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.github/SECURITY.md vendored
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# Reporting Security Issues
To report a security issue, please use the GitHub Security Advisory ["Report a Vulnerability"](https://github.com/electron/electron/security/advisories/new) tab.
To report a security issue, please use the GitHub Security Advisory ["Report a Vulnerability"](https://github.com/hiyouga/LLaMA-Factory/security/advisories/new) tab.
We will send a response indicating the next steps in handling your report. After the initial reply to your report, the security team will keep you informed of the progress towards a fix and full announcement, and may ask for additional information or guidance.

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@ -6,9 +6,9 @@ COPY requirements.txt /app/
RUN pip install -r requirements.txt
COPY . /app/
RUN pip install -e .[deepspeed,metrics,bitsandbytes,qwen]
RUN pip install -e .[metrics,bitsandbytes,qwen]
VOLUME [ "/root/.cache/huggingface/", "/app/data", "/app/output" ]
EXPOSE 7860
CMD [ "python", "src/train_web.py" ]
CMD [ "llamafactory-cli", "webui" ]

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README.md
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@ -3,9 +3,8 @@
[![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Factory?style=social)](https://github.com/hiyouga/LLaMA-Factory/stargazers)
[![GitHub Code License](https://img.shields.io/github/license/hiyouga/LLaMA-Factory)](LICENSE)
[![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Factory)](https://github.com/hiyouga/LLaMA-Factory/commits/main)
[![PyPI](https://img.shields.io/pypi/v/llmtuner)](https://pypi.org/project/llmtuner/)
[![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/)
[![Citation](https://img.shields.io/badge/citation-26-green)](#projects-using-llama-factory)
[![PyPI](https://img.shields.io/pypi/v/llamafactory)](https://pypi.org/project/llamafactory/)
[![Citation](https://img.shields.io/badge/citation-44-green)](#projects-using-llama-factory)
[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
[![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
@ -13,6 +12,8 @@
[![Studios](https://img.shields.io/badge/ModelScope-Open%20in%20Studios-blue)](https://modelscope.cn/studios/hiyouga/LLaMA-Board)
[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)
[![GitHub Tread](https://trendshift.io/api/badge/repositories/4535)](https://trendshift.io/repositories/4535)
👋 Join our [WeChat](assets/wechat.jpg).
\[ English | [中文](README_zh.md) \]
@ -43,17 +44,17 @@ Choose your path:
## Features
- **Various models**: LLaMA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
- **Integrated methods**: (Continuous) pre-training, supervised fine-tuning, reward modeling, PPO and DPO.
- **Various models**: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen, Yi, Gemma, Baichuan, ChatGLM, Phi, etc.
- **Integrated methods**: (Continuous) pre-training, (multimodal) supervised fine-tuning, reward modeling, PPO, DPO, KTO and ORPO.
- **Scalable resources**: 32-bit full-tuning, 16-bit freeze-tuning, 16-bit LoRA and 2/4/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8.
- **Advanced algorithms**: GaLore, DoRA, LongLoRA, LLaMA Pro, LoRA+, LoftQ and Agent tuning.
- **Advanced algorithms**: GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and Agent tuning.
- **Practical tricks**: FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA.
- **Experiment monitors**: LlamaBoard, TensorBoard, Wandb, MLflow, etc.
- **Faster inference**: OpenAI-style API, Gradio UI and CLI with vLLM worker.
## Benchmark
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.
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.
![benchmark](assets/benchmark.svg)
@ -62,51 +63,69 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
- **Training Speed**: the number of training samples processed per second during the training. (bs=4, cutoff_len=1024)
- **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)
- **GPU Memory**: Peak GPU memory usage in 4-bit quantized training. (bs=1, cutoff_len=1024)
- We adopt `pre_seq_len=128` for ChatGLM's P-Tuning and `lora_rank=32` for LLaMA-Factory's LoRA tuning.
- We adopt `pre_seq_len=128` for ChatGLM's P-Tuning and `lora_rank=32` for LLaMA Factory's LoRA tuning.
</details>
## Changelog
[24/03/21] Our paper "[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://arxiv.org/abs/2403.13372)" is available at arXiv!
[24/05/18] We supported **[KTO](https://arxiv.org/abs/2402.01306)** algorithm for preference learning. See [examples](examples/README.md) for usage.
[24/03/20] We supported **FSDP+QLoRA** that fine-tunes a 70B model on 2x24GB GPUs. See `examples/fsdp_qlora` for usage.
[24/05/14] We supported training and inference on the Ascend NPU devices. Check [installation](#installation) section for details.
[24/03/13] We supported **[LoRA+](https://arxiv.org/abs/2402.12354)**. Try `loraplus_lr_ratio=16.0` to enable LoRA+ algorithm.
[24/03/07] We supported gradient low-rank projection (**[GaLore](https://arxiv.org/abs/2403.03507)**) algorithm. Try `--use_galore` to use the memory-efficient optimizer.
[24/03/07] We integrated **[vLLM](https://github.com/vllm-project/vllm)** for faster and concurrent inference. Try `--infer_backend vllm` to enjoy **270%** inference speed. (LoRA is not yet supported, merge it first.)
[24/05/13] We supported fine-tuning the **Yi-1.5** series models.
<details><summary>Full Changelog</summary>
[24/02/28] We supported weight-decomposed LoRA (**[DoRA](https://arxiv.org/abs/2402.09353)**). Try `--use_dora` to activate DoRA training.
[24/04/26] We supported fine-tuning the **LLaVA-1.5** multimodal LLMs. See [examples](examples/README.md) for usage.
[24/02/15] We supported **block expansion** proposed by [LLaMA Pro](https://github.com/TencentARC/LLaMA-Pro). See `examples/extras/llama_pro` for usage.
[24/04/22] We provided a **[Colab notebook](https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing)** for fine-tuning the Llama-3 model on a free T4 GPU. Two Llama-3-derived models fine-tuned using LLaMA Factory are available at Hugging Face, check [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) and [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese) for details.
[24/04/21] We supported **[Mixture-of-Depths](https://arxiv.org/abs/2404.02258)** according to [AstraMindAI's implementation](https://github.com/astramind-ai/Mixture-of-depths). See [examples](examples/README.md) for usage.
[24/04/16] We supported **[BAdam](https://arxiv.org/abs/2404.02827)**. See [examples](examples/README.md) for usage.
[24/04/16] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s long-sequence training (Llama-2-7B-56k within 24GB). It achieves **117%** speed and **50%** memory compared with FlashAttention-2, more benchmarks can be found in [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison).
[24/03/31] We supported **[ORPO](https://arxiv.org/abs/2403.07691)**. See [examples](examples/README.md) for usage.
[24/03/21] Our paper "[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://arxiv.org/abs/2403.13372)" is available at arXiv!
[24/03/20] We supported **FSDP+QLoRA** that fine-tunes a 70B model on 2x24GB GPUs. See [examples](examples/README.md) for usage.
[24/03/13] We supported **[LoRA+](https://arxiv.org/abs/2402.12354)**. See [examples](examples/README.md) for usage.
[24/03/07] We supported gradient low-rank projection (**[GaLore](https://arxiv.org/abs/2403.03507)**) algorithm. See [examples](examples/README.md) for usage.
[24/03/07] We integrated **[vLLM](https://github.com/vllm-project/vllm)** for faster and concurrent inference. Try `infer_backend: vllm` to enjoy **270%** inference speed.
[24/02/28] We supported weight-decomposed LoRA (**[DoRA](https://arxiv.org/abs/2402.09353)**). Try `use_dora: true` to activate DoRA training.
[24/02/15] We supported **block expansion** proposed by [LLaMA Pro](https://github.com/TencentARC/LLaMA-Pro). See [examples](examples/README.md) for usage.
[24/02/05] Qwen1.5 (Qwen2 beta version) series models are supported in LLaMA-Factory. Check this [blog post](https://qwenlm.github.io/blog/qwen1.5/) for details.
[24/01/18] We supported **agent tuning** for most models, equipping model with tool using abilities by fine-tuning with `--dataset glaive_toolcall`.
[24/01/18] We supported **agent tuning** for most models, equipping model with tool using abilities by fine-tuning with `dataset: glaive_toolcall`.
[23/12/23] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s implementation to boost LoRA tuning for the LLaMA, Mistral and Yi models. Try `--use_unsloth` argument to activate unsloth patch. It achieves **170%** speed in our benchmark, check [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison) for details.
[23/12/23] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s implementation to boost LoRA tuning for the LLaMA, Mistral and Yi models. Try `use_unsloth: true` argument to activate unsloth patch. It achieves **170%** speed in our benchmark, check [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison) for details.
[23/12/12] We supported fine-tuning the latest MoE model **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)** in our framework. See hardware requirement [here](#hardware-requirement).
[23/12/01] We supported downloading pre-trained models and datasets from the **[ModelScope Hub](https://modelscope.cn/models)** for Chinese mainland users. See [this tutorial](#use-modelscope-hub-optional) for usage.
[23/12/01] We supported downloading pre-trained models and datasets from the **[ModelScope Hub](https://modelscope.cn/models)** for Chinese mainland users. See [this tutorial](#download-from-modelscope-hub) for usage.
[23/10/21] We supported **[NEFTune](https://arxiv.org/abs/2310.05914)** trick for fine-tuning. Try `--neftune_noise_alpha` argument to activate NEFTune, e.g., `--neftune_noise_alpha 5`.
[23/10/21] We supported **[NEFTune](https://arxiv.org/abs/2310.05914)** trick for fine-tuning. Try `neftune_noise_alpha: 5` argument to activate NEFTune.
[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.
[23/09/27] We supported **$S^2$-Attn** proposed by [LongLoRA](https://github.com/dvlab-research/LongLoRA) for the LLaMA models. Try `shift_attn: true` argument to enable shift short attention.
[23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [this example](#evaluation) to evaluate your models.
[23/09/23] We integrated MMLU, C-Eval and CMMLU benchmarks in this repo. See [examples](examples/README.md) for usage.
[23/09/10] We supported **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**. Try `--flash_attn` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs.
[23/09/10] We supported **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**. Try `flash_attn: fa2` argument to enable FlashAttention-2 if you are using RTX4090, A100 or H100 GPUs.
[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.
[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.
[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.
[23/08/11] We supported **[DPO training](https://arxiv.org/abs/2305.18290)** for instruction-tuned models. See [examples](examples/README.md) for usage.
[23/07/31] We supported **dataset streaming**. Try `--streaming` and `--max_steps 10000` arguments to load your dataset in streaming mode.
[23/07/31] We supported **dataset streaming**. Try `streaming: true` and `max_steps: 10000` arguments to load your dataset in streaming mode.
[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.
@ -118,43 +137,49 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
[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**.
[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.
[23/06/03] We supported quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). See [examples](examples/README.md) for usage.
</details>
## Supported Models
| Model | Model size | Default module | Template |
| -------------------------------------------------------- | --------------------------- | ----------------- | --------- |
| -------------------------------------------------------- | -------------------------------- | ----------------- | --------- |
| [Baichuan2](https://huggingface.co/baichuan-inc) | 7B/13B | W_pack | baichuan2 |
| [BLOOM](https://huggingface.co/bigscience/bloom) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
| [BLOOMZ](https://huggingface.co/bigscience/bloomz) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
| [ChatGLM3](https://huggingface.co/THUDM/chatglm3-6b) | 6B | query_key_value | chatglm3 |
| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B | q_proj,v_proj | deepseek |
| [Falcon](https://huggingface.co/tiiuae) | 7B/40B/180B | query_key_value | falcon |
| [Gemma](https://huggingface.co/google) | 2B/7B | q_proj,v_proj | gemma |
| [BLOOM](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
| [BLOOMZ](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
| [ChatGLM3](https://huggingface.co/THUDM) | 6B | query_key_value | chatglm3 |
| [Command-R](https://huggingface.co/CohereForAI) | 35B/104B | q_proj,v_proj | cohere |
| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | q_proj,v_proj | deepseek |
| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | query_key_value | falcon |
| [Gemma/CodeGemma](https://huggingface.co/google) | 2B/7B | q_proj,v_proj | gemma |
| [InternLM2](https://huggingface.co/internlm) | 7B/20B | wqkv | intern2 |
| [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | q_proj,v_proj | - |
| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | q_proj,v_proj | llama2 |
| [Mistral](https://huggingface.co/mistralai) | 7B | q_proj,v_proj | mistral |
| [Mixtral](https://huggingface.co/mistralai) | 8x7B | q_proj,v_proj | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | att_proj | olmo |
| [LLaMA-3](https://huggingface.co/meta-llama) | 8B/70B | q_proj,v_proj | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | q_proj,v_proj | vicuna |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | q_proj,v_proj | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | q_proj,v_proj | - |
| [Phi-1.5/2](https://huggingface.co/microsoft) | 1.3B/2.7B | q_proj,v_proj | - |
| [Phi-3](https://huggingface.co/microsoft) | 3.8B | qkv_proj | phi |
| [Qwen](https://huggingface.co/Qwen) | 1.8B/7B/14B/72B | c_attn | qwen |
| [Qwen1.5](https://huggingface.co/Qwen) | 0.5B/1.8B/4B/7B/14B/72B | q_proj,v_proj | qwen |
