554 lines
23 KiB
Markdown
554 lines
23 KiB
Markdown
# LLaMA Factory: Training and Evaluating Large Language Models with Minimal Effort
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[](https://github.com/hiyouga/LLaMA-Factory/stargazers)
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[](LICENSE)
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[](https://github.com/hiyouga/LLaMA-Factory/commits/main)
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[](https://pypi.org/project/llmtuner/)
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[](https://pypi.org/project/llmtuner/)
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[](https://github.com/hiyouga/LLaMA-Factory/pulls)
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[](https://discord.gg/c2EPEt5NU)
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[](https://huggingface.co/spaces/hiyouga/LLaMA-Board)
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[](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
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👋 Join our [WeChat](assets/wechat.jpg).
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\[ English | [中文](README_zh.md) \]
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## LLaMA Board: A One-stop Web UI for Getting Started with LLaMA Factory
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Preview LLaMA Board at **[🤗 Spaces](https://huggingface.co/spaces/hiyouga/LLaMA-Board)** or **[ModelScope](https://modelscope.cn/studios/hiyouga/LLaMA-Board)**.
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Launch LLaMA Board via `CUDA_VISIBLE_DEVICES=0 python src/train_web.py`. (multiple GPUs are not supported yet in this mode)
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Here is an example of altering the self-cognition of an instruction-tuned language model within 10 minutes on a single GPU.
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https://github.com/hiyouga/LLaMA-Factory/assets/16256802/6ba60acc-e2e2-4bec-b846-2d88920d5ba1
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## Table of Contents
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- [Benchmark](#benchmark)
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- [Changelog](#changelog)
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- [Supported Models](#supported-models)
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- [Supported Training Approaches](#supported-training-approaches)
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- [Provided Datasets](#provided-datasets)
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- [Requirement](#requirement)
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- [Getting Started](#getting-started)
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- [Projects using LLaMA Factory](#projects-using-llama-factory)
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- [License](#license)
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- [Citation](#citation)
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- [Acknowledgement](#acknowledgement)
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## Benchmark
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Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/ptuning), LLaMA-Factory's LoRA tuning offers up to **3.7 times faster** training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA-Factory's QLoRA further improves the efficiency regarding the GPU memory.
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- **Training Speed**: the number of training samples processed per second during the training. (bs=4, cutoff_len=1024)
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- **Rouge Score**: Rouge-2 score on the development set of the [advertising text generation](https://aclanthology.org/D19-1321.pdf) task. (bs=4, cutoff_len=1024)
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- **GPU Memory**: Peak GPU memory usage in 4-bit quantized training. (bs=1, cutoff_len=1024)
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- We adopt `pre_seq_len=128` for ChatGLM's P-Tuning and `lora_rank=32` for LLaMA-Factory's LoRA tuning.
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## Changelog
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[23/10/21] We supported **[NEFTune](https://arxiv.org/abs/2310.05914)** trick for fine-tuning. Try `--neft_alpha` argument to activate NEFTune, e.g., `--neft_alpha 5`.
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[23/09/27] We supported **$S^2$-Attn** proposed by [LongLoRA](https://github.com/dvlab-research/LongLoRA) for the LLaMA models. Try `--shift_attn` argument to enable shift short attention.
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[23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [this example](#evaluation) to evaluate your models.
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[23/09/10] We supported using **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)** for the LLaMA models. Try `--flash_attn` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs.
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[23/08/12] We supported **RoPE scaling** to extend the context length of the LLaMA models. Try `--rope_scaling linear` argument in training and `--rope_scaling dynamic` argument at inference to extrapolate the position embeddings.
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[23/08/11] We supported **[DPO training](https://arxiv.org/abs/2305.18290)** for instruction-tuned models. See [this example](#dpo-training) to train your models.
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[23/07/31] We supported **dataset streaming**. Try `--streaming` and `--max_steps 10000` arguments to load your dataset in streaming mode.
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[23/07/29] We released two instruction-tuned 13B models at Hugging Face. See these Hugging Face Repos ([LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/Baichuan-13B-sft)) for details.
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[23/07/18] We developed an **all-in-one Web UI** for training, evaluation and inference. Try `train_web.py` to fine-tune models in your Web browser. Thank [@KanadeSiina](https://github.com/KanadeSiina) and [@codemayq](https://github.com/codemayq) for their efforts in the development.
