install", "-r", "reqs_optional/requirements_optional_gpt4all.txt", "pip", "install", "-r", "reqs_optional/requirements_optional_langchain.urls.txt" ], [ "amd roc , comment-out except correct roc wheel pip install -r reqs_optional/requirements_optional_gpt4all.txt pip install -r reqs_optional/requirements_optional_langchain.urls.txt" ] ]

[ "ai", "chatgpt", "embeddings", "generative", "gpt", "gpt4all", "llama2", "llm", "mixtral", "pdf", "private", "privategpt", "vectorstore" ]

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[ "ai", "chatgpt", "embeddings", "generative", "gpt", "gpt4all", "llama2", "llm", "mixtral", "pdf", "private", "privategpt", "vectorstore" ]

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\\users\\pseud\\appdata\\local\\programs\\h2ogpt\\h2ogpt.launch.pyw '' `` ` start-up exception appended log , e.g .", "` c : \\users\\pseud\\h2ogpt_exception.log ` .", "* control startup , tweak python startup file , e.g .", "user ` pseud ` : ` c : \\users\\pseud\\appdata\\local\\programs\\h2ogpt\\pkgs\\win_run_app.py ` * python code , set envs anywhere main_h2ogpt ( ) called * e.g .", "` os.environ [ 'name ' ] = 'value ' ` , e.g .", "` os.environ [ 'n_jobs ' ] = '10 ' ` ( must always string ) .", "* environment variable changed , e.g .", ": * ` n_jobs ` : number core various task * ` omp_num_threads ` thread count llama * ` cuda_visible_devices ` gpus used .", "recommend set single fast gpu , e.g .", "` cuda_visible_devices=0 ` multiple gpus .", "note ui control gpus ( cpu mode ) llama model .", "* cli argument ` python generate.py -- help ` environment variable set ` h2ogpt_x ` , e.g .", "` h2ogpt_h2ocolors ` ` false ` .", "* set env ` h2ogpt_server_name ` actual ip address lan see app , e.g .", "` h2ogpt_server_name ` ` 192.168.1.172 ` allow access firewall window defender activated .", "* one tweak installed h2ogpt code , e.g .", "` c : \\users\\pseud\\appdata\\local\\programs\\h2ogpt ` .", "* terminate app , go system tab click admin click shutdown h2ogpt .", "* startup fails , run console check error , e.g .", "kill old python process .", "* [ full window 10/11 manual installation script ] ( docs/readme_windows.md ) * single ` .bat ` file installation ( skip optional package , take 9gb filled disk ) .", "* recommend base conda env , allows doctr requires pygobject otherwise support ( except ` mysys2 ` used h2ogpt ) .", "* also allows tt package coqui , otherwise currently enabled one-click installer .", "-- -" ] ]

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[ "ai", "chatgpt", "embeddings", "generative", "gpt", "gpt4all", "llama2", "llm", "mixtral", "pdf", "private", "privategpt", "vectorstore" ]

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[ "ai", "chatgpt", "embeddings", "generative", "gpt", "gpt4all", "llama2", "llm", "mixtral", "pdf", "private", "privategpt", "vectorstore" ]

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[ "ai", "chatgpt", "embeddings", "generative", "gpt", "gpt4all", "llama2", "llm", "mixtral", "pdf", "private", "privategpt", "vectorstore" ]

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[ "ai", "chatgpt", "embeddings", "generative", "gpt", "gpt4all", "llama2", "llm", "mixtral", "pdf", "private", "privategpt", "vectorstore" ]

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[ "ai", "chatgpt", "embeddings", "generative", "gpt", "gpt4all", "llama2", "llm", "mixtral", "pdf", "private", "privategpt", "vectorstore" ]

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[ "ai", "chatgpt", "embeddings", "generative", "gpt", "gpt4all", "llama2", "llm", "mixtral", "pdf", "private", "privategpt", "vectorstore" ]

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[ "api", "api-documentation", "chatgpt", "claude-api", "gpt-4-api", "llm", "openai-api", "openai-functions" ]