| [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen) | 0.5B/1.8B/4B/7B/14B/32B/72B/110B | q_proj,v_proj | qwen |
| [StarCoder2](https://huggingface.co/bigcode) | 3B/7B/15B | q_proj,v_proj | - |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | q_proj,v_proj | xverse |
| [Yi](https://huggingface.co/01-ai) | 6B/9B/34B | q_proj,v_proj | yi |
| [Yi (1/1.5)](https://huggingface.co/01-ai) | 6B/9B/34B | q_proj,v_proj | yi |
| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | q_proj,v_proj | yi_vl |
| [Yuan](https://huggingface.co/IEITYuan) | 2B/51B/102B | q_proj,v_proj | yuan |
> [!NOTE]
> **Default module** is used for the `--lora_target` argument, you can use `--lora_target all` to specify all the available modules.
> **Default module** is used for the `--lora_target` argument, you can use `--lora_target all` to specify all the available modules for better convergence.
>
> 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.
> 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 "instruct/chat" models.
>
> Remember to use the **SAME** template in training and inference.
Please refer to [constants.py](src/llmtuner/extras/constants.py) for a full list of models we supported.
Please refer to [constants.py](src/llamafactory/extras/constants.py) for a full list of models we supported.
You also can add a custom chat template to [template.py](src/llmtuner/data/template.py).
You also can add a custom chat template to [template.py](src/llamafactory/data/template.py).
## Supported Training Approaches
@ -165,9 +190,8 @@ You also can add a custom chat template to [template.py](src/llmtuner/data/templ
| Reward Modeling | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| PPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| DPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
> [!NOTE]
> Use `--quantization_bit 4` argument to enable QLoRA.
| KTO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| ORPO Training | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
## Provided Datasets
@ -187,12 +211,12 @@ You also can add a custom chat template to [template.py](src/llmtuner/data/templ
<details><summary>Supervised fine-tuning datasets</summary>
- [Identity (en&zh)](data/identity.json)
- [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3)
- [Alpaca GPT4 (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
- [Self Cognition (zh)](data/self_cognition.json)
- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection)
- [Glaive Function Calling V2 (en&zh)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
- [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
- [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
- [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
@ -201,7 +225,6 @@ You also can add a custom chat template to [template.py](src/llmtuner/data/templ
- [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
- [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
- [UltraChat (en)](https://github.com/thunlp/UltraChat)
- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
- [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
- [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
@ -214,15 +237,17 @@ You also can add a custom chat template to [template.py](src/llmtuner/data/templ
- [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
- [deepctrl (en&zh)](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data)
- [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
- [Advertise Generating (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
- [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k)
- [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)
- [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
- [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct)
- [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)
- [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
- [Glaive Function Calling V2 (en)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
- [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
- [STEM (zh)](https://huggingface.co/datasets/hfl/stem_zh_instruction)
- [Ruozhiba (zh)](https://huggingface.co/datasets/hfl/ruozhiba_gpt4_turbo)
- [LLaVA mixed (en&zh)](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k)
- [Open Assistant (de)](https://huggingface.co/datasets/mayflowergmbh/oasst_de)
- [Dolly 15k (de)](https://huggingface.co/datasets/mayflowergmbh/dolly-15k_de)
- [Alpaca GPT4 (de)](https://huggingface.co/datasets/mayflowergmbh/alpaca-gpt4_de)
@ -237,17 +262,15 @@ You also can add a custom chat template to [template.py](src/llmtuner/data/templ
<details><summary>Preference datasets</summary>
- [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k)
- [Orca DPO Pairs (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
- [Orca DPO (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
- [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de)
- [KTO mixed (en)](https://huggingface.co/datasets/argilla/kto-mix-15k)
</details>
Please refer to [data/README.md](data/README.md) for details.
Some datasets require confirmation before using them, so we recommend logging in with your Hugging Face account using these commands.
```bash
@ -261,54 +284,55 @@ huggingface-cli login
| ------------ | ------- | --------- |
| python | 3.8 | 3.10 |
| torch | 1.13.1 | 2.2.0 |
| transformers | 4.37.2 | 4.39.1 |
| datasets | 2.14.3 | 2.17.1 |
| accelerate | 0.27.2 | 0.28.0 |
| transformers | 4.37.2 | 4.40.1 |
| datasets | 2.14.3 | 2.19.1 |
| accelerate | 0.27.2 | 0.30.0 |
| peft | 0.9.0 | 0.10.0 |
| trl | 0.8.1 | 0.8.1 |
| trl | 0.8.1 | 0.8.6 |
| Optional | Minimum | Recommend |
| ------------ | ------- | --------- |
| CUDA | 11.6 | 12.2 |
| deepspeed | 0.10.0 | 0.14.0 |
| bitsandbytes | 0.39.0 | 0.43.0 |
| flash-attn | 2.3.0 | 2.5.6 |
| bitsandbytes | 0.39.0 | 0.43.1 |
| vllm | 0.4.0 | 0.4.2 |
| flash-attn | 2.3.0 | 2.5.8 |
### Hardware Requirement
\* *estimated*
| Method | Bits | 7B | 13B | 30B | 70B | 8x7B |
| ------ | ---- | ----- | ----- | ----- | ------ | ------ |
| Full | AMP | 120GB | 240GB | 600GB | 1200GB | 900GB |
| Full | 16 | 60GB | 120GB | 300GB | 600GB | 400GB |
| GaLore | 16 | 16GB | 32GB | 64GB | 160GB | 120GB |
| Freeze | 16 | 20GB | 40GB | 80GB | 200GB | 160GB |
| LoRA | 16 | 16GB | 32GB | 64GB | 160GB | 120GB |
| QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | 60GB |
| QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | 30GB |
| QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | 18GB |
| Method | Bits | 7B | 13B | 30B | 70B | 110B | 8x7B | 8x22B |
| ----------------- | ---- | ----- | ----- | ----- | ------ | ------ | ----- | ------ |
| Full | AMP | 120GB | 240GB | 600GB | 1200GB | 2000GB | 900GB | 2400GB |
| Full | 16 | 60GB | 120GB | 300GB | 600GB | 900GB | 400GB | 1200GB |
| Freeze | 16 | 20GB | 40GB | 80GB | 200GB | 360GB | 160GB | 400GB |
| LoRA/GaLore/BAdam | 16 | 16GB | 32GB | 64GB | 160GB | 240GB | 120GB | 320GB |
| QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | 140GB | 60GB | 160GB |
| QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | 72GB | 30GB | 96GB |
| QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | 48GB | 18GB | 48GB |
## Getting Started
### Data Preparation (optional)
### Installation
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.
> [!NOTE]
> Please update `data/dataset_info.json` to use your custom dataset. About the format of this file, please refer to `data/README.md`.
### Dependence Installation (optional)
> [!IMPORTANT]
> Installation is mandatory.
```bash
git clone https://github.com/hiyouga/LLaMA-Factory.git
conda create -n llama_factory python=3.10
conda activate llama_factory
git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
pip install -r requirements.txt
pip install -e .[torch,metrics]
```
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 12.2, please select the appropriate [release version](https://github.com/jllllll/bitsandbytes-windows-webui/releases/tag/wheels) based on your CUDA version.
Extra dependencies available: torch, metrics, deepspeed, bitsandbytes, vllm, galore, badam, gptq, awq, aqlm, qwen, modelscope, quality
> [!TIP]
> Use `pip install --no-deps -e .` to resolve package conflicts.
<details><summary>For Windows users</summary>
If you want to enable the quantized LoRA (QLoRA) on the Windows platform, you need to install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.2, please select the appropriate [release version](https://github.com/jllllll/bitsandbytes-windows-webui/releases/tag/wheels) based on your CUDA version.
```bash
pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.2.post2-py3-none-win_amd64.whl
@ -316,378 +340,130 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl
To enable FlashAttention-2 on the Windows platform, you need to install the precompiled `flash-attn` library, which supports CUDA 12.1 to 12.2. Please download the corresponding version from [flash-attention](https://github.com/bdashore3/flash-attention/releases) based on your requirements.
### Use ModelScope Hub (optional)
</details>
If you have trouble with downloading models and datasets from Hugging Face, you can use LLaMA-Factory together with ModelScope in the following manner.
<details><summary>For Ascend NPU users</summary>
To utilize Ascend NPU devices for (distributed) training and inference, you need to install the **[torch-npu](https://gitee.com/ascend/pytorch)** library and the **[Ascend CANN Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**.
| Requirement | Minimum | Recommend |
| ------------ | ------- | --------- |
| CANN | 8.0.RC1 | 8.0.RC1 |
| torch | 2.2.0 | 2.2.0 |
| torch-npu | 2.2.0 | 2.2.0 |
| deepspeed | 0.13.2 | 0.13.2 |
Docker image:
- 32GB: [Download page](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html)
- 64GB: Coming soon
Remember to use `ASCEND_RT_VISIBLE_DEVICES` instead of `CUDA_VISIBLE_DEVICES` to specify the device to use.
If you cannot infer model on NPU devices, try setting `do_sample: false` in the configurations.
</details>
### Data Preparation
Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use datasets on HuggingFace / ModelScope hub or load the dataset in local disk.
> [!NOTE]
> Please update `data/dataset_info.json` to use your custom dataset.
### Quickstart
Use the following 3 commands to run LoRA **fine-tuning**, **inference** and **merging** of the Llama3-8B-Instruct model, respectively.
```bash
export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
```
Then you can train the corresponding model by specifying a model ID of the ModelScope Hub. (find a full list of model IDs at [ModelScope Hub](https://modelscope.cn/models))
See [examples/README.md](examples/README.md) for advanced usage (including distributed training).
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--model_name_or_path modelscope/Llama-2-7b-ms \
... # arguments (same as below)
```
> [!TIP]
> Use `llamafactory-cli help` to show help information.
LLaMA Board also supports using the models and datasets on the ModelScope Hub.
```bash
CUDA_VISIBLE_DEVICES=0 USE_MODELSCOPE_HUB=1 python src/train_web.py
```
### Train on a single GPU
### Fine-Tuning with LLaMA Board GUI (powered by [Gradio](https://github.com/gradio-app/gradio))
> [!IMPORTANT]
> If you want to train models on multiple GPUs, please refer to [Distributed Training](#distributed-training).
> LLaMA Board GUI only supports training on a single GPU.
#### LLaMA Board GUI
#### Use local environment
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_web.py
CUDA_VISIBLE_DEVICES=0 GRADIO_SHARE=1 llamafactory-cli webui
```
#### Pre-Training
<details><summary>For Alibaba Cloud PAI or AutoDL users</summary>
If you encountered display problems in LLaMA Board on Alibaba Cloud PAI, try using the following command to set environment variables before starting LLaMA Board:
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage pt \
--do_train \
--model_name_or_path path_to_llama_model \
--dataset wiki_demo \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir path_to_pt_checkpoint \
--overwrite_cache \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--plot_loss \
--fp16
export GRADIO_SERVER_PORT=7860 GRADIO_ROOT_PATH=/${JUPYTER_NAME}/proxy/7860/
```
#### Supervised Fine-Tuning
If you are using AutoDL, please install a specific version of Gradio:
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage sft \
--do_train \
--model_name_or_path path_to_llama_model \
--dataset alpaca_gpt4_en \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir path_to_sft_checkpoint \
--overwrite_cache \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--plot_loss \
--fp16
```
#### Reward Modeling
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage rm \
--do_train \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_sft_checkpoint \
--create_new_adapter \
--dataset comparison_gpt4_en \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir path_to_rm_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
```
#### PPO Training
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage ppo \
--do_train \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_sft_checkpoint \
--create_new_adapter \
--dataset alpaca_gpt4_en \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--reward_model path_to_rm_checkpoint \
--output_dir path_to_ppo_checkpoint \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--top_k 0 \
--top_p 0.9 \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 1e-5 \
--num_train_epochs 1.0 \
--plot_loss \
--fp16
```
> [!TIP]
> Use `--adapter_name_or_path path_to_sft_checkpoint,path_to_ppo_checkpoint` to infer the fine-tuned model.
> [!WARNING]
> Use `--per_device_train_batch_size=1` for LLaMA-2 models in fp16 PPO training.
#### DPO Training
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage dpo \
--do_train \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_sft_checkpoint \
--create_new_adapter \
--dataset comparison_gpt4_en \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--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
```
> [!TIP]
> Use `--adapter_name_or_path path_to_sft_checkpoint,path_to_dpo_checkpoint` to infer the fine-tuned model.
### Distributed Training
#### Use Huggingface Accelerate
```bash
accelerate launch --config_file config.yaml src/train_bash.py \
--ddp_timeout 180000000 \
... # arguments (same as above)
```
<details><summary>Example config.yaml for LoRA training</summary>