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[23/07/09] We released **[FastEdit](https://github.com/hiyouga/FastEdit)** ⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow [FastEdit](https://github.com/hiyouga/FastEdit) if you are interested.
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[23/06/29] We provided a **reproducible example** of training a chat model using instruction-following datasets, see [Baichuan-7B-sft](https://huggingface.co/hiyouga/Baichuan-7B-sft) for details.
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[23/06/22] We aligned the [demo API](src/api_demo.py) with the [OpenAI's](https://platform.openai.com/docs/api-reference/chat) format where you can insert the fine-tuned model in **arbitrary ChatGPT-based applications**.
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[23/06/03] We supported quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). Try `--quantization_bit 4/8` argument to work with quantized models.
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## Supported Models
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| Model | Model size | Default module | Template |
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| -------------------------------------------------------- | --------------------------- | ----------------- | --------- |
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| [Baichuan](https://github.com/baichuan-inc/Baichuan-13B) | 7B/13B | W_pack | baichuan |
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| [Baichuan2](https://github.com/baichuan-inc/Baichuan2) | 7B/13B | W_pack | baichuan2 |
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| [BLOOM](https://huggingface.co/bigscience/bloom) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
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| [BLOOMZ](https://huggingface.co/bigscience/bloomz) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
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| [ChatGLM3](https://github.com/THUDM/ChatGLM3) | 6B | query_key_value | chatglm3 |
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| [Falcon](https://huggingface.co/tiiuae/falcon-7b) | 7B/40B/180B | query_key_value | falcon |
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| [InternLM](https://github.com/InternLM/InternLM) | 7B/20B | q_proj,v_proj | intern |
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| [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | q_proj,v_proj | - |
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| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | q_proj,v_proj | llama2 |
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| [Mistral](https://huggingface.co/mistralai) | 7B | q_proj,v_proj | mistral |
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| [Phi-1.5](https://huggingface.co/microsoft/phi-1_5) | 1.3B | Wqkv | - |
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| [Qwen](https://github.com/QwenLM/Qwen) | 1.8B/7B/14B/72B | c_attn | qwen |
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| [XVERSE](https://github.com/xverse-ai) | 7B/13B/65B | q_proj,v_proj | xverse |
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> [!NOTE]
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> **Default module** is used for the `--lora_target` argument, you can use `--lora_target all` to specify all the available modules.
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>
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> For the "base" models, the `--template` argument can be chosen from `default`, `alpaca`, `vicuna` etc. But make sure to use the **corresponding template** for the "chat" models.
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Please refer to [constants.py](src/llmtuner/extras/constants.py) for a full list of models we supported.
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## Supported Training Approaches
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| Approach | Full-parameter | Partial-parameter | LoRA | QLoRA |
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| ---------------------- | ------------------ | ------------------ | ------------------ | ------------------ |
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| Pre-Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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| Supervised Fine-Tuning | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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| Reward Modeling | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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| PPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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| DPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
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> [!NOTE]
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> Use `--quantization_bit 4/8` argument to enable QLoRA.
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## Provided Datasets
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<details><summary>Pre-training datasets</summary>
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- [Wiki Demo (en)](data/wiki_demo.txt)
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- [RefinedWeb (en)](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
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- [RedPajama V2 (en)](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2)
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- [Wikipedia (en)](https://huggingface.co/datasets/olm/olm-wikipedia-20221220)
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- [Wikipedia (zh)](https://huggingface.co/datasets/pleisto/wikipedia-cn-20230720-filtered)
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- [Pile (en)](https://huggingface.co/datasets/EleutherAI/pile)
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- [SkyPile (zh)](https://huggingface.co/datasets/Skywork/SkyPile-150B)
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- [The Stack (en)](https://huggingface.co/datasets/bigcode/the-stack)
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- [StarCoder (en)](https://huggingface.co/datasets/bigcode/starcoderdata)
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</details>
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<details><summary>Supervised fine-tuning datasets</summary>
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- [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
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- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
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- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
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- [Self-cognition (zh)](data/self_cognition.json)
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- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
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- [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection)
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- [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
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- [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
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- [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
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- [BELLE 0.5M (zh)](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN)
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- [BELLE Dialogue 0.4M (zh)](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M)
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- [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
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- [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
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- [UltraChat (en)](https://github.com/thunlp/UltraChat)
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- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
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- [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
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- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
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- [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
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- [OpenOrca (en)](https://huggingface.co/datasets/Open-Orca/OpenOrca)
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- [MathInstruct (en)](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
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- [Firefly 1.1M (zh)](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M)
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- [Web QA (zh)](https://huggingface.co/datasets/suolyer/webqa)
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- [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
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- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
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- [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
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- [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k)
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- [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)
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- [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
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- [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct)
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- [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)
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- [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
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</details>
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<details><summary>Preference datasets</summary>
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- [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
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- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
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- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
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- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
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</details>
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Please refer to [data/README.md](data/README.md) for details.