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"get_llm_responses.py", "│", "├──", "eval-scripts", "│", "│", "├──", "ast_eval_", "{", "api_name", "}", ".py", "│", "├──", "eval-data", "│", "│", "├──", "question", "│", "│", "│", "├──", "api", "name", "│", "│", "│", "│", "├──", "questions_", "{", "api_name", "}", "_", "{", "eval_metric", "}", ".jsonl", "│", "│", "├──", "response", "│", "│", "│", "├──", "api", "name", "│", "│", "│", "│", "├──", "responses_", "{", "api_name", "}", "_gorilla_ft_", "{", "eval_metric", "}", ".jsonl", "│", "│", "│", "│", "├──", "responses_", "{", "api_name", "}", "_gorilla_rt_", "{", "eval_metric", "}", ".jsonl", "├──", "inference", "│", "├──", "readme.md", "│", "├──", "serve", "│", "│", "├──", "gorilla_cli.py", "│", "│", "├──", "conv_template.py", "├──", "train", "(", "coming", "soon", "!", ")", "``", "`" ], [ "repository organization repository organization shown .", "- ` data ` folder contains evaluation apis ` ( apibench ) ` community contributed apis .", "- ` eval ` folder contains evaluation code well gorilla output .", "- ` inference ` folder contains inference code running gorilla locally .", "- < span style= '' color : hr '' > [ coming soon !", "] < /span > ` train ` folder contains training code associated gorilla finetuning .", "dataset collection , 1640 api documentation ` data/api ` .", "also include ` apibench ` dataset created self-instruct ` data/apibench ` .", "evaluation , convert llm-friendly chat format , question ` eval/eval-data/questions ` , corresponding response ` eval/eval-data/responses ` .", "also included evaluation script ` eval/eval-scripts ` .", "would entirely sufficient train gorilla , reproduce result .", "please see [ evaluation ] ( http : //github.com/shishirpatil/gorilla/tree/main/eval ) detail use evaluation pipeline .", "additionally , released model weight .", "` gorilla-7b-hf-v0 ` let invoke 925 hugging face apis .", "similarly , ` gorilla-7b-tf-v0 ` ` gorilla-7b-th-v0 ` 626 ( exhaustive ) tensorflow v2 , 94 ( exhaustive ) torch hub apis .", "` gorilla-mpt-7b-hf-v0 ` ` gorilla-falcon-7b-hf-v0 ` apache 2.0 licensed model ( commercially usable ) fine-tuned mpt-7b falcon-7b respectively .", "release model three combined generic chat capability community contributed apis soon scale serving infrastructure .", "run gorilla locally instruction ` inference/ ` sub-directory , also provide hosted gorilla chat completion api ( see colab ) !", "suggestion , run issue please feel free reach u either discord email raise github issue .", "`` ` gorilla ├── data │ ├── api ( tf/hf/th apis used generating apibench ) │ │ ├── { api_name } _api.jsonl │ ├── apibench ( evaluating llm model ) v-1.0 │ │ ├── { api_name } _train.jsonl , { api_name } _eval.jsonl | |── apizoo ( contributed community - evolving ) │ | ├── username1.json │ │ ├── username2.json │ │ ├── ... ├── eval │ ├── readme.md │ ├── get_llm_responses.py │ ├── eval-scripts │ │ ├── ast_eval_ { api_name } .py │ ├── eval-data │ │ ├── question │ │ │ ├── api name │ │ │ │ ├── questions_ { api_name } _ { eval_metric } .jsonl │ │ ├── response │ │ │ ├── api name │ │ │ │ ├── responses_ { api_name } _gorilla_ft_ { eval_metric } .jsonl │ │ │ │ ├── responses_ { api_name } _gorilla_rt_ { eval_metric } .jsonl ├── inference │ ├── readme.md │ ├── serve │ │ ├── gorilla_cli.py │ │ ├── conv_template.py ├── train ( coming soon ! )", "`` `" ] ]

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gorilla output .", "- ` inference ` folder contains inference code running gorilla locally .", "- < span style= '' color : hr '' > [ coming soon !", "] < /span > ` train ` folder contains training code associated gorilla finetuning .", "dataset collection , 1640 api documentation ` data/api ` .", "also include ` apibench ` dataset created self-instruct ` data/apibench ` .", "evaluation , convert llm-friendly chat format , question ` eval/eval-data/questions ` , corresponding response ` eval/eval-data/responses ` .", "also included evaluation script ` eval/eval-scripts ` .", "would entirely sufficient train gorilla , reproduce result .", "please see [ evaluation ] ( http : //github.com/shishirpatil/gorilla/tree/main/eval ) detail use evaluation pipeline .", "additionally , released model weight .", "` gorilla-7b-hf-v0 ` let invoke 925 hugging face apis .", "similarly , ` gorilla-7b-tf-v0 ` ` gorilla-7b-th-v0 ` 626 ( exhaustive ) tensorflow v2 , 94 ( exhaustive ) torch hub apis .", "` gorilla-mpt-7b-hf-v0 ` ` gorilla-falcon-7b-hf-v0 ` apache 2.0 licensed model ( commercially usable ) fine-tuned mpt-7b falcon-7b respectively .", "release model three combined generic chat capability community contributed apis soon scale serving infrastructure .", "run gorilla locally instruction ` inference/ ` sub-directory , also provide hosted gorilla chat completion api ( see colab ) !", "suggestion , run issue please feel free reach u either discord email raise github issue .", "`` ` gorilla ├── data │ ├── api ( tf/hf/th apis used generating apibench ) │ │ ├── { api_name } _api.jsonl │ ├── apibench ( evaluating llm model ) v-1.0 │ │ ├── { api_name } _train.jsonl , { api_name } _eval.jsonl | |── apizoo ( contributed community - evolving ) │ | ├── username1.json │ │ ├── username2.json │ │ ├── ... ├── eval │ ├── readme.md │ ├── get_llm_responses.py │ ├── eval-scripts │ │ ├── ast_eval_ { api_name } .py │ ├── eval-data │ │ ├── question │ │ │ ├── api name │ │ │ │ ├── questions_ { api_name } _ { eval_metric } .jsonl │ │ ├── response │ │ │ ├── api name │ │ │ │ ├── responses_ { api_name } _gorilla_ft_ { eval_metric } .jsonl │ │ │ │ ├── responses_ { api_name } _gorilla_rt_ { eval_metric } .jsonl ├── inference │ ├── readme.md │ ├── serve │ │ ├── gorilla_cli.py │ │ ├── conv_template.py ├── train ( coming soon ! )", "`` `" ] ]