```yaml
compute_environment: LOCAL_MACHINE
debug: false
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
pip install gradio==4.10.0
```
</details>
> [!TIP]
> We commend using Accelerate for LoRA tuning.
#### Use DeepSpeed
```bash
deepspeed --num_gpus 8 src/train_bash.py \
--deepspeed ds_config.json \
--ddp_timeout 180000000 \
... # arguments (same as above)
```
<details><summary>Example ds_config.json 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,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"bf16": {
"enabled": "auto"
},
"zero_optimization": {
"stage": 2,
"allgather_partitions": true,
"allgather_bucket_size": 5e8,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 5e8,
"contiguous_gradients": true,
"round_robin_gradients": true
}
}
```
</details>
> [!TIP]
> Refer to [examples](examples) for more training scripts.
### Merge LoRA weights and export model
```bash
CUDA_VISIBLE_DEVICES=0 python src/export_model.py \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_checkpoint \
--template default \
--finetuning_type lora \
--export_dir path_to_export \
--export_size 2 \
--export_legacy_format False
```
> [!WARNING]
> Merging LoRA weights into a quantized model is not supported.
> [!TIP]
> Use `--model_name_or_path path_to_export` solely to use the exported model.
>
> Use `--export_quantization_bit 4` and `--export_quantization_dataset data/c4_demo.json` to quantize the model with AutoGPTQ after merging the LoRA weights.
### Inference with OpenAI-style API
```bash
CUDA_VISIBLE_DEVICES=0 API_PORT=8000 python src/api_demo.py \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_checkpoint \
--template default \
--finetuning_type lora
```
> [!TIP]
> Visit `http://localhost:8000/docs` for API documentation.
### Inference with command line
```bash
CUDA_VISIBLE_DEVICES=0 python src/cli_demo.py \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_checkpoint \
--template default \
--finetuning_type lora
```
### Inference with web browser
```bash
CUDA_VISIBLE_DEVICES=0 python src/web_demo.py \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_checkpoint \
--template default \
--finetuning_type lora
```
### Evaluation
```bash
CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_checkpoint \
--template vanilla \
--finetuning_type lora \
--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 \
--do_predict \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_checkpoint \
--dataset alpaca_gpt4_en \
--template default \
--finetuning_type lora \
--output_dir path_to_predict_result \
--per_device_eval_batch_size 1 \
--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.
### Dockerize Training
#### Get ready
Necessary dockerized environment is needed, such as Docker or Docker Compose.
#### Docker support
#### Use Docker
```bash
docker build -f ./Dockerfile -t llama-factory:latest .
docker run --gpus=all -v ./hf_cache:/root/.cache/huggingface/ -v ./data:/app/data -v ./output:/app/output -p 7860:7860 --shm-size 16G --name llama_factory -d llama-factory:latest
docker run --gpus=all \
-v ./hf_cache:/root/.cache/huggingface/ \
-v ./data:/app/data \
-v ./output:/app/output \
-e CUDA_VISIBLE_DEVICES=0 \
-p 7860:7860 \
--shm-size 16G \
--name llama_factory \
-d llama-factory:latest
```
#### Docker Compose support
#### Use Docker Compose
```bash
docker compose -f ./docker-compose.yml up -d
```
> [!TIP]
> Details about volume:
> * hf_cache: Utilize Huggingface cache on the host machine. Reassignable if a cache already exists in a different directory.
> * data: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI.
> * output: Set export dir to this location so that the merged result can be accessed directly on the host machine.
<details><summary>Details about volume</summary>
- hf_cache: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory.
- data: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI.
- output: Set export dir to this location so that the merged result can be accessed directly on the host machine.
</details>
### Deploy with OpenAI-style API and vLLM
```bash
CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
```
### Download from ModelScope Hub
If you have trouble with downloading models and datasets from Hugging Face, you can use ModelScope.
```bash
export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows
```
Train the model by specifying a model ID of the ModelScope Hub as the `--model_name_or_path`. You can find a full list of model IDs at [ModelScope Hub](https://modelscope.cn/models), e.g., `LLM-Research/Meta-Llama-3-8B-Instruct`.
## Projects using LLaMA Factory
If you have a project that should be incorporated, please contact via email or create a pull request.
<details><summary>Click to show</summary>
1. Wang et al. ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation. 2023. [[arxiv]](https://arxiv.org/abs/2308.02223)
1. Yu et al. Open, Closed, or Small Language Models for Text Classification? 2023. [[arxiv]](https://arxiv.org/abs/2308.10092)
1. Wang et al. UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language. 2023. [[arxiv]](https://arxiv.org/abs/2308.10526)
@ -709,20 +485,37 @@ docker compose -f ./docker-compose.yml up -d
1. Huang et al. Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2403.02333)
1. Duan et al. Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization. 2024. [[arxiv]](https://arxiv.org/abs/2403.03419)
1. Xie and Schwertfeger. Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2403.08228)
1. Wu et al. Large Language Models are Parallel Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2403.09073)
1. Zhang et al. EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling. 2024. [[arxiv]](https://arxiv.org/abs/2403.14541)
1. Weller et al. FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions. 2024. [[arxiv]](https://arxiv.org/abs/2403.15246)
1. Hongbin Na. CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering. 2024. [[arxiv]](https://arxiv.org/abs/2403.16008)
1. Zan et al. CodeS: Natural Language to Code Repository via Multi-Layer Sketch. 2024. [[arxiv]](https://arxiv.org/abs/2403.16443)
1. Liu et al. Extensive Self-Contrast Enables Feedback-Free Language Model Alignment. 2024. [[arxiv]](https://arxiv.org/abs/2404.00604)
1. Luo et al. BAdam: A Memory Efficient Full Parameter Training Method for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.02827)
1. Du et al. Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2404.04167)
1. Ma et al. Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation. 2024. [[arxiv]](https://arxiv.org/abs/2404.04316)
1. Liu et al. Dynamic Generation of Personalities with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.07084)
1. Shang et al. How Far Have We Gone in Stripped Binary Code Understanding Using Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.09836)
1. Huang et al. LLMTune: Accelerate Database Knob Tuning with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.11581)
1. Deng et al. Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction. 2024. [[arxiv]](https://arxiv.org/abs/2404.14215)
1. Acikgoz et al. Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare. 2024. [[arxiv]](https://arxiv.org/abs/2404.16621)
1. Zhang et al. Small Language Models Need Strong Verifiers to Self-Correct Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2404.17140)
1. Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. 2024. [[arxiv]](https://arxiv.org/abs/2404.18585)
1. **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B.
1. **[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.
1. **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
1. **[Sunsimiao](https://github.com/X-D-Lab/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
1. **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B.
1. **[MachineMindset](https://github.com/PKU-YuanGroup/Machine-Mindset/)**: A series of MBTI Personality large language models, capable of giving any LLM 16 different personality types based on different datasets and training methods.
1. **[Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-v3)**: A large language model specialized in generate metadata for stable diffusion. [[🤗Demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt)
1. **[Chinese-LLaVA-Med](https://github.com/BUAADreamer/Chinese-LLaVA-Med)**: A multimodal large language model specialized in Chinese medical domain, based on LLaVA-1.5-7B.
> [!TIP]
> If you have a project that should be incorporated, please contact via email or create a pull request.
</details>
## License
This repository is licensed under the [Apache-2.0 License](LICENSE).
Please follow the model licenses to use the corresponding model weights: [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](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) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
Please follow the model licenses to use the corresponding model weights: [Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command-R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2 (LLaVA-1.5)](https://ai.meta.com/llama/license/) / [LLaMA-3](https://llama.meta.com/llama3/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
## Citation
@ -740,7 +533,7 @@ If this work is helpful, please kindly cite as:
## 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.
This repo benefits from [PEFT](https://github.com/huggingface/peft), [TRL](https://github.com/huggingface/trl), [QLoRA](https://github.com/artidoro/qlora) and [FastChat](https://github.com/lm-sys/FastChat). Thanks for their wonderful works.
## Star History

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@ -3,15 +3,16 @@
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@ -23,7 +24,7 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
选择你的打开方式：
- **Colab**：https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
- **Colab**：https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing
- **本地机器**：请见[如何使用](#如何使用)
## 目录
@ -43,17 +44,17 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
## 项目特色
- **多种模型**：LLaMA、Mistral、Mixtral-MoE、Qwen、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
- **集成方法**：（增量）预训练、指令监督微调、奖励模型训练、PPO 训练和 DPO 训练。
- **多种模型**：LLaMA、LLaVA、Mistral、Mixtral-MoE、Qwen、Yi、Gemma、Baichuan、ChatGLM、Phi 等等。
- **集成方法**：（增量）预训练、（多模态）指令监督微调、奖励模型训练、PPO 训练、DPO 训练、KTO 训练和 ORPO 训练。
- **多种精度**：32 比特全参数微调、16 比特冻结微调、16 比特 LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8 的 2/4/8 比特 QLoRA 微调。
- **先进算法**：GaLore、DoRA、LongLoRA、LLaMA Pro、LoRA+、LoftQ 和 Agent 微调。
- **先进算法**：GaLore、BAdam、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ 和 Agent 微调。
- **实用技巧**：FlashAttention-2、Unsloth、RoPE scaling、NEFTune 和 rsLoRA。
- **实验监控**：LlamaBoard、TensorBoard、Wandb、MLflow 等等。
- **极速推理**：基于 vLLM 的 OpenAI 风格 API、浏览器界面和命令行接口。
## 性能指标
与 ChatGLM 官方的 [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/ptuning) 微调相比，LLaMA-Factory 的 LoRA 微调提供了 **3.7 倍**的加速比，同时在广告文案生成任务上取得了更高的 Rouge 分数。结合 4 比特量化技术，LLaMA-Factory 的 QLoRA 微调进一步降低了 GPU 显存消耗。
与 ChatGLM 官方的 [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/ptuning) 微调相比，LLaMA Factory 的 LoRA 微调提供了 **3.7 倍**的加速比，同时在广告文案生成任务上取得了更高的 Rouge 分数。结合 4 比特量化技术，LLaMA Factory 的 QLoRA 微调进一步降低了 GPU 显存消耗。
![benchmark](assets/benchmark.svg)
@ -62,51 +63,69 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
- **Training Speed**: 训练阶段每秒处理的样本数量。（批处理大小=4，截断长度=1024）
- **Rouge Score**: [广告文案生成](https://aclanthology.org/D19-1321.pdf)任务验证集上的 Rouge-2 分数。（批处理大小=4，截断长度=1024）
- **GPU Memory**: 4 比特量化训练的 GPU 显存峰值。（批处理大小=1，截断长度=1024）
- 我们在 ChatGLM 的 P-Tuning 中采用 `pre_seq_len=128`，在 LLaMA-Factory 的 LoRA 微调中采用 `lora_rank=32`。
- 我们在 ChatGLM 的 P-Tuning 中采用 `pre_seq_len=128`，在 LLaMA Factory 的 LoRA 微调中采用 `lora_rank=32`。
</details>
## 更新日志
[24/03/21] 我们的论文 "[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://arxiv.org/abs/2403.13372)" 可在 arXiv 上查看！
[24/05/18] 我们支持了 **[KTO](https://arxiv.org/abs/2402.01306)** 偏好对齐算法。详细用法请参照 [examples](examples/README_zh.md)。
[24/03/20] 我们支持了能在 2x24GB GPU 上微调 70B 模型的 **FSDP+QLoRA**。详细用法请参照 `examples/fsdp_qlora`。
[24/05/14] 我们支持了昇腾 NPU 设备的训练和推理。详情请查阅[安装](#安装-llama-factory)部分。
[24/03/13] 我们支持了 **[LoRA+](https://arxiv.org/abs/2402.12354)**。请使用 `loraplus_lr_ratio=16.0` 参数开启 LoRA+ 方法。
[24/03/07] 我们支持了梯度低秩投影（**[GaLore](https://arxiv.org/abs/2403.03507)**）算法。请使用 `--use_galore` 参数切换显存高效的优化器。
[24/03/07] 我们集成了 **[vLLM](https://github.com/vllm-project/vllm)** 以实现极速并发推理。请使用 `--infer_backend vllm` 来获得 **270%** 的推理速度。（尚不支持 LoRA，请先合并权重。）
[24/05/13] 我们支持了 Yi-1.5 系列模型的微调。
<details><summary>展开日志</summary>
[24/02/28] 我们支持了 **[DoRA](https://arxiv.org/abs/2402.09353)** 微调。请使用 `--use_dora` 参数进行 DoRA 微调。
[24/04/26] 我们支持了多模态模型 **LLaVA-1.5** 的微调。详细用法请参照 [examples](examples/README_zh.md)。
[24/02/15] 我们支持了 [LLaMA Pro](https://github.com/TencentARC/LLaMA-Pro) 提出的**块扩展**方法。详细用法请参照 `examples/extras/llama_pro`。
[24/04/22] 我们提供了在免费 T4 GPU 上微调 Llama-3 模型的 **[Colab 笔记本](https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing)**。Hugging Face 社区公开了两个利用 LLaMA Factory 微调的 Llama-3 模型，详情请见 [Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat) 和 [Llama3-Chinese](https://huggingface.co/zhichen/Llama3-Chinese)。
[24/04/21] 我们基于 [AstraMindAI 的仓库](https://github.com/astramind-ai/Mixture-of-depths)支持了 **[混合深度训练](https://arxiv.org/abs/2404.02258)**。详细用法请参照 [examples](examples/README_zh.md)。
[24/04/16] 我们支持了 **[BAdam](https://arxiv.org/abs/2404.02827)**。详细用法请参照 [examples](examples/README_zh.md)。
[24/04/16] 我们支持了 **[unsloth](https://github.com/unslothai/unsloth)** 的长序列训练（24GB 可训练 Llama-2-7B-56k）。该方法相比 FlashAttention-2 提供了 **117%** 的训练速度和 **50%** 的显存节约。更多数据请见[此页面](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison)。
[24/03/31] 我们支持了 **[ORPO](https://arxiv.org/abs/2403.07691)**。详细用法请参照 [examples](examples/README_zh.md)。
[24/03/21] 我们的论文 "[LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://arxiv.org/abs/2403.13372)" 可在 arXiv 上查看！
[24/03/20] 我们支持了能在 2x24GB GPU 上微调 70B 模型的 **FSDP+QLoRA**。详细用法请参照 [examples](examples/README_zh.md)。
[24/03/13] 我们支持了 **[LoRA+](https://arxiv.org/abs/2402.12354)**。详细用法请参照 [examples](examples/README_zh.md)。
[24/03/07] 我们支持了梯度低秩投影（**[GaLore](https://arxiv.org/abs/2403.03507)**）算法。详细用法请参照 [examples](examples/README_zh.md)。
[24/03/07] 我们集成了 **[vLLM](https://github.com/vllm-project/vllm)** 以实现极速并发推理。请使用 `infer_backend: vllm` 来获得 **270%** 的推理速度。
[24/02/28] 我们支持了 **[DoRA](https://arxiv.org/abs/2402.09353)** 微调。请使用 `use_dora: true` 参数进行 DoRA 微调。