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Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands.
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```bash
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pip install --upgrade huggingface_hub
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huggingface-cli login
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```
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## Requirement
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- Python 3.8+ and PyTorch 1.13.1+
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- 🤗Transformers, Datasets, Accelerate, PEFT and TRL
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- sentencepiece, protobuf and tiktoken
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- jieba, rouge-chinese and nltk (used at evaluation and predict)
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- gradio and matplotlib (used in web UI)
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- uvicorn, fastapi and sse-starlette (used in API)
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### Hardware Requirement
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| Method | Bits | 7B | 13B | 30B | 65B |
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| ------ | ---- | ----- | ----- | ----- | ------ |
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| Full | 16 | 140GB | 240GB | 520GB | 1200GB |
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| Freeze | 16 | 20GB | 40GB | 120GB | 240GB |
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| LoRA | 16 | 16GB | 32GB | 80GB | 160GB |
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| QLoRA | 8 | 10GB | 16GB | 40GB | 80GB |
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| QLoRA | 4 | 6GB | 12GB | 24GB | 48GB |
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## Getting Started
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### Data Preparation (optional)
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Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use a single `.json` file or a [dataset loading script](https://huggingface.co/docs/datasets/dataset_script) with multiple files to create a custom dataset.
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> [!NOTE]
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> Please update `data/dataset_info.json` to use your custom dataset. About the format of this file, please refer to `data/README.md`.
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### Dependence Installation (optional)
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```bash
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git clone https://github.com/hiyouga/LLaMA-Factory.git
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conda create -n llama_factory python=3.10
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conda activate llama_factory
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cd LLaMA-Factory
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pip install -r requirements.txt
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```
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If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you will be required to install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.1.
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```bash
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pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl
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```
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### Train on a single GPU
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> [!IMPORTANT]
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> If you want to train models on multiple GPUs, please refer to [Distributed Training](#distributed-training).
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#### Pre-Training
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
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--stage pt \
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--model_name_or_path path_to_llama_model \
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--do_train \
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--dataset wiki_demo \
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--finetuning_type lora \
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--lora_target q_proj,v_proj \
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--output_dir path_to_pt_checkpoint \
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--overwrite_cache \
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--per_device_train_batch_size 4 \
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--gradient_accumulation_steps 4 \
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--lr_scheduler_type cosine \
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--logging_steps 10 \
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--save_steps 1000 \
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--learning_rate 5e-5 \
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--num_train_epochs 3.0 \
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--plot_loss \
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--fp16
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```
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#### Supervised Fine-Tuning
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
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--stage sft \
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--model_name_or_path path_to_llama_model \
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--do_train \
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--dataset alpaca_gpt4_en \
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--template default \
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--finetuning_type lora \
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--lora_target q_proj,v_proj \
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--output_dir path_to_sft_checkpoint \
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--overwrite_cache \
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--per_device_train_batch_size 4 \
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--gradient_accumulation_steps 4 \
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--lr_scheduler_type cosine \
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--logging_steps 10 \
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--save_steps 1000 \
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--learning_rate 5e-5 \
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--num_train_epochs 3.0 \
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--plot_loss \
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--fp16
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```
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#### Reward Modeling
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
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--stage rm \
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--model_name_or_path path_to_llama_model \
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--do_train \
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--dataset comparison_gpt4_en \
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--template default \
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--finetuning_type lora \
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--lora_target q_proj,v_proj \
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--resume_lora_training False \
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--checkpoint_dir path_to_sft_checkpoint \
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--output_dir path_to_rm_checkpoint \
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--per_device_train_batch_size 2 \
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--gradient_accumulation_steps 4 \
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--lr_scheduler_type cosine \
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--logging_steps 10 \
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--save_steps 1000 \
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--learning_rate 1e-6 \
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--num_train_epochs 1.0 \
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--plot_loss \
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--fp16
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```
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#### PPO Training
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
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--stage ppo \
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--model_name_or_path path_to_llama_model \
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--do_train \
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--dataset alpaca_gpt4_en \
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--template default \
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--finetuning_type lora \
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--lora_target q_proj,v_proj \
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--resume_lora_training False \
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--checkpoint_dir path_to_sft_checkpoint \
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--reward_model path_to_rm_checkpoint \
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--output_dir path_to_ppo_checkpoint \
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--per_device_train_batch_size 2 \
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--gradient_accumulation_steps 4 \
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--lr_scheduler_type cosine \
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--top_k 0 \
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--top_p 0.9 \
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--logging_steps 10 \
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--save_steps 1000 \
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--learning_rate 1e-5 \
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--num_train_epochs 1.0 \
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--plot_loss \
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--fp16
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```
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> [!WARNING]
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> Use `--per_device_train_batch_size=1` for LLaMA-2 models in fp16 PPO training.