[ "api", "api-documentation", "chatgpt", "claude-api", "gpt-4-api", "llm", "openai-api", "openai-functions" ]

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[ [ "optional", ".", "installing", "might", "slow", "." ], [ "optional .", "installing might slow ." ] ]

[ [ "optional", ".", "installing", "might", "slow", "." ], [ "optional .", "installing might slow ." ] ]

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[ [ "pip", "install", "csrc/layer_norm" ], [ "pip install csrc/layer_norm" ] ]

[ [ "pip", "install", "csrc/layer_norm" ], [ "pip install csrc/layer_norm" ] ]

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[ "chinese", "flash-attention", "large-language-models", "llm", "natural-language-processing", "pretrained-models" ]

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[ "chinese", "flash-attention", "large-language-models", "llm", "natural-language-processing", "pretrained-models" ]

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[ "chinese", "flash-attention", "large-language-models", "llm", "natural-language-processing", "pretrained-models" ]

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[ "chinese", "flash-attention", "large-language-models", "llm", "natural-language-processing", "pretrained-models" ]

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[ "chinese", "flash-attention", "large-language-models", "llm", "natural-language-processing", "pretrained-models" ]

[ [ "quantize", "fine-tuned", "model", "section", "applies", "full-parameter/lora", "fine-tuned", "model", ".", "(", "note", ":", "need", "quantize", "q-lora", "fine-tuned", "model", "already", "quantized", ".", ")", "use", "lora", ",", "please", "follow", "instruction", "merge", "model", "quantization", ".", "recommend", "using", "[", "auto_gptq", "]", "(", "http", ":", "//github.com/panqiwei/autogptq", ")", "quantize", "finetuned", "model", ".", "``", "`", "bash", "pip", "install", "auto-gptq", "optimum", "``", "`", "note", ":", "currently", "autogptq", "bug", "referred", "[", "issue", "]", "(", "http", ":", "//github.com/panqiwei/autogptq/issues/370", ")", ".", "[", "workaround", "pr", "]", "(", "http", ":", "//github.com/panqiwei/autogptq/pull/495", ")", ",", "pull", "branch", "install", "source", ".", "first", ",", "prepare", "calibration", "data", ".", "reuse", "fine-tuning", "data", ",", "use", "data", "following", "format", ".", "second", ",", "run", "following", "script", ":", "``", "`", "bash", "python", "run_gptq.py", "\\", "--", "model_name_or_path", "$", "your_lora_model_path", "\\", "--", "data_path", "$", "data", "\\", "--", "out_path", "$", "output_path", "\\", "--", "bit", "4" ], [ "quantize fine-tuned model section applies full-parameter/lora fine-tuned model .", "( note : need quantize q-lora fine-tuned model already quantized . )", "use lora , please follow instruction merge model quantization .", "recommend using [ auto_gptq ] ( http : //github.com/panqiwei/autogptq ) quantize finetuned model .", "`` ` bash pip install auto-gptq optimum `` ` note : currently autogptq bug referred [ issue ] ( http : //github.com/panqiwei/autogptq/issues/370 ) .", "[ workaround pr ] ( http : //github.com/panqiwei/autogptq/pull/495 ) , pull branch install source .", "first , prepare calibration data .", "reuse fine-tuning data , use data following format .", "second , run following script : `` ` bash python run_gptq.py \\ -- model_name_or_path $ your_lora_model_path \\ -- data_path $ data \\ -- out_path $ output_path \\ -- bit 4" ] ]

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[ "chinese", "flash-attention", "large-language-models", "llm", "natural-language-processing", "pretrained-models" ]