[24/02/15] 我们支持了 [LLaMA Pro](https://github.com/TencentARC/LLaMA-Pro) 提出的**块扩展**方法。详细用法请参照 [examples](examples/README_zh.md)。
[24/02/05] Qwen1.5（Qwen2 测试版）系列模型已在 LLaMA-Factory 中实现微调支持。详情请查阅该[博客页面](https://qwenlm.github.io/zh/blog/qwen1.5/)。
[24/01/18] 我们针对绝大多数模型实现了 **Agent 微调**，微调时指定 `--dataset glaive_toolcall` 即可使模型获得工具调用能力。
[24/01/18] 我们针对绝大多数模型实现了 **Agent 微调**，微调时指定 `dataset: glaive_toolcall` 即可使模型获得工具调用能力。
[23/12/23] 我们针对 LLaMA, Mistral 和 Yi 模型支持了 **[unsloth](https://github.com/unslothai/unsloth)** 的 LoRA 训练加速。请使用 `--use_unsloth` 参数启用 unsloth 优化。该方法可提供 **170%** 的训练速度，详情请查阅[此页面](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison)。
[23/12/23] 我们针对 LLaMA, Mistral 和 Yi 模型支持了 **[unsloth](https://github.com/unslothai/unsloth)** 的 LoRA 训练加速。请使用 `use_unsloth: true` 参数启用 unsloth 优化。该方法可提供 **170%** 的训练速度，详情请查阅[此页面](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison)。
[23/12/12] 我们支持了微调最新的混合专家模型 **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)**。硬件需求请查阅[此处](#硬件依赖)。
[23/12/01] 我们支持了从 **[魔搭社区](https://modelscope.cn/models)** 下载预训练模型和数据集。详细用法请参照 [此教程](#使用魔搭社区可跳过)。
[23/12/01] 我们支持了从 **[魔搭社区](https://modelscope.cn/models)** 下载预训练模型和数据集。详细用法请参照 [此教程](#从魔搭社区下载)。
[23/10/21] 我们支持了 **[NEFTune](https://arxiv.org/abs/2310.05914)** 训练技巧。请使用 `--neftune_noise_alpha` 参数启用 NEFTune，例如 `--neftune_noise_alpha 5`。
[23/10/21] 我们支持了 **[NEFTune](https://arxiv.org/abs/2310.05914)** 训练技巧。请使用 `neftune_noise_alpha: 5` 参数启用 NEFTune。
[23/09/27] 我们针对 LLaMA 模型支持了 [LongLoRA](https://github.com/dvlab-research/LongLoRA) 提出的 **$S^2$-Attn**。请使用 `--shift_attn` 参数以启用该功能。
[23/09/27] 我们针对 LLaMA 模型支持了 [LongLoRA](https://github.com/dvlab-research/LongLoRA) 提出的 **$S^2$-Attn**。请使用 `shift_attn: true` 参数以启用该功能。
[23/09/23] 我们在项目中集成了 MMLU、C-Eval 和 CMMLU 评估集。使用方法请参阅[此示例](#模型评估)。
[23/09/23] 我们在项目中集成了 MMLU、C-Eval 和 CMMLU 评估集。详细用法请参照 [examples](examples/README_zh.md)。
[23/09/10] 我们支持了 **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**。如果您使用的是 RTX4090、A100 或 H100 GPU，请使用 `--flash_attn` 参数以启用 FlashAttention-2。
[23/09/10] 我们支持了 **[FlashAttention-2](https://github.com/Dao-AILab/flash-attention)**。如果您使用的是 RTX4090、A100 或 H100 GPU，请使用 `flash_attn: fa2` 参数以启用 FlashAttention-2。
[23/08/12] 我们支持了 **RoPE 插值**来扩展 LLaMA 模型的上下文长度。请使用 `--rope_scaling linear` 参数训练模型或使用 `--rope_scaling dynamic` 参数评估模型。
[23/08/12] 我们支持了 **RoPE 插值**来扩展 LLaMA 模型的上下文长度。请使用 `rope_scaling: linear` 参数训练模型或使用 `rope_scaling: dynamic` 参数评估模型。
[23/08/11] 我们支持了指令模型的 **[DPO 训练](https://arxiv.org/abs/2305.18290)**。使用方法请参阅[此示例](#dpo-训练)。
[23/08/11] 我们支持了指令模型的 **[DPO 训练](https://arxiv.org/abs/2305.18290)**。详细用法请参照 [examples](examples/README_zh.md)。
[23/07/31] 我们支持了**数据流式加载**。请使用 `--streaming` 和 `--max_steps 10000` 参数来流式加载数据集。
[23/07/31] 我们支持了**数据流式加载**。请使用 `streaming: true` 和 `max_steps: 10000` 参数来流式加载数据集。
[23/07/29] 我们在 Hugging Face 发布了两个 13B 指令微调模型。详细内容请查阅我们的 Hugging Face 项目（[LLaMA-2](https://huggingface.co/hiyouga/Llama-2-Chinese-13b-chat) / [Baichuan](https://huggingface.co/hiyouga/Baichuan-13B-sft)）。
@ -118,43 +137,49 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
[23/06/22] 我们对齐了[示例 API](src/api_demo.py) 与 [OpenAI API](https://platform.openai.com/docs/api-reference/chat) 的格式，您可以将微调模型接入**任意基于 ChatGPT 的应用**中。
[23/06/03] 我们实现了 4 比特的 LoRA 训练（也称 **[QLoRA](https://github.com/artidoro/qlora)**）。请使用 `--quantization_bit 4` 参数进行 4 比特量化微调。
[23/06/03] 我们实现了 4 比特的 LoRA 训练（也称 **[QLoRA](https://github.com/artidoro/qlora)**）。详细用法请参照 [examples](examples/README_zh.md)。
</details>
## 模型
| 模型名 | 模型大小 | 默认模块 | Template |
| -------------------------------------------------------- | --------------------------- | ----------------- | --------- |
| -------------------------------------------------------- | -------------------------------- | ----------------- | --------- |
| [Baichuan2](https://huggingface.co/baichuan-inc) | 7B/13B | W_pack | baichuan2 |
| [BLOOM](https://huggingface.co/bigscience/bloom) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
| [BLOOMZ](https://huggingface.co/bigscience/bloomz) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
| [ChatGLM3](https://huggingface.co/THUDM/chatglm3-6b) | 6B | query_key_value | chatglm3 |
| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B | q_proj,v_proj | deepseek |
| [Falcon](https://huggingface.co/tiiuae) | 7B/40B/180B | query_key_value | falcon |
| [Gemma](https://huggingface.co/google) | 2B/7B | q_proj,v_proj | gemma |
| [BLOOM](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
| [BLOOMZ](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | query_key_value | - |
| [ChatGLM3](https://huggingface.co/THUDM) | 6B | query_key_value | chatglm3 |
| [Command-R](https://huggingface.co/CohereForAI) | 35B/104B | q_proj,v_proj | cohere |
| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | q_proj,v_proj | deepseek |
| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | query_key_value | falcon |
| [Gemma/CodeGemma](https://huggingface.co/google) | 2B/7B | q_proj,v_proj | gemma |
| [InternLM2](https://huggingface.co/internlm) | 7B/20B | wqkv | intern2 |
| [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | q_proj,v_proj | - |
| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | q_proj,v_proj | llama2 |
| [Mistral](https://huggingface.co/mistralai) | 7B | q_proj,v_proj | mistral |
| [Mixtral](https://huggingface.co/mistralai) | 8x7B | q_proj,v_proj | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | att_proj | olmo |
| [LLaMA-3](https://huggingface.co/meta-llama) | 8B/70B | q_proj,v_proj | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | q_proj,v_proj | vicuna |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | q_proj,v_proj | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | q_proj,v_proj | - |
| [Phi-1.5/2](https://huggingface.co/microsoft) | 1.3B/2.7B | q_proj,v_proj | - |
| [Phi-3](https://huggingface.co/microsoft) | 3.8B | qkv_proj | phi |
| [Qwen](https://huggingface.co/Qwen) | 1.8B/7B/14B/72B | c_attn | qwen |
| [Qwen1.5](https://huggingface.co/Qwen) | 0.5B/1.8B/4B/7B/14B/72B | q_proj,v_proj | qwen |
| [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen) | 0.5B/1.8B/4B/7B/14B/32B/72B/110B | q_proj,v_proj | qwen |
| [StarCoder2](https://huggingface.co/bigcode) | 3B/7B/15B | q_proj,v_proj | - |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | q_proj,v_proj | xverse |
| [Yi](https://huggingface.co/01-ai) | 6B/9B/34B | q_proj,v_proj | yi |
| [Yi (1/1.5)](https://huggingface.co/01-ai) | 6B/9B/34B | q_proj,v_proj | yi |
| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | q_proj,v_proj | yi_vl |
| [Yuan](https://huggingface.co/IEITYuan) | 2B/51B/102B | q_proj,v_proj | yuan |
> [!NOTE]
> **默认模块**应作为 `--lora_target` 参数的默认值，可使用 `--lora_target all` 参数指定全部模块。
> **默认模块**应作为 `--lora_target` 参数的默认值，可使用 `--lora_target all` 参数指定全部模块以取得更好的效果。
>
> 对于所有“基座”（Base）模型，`--template` 参数可以是 `default`, `alpaca`, `vicuna` 等任意值。但“对话”（Chat）模型请务必使用**对应的模板**。
> 对于所有“基座”（Base）模型，`--template` 参数可以是 `default`, `alpaca`, `vicuna` 等任意值。但“对话”（Instruct/Chat）模型请务必使用**对应的模板**。
>
> 请务必在训练和推理时使用**完全一致**的模板。
项目所支持模型的完整列表请参阅 [constants.py](src/llmtuner/extras/constants.py)。
项目所支持模型的完整列表请参阅 [constants.py](src/llamafactory/extras/constants.py)。
您也可以在 [template.py](src/llmtuner/data/template.py) 中添加自己的对话模板。
您也可以在 [template.py](src/llamafactory/data/template.py) 中添加自己的对话模板。
## 训练方法
@ -165,9 +190,8 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
| 奖励模型训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| PPO 训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| DPO 训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
> [!NOTE]
> 请使用 `--quantization_bit 4` 参数来启用 QLoRA 训练。
| KTO 训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
| ORPO 训练 | :white_check_mark: | :white_check_mark: | :white_check_mark: | :white_check_mark: |
## 数据集
@ -187,12 +211,12 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
<details><summary>指令微调数据集</summary>
- [Identity (en&zh)](data/identity.json)
- [Stanford Alpaca (en)](https://github.com/tatsu-lab/stanford_alpaca)
- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca)
- [Stanford Alpaca (zh)](https://github.com/ymcui/Chinese-LLaMA-Alpaca-3)
- [Alpaca GPT4 (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
- [Self Cognition (zh)](data/self_cognition.json)
- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [ShareGPT (zh)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT/tree/main/Chinese-instruction-collection)
- [Glaive Function Calling V2 (en&zh)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
- [Guanaco Dataset (multilingual)](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset)
- [BELLE 2M (zh)](https://huggingface.co/datasets/BelleGroup/train_2M_CN)
- [BELLE 1M (zh)](https://huggingface.co/datasets/BelleGroup/train_1M_CN)
@ -201,7 +225,6 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
- [BELLE School Math 0.25M (zh)](https://huggingface.co/datasets/BelleGroup/school_math_0.25M)
- [BELLE Multiturn Chat 0.8M (zh)](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M)
- [UltraChat (en)](https://github.com/thunlp/UltraChat)
- [LIMA (en)](https://huggingface.co/datasets/GAIR/lima)
- [OpenPlatypus (en)](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
- [CodeAlpaca 20k (en)](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k)
- [Alpaca CoT (multilingual)](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT)
@ -214,15 +237,17 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
- [WebNovel (zh)](https://huggingface.co/datasets/zxbsmk/webnovel_cn)
- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
- [deepctrl (en&zh)](https://www.modelscope.cn/datasets/deepctrl/deepctrl-sft-data)
- [Ad Gen (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
- [Advertise Generating (zh)](https://huggingface.co/datasets/HasturOfficial/adgen)
- [ShareGPT Hyperfiltered (en)](https://huggingface.co/datasets/totally-not-an-llm/sharegpt-hyperfiltered-3k)
- [ShareGPT4 (en&zh)](https://huggingface.co/datasets/shibing624/sharegpt_gpt4)
- [UltraChat 200k (en)](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)
- [AgentInstruct (en)](https://huggingface.co/datasets/THUDM/AgentInstruct)
- [LMSYS Chat 1M (en)](https://huggingface.co/datasets/lmsys/lmsys-chat-1m)
- [Evol Instruct V2 (en)](https://huggingface.co/datasets/WizardLM/WizardLM_evol_instruct_V2_196k)
- [Glaive Function Calling V2 (en)](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
- [Cosmopedia (en)](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
- [STEM (zh)](https://huggingface.co/datasets/hfl/stem_zh_instruction)
- [Ruozhiba (zh)](https://huggingface.co/datasets/hfl/ruozhiba_gpt4_turbo)
- [LLaVA mixed (en&zh)](https://huggingface.co/datasets/BUAADreamer/llava-en-zh-300k)
- [Open Assistant (de)](https://huggingface.co/datasets/mayflowergmbh/oasst_de)
- [Dolly 15k (de)](https://huggingface.co/datasets/mayflowergmbh/dolly-15k_de)
- [Alpaca GPT4 (de)](https://huggingface.co/datasets/mayflowergmbh/alpaca-gpt4_de)
@ -237,17 +262,15 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
<details><summary>偏好数据集</summary>
- [DPO mixed (en&zh)](https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k)
- [Orca DPO Pairs (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- [HH-RLHF (en)](https://huggingface.co/datasets/Anthropic/hh-rlhf)
- [Open Assistant (multilingual)](https://huggingface.co/datasets/OpenAssistant/oasst1)
- [GPT-4 Generated Data (en&zh)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
- [Orca DPO (en)](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- [Nectar (en)](https://huggingface.co/datasets/berkeley-nest/Nectar)
- [Orca DPO (de)](https://huggingface.co/datasets/mayflowergmbh/intel_orca_dpo_pairs_de)
- [KTO mixed (en)](https://huggingface.co/datasets/argilla/kto-mix-15k)
</details>
使用方法请参考 [data/README_zh.md](data/README_zh.md) 文件。
部分数据集的使用需要确认，我们推荐使用下述命令登录您的 Hugging Face 账户。
```bash
@ -261,53 +284,54 @@ huggingface-cli login
| ------------ | ------- | --------- |
| python | 3.8 | 3.10 |
| torch | 1.13.1 | 2.2.0 |
| transformers | 4.37.2 | 4.39.1 |
| datasets | 2.14.3 | 2.17.1 |
| accelerate | 0.27.2 | 0.28.0 |
| transformers | 4.37.2 | 4.40.1 |
| datasets | 2.14.3 | 2.19.1 |
| accelerate | 0.27.2 | 0.30.0 |
| peft | 0.9.0 | 0.10.0 |
| trl | 0.8.1 | 0.8.1 |
| trl | 0.8.1 | 0.8.6 |
| 可选项 | 至少 | 推荐 |
| ------------ | ------- | --------- |
| CUDA | 11.6 | 12.2 |
| deepspeed | 0.10.0 | 0.14.0 |
| bitsandbytes | 0.39.0 | 0.43.0 |
| flash-attn | 2.3.0 | 2.5.6 |
| bitsandbytes | 0.39.0 | 0.43.1 |
| vllm | 0.4.0 | 0.4.2 |
| flash-attn | 2.3.0 | 2.5.8 |
### 硬件依赖
\* *估算值*
| 训练方法 | 精度 | 7B | 13B | 30B | 70B | 8x7B |
| ------- | ---- | ----- | ----- | ----- | ------ | ------ |
| 全参数 | AMP | 120GB | 240GB | 600GB | 1200GB | 900GB |
| 全参数 | 16 | 60GB | 120GB | 300GB | 600GB | 400GB |
| GaLore | 16 | 16GB | 32GB | 64GB | 160GB | 120GB |
| 部分参数 | 16 | 20GB | 40GB | 80GB | 200GB | 160GB |
| LoRA | 16 | 16GB | 32GB | 64GB | 160GB | 120GB |
| QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | 60GB |
| QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | 30GB |
| QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | 18GB |
| 方法 | 精度 | 7B | 13B | 30B | 70B | 110B | 8x7B | 8x22B |
| ----------------- | ---- | ----- | ----- | ----- | ------ | ------ | ----- | ------ |
| Full | AMP | 120GB | 240GB | 600GB | 1200GB | 2000GB | 900GB | 2400GB |
| Full | 16 | 60GB | 120GB | 300GB | 600GB | 900GB | 400GB | 1200GB |
| Freeze | 16 | 20GB | 40GB | 80GB | 200GB | 360GB | 160GB | 400GB |
| LoRA/GaLore/BAdam | 16 | 16GB | 32GB | 64GB | 160GB | 240GB | 120GB | 320GB |
| QLoRA | 8 | 10GB | 20GB | 40GB | 80GB | 140GB | 60GB | 160GB |
| QLoRA | 4 | 6GB | 12GB | 24GB | 48GB | 72GB | 30GB | 96GB |
| QLoRA | 2 | 4GB | 8GB | 16GB | 24GB | 48GB | 18GB | 48GB |
## 如何使用
### 数据准备（可跳过）
### 安装 LLaMA Factory
关于数据集文件的格式，请参考 [data/README_zh.md](data/README_zh.md) 的内容。构建自定义数据集时，既可以使用单个 `.json` 文件，也可以使用一个[数据加载脚本](https://huggingface.co/docs/datasets/dataset_script)和多个文件。
> [!NOTE]
> 使用自定义数据集时，请更新 `data/dataset_info.json` 文件，该文件的格式请参考 `data/README_zh.md`。
### 环境搭建（可跳过）
> [!IMPORTANT]
> 此步骤为必需。
```bash
git clone https://github.com/hiyouga/LLaMA-Factory.git
conda create -n llama_factory python=3.10
conda activate llama_factory
git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
pip install -r requirements.txt
pip install -e .[torch,metrics]
```
可选的额外依赖项：torch、metrics、deepspeed、bitsandbytes、vllm、galore、badam、gptq、awq、aqlm、qwen、modelscope、quality
> [!TIP]
> 遇到包冲突时，可使用 `pip install --no-deps -e .` 解决。
<details><summary>Windows 用户指南</summary>