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#### DPO Training
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
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--stage dpo \
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--model_name_or_path path_to_llama_model \
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--do_train \
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--dataset comparison_gpt4_en \
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--template default \
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|
--finetuning_type lora \
|
|
--lora_target q_proj,v_proj \
|
|
--resume_lora_training False \
|
|
--checkpoint_dir path_to_sft_checkpoint \
|
|
--output_dir path_to_dpo_checkpoint \
|
|
--per_device_train_batch_size 2 \
|
|
--gradient_accumulation_steps 4 \
|
|
--lr_scheduler_type cosine \
|
|
--logging_steps 10 \
|
|
--save_steps 1000 \
|
|
--learning_rate 1e-5 \
|
|
--num_train_epochs 1.0 \
|
|
--plot_loss \
|
|
--fp16
|
|
```
|
|
|
|
### Distributed Training
|
|
|
|
#### Use Huggingface Accelerate
|
|
|
|
```bash
|
|
accelerate config # configure the environment
|
|
accelerate launch src/train_bash.py # arguments (same as above)
|
|
```
|
|
|
|
<details><summary>Example config for LoRA training</summary>
|
|
|
|
```yaml
|
|
compute_environment: LOCAL_MACHINE
|
|
distributed_type: MULTI_GPU
|
|
downcast_bf16: 'no'
|
|
gpu_ids: all
|
|
machine_rank: 0
|
|
main_training_function: main
|
|
mixed_precision: fp16
|
|
num_machines: 1
|
|
num_processes: 4
|
|
rdzv_backend: static
|
|
same_network: true
|
|
tpu_env: []
|
|
tpu_use_cluster: false
|
|
tpu_use_sudo: false
|
|
use_cpu: false
|
|
```
|
|
|
|
</details>
|
|
|
|
#### Use DeepSpeed
|
|
|
|
```bash
|
|
deepspeed --num_gpus 8 --master_port=9901 src/train_bash.py \
|
|
--deepspeed ds_config.json \
|
|
... # arguments (same as above)
|
|
```
|
|
|
|
<details><summary>Example config for full-parameter training with DeepSpeed ZeRO-2</summary>
|
|
|
|
```json
|
|
{
|
|
"train_batch_size": "auto",
|
|
"train_micro_batch_size_per_gpu": "auto",
|
|
"gradient_accumulation_steps": "auto",
|
|
"gradient_clipping": "auto",
|
|
"zero_allow_untested_optimizer": true,
|
|
"fp16": {
|
|
"enabled": "auto",
|
|
"loss_scale": 0,
|
|
"initial_scale_power": 16,
|
|
"loss_scale_window": 1000,
|
|
"hysteresis": 2,
|
|
"min_loss_scale": 1
|
|
},
|
|
"zero_optimization": {
|
|
"stage": 2,
|
|
"allgather_partitions": true,
|
|
"allgather_bucket_size": 5e8,
|
|
"reduce_scatter": true,
|
|
"reduce_bucket_size": 5e8,
|
|
"overlap_comm": false,
|
|
"contiguous_gradients": true
|
|
}
|
|
}
|
|
```
|
|
|
|
</details>
|
|
|
|
### Merge LoRA weights and export model
|
|
|
|
```bash
|
|
python src/export_model.py \
|
|
--model_name_or_path path_to_llama_model \
|
|
--template default \
|
|
--finetuning_type lora \
|
|
--checkpoint_dir path_to_checkpoint \
|
|
--export_dir path_to_export
|
|
```
|
|
|
|
### API Demo
|
|
|
|
```bash
|
|
python src/api_demo.py \
|
|
--model_name_or_path path_to_llama_model \
|
|
--template default \
|
|
--finetuning_type lora \
|
|
--checkpoint_dir path_to_checkpoint
|
|
```
|
|
|
|
> [!TIP]
|
|
> Visit `http://localhost:8000/docs` for API documentation.