如果要在 Windows 平台上开启量化 LoRA（QLoRA），需要安装预编译的 `bitsandbytes` 库, 支持 CUDA 11.1 到 12.2, 请根据您的 CUDA 版本情况选择适合的[发布版本](https://github.com/jllllll/bitsandbytes-windows-webui/releases/tag/wheels)。
```bash
@ -316,7 +340,115 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl
如果要在 Windows 平台上开启 FlashAttention-2，需要安装预编译的 `flash-attn` 库，支持 CUDA 12.1 到 12.2，请根据需求到 [flash-attention](https://github.com/bdashore3/flash-attention/releases) 下载对应版本安装。
### 使用魔搭社区（可跳过）
</details>
<details><summary>昇腾 NPU 用户指南</summary>
如果使用昇腾 NPU 设备进行（分布式）训练或推理，需要安装 **[torch-npu](https://gitee.com/ascend/pytorch)** 库和 **[Ascend CANN Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**。
| 依赖项 | 至少 | 推荐 |
| ------------ | ------- | --------- |
| CANN | 8.0.RC1 | 8.0.RC1 |
| torch | 2.2.0 | 2.2.0 |
| torch-npu | 2.2.0 | 2.2.0 |
| deepspeed | 0.13.2 | 0.13.2 |
Docker 镜像：
- 32GB：[下载地址](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html)
- 64GB：敬请期待
请记得使用 `ASCEND_RT_VISIBLE_DEVICES` 而非 `CUDA_VISIBLE_DEVICES` 来指定您使用的设备。
如果遇到无法正常推理的情况，请尝试设置 `do_sample: false`。
</details>
### 数据准备
关于数据集文件的格式，请参考 [data/README_zh.md](data/README_zh.md) 的内容。你可以使用 HuggingFace / ModelScope 上的数据集或加载本地数据集。
> [!NOTE]
> 使用自定义数据集时，请更新 `data/dataset_info.json` 文件。
### 快速开始
下面三行命令分别对 Llama3-8B-Instruct 模型进行 LoRA **微调**、**推理**和**合并**。
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
```
高级用法请参考 [examples/README_zh.md](examples/README_zh.md)（包括多 GPU 微调）。
> [!TIP]
> 使用 `llamafactory-cli help` 显示帮助信息。
### LLaMA Board 可视化微调（由 [Gradio](https://github.com/gradio-app/gradio) 驱动）
> [!IMPORTANT]
> LLaMA Board 可视化界面目前仅支持单 GPU 训练。
#### 使用本地环境
```bash
CUDA_VISIBLE_DEVICES=0 GRADIO_SHARE=1 llamafactory-cli webui
```
<details><summary>阿里云 PAI 和 AutoDL 用户指南</summary>
如果您在阿里云 PAI 上使用 LLaMA Board 时遇到显示问题，请尝试在启动前使用以下命令设置环境变量：
```bash
export GRADIO_SERVER_PORT=7860 GRADIO_ROOT_PATH=/${JUPYTER_NAME}/proxy/7860/
```
如果您正在使用 AutoDL，请安装下述 Gradio 版本：
```bash
pip install gradio==4.10.0
```
</details>
#### 使用 Docker
```bash
docker build -f ./Dockerfile -t llama-factory:latest .
docker run --gpus=all \
-v ./hf_cache:/root/.cache/huggingface/ \
-v ./data:/app/data \
-v ./output:/app/output \
-e CUDA_VISIBLE_DEVICES=0 \
-p 7860:7860 \
--shm-size 16G \
--name llama_factory \
-d llama-factory:latest
```
#### 使用 Docker Compose
```bash
docker compose -f ./docker-compose.yml up -d
```
<details><summary>数据卷详情</summary>
- hf_cache：使用宿主机的 Hugging Face 缓存文件夹，允许更改为新的目录。
- data：宿主机中存放数据集的文件夹路径。
- output：将导出目录设置为该路径后，即可在宿主机中访问导出后的模型。
</details>
### 利用 vLLM 部署 OpenAI API
```bash
CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
```
### 从魔搭社区下载
如果您在 Hugging Face 模型和数据集的下载中遇到了问题，可以通过下述方法使用魔搭社区。
@ -324,343 +456,14 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl
export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
```
接着即可通过指定模型名称来训练对应的模型。（在[魔搭社区](https://modelscope.cn/models)查看所有可用的模型）
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--model_name_or_path modelscope/Llama-2-7b-ms \
... # 参数同下
```
LLaMA Board 同样支持魔搭社区的模型和数据集下载。
```bash
CUDA_VISIBLE_DEVICES=0 USE_MODELSCOPE_HUB=1 python src/train_web.py
```
### 单 GPU 训练
> [!IMPORTANT]
> 如果您使用多张 GPU 训练模型，请移步[多 GPU 分布式训练](#多-gpu-分布式训练)部分。
#### LLaMA Board GUI
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_web.py
```
#### 预训练
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage pt \
--do_train \
--model_name_or_path path_to_llama_model \
--dataset wiki_demo \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir path_to_pt_checkpoint \
--overwrite_cache \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--plot_loss \
--fp16
```
#### 指令监督微调
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage sft \
--do_train \
--model_name_or_path path_to_llama_model \
--dataset alpaca_gpt4_zh \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir path_to_sft_checkpoint \
--overwrite_cache \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--plot_loss \
--fp16
```
#### 奖励模型训练
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage rm \
--do_train \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_sft_checkpoint \
--create_new_adapter \
--dataset comparison_gpt4_zh \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir path_to_rm_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
```
#### PPO 训练
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage ppo \
--do_train \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_sft_checkpoint \
--create_new_adapter \
--dataset alpaca_gpt4_zh \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--reward_model path_to_rm_checkpoint \
--output_dir path_to_ppo_checkpoint \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--top_k 0 \
--top_p 0.9 \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 1e-5 \
--num_train_epochs 1.0 \
--plot_loss \
--fp16
```
> [!TIP]
> 使用 `--adapter_name_or_path path_to_sft_checkpoint,path_to_ppo_checkpoint` 来进行微调模型的推理。
> [!WARNING]
> 如果使用 fp16 精度进行 LLaMA-2 模型的 PPO 训练，请使用 `--per_device_train_batch_size=1`。
#### DPO 训练
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage dpo \
--do_train \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_sft_checkpoint \
--create_new_adapter \
--dataset comparison_gpt4_zh \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--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
```
> [!TIP]
> 使用 `--adapter_name_or_path path_to_sft_checkpoint,path_to_dpo_checkpoint` 来进行微调模型的推理。
### 多 GPU 分布式训练
#### 使用 Huggingface Accelerate
```bash
accelerate launch --config_file config.yaml src/train_bash.py \
--ddp_timeout 180000000 \
... # 参数同上
```
<details><summary>使用 Accelerate 进行 LoRA 训练的 config.yaml 示例</summary>
```yaml
compute_environment: LOCAL_MACHINE
debug: false
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>
> [!TIP]
> 我们推荐使用 Accelerate 进行 LoRA 训练。
#### 使用 DeepSpeed
```bash
deepspeed --num_gpus 8 src/train_bash.py \
--deepspeed ds_config.json \
--ddp_timeout 180000000 \
... # 参数同上
```
<details><summary>使用 DeepSpeed ZeRO-2 进行全参数训练的 ds_config.json 示例</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,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"bf16": {
"enabled": "auto"
},
"zero_optimization": {
"stage": 2,
"allgather_partitions": true,
"allgather_bucket_size": 5e8,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 5e8,
"contiguous_gradients": true,
"round_robin_gradients": true
}
}
```
</details>
> [!TIP]
> 更多训练脚本请查看 [examples](examples)。
### 合并 LoRA 权重并导出模型
```bash
CUDA_VISIBLE_DEVICES=0 python src/export_model.py \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_checkpoint \
--template default \
--finetuning_type lora \
--export_dir path_to_export \
--export_size 2 \
--export_legacy_format False
```
> [!WARNING]
> 尚不支持量化模型的 LoRA 权重合并及导出。
> [!TIP]
> 仅使用 `--model_name_or_path path_to_export` 来加载导出后的模型。
>
> 合并 LoRA 权重之后可再次使用 `--export_quantization_bit 4` 和 `--export_quantization_dataset data/c4_demo.json` 基于 AutoGPTQ 量化模型。
### 使用 OpenAI 风格 API 推理
```bash
CUDA_VISIBLE_DEVICES=0 API_PORT=8000 python src/api_demo.py \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_checkpoint \
--template default \
--finetuning_type lora
```
> [!TIP]
> 关于 API 文档请见 `http://localhost:8000/docs`。
### 使用命令行推理
```bash
CUDA_VISIBLE_DEVICES=0 python src/cli_demo.py \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_checkpoint \
--template default \
--finetuning_type lora
```
### 使用浏览器推理
```bash
CUDA_VISIBLE_DEVICES=0 python src/web_demo.py \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_checkpoint \
--template default \
--finetuning_type lora
```
### 模型评估
```bash
CUDA_VISIBLE_DEVICES=0 python src/evaluate.py \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_checkpoint \
--template vanilla \
--finetuning_type lora \
--task ceval \
--split validation \
--lang zh \
--n_shot 5 \
--batch_size 4
```
### 模型预测
```bash
CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage sft \
--do_predict \
--model_name_or_path path_to_llama_model \
--adapter_name_or_path path_to_checkpoint \
--dataset alpaca_gpt4_zh \
--template default \
--finetuning_type lora \
--output_dir path_to_predict_result \
--per_device_eval_batch_size 1 \
--max_samples 100 \
--predict_with_generate \
--fp16
```
> [!WARNING]
> 如果使用 fp16 精度进行 LLaMA-2 模型的预测，请使用 `--per_device_eval_batch_size=1`。
> [!TIP]
> 我们建议在量化模型的预测中使用 `--per_device_eval_batch_size=1` 和 `--max_target_length 128`。
将 `--model_name_or_path` 设置为模型 ID 来加载对应的模型。在[魔搭社区](https://modelscope.cn/models)查看所有可用的模型，例如 `LLM-Research/Meta-Llama-3-8B-Instruct`。
## 使用了 LLaMA Factory 的项目
如果您有项目希望添加至下述列表，请通过邮件联系或者创建一个 PR。
<details><summary>点击显示</summary>
1. Wang et al. ESRL: Efficient Sampling-based Reinforcement Learning for Sequence Generation. 2023. [[arxiv]](https://arxiv.org/abs/2308.02223)
1. Yu et al. Open, Closed, or Small Language Models for Text Classification? 2023. [[arxiv]](https://arxiv.org/abs/2308.10092)
1. Wang et al. UbiPhysio: Support Daily Functioning, Fitness, and Rehabilitation with Action Understanding and Feedback in Natural Language. 2023. [[arxiv]](https://arxiv.org/abs/2308.10526)
@ -682,20 +485,37 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
1. Huang et al. Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2403.02333)
1. Duan et al. Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization. 2024. [[arxiv]](https://arxiv.org/abs/2403.03419)
1. Xie and Schwertfeger. Empowering Robotics with Large Language Models: osmAG Map Comprehension with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2403.08228)
1. Wu et al. Large Language Models are Parallel Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2403.09073)
1. Zhang et al. EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling. 2024. [[arxiv]](https://arxiv.org/abs/2403.14541)
1. Weller et al. FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions. 2024. [[arxiv]](https://arxiv.org/abs/2403.15246)
1. Hongbin Na. CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering. 2024. [[arxiv]](https://arxiv.org/abs/2403.16008)
1. Zan et al. CodeS: Natural Language to Code Repository via Multi-Layer Sketch. 2024. [[arxiv]](https://arxiv.org/abs/2403.16443)
1. Liu et al. Extensive Self-Contrast Enables Feedback-Free Language Model Alignment. 2024. [[arxiv]](https://arxiv.org/abs/2404.00604)
1. Luo et al. BAdam: A Memory Efficient Full Parameter Training Method for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.02827)
1. Du et al. Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2404.04167)
1. Ma et al. Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation. 2024. [[arxiv]](https://arxiv.org/abs/2404.04316)
1. Liu et al. Dynamic Generation of Personalities with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.07084)
1. Shang et al. How Far Have We Gone in Stripped Binary Code Understanding Using Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.09836)
1. Huang et al. LLMTune: Accelerate Database Knob Tuning with Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2404.11581)
1. Deng et al. Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction. 2024. [[arxiv]](https://arxiv.org/abs/2404.14215)
1. Acikgoz et al. Hippocrates: An Open-Source Framework for Advancing Large Language Models in Healthcare. 2024. [[arxiv]](https://arxiv.org/abs/2404.16621)
1. Zhang et al. Small Language Models Need Strong Verifiers to Self-Correct Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2404.17140)
1. Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. 2024. [[arxiv]](https://arxiv.org/abs/2404.18585)
1. **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: 天文大模型 StarWhisper，基于 ChatGLM2-6B 和 Qwen-14B 在天文数据上微调而得。
1. **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: 中文法律领域大模型 DISC-LawLLM，基于 Baichuan-13B 微调而得，具有法律推理和知识检索能力。
1. **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: 孙思邈中文医疗大模型 Sumsimiao，基于 Baichuan-7B 和 ChatGLM-6B 在中文医疗数据上微调而得。
1. **[Sunsimiao](https://github.com/X-D-Lab/Sunsimiao)**: 孙思邈中文医疗大模型 Sumsimiao，基于 Baichuan-7B 和 ChatGLM-6B 在中文医疗数据上微调而得。
1. **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: 医疗大模型项目 CareGPT，基于 LLaMA2-7B 和 Baichuan-13B 在中文医疗数据上微调而得。
1. **[MachineMindset](https://github.com/PKU-YuanGroup/Machine-Mindset/)**：MBTI性格大模型项目，根据数据集与训练方式让任意 LLM 拥有 16 个不同的性格类型。
1. **[Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-v3)**：一个用于生成 Stable Diffusion 提示词的大型语言模型。[[🤗Demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt)
1. **[Chinese-LLaVA-Med](https://github.com/BUAADreamer/Chinese-LLaVA-Med)**：中文多模态医学大模型，基于 LLaVA-1.5-7B 在中文多模态医疗数据上微调而得。
> [!TIP]
> 如果您有项目希望添加至上述列表，请通过邮件联系或者创建一个 PR。
</details>
## 协议
本仓库的代码依照 [Apache-2.0](LICENSE) 协议开源。
使用模型权重时，请遵循对应的模型协议：[Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](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) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
使用模型权重时，请遵循对应的模型协议：[Baichuan2](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/blob/main/Community%20License%20for%20Baichuan%202%20Model.pdf) / [BLOOM](https://huggingface.co/spaces/bigscience/license) / [ChatGLM3](https://github.com/THUDM/ChatGLM3/blob/main/MODEL_LICENSE) / [Command-R](https://cohere.com/c4ai-cc-by-nc-license) / [DeepSeek](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) / [Falcon](https://huggingface.co/tiiuae/falcon-180B/blob/main/LICENSE.txt) / [Gemma](https://ai.google.dev/gemma/terms) / [InternLM2](https://github.com/InternLM/InternLM#license) / [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) / [LLaMA-2 (LLaVA-1.5)](https://ai.meta.com/llama/license/) / [LLaMA-3](https://llama.meta.com/llama3/license/) / [Mistral](LICENSE) / [OLMo](LICENSE) / [Phi-1.5/2](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx) / [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) / [Qwen](https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT) / [StarCoder2](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) / [XVERSE](https://github.com/xverse-ai/XVERSE-13B/blob/main/MODEL_LICENSE.pdf) / [Yi](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE) / [Yi-1.5](LICENSE) / [Yuan](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
## 引用
@ -713,7 +533,7 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
## 致谢
本项目受益于 [PEFT](https://github.com/huggingface/peft)、[QLoRA](https://github.com/artidoro/qlora) 和 [FastChat](https://github.com/lm-sys/FastChat)，感谢以上诸位作者的付出。
本项目受益于 [PEFT](https://github.com/huggingface/peft)、[TRL](https://github.com/huggingface/trl)、[QLoRA](https://github.com/artidoro/qlora) 和 [FastChat](https://github.com/lm-sys/FastChat)，感谢以上诸位作者的付出。
## Star History

291
data/README.md
View File

@ -1,16 +1,17 @@
If you are using a custom dataset, please provide your dataset definition in the following format in `dataset_info.json`.
The [dataset_info.json](dataset_info.json) contains all available datasets. If you are using a custom dataset, please **make sure** to add a *dataset description* in `dataset_info.json` and specify `dataset: dataset_name` before training to use it.
Currently we support datasets in **alpaca** and **sharegpt** format.
```json
"dataset_name": {
"hf_hub_url": "the name of the dataset repository on the Hugging Face hub. (if specified, ignore script_url and file_name)",
"ms_hub_url": "the name of the dataset repository on the Model Scope hub. (if specified, ignore script_url and file_name)",
"script_url": "the name of the directory containing a dataset loading script. (if specified, ignore file_name)",
"file_name": "the name of the dataset file in this directory. (required if above are not specified)",
"file_sha1": "the SHA-1 hash value of the dataset file. (optional, does not affect training)",
"file_name": "the name of the dataset folder or dataset file in this directory. (required if above are not specified)",
"formatting": "the format of the dataset. (optional, default: alpaca, can be chosen from {alpaca, sharegpt})",
"ranking": "whether the dataset is a preference dataset or not. (default: False)",
"subset": "the name of the subset. (optional, default: None)",
"folder": "the name of the folder of the dataset repository on the Hugging Face hub. (optional, default: None)",
"ranking": "whether the dataset is a preference dataset or not. (default: false)",
"formatting": "the format of the dataset. (optional, default: alpaca, can be chosen from {alpaca, sharegpt})",
"columns (optional)": {
"prompt": "the column name in the dataset containing the prompts. (default: instruction)",
"query": "the column name in the dataset containing the queries. (default: input)",
@ -18,7 +19,11 @@ If you are using a custom dataset, please provide your dataset definition in the
"history": "the column name in the dataset containing the histories. (default: None)",
"messages": "the column name in the dataset containing the messages. (default: conversations)",
"system": "the column name in the dataset containing the system prompts. (default: None)",
"tools": "the column name in the dataset containing the tool description. (default: None)"
"tools": "the column name in the dataset containing the tool description. (default: None)",
"images": "the column name in the dataset containing the image inputs. (default: None)",
"chosen": "the column name in the dataset containing the chosen answers. (default: None)",
"rejected": "the column name in the dataset containing the rejected answers. (default: None)",
"kto_tag": "the column name in the dataset containing the kto tags. (default: None)"
},
"tags (optional, used for the sharegpt format)": {
"role_tag": "the key in the message represents the identity. (default: from)",
@ -33,29 +38,38 @@ If you are using a custom dataset, please provide your dataset definition in the
}
```
Given above, you can use the custom dataset via specifying `--dataset dataset_name`.
## Alpaca Format
Currently we support dataset in **alpaca** or **sharegpt** format, the dataset in alpaca format should follow the below format:
### Supervised Fine-Tuning Dataset
* [Example dataset](alpaca_en_demo.json)
In supervised fine-tuning, the `instruction` column will be concatenated with the `input` column and used as the human prompt, then the human prompt would be `instruction\ninput`. The `output` column represents the model response.
The `system` column will be used as the system prompt if specified.
The `history` column is a list consisting of string tuples representing prompt-response pairs in the history messages. Note that the responses in the history **will also be learned by the model** in supervised fine-tuning.
```json
[
{
"instruction": "user instruction (required)",
"input": "user input (optional)",
"instruction": "human instruction (required)",
"input": "human input (optional)",
"output": "model response (required)",
"system": "system prompt (optional)",
"history": [
["user instruction in the first round (optional)", "model response in the first round (optional)"],
["user instruction in the second round (optional)", "model response in the second round (optional)"]
["human instruction in the first round (optional)", "model response in the first round (optional)"],
["human instruction in the second round (optional)", "model response in the second round (optional)"]
]
}
]
```
Regarding the above dataset, the `columns` in `dataset_info.json` should be:
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
```json
"dataset_name": {
"file_name": "data.json",
"columns": {
"prompt": "instruction",
"query": "input",
@ -66,26 +80,135 @@ Regarding the above dataset, the `columns` in `dataset_info.json` should be:
}
```
The `query` column will be concatenated with the `prompt` column and used as the user prompt, then the user prompt would be `prompt\nquery`. The `response` column represents the model response.
### Pre-training Dataset
The `system` column will be used as the system prompt. The `history` column is a list consisting string tuples representing prompt-response pairs in the history. Note that the responses in the history **will also be used for training**.
- [Example dataset](c4_demo.json)
For the pre-training datasets, only the `prompt` column will be used for training.
For the preference datasets, the `response` column should be a string list whose length is 2, with the preferred answers appearing first, for example:
In pre-training, only the `text` column will be used for model learning.
```json
{
"instruction": "user instruction",
"input": "user input",
"output": [
"chosen answer",
"rejected answer"
[
{"text": "document"},
{"text": "document"}
]
```
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
```json
"dataset_name": {
"file_name": "data.json",
"columns": {
"prompt": "text"
}
}
```
The dataset in sharegpt format should follow the below format:
### Preference Dataset
Preference datasets are used for reward modeling, DPO training and ORPO training.
It requires a better response in `chosen` column and a worse response in `rejected` column.
```json
[
{
"instruction": "human instruction (required)",
"input": "human input (optional)",
"chosen": "chosen answer (required)",
"rejected": "rejected answer (required)"
}
]
```
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
```json
"dataset_name": {
"file_name": "data.json",
"ranking": true,
"columns": {
"prompt": "instruction",
"query": "input",
"chosen": "chosen",
"rejected": "rejected"
}
}
```
### KTO Dataset
- [Example dataset](kto_en_demo.json)
KTO datasets require a extra `kto_tag` column containing the boolean human feedback.
```json
[
{
"instruction": "human instruction (required)",
"input": "human input (optional)",
"output": "model response (required)",
"kto_tag": "human feedback [true/false] (required)"
}
]
```
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
```json
"dataset_name": {
"file_name": "data.json",
"columns": {
"prompt": "instruction",
"query": "input",
"response": "output",
"kto_tag": "kto_tag"
}
}
```
### Multimodal Dataset
- [Example dataset](mllm_demo.json)
Multimodal datasets require a `images` column containing the paths to the input images. Currently we only support one image.
```json
[
{
"instruction": "human instruction (required)",
"input": "human input (optional)",
"output": "model response (required)",
"images": [
"image path (required)"
]
}
]
```
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
```json
"dataset_name": {
"file_name": "data.json",
"columns": {
"prompt": "instruction",
"query": "input",
"response": "output",
"images": "images"
}
}
```
## Sharegpt Format
### Supervised Fine-Tuning Dataset
- [Example dataset](glaive_toolcall_en_demo.json)
Compared to the alpaca format, the sharegpt format allows the datasets have **more roles**, such as human, gpt, observation and function. They are presented in a list of objects in the `conversations` column.
Note that the human and observation should appear in odd positions, while gpt and function should appear in even positions.
```json
[
@ -93,7 +216,15 @@ The dataset in sharegpt format should follow the below format:
"conversations": [
{
"from": "human",
"value": "user instruction"
"value": "human instruction"
},
{
"from": "function_call",
"value": "tool arguments"
},
{
"from": "observation",
"value": "tool result"
},
{
"from": "gpt",
@ -106,24 +237,114 @@ The dataset in sharegpt format should follow the below format:
]
```
Regarding the above dataset, the `columns` in `dataset_info.json` should be:
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
```json
"dataset_name": {
"file_name": "data.json",
"formatting": "sharegpt",
"columns": {
"messages": "conversations",
"system": "system",
"tools": "tools"
},
"tags": {
"role_tag": "from",
"content_tag": "value",
"user_tag": "human",
"assistant_tag": "gpt"
}
}
```
where the `messages` column should be a list following the `u/a/u/a/u/a` order.
### Preference Dataset
Pre-training datasets and preference datasets are incompatible with the sharegpt format yet.
- [Example dataset](dpo_en_demo.json)
Preference datasets in sharegpt format also require a better message in `chosen` column and a worse message in `rejected` column.
```json
[
{
"conversations": [
{
"from": "human",
"value": "human instruction"
},
{
"from": "gpt",
"value": "model response"
},
{
"from": "human",
"value": "human instruction"
}
],
"chosen": {
"from": "gpt",
"value": "chosen answer (required)"
},
"rejected": {
"from": "gpt",
"value": "rejected answer (required)"
}
}
]
```
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
```json
"dataset_name": {
"file_name": "data.json",
"formatting": "sharegpt",
"ranking": true,
"columns": {
"messages": "conversations",
"chosen": "chosen",
"rejected": "rejected"
}
}
```
### OpenAI Format
The openai format is simply a special case of the sharegpt format, where the first message may be a system prompt.
```json
[
{
"messages": [
{
"role": "system",
"content": "system prompt (optional)"
},
{
"role": "user",
"content": "human instruction"
},
{
"role": "assistant",
"content": "model response"
}
]
}
]
```
Regarding the above dataset, the *dataset description* in `dataset_info.json` should be:
```json
"dataset_name": {
"file_name": "data.json",
"formatting": "sharegpt",
"columns": {
"messages": "messages"
},
"tags": {
"role_tag": "role",
"content_tag": "content",
"user_tag": "user",
"assistant_tag": "assistant",
"system_tag": "system"
}
}
```
The KTO datasets and multimodal datasets in sharegpt format are similar to the alpaca format.
Pre-training datasets are **incompatible** with the sharegpt format.

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@ -1,4 +1,6 @@
如果您使用自定义数据集，请务必在 `dataset_info.json` 文件中按照以下格式提供数据集定义。
[dataset_info.json](dataset_info.json) 包含了所有可用的数据集。如果您希望使用自定义数据集，请**务必**在 `dataset_info.json` 文件中添加*数据集描述*，并通过修改 `dataset: 数据集名称` 配置来使用数据集。
目前我们支持 **alpaca** 格式和 **sharegpt** 格式的数据集。
```json
"数据集名称": {
@ -6,11 +8,10 @@
"ms_hub_url": "ModelScope 的数据集仓库地址（若指定，则忽略 script_url 和 file_name）",
"script_url": "包含数据加载脚本的本地文件夹名称（若指定，则忽略 file_name）",
"file_name": "该目录下数据集文件的名称（若上述参数未指定，则此项必需）",
"file_sha1": "数据集文件的 SHA-1 哈希值（可选，留空不影响训练）",
"formatting": "数据集格式（可选，默认：alpaca，可以为 alpaca 或 sharegpt）",
"ranking": "是否为偏好数据集（可选，默认：False）",
"subset": "数据集子集的名称（可选，默认：None）",
"folder": "Hugging Face 仓库的文件夹名称（可选，默认：None）",
"ranking": "是否为偏好数据集（可选，默认：False）",
"formatting": "数据集格式（可选，默认：alpaca，可以为 alpaca 或 sharegpt）",
"columns（可选）": {
"prompt": "数据集代表提示词的表头名称（默认：instruction）",
"query": "数据集代表请求的表头名称（默认：input）",
@ -18,7 +19,11 @@
"history": "数据集代表历史对话的表头名称（默认：None）",
"messages": "数据集代表消息列表的表头名称（默认：conversations）",
"system": "数据集代表系统提示的表头名称（默认：None）",
"tools": "数据集代表工具描述的表头名称（默认：None）"
"tools": "数据集代表工具描述的表头名称（默认：None）",
"images": "数据集代表图像输入的表头名称（默认：None）",
"chosen": "数据集代表更优回答的表头名称（默认：None）",
"rejected": "数据集代表更差回答的表头名称（默认：None）",
"kto_tag": "数据集代表 KTO 标签的表头名称（默认：None）"
},
"tags（可选，用于 sharegpt 格式）": {
"role_tag": "消息中代表发送者身份的键名（默认：from）",
@ -33,15 +38,23 @@
}
```
添加后可通过指定 `--dataset 数据集名称` 参数使用自定义数据集。
## Alpaca 格式
该项目目前支持两种格式的数据集：**alpaca** 和 **sharegpt**，其中 alpaca 格式的数据集按照以下方式组织：
### 指令监督微调数据集
- [样例数据集](alpaca_zh_demo.json)
在指令监督微调时，`instruction` 列对应的内容会与 `input` 列对应的内容拼接后作为人类指令，即人类指令为 `instruction\ninput`。而 `output` 列对应的内容为模型回答。
如果指定，`system` 列对应的内容将被作为系统提示词。
`history` 列是由多个字符串二元组构成的列表，分别代表历史消息中每轮对话的指令和回答。注意在指令监督微调时，历史消息中的回答内容**也会被用于模型学习**。
```json
[
{
"instruction": "用户指令（必填）",
"input": "用户输入（选填）",
"instruction": "人类指令（必填）",
"input": "人类输入（选填）",
"output": "模型回答（必填）",
"system": "系统提示词（选填）",
"history": [
@ -52,10 +65,11 @@
]
```
对于上述格式的数据，`dataset_info.json` 中的 `columns` 应为：
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
```json
"数据集名称": {
"file_name": "data.json",
"columns": {
"prompt": "instruction",
"query": "input",
@ -66,26 +80,135 @@
}
```
其中 `query` 列对应的内容会与 `prompt` 列对应的内容拼接后作为用户指令，即用户指令为 `prompt\nquery`。`response` 列对应的内容为模型回答。
### 预训练数据集
`system` 列对应的内容将被作为系统提示词。`history` 列是由多个字符串二元组构成的列表，分别代表历史消息中每轮的指令和回答。注意历史消息中的回答**也会被用于训练**。
- [样例数据集](c4_demo.json)
对于预训练数据集，仅 `prompt` 列中的内容会用于模型训练。
对于偏好数据集，`response` 列应当是一个长度为 2 的字符串列表，排在前面的代表更优的回答，例如：
在预训练时，只有 `text` 列中的内容会用于模型学习。
```json
{
"instruction": "用户指令",
"input": "用户输入",
"output": [
"优质回答",
"劣质回答"
[
{"text": "document"},
{"text": "document"}
]
```
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
```json
"数据集名称": {
"file_name": "data.json",
"columns": {
"prompt": "text"
}
}
```
而 sharegpt 格式的数据集按照以下方式组织：
### 偏好数据集
偏好数据集用于奖励模型训练、DPO 训练和 ORPO 训练。
它需要在 `chosen` 列中提供更优的回答，并在 `rejected` 列中提供更差的回答。
```json
[
{
"instruction": "人类指令（必填）",
"input": "人类输入（选填）",
"chosen": "优质回答（必填）",
"rejected": "劣质回答（必填）"
}
]
```
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
```json
"数据集名称": {
"file_name": "data.json",
"ranking": true,
"columns": {
"prompt": "instruction",
"query": "input",
"chosen": "chosen",
"rejected": "rejected"
}
}
```
### KTO 数据集
- [样例数据集](kto_en_demo.json)
KTO 数据集需要额外添加一个 `kto_tag` 列，包含 bool 类型的人类反馈。
```json
[
{
"instruction": "人类指令（必填）",
"input": "人类输入（选填）",
"output": "模型回答（必填）",
"kto_tag": "人类反馈 [true/false]（必填）"
}
]
```
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
```json
"数据集名称": {
"file_name": "data.json",
"columns": {
"prompt": "instruction",
"query": "input",
"response": "output",
"kto_tag": "kto_tag"
}
}
```
### 多模态数据集
- [样例数据集](mllm_demo.json)
多模态数据集需要额外添加一个 `images` 列，包含输入图像的路径。目前我们仅支持单张图像输入。
```json
[
{
"instruction": "人类指令（必填）",
"input": "人类输入（选填）",
"output": "模型回答（必填）",
"images": [
"图像路径（必填）"
]
}
]
```
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
```json
"数据集名称": {
"file_name": "data.json",
"columns": {
"prompt": "instruction",
"query": "input",
"response": "output",
"images": "images"
}
}
```
## Sharegpt 格式
### 指令监督微调数据集
- [样例数据集](glaive_toolcall_zh_demo.json)
相比 alpaca 格式的数据集，sharegpt 格式支持**更多的角色种类**，例如 human、gpt、observation、function 等等。它们构成一个对象列表呈现在 `conversations` 列中。
注意其中 human 和 observation 必须出现在奇数位置，gpt 和 function 必须出现在偶数位置。
```json
[
@ -93,7 +216,15 @@
"conversations": [
{
"from": "human",
"value": "用户指令"
"value": "人类指令"
},
{
"from": "function_call",
"value": "工具参数"
},
{
"from": "observation",
"value": "工具结果"
},
{
"from": "gpt",
@ -106,24 +237,114 @@
]
```
对于上述格式的数据，`dataset_info.json` 中的 `columns` 应为：
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
```json
"数据集名称": {
"file_name": "data.json",
"formatting": "sharegpt",
"columns": {
"messages": "conversations",
"system": "system",
"tools": "tools"
},
"tags": {
"role_tag": "from",
"content_tag": "value",
"user_tag": "human",
"assistant_tag": "gpt"
}
}
```
其中 `messages` 列应当是一个列表，且符合 `用户/模型/用户/模型/用户/模型` 的顺序。
### 偏好数据集
预训练数据集和偏好数据集尚不支持 sharegpt 格式。
- [样例数据集](dpo_zh_demo.json)
Sharegpt 格式的偏好数据集同样需要在 `chosen` 列中提供更优的消息，并在 `rejected` 列中提供更差的消息。
```json
[
{
"conversations": [
{
"from": "human",
"value": "人类指令"
},
{
"from": "gpt",
"value": "模型回答"
},
{
"from": "human",
"value": "人类指令"
}
],
"chosen": {
"from": "gpt",
"value": "优质回答"
},
"rejected": {
"from": "gpt",
"value": "劣质回答"
}
}
]
```
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
```json
"数据集名称": {
"file_name": "data.json",
"formatting": "sharegpt",
"ranking": true,
"columns": {
"messages": "conversations",
"chosen": "chosen",
"rejected": "rejected"
}
}
```
### OpenAI 格式
OpenAI 格式仅仅是 sharegpt 格式的一种特殊情况，其中第一条消息可能是系统提示词。
```json
[
{
"messages": [
{
"role": "system",
"content": "系统提示词（选填）"
},
{
"role": "user",
"content": "人类指令"
},
{
"role": "assistant",
"content": "模型回答"
}
]
}
]
```
对于上述格式的数据，`dataset_info.json` 中的*数据集描述*应为：
```json
"数据集名称": {
"file_name": "data.json",
"formatting": "sharegpt",
"columns": {
"messages": "messages"
},
"tags": {
"role_tag": "role",
"content_tag": "content",
"user_tag": "user",
"assistant_tag": "assistant",
"system_tag": "system"
}
}
```
Sharegpt 格式中的 KTO 数据集和多模态数据集与 alpaca 格式的类似。
预训练数据集**不支持** sharegpt 格式。

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7cb6a7d11455bddc3d495750a2392683d775b184

25
data/belle_multiturn/belle_multiturn.py
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@ -1,5 +1,6 @@
import os
import json
import os
import datasets
@ -22,31 +23,19 @@ _URL = "{}/datasets/BelleGroup/multiturn_chat_0.8M/resolve/main/multiturn_chat_0
class BelleMultiturn(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.0.0")
def _info(self):
features = datasets.Features({
"conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]
})
features = datasets.Features(
{"conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION
description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
)
def _split_generators(self, dl_manager: datasets.DownloadManager):
file_path = dl_manager.download(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": file_path
}
)
]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": file_path})]
def _generate_examples(self, filepath: str):
with open(filepath, "r", encoding="utf-8") as f:

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46
data/example_dataset/example_dataset.py
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@ -1,46 +0,0 @@
import json
import datasets
from typing import Any, Dict, Generator, List, Tuple
_DESCRIPTION = "An example of dataset."
_CITATION = ""
_HOMEPAGE = ""
_LICENSE = ""
_URL = "examples.json"
class ExampleDataset(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.0.0")
def _info(self) -> datasets.DatasetInfo:
features = datasets.Features({
"instruction": datasets.Value("string"),
"input": datasets.Value("string"),
"output": datasets.Value("string"),
"history": datasets.Sequence(datasets.Sequence(datasets.Value("string")))
})
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION
)
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
file_path = dl_manager.download(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": file_path
}
)
]
def _generate_examples(self, filepath: str) -> Generator[Tuple[int, Dict[str, Any]], None, None]:
example_dataset = json.load(open(filepath, "r", encoding="utf-8"))
for key, example in enumerate(example_dataset):
yield key, example

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47
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@ -1,8 +1,10 @@
import os
import json
import datasets
import os
from typing import List
import datasets
_HF_ENDPOINT = os.getenv("HF_ENDPOINT", "https://huggingface.co")
_DESCRIPTION = "Human preference data about helpfulness and harmlessness."
_CITATION = ""
@ -14,50 +16,37 @@ _URLS = {
_URL + "harmless-base/train.jsonl.gz",
_URL + "helpful-base/train.jsonl.gz",
_URL + "helpful-online/train.jsonl.gz",
_URL + "helpful-rejection-sampled/train.jsonl.gz"
_URL + "helpful-rejection-sampled/train.jsonl.gz",
],
"test": [
_URL + "harmless-base/test.jsonl.gz",
_URL + "helpful-base/test.jsonl.gz",
_URL + "helpful-online/test.jsonl.gz",
_URL + "helpful-rejection-sampled/test.jsonl.gz"
]
_URL + "helpful-rejection-sampled/test.jsonl.gz",
],
}
class HhRlhfEn(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.0.0")
def _info(self) -> datasets.DatasetInfo:
features = datasets.Features({
features = datasets.Features(
{
"instruction": datasets.Value("string"),
"output": datasets.Sequence(datasets.Value("string")),
"history": datasets.Sequence(datasets.Sequence(datasets.Value("string")))
})
"history": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION
description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
)
def _split_generators(self, dl_manager: datasets.DownloadManager):
file_path = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepaths": file_path["train"]
}
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepaths": file_path["test"]
}
)
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": file_path["train"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepaths": file_path["test"]}),
]
def _generate_examples(self, filepaths: List[str]):
@ -90,9 +79,5 @@ class HhRlhfEn(datasets.GeneratorBasedBuilder):
break
prompt = prompt[:human_idx]
yield key, {
"instruction": query,
"output": [r_accept, r_reject],
"history": history
}
yield key, {"instruction": query, "chosen": r_accept, "rejected": r_reject, "history": history}
key += 1

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@ -1 +0,0 @@
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@ -1 +0,0 @@
57fd080be5bffe4153fe3ee26a175e3d56da30f3

1
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@ -1 +0,0 @@
736bcedea2b24a1414765c6d69cbdafaea839f3c

37
data/ultra_chat/ultra_chat.py
View File

@ -1,8 +1,10 @@
import os
import json
import datasets
import os
from typing import List
import datasets
_HF_ENDPOINT = os.getenv("HF_ENDPOINT", "https://huggingface.co")
_DESCRIPTION = "UltraChat: Large-scale, Informative, and Diverse Multi-round Dialogue Data."
@ -24,31 +26,19 @@ _BASE_DATA_URL = "{}/datasets/stingning/ultrachat/resolve/main/train_{{idx}}.jso
class UltraChat(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("0.0.0")
def _info(self):
features = datasets.Features({
"conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]
})
features = datasets.Features(
{"conversations": [{"from": datasets.Value("string"), "value": datasets.Value("string")}]}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION
description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION
)
def _split_generators(self, dl_manager: datasets.DownloadManager):
file_paths = [dl_manager.download(_BASE_DATA_URL.format(idx=idx)) for idx in range(10)] # multiple shards
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepaths": file_paths
}
)
]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": file_paths})]
def _generate_examples(self, filepaths: List[str]):
for filepath in filepaths:
@ -56,7 +46,7 @@ class UltraChat(datasets.GeneratorBasedBuilder):
for row in f:
try:
data = json.loads(row)
except:
except Exception:
continue
key: int = data["id"]
content: List[str] = data["data"]
@ -64,8 +54,7 @@ class UltraChat(datasets.GeneratorBasedBuilder):
content.pop(-1)
if len(content) < 2:
continue
conversations = [{
"from": "human" if i % 2 == 0 else "gpt",
"value": content[i]
} for i in range(len(content))]
conversations = [
{"from": "human" if i % 2 == 0 else "gpt", "value": content[i]} for i in range(len(content))
]
yield key, {"conversations": conversations}

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2
docker-compose.yml
View File

@ -10,6 +10,8 @@ services:
- ./hf_cache:/root/.cache/huggingface/
- ./data:/app/data
- ./output:/app/output
environment:
- CUDA_VISIBLE_DEVICES=0
ports:
- "7860:7860"
ipc: host

12
evaluation/ceval/ceval.py
View File

@ -133,25 +133,19 @@ class Ceval(datasets.GeneratorBasedBuilder):
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(
data_dir, "test", f"{task_name}_test.csv"
),
"filepath": os.path.join(data_dir, "test", f"{task_name}_test.csv"),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(
data_dir, "val", f"{task_name}_val.csv"
),
"filepath": os.path.join(data_dir, "val", f"{task_name}_val.csv"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(
data_dir, "dev", f"{task_name}_dev.csv"
),
"filepath": os.path.join(data_dir, "dev", f"{task_name}_dev.csv"),
},
),
]

134
evaluation/cmmlu/cmmlu.py
View File

@ -37,73 +37,73 @@ _LICENSE = "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Internatio
_URL = "cmmlu.zip"
task_list = [
'agronomy',
'anatomy',
'ancient_chinese',
'arts',
'astronomy',
'business_ethics',
'chinese_civil_service_exam',
'chinese_driving_rule',
'chinese_food_culture',
'chinese_foreign_policy',
'chinese_history',
'chinese_literature',
'chinese_teacher_qualification',
'clinical_knowledge',
'college_actuarial_science',
'college_education',
'college_engineering_hydrology',
'college_law',
'college_mathematics',
'college_medical_statistics',
'college_medicine',
'computer_science',
'computer_security',
'conceptual_physics',
'construction_project_management',
'economics',
'education',
'electrical_engineering',
'elementary_chinese',
'elementary_commonsense',
'elementary_information_and_technology',
'elementary_mathematics',
'ethnology',
'food_science',
'genetics',
'global_facts',
'high_school_biology',
'high_school_chemistry',
'high_school_geography',
'high_school_mathematics',
'high_school_physics',
'high_school_politics',
'human_sexuality',
'international_law',
'journalism',
'jurisprudence',
'legal_and_moral_basis',
'logical',
'machine_learning',
'management',
'marketing',
'marxist_theory',
'modern_chinese',
'nutrition',
'philosophy',
'professional_accounting',
'professional_law',
'professional_medicine',
'professional_psychology',
'public_relations',
'security_study',
'sociology',
'sports_science',
'traditional_chinese_medicine',
'virology',
'world_history',
'world_religions',
"agronomy",
"anatomy",
"ancient_chinese",
"arts",
"astronomy",
"business_ethics",
"chinese_civil_service_exam",
"chinese_driving_rule",
"chinese_food_culture",
"chinese_foreign_policy",
"chinese_history",
"chinese_literature",
"chinese_teacher_qualification",
"clinical_knowledge",
"college_actuarial_science",
"college_education",
"college_engineering_hydrology",
"college_law",
"college_mathematics",
"college_medical_statistics",
"college_medicine",
"computer_science",
"computer_security",
"conceptual_physics",
"construction_project_management",
"economics",
"education",
"electrical_engineering",
"elementary_chinese",
"elementary_commonsense",
"elementary_information_and_technology",
"elementary_mathematics",
"ethnology",
"food_science",
"genetics",
"global_facts",
"high_school_biology",
"high_school_chemistry",
"high_school_geography",
"high_school_mathematics",
"high_school_physics",
"high_school_politics",
"human_sexuality",
"international_law",
"journalism",
"jurisprudence",
"legal_and_moral_basis",
"logical",
"machine_learning",
"management",
"marketing",
"marxist_theory",
"modern_chinese",
"nutrition",
"philosophy",
"professional_accounting",
"professional_law",
"professional_medicine",
"professional_psychology",
"public_relations",
"security_study",
"sociology",
"sports_science",
"traditional_chinese_medicine",
"virology",
"world_history",
"world_religions",
]

12
evaluation/mmlu/mmlu.py
View File

@ -136,25 +136,19 @@ class MMLU(datasets.GeneratorBasedBuilder):
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": os.path.join(
data_dir, "data", "test", f"{task_name}_test.csv"
),
"filepath": os.path.join(data_dir, "data", "test", f"{task_name}_test.csv"),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"filepath": os.path.join(
data_dir, "data", "val", f"{task_name}_val.csv"
),
"filepath": os.path.join(data_dir, "data", "val", f"{task_name}_val.csv"),
},
),
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": os.path.join(
data_dir, "data", "dev", f"{task_name}_dev.csv"
),
"filepath": os.path.join(data_dir, "data", "dev", f"{task_name}_dev.csv"),
},
),
]

237
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@ -0,0 +1,237 @@
We provide diverse examples about fine-tuning LLMs.
Make sure to execute these commands in the `LLaMA-Factory` directory.
## Table of Contents
- [LoRA Fine-Tuning on A Single GPU](#lora-fine-tuning-on-a-single-gpu)
- [QLoRA Fine-Tuning on a Single GPU](#qlora-fine-tuning-on-a-single-gpu)
- [LoRA Fine-Tuning on Multiple GPUs](#lora-fine-tuning-on-multiple-gpus)
- [LoRA Fine-Tuning on Multiple NPUs](#lora-fine-tuning-on-multiple-npus)
- [Full-Parameter Fine-Tuning on Multiple GPUs](#full-parameter-fine-tuning-on-multiple-gpus)
- [Merging LoRA Adapters and Quantization](#merging-lora-adapters-and-quantization)
- [Inferring LoRA Fine-Tuned Models](#inferring-lora-fine-tuned-models)
- [Extras](#extras)
## Examples
### LoRA Fine-Tuning on A Single GPU
#### (Continuous) Pre-Training
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_pretrain.yaml
```
#### Supervised Fine-Tuning
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
```
#### Multimodal Supervised Fine-Tuning
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llava1_5_lora_sft.yaml
```
#### Reward Modeling
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_reward.yaml
```
#### PPO Training
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_ppo.yaml
```
#### DPO Training
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_dpo.yaml
```
#### KTO Training
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_kto.yaml
```
#### ORPO Training
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_orpo.yaml
```
#### Preprocess Dataset
It is useful for large dataset, use `tokenized_path` in config to load the preprocessed dataset.
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_preprocess.yaml
```
#### Evaluating on MMLU/CMMLU/C-Eval Benchmarks
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli eval examples/lora_single_gpu/llama3_lora_eval.yaml
```
#### Batch Predicting and Computing BLEU and ROUGE Scores
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_predict.yaml
```
### QLoRA Fine-Tuning on a Single GPU
#### Supervised Fine-Tuning with 4/8-bit Bitsandbytes Quantization (Recommended)
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_bitsandbytes.yaml
```
#### Supervised Fine-Tuning with 4/8-bit GPTQ Quantization
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_gptq.yaml
```
#### Supervised Fine-Tuning with 4-bit AWQ Quantization
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_awq.yaml
```
#### Supervised Fine-Tuning with 2-bit AQLM Quantization
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_aqlm.yaml
```
### LoRA Fine-Tuning on Multiple GPUs
#### Supervised Fine-Tuning with Accelerate on Single Node
```bash
bash examples/lora_multi_gpu/single_node.sh
```
#### Supervised Fine-Tuning with Accelerate on Multiple Nodes
```bash
bash examples/lora_multi_gpu/multi_node.sh
```
#### Supervised Fine-Tuning with DeepSpeed ZeRO-3 (Weight Sharding)
```bash
bash examples/lora_multi_gpu/ds_zero3.sh
```
### LoRA Fine-Tuning on Multiple NPUs
#### Supervised Fine-Tuning with DeepSpeed ZeRO-0
```bash
bash examples/lora_multi_npu/ds_zero0.sh
```
### Full-Parameter Fine-Tuning on Multiple GPUs
#### Supervised Fine-Tuning with Accelerate on Single Node
```bash
bash examples/full_multi_gpu/single_node.sh
```
#### Supervised Fine-Tuning with Accelerate on Multiple Nodes
```bash
bash examples/full_multi_gpu/multi_node.sh
```
#### Batch Predicting and Computing BLEU and ROUGE Scores
```bash
bash examples/full_multi_gpu/predict.sh
```
### Merging LoRA Adapters and Quantization
#### Merge LoRA Adapters
Note: DO NOT use quantized model or `quantization_bit` when merging LoRA adapters.
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
```
#### Quantizing Model using AutoGPTQ
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_gptq.yaml
```
### Inferring LoRA Fine-Tuned Models
Use `CUDA_VISIBLE_DEVICES=0,1` to infer models on multiple devices.
#### Use CLI
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
```
#### Use Web UI
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli webchat examples/inference/llama3_lora_sft.yaml
```
#### Launch OpenAI-style API
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli api examples/inference/llama3_lora_sft.yaml
```
### Extras
#### Full-Parameter Fine-Tuning using GaLore
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/galore/llama3_full_sft.yaml
```
#### Full-Parameter Fine-Tuning using BAdam
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/badam/llama3_full_sft.yaml
```
#### LoRA+ Fine-Tuning
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/loraplus/llama3_lora_sft.yaml
```
#### Mixture-of-Depths Fine-Tuning
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/mod/llama3_full_sft.yaml
```
#### LLaMA-Pro Fine-Tuning
```bash
bash examples/extras/llama_pro/expand.sh
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
```
#### FSDP+QLoRA Fine-Tuning
```bash
bash examples/extras/fsdp_qlora/single_node.sh
```

237
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@ -0,0 +1,237 @@
我们提供了多样化的大模型微调示例脚本。
请确保在 `LLaMA-Factory` 目录下执行下述命令。
## 目录
- [单 GPU LoRA 微调](#单-gpu-lora-微调)
- [单 GPU QLoRA 微调](#单-gpu-qlora-微调)
- [多 GPU LoRA 微调](#多-gpu-lora-微调)
- [多 NPU LoRA 微调](#多-npu-lora-微调)
- [多 GPU 全参数微调](#多-gpu-全参数微调)
- [合并 LoRA 适配器与模型量化](#合并-lora-适配器与模型量化)
- [推理 LoRA 模型](#推理-lora-模型)
- [杂项](#杂项)
## 示例
### 单 GPU LoRA 微调
#### （增量）预训练
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_pretrain.yaml
```
#### 指令监督微调
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
```
#### 多模态指令监督微调
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llava1_5_lora_sft.yaml
```
#### 奖励模型训练
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_reward.yaml
```
#### PPO 训练
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_ppo.yaml
```
#### DPO 训练
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_dpo.yaml
```
#### KTO 训练
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_kto.yaml
```
#### ORPO 训练
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_orpo.yaml
```
#### 预处理数据集
对于大数据集有帮助，在配置中使用 `tokenized_path` 以加载预处理后的数据集。
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_preprocess.yaml
```
#### 在 MMLU/CMMLU/C-Eval 上评估
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli eval examples/lora_single_gpu/llama3_lora_eval.yaml
```
#### 批量预测并计算 BLEU 和 ROUGE 分数
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_predict.yaml
```
### 单 GPU QLoRA 微调
#### 基于 4/8 比特 Bitsandbytes 量化进行指令监督微调（推荐）
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_bitsandbytes.yaml
```
#### 基于 4/8 比特 GPTQ 量化进行指令监督微调
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_gptq.yaml
```
#### 基于 4 比特 AWQ 量化进行指令监督微调
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_awq.yaml
```
#### 基于 2 比特 AQLM 量化进行指令监督微调
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/qlora_single_gpu/llama3_lora_sft_aqlm.yaml
```
### 多 GPU LoRA 微调
#### 使用 Accelerate 进行单节点训练
```bash
bash examples/lora_multi_gpu/single_node.sh
```
#### 使用 Accelerate 进行多节点训练
```bash
bash examples/lora_multi_gpu/multi_node.sh
```
#### 使用 DeepSpeed ZeRO-3 平均分配显存
```bash
bash examples/lora_multi_gpu/ds_zero3.sh
```
### 多 NPU LoRA 微调
#### 使用 DeepSpeed ZeRO-0 训练
```bash
bash examples/lora_multi_npu/ds_zero0.sh
```
### 多 GPU 全参数微调
#### 使用 DeepSpeed 进行单节点训练
```bash
bash examples/full_multi_gpu/single_node.sh
```
#### 使用 DeepSpeed 进行多节点训练
```bash
bash examples/full_multi_gpu/multi_node.sh
```
#### 批量预测并计算 BLEU 和 ROUGE 分数
```bash
bash examples/full_multi_gpu/predict.sh
```
### 合并 LoRA 适配器与模型量化
#### 合并 LoRA 适配器
注：请勿使用量化后的模型或 `quantization_bit` 参数来合并 LoRA 适配器。
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
```
#### 使用 AutoGPTQ 量化模型
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_gptq.yaml
```
### 推理 LoRA 模型
使用 `CUDA_VISIBLE_DEVICES=0,1` 进行多卡推理。
#### 使用命令行接口
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
```
#### 使用浏览器界面
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli webchat examples/inference/llama3_lora_sft.yaml
```
#### 启动 OpenAI 风格 API
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli api examples/inference/llama3_lora_sft.yaml
```
### 杂项
#### 使用 GaLore 进行全参数训练
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/galore/llama3_full_sft.yaml
```
#### 使用 BAdam 进行全参数训练
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/badam/llama3_full_sft.yaml
```
#### LoRA+ 微调
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/loraplus/llama3_lora_sft.yaml
```
#### 深度混合微调
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/mod/llama3_full_sft.yaml
```
#### LLaMA-Pro 微调
```bash
bash examples/extras/llama_pro/expand.sh
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
```
#### FSDP+QLoRA 微调
```bash
bash examples/extras/fsdp_qlora/single_node.sh
```

4
examples/accelerate/fsdp_config.yaml
View File

@ -15,8 +15,8 @@ fsdp_config:
machine_rank: 0
main_training_function: main
mixed_precision: fp16
num_machines: 1
num_processes: 2
num_machines: 1 # the number of nodes
num_processes: 2 # the number of GPUs in all nodes
rdzv_backend: static
same_network: true
tpu_env: []

4
examples/accelerate/master_config.yaml
View File

@ -8,8 +8,8 @@ main_process_ip: 192.168.0.1
main_process_port: 29555
main_training_function: main
mixed_precision: fp16
num_machines: 2
num_processes: 16
num_machines: 2 # the number of nodes
num_processes: 8 # the number of GPUs in all nodes
rdzv_backend: static
same_network: true
tpu_env: []

4
examples/accelerate/single_config.yaml
View File

@ -6,8 +6,8 @@ gpu_ids: all
machine_rank: 0
main_training_function: main
mixed_precision: fp16
num_machines: 1
num_processes: 4
num_machines: 1 # the number of nodes
num_processes: 4 # the number of GPUs in all nodes
rdzv_backend: static
same_network: true
tpu_env: []

4
examples/accelerate/slave_config.yaml
View File

@ -8,8 +8,8 @@ main_process_ip: 192.168.0.1
main_process_port: 29555
main_training_function: main
mixed_precision: fp16
num_machines: 2
num_processes: 16
num_machines: 2 # the number of nodes
num_processes: 8 # the number of GPUs in all nodes
rdzv_backend: static
same_network: true
tpu_env: []

41
examples/extras/badam/llama3_lora_sft.yaml Normal file
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@ -0,0 +1,41 @@
### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
### method
stage: sft
do_train: true
finetuning_type: full
use_badam: true
badam_switch_mode: ascending
badam_switch_interval: 50
badam_verbose: 2
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/full/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
pure_bf16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

42
examples/extras/fsdp_qlora/llama3_lora_sft.yaml Normal file
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@ -0,0 +1,42 @@
### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
quantization_bit: 4
### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
### ddp
ddp_timeout: 180000000
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

10
examples/extras/fsdp_qlora/single_node.sh Normal file
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@ -0,0 +1,10 @@
#!/bin/bash
# DO NOT use GPTQ/AWQ model in FSDP+QLoRA
pip install "transformers>=4.39.1"
pip install "accelerate>=0.28.0"
pip install "bitsandbytes>=0.43.0"
CUDA_VISIBLE_DEVICES=0,1 accelerate launch \
--config_file examples/accelerate/fsdp_config.yaml \
src/train.py examples/extras/fsdp_qlora/llama3_lora_sft.yaml

31
examples/extras/galore/adamw.sh
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@ -1,31 +0,0 @@
#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../../src/train_bash.py \
--stage sft \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../../data \
--template default \
--finetuning_type full \
--output_dir ../../../saves/LLaMA2-7B/galore/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 1 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--plot_loss \
--fp16

32
examples/extras/galore/adamw_8bit_bf16.sh
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@ -1,32 +0,0 @@
#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../../src/train_bash.py \
--stage sft \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../../data \
--template default \
--finetuning_type full \
--optim adamw_8bit \
--output_dir ../../../saves/LLaMA2-7B/galore/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 1 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--plot_loss \
--pure_bf16

35
examples/extras/galore/galore_adamw.sh
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@ -1,35 +0,0 @@
#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../../src/train_bash.py \
--stage sft \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../../data \
--template default \
--finetuning_type full \
--use_galore \
--galore_layerwise \
--galore_target mlp,self_attn \
--galore_rank 128 \
--output_dir ../../../saves/LLaMA2-7B/galore/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 1 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--plot_loss \
--fp16

36
examples/extras/galore/galore_adamw_8bit_bf16.sh
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@ -1,36 +0,0 @@
#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../../src/train_bash.py \
--stage sft \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../../data \
--template default \
--finetuning_type full \
--optim adamw_8bit \
--use_galore \
--galore_layerwise \
--galore_target mlp,self_attn \
--galore_rank 128 \
--output_dir ../../../saves/LLaMA2-7B/galore/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 1 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--plot_loss \
--pure_bf16

42
examples/extras/galore/llama3_full_sft.yaml Normal file
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@ -0,0 +1,42 @@
### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
### method
stage: sft
do_train: true
finetuning_type: full
use_galore: true
galore_layerwise: true
galore_target: mlp,self_attn
galore_rank: 128
galore_scale: 2.0
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/full/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 1
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
pure_bf16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

6
examples/extras/llama_pro/expand.sh
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@ -1,6 +1,6 @@
#!/bin/bash
python ../../../scripts/llama_pro.py \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--output_dir ../../../models/llama2-7b-pro \
python scripts/llama_pro.py \
--model_name_or_path meta-llama/Meta-Llama-3-8B-Instruct \
--output_dir models/llama3-8b-instruct-pro \
--num_expand 8

40
examples/extras/llama_pro/llama3_freeze_sft.yaml Normal file
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@ -0,0 +1,40 @@
### model
model_name_or_path: models/llama3-8b-instruct-pro
### method
stage: sft
do_train: true
finetuning_type: freeze
freeze_trainable_layers: 8
freeze_trainable_modules: all
use_llama_pro: true
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b-instruct-pro/freeze/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

34
examples/extras/llama_pro/sft.sh
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@ -1,34 +0,0 @@
#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../../src/train_bash.py \
--stage sft \
--do_train \
--model_name_or_path ../../../models/llama2-7b-pro \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../../data \
--template default \
--finetuning_type freeze \
--name_module_trainable all \
--num_layer_trainable 8 \
--use_llama_pro \
--output_dir ../../../saves/LLaMA2-7B-Pro/lora/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--plot_loss \
--fp16

39
examples/extras/loraplus/llama3_lora_sft.yaml Normal file
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@ -0,0 +1,39 @@
### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
loraplus_lr_ratio: 16.0
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

33
examples/extras/loraplus/sft.sh
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@ -1,33 +0,0 @@
#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
--stage sft \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/loraplus/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--plot_loss \
--fp16 \
--loraplus_lr_ratio 16.0

39
examples/extras/mod/llama3_full_sft.yaml Normal file
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@ -0,0 +1,39 @@
### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
### method
stage: sft
do_train: true
finetuning_type: full
mixture_of_depths: convert
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b-mod/full/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
optim: paged_adamw_8bit
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
pure_bf16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

5
examples/fsdp_qlora/README.md
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@ -1,5 +0,0 @@
```bash
pip install "transformers>=4.39.1"
pip install "accelerate>=0.28.0"
pip install "bitsandbytes>=0.43.0"
```

33
examples/fsdp_qlora/fsdp.sh
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@ -1,33 +0,0 @@
#!/bin/bash
CUDA_VISIBLE_DEVICES=0,1 accelerate launch \
--config_file ../accelerate/fsdp_config.yaml \
../../src/train_bash.py \
--stage sft \
--do_train \
--model_name_or_path meta-llama/Llama-2-70b-hf \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-70B/lora/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--quantization_bit 4 \
--plot_loss \
--fp16

23
examples/full_multi_gpu/llama3_full_predict.yaml Normal file
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@ -0,0 +1,23 @@
### model
model_name_or_path: saves/llama3-8b/full/sft
### method
stage: sft
do_predict: true
finetuning_type: full
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 50
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/full/predict
overwrite_output_dir: true
### eval
per_device_eval_batch_size: 1
predict_with_generate: true

41
examples/full_multi_gpu/llama3_full_sft.yaml Normal file
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@ -0,0 +1,41 @@
### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
### method
stage: sft
do_train: true
finetuning_type: full
### ddp
ddp_timeout: 180000000
deepspeed: examples/deepspeed/ds_z3_config.json
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/full/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 2
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

39
examples/full_multi_gpu/multi_node.sh
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@ -1,38 +1,15 @@
#!/bin/bash
python -m torch.distributed.run \
NPROC_PER_NODE=4
NNODES=2
RANK=0
MASTER_ADDR=192.168.0.1
MASTER_PORT=29500
CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun \
--nproc_per_node $NPROC_PER_NODE \
--nnodes $NNODES \
--node_rank $RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT \
../../src/train_bash.py \
--deepspeed ../deepspeed/ds_z3_config.json \
--stage sft \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../data \
--template default \
--finetuning_type full \
--output_dir ../../saves/LLaMA2-7B/full/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 2 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--ddp_timeout 1800000 \
--plot_loss \
--fp16
src/train.py examples/full_multi_gpu/llama3_full_sft.yaml

5
examples/full_multi_gpu/predict.sh Normal file
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@ -0,0 +1,5 @@
#!/bin/bash
CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch \
--config_file examples/accelerate/single_config.yaml \
src/train.py examples/full_multi_gpu/llama3_full_predict.yaml

43
examples/full_multi_gpu/single_node.sh
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@ -1,32 +1,15 @@
#!/bin/bash
deepspeed --num_gpus 4 ../../src/train_bash.py \
--deepspeed ../deepspeed/ds_z3_config.json \
--stage sft \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../data \
--template default \
--finetuning_type full \
--output_dir ../../saves/LLaMA2-7B/full/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 2 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--ddp_timeout 1800000 \
--plot_loss \
--fp16
NPROC_PER_NODE=4
NNODES=1
RANK=0
MASTER_ADDR=127.0.0.1
MASTER_PORT=29500
CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun \
--nproc_per_node $NPROC_PER_NODE \
--nnodes $NNODES \
--node_rank $RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT \
src/train.py examples/full_multi_gpu/llama3_full_sft.yaml

2
examples/inference/llama3.yaml Normal file
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@ -0,0 +1,2 @@
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
template: llama3

4
examples/inference/llama3_lora_sft.yaml Normal file
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@ -0,0 +1,4 @@
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
adapter_name_or_path: saves/llama3-8b/lora/sft
template: llama3
finetuning_type: lora

4
examples/inference/llama3_vllm.yaml Normal file
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@ -0,0 +1,4 @@
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
template: llama3
infer_backend: vllm
vllm_enforce_eager: true

15
examples/lora_multi_gpu/ds_zero3.sh Normal file
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@ -0,0 +1,15 @@
#!/bin/bash
NPROC_PER_NODE=4
NNODES=1
RANK=0
MASTER_ADDR=127.0.0.1
MASTER_PORT=29500
CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun \
--nproc_per_node $NPROC_PER_NODE \
--nnodes $NNODES \
--node_rank $RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT \
src/train.py examples/lora_multi_gpu/llama3_lora_sft_ds.yaml

41
examples/lora_multi_gpu/llama3_lora_sft.yaml Normal file
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@ -0,0 +1,41 @@
### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
### ddp
ddp_timeout: 180000000
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 2
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

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### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
### ddp
ddp_timeout: 180000000
deepspeed: examples/deepspeed/ds_z3_config.json
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 2
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

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#!/bin/bash
# also launch it on slave machine using slave_config.yaml
CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch \
--config_file ../accelerate/master_config.yaml \
../../src/train_bash.py \
--stage sft \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 2 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--ddp_timeout 1800000 \
--plot_loss \
--fp16
--config_file examples/accelerate/master_config.yaml \
src/train.py examples/lora_multi_gpu/llama3_lora_sft.yaml

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#!/bin/bash
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 accelerate launch \
--config_file ../accelerate/single_config.yaml \
../../src/train_bash.py \
--stage sft \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 2 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--ddp_timeout 1800000 \
--plot_loss \
--fp16
CUDA_VISIBLE_DEVICES=0,1,2,3 accelerate launch \
--config_file examples/accelerate/single_config.yaml \
src/train.py examples/lora_multi_gpu/llama3_lora_sft.yaml

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#!/bin/bash
NPROC_PER_NODE=4
NNODES=1
RANK=0
MASTER_ADDR=127.0.0.1
MASTER_PORT=29500
ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 torchrun \
--nproc_per_node $NPROC_PER_NODE \
--nnodes $NNODES \
--node_rank $RANK \
--master_addr $MASTER_ADDR \
--master_port $MASTER_PORT \
src/train.py examples/lora_multi_npu/llama3_lora_sft_ds.yaml

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### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
### ddp
ddp_timeout: 180000000
deepspeed: examples/deepspeed/ds_z0_config.json
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 2
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

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examples/lora_single_gpu/README.md
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Usage:
- `pretrain.sh`: do pre-train (optional)
- `sft.sh`: do supervised fine-tune
- `reward.sh`: do reward modeling (must after sft.sh)
- `ppo.sh`: do PPO training (must after sft.sh and reward.sh)
- `dpo.sh`: do DPO training (must after sft.sh)
- `predict.sh`: do predict (must after sft.sh and dpo.sh)

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examples/lora_single_gpu/dpo.sh
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#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
--stage dpo \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
--create_new_adapter \
--dataset comparison_gpt4_en \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/dpo \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 1e-5 \
--num_train_epochs 1.0 \
--max_samples 1000 \
--val_size 0.1 \
--dpo_ftx 1.0 \
--plot_loss \
--fp16

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### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
### method
stage: dpo
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
dpo_ftx: 1.0
### dataset
dataset: dpo_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/dpo
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.000005
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

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### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
adapter_name_or_path: saves/llama3-8b/lora/sft
### method