|
|
|
|
### CLI Demo
|
|
|
|
```bash
|
|
python src/cli_demo.py \
|
|
--model_name_or_path path_to_llama_model \
|
|
--template default \
|
|
--finetuning_type lora \
|
|
--checkpoint_dir path_to_checkpoint
|
|
```
|
|
|
|
### Web Demo
|
|
|
|
```bash
|
|
python src/web_demo.py \
|
|
--model_name_or_path path_to_llama_model \
|
|
--template default \
|
|
--finetuning_type lora \
|
|
--checkpoint_dir path_to_checkpoint
|
|
```
|
|
|
|
### Evaluation
|
|
|
|
```bash
|
|
CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \
|
|
--model_name_or_path path_to_llama_model \
|
|
--finetuning_type lora \
|
|
--checkpoint_dir path_to_checkpoint \
|
|
--template vanilla \
|
|
--task mmlu \
|
|
--split test \
|
|
--lang en \
|
|
--n_shot 5 \
|
|
--batch_size 4
|
|
```
|
|
|
|
### Predict
|
|
|
|
```bash
|
|
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
|
|
--stage sft \
|
|
--model_name_or_path path_to_llama_model \
|
|
--do_predict \
|
|
--dataset alpaca_gpt4_en \
|
|
--template default \
|
|
--finetuning_type lora \
|
|
--checkpoint_dir path_to_checkpoint \
|
|
--output_dir path_to_predict_result \
|
|
--per_device_eval_batch_size 8 \
|
|
--max_samples 100 \
|
|
--predict_with_generate \
|
|
--fp16
|
|
```
|
|
|
|
> [!WARNING]
|
|
> Use `--per_device_train_batch_size=1` for LLaMA-2 models in fp16 predict.
|
|
|
|
> [!TIP]
|
|
> We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` at 4/8-bit predict.
|
|
|
|
## Projects using LLaMA Factory
|
|
|
|
- **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B.
|
|
- **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge.
|
|
- **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
|
|
- **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B.
|
|
|
|
> [!TIP]
|
|
> If you have a project that should be incorporated, please contact via email or create a pull request.
|
|
|
|
## License
|
|
|
|
This repository is licensed under the [Apache-2.0 License](LICENSE).
|
|
|
|
Please follow the model licenses to use the corresponding model weights: [Baichuan](https://huggingface.co/baichuan-inc/Baichuan-13B-Base/resolve/main/Community%20License%20for%20Baichuan-13B%20Model.pdf) / [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat/resolve/main/Community%20License%20for%20Baichuan2%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [InternLM](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2](https://ai.meta.com/llama/license/) / [Mistral](LICENSE) / [Phi-1.5](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/LICENSE) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf)
|
|
|
|
## Citation
|
|
|
|
If this work is helpful, please kindly cite as:
|
|
|
|
```bibtex
|
|
@Misc{llama-factory,
|
|
title = {LLaMA Factory},
|
|
author = {hiyouga},
|
|
howpublished = {\url{https://github.com/hiyouga/LLaMA-Factory}},
|
|
year = {2023}
|
|
}
|
|
```
|
|
|
|
## Acknowledgement
|
|
|
|
This repo benefits from [PEFT](https://github.com/huggingface/peft), [QLoRA](https://github.com/artidoro/qlora) and [FastChat](https://github.com/lm-sys/FastChat). Thanks for their wonderful works.
|
|
|
|
## Star History
|
|
|
|
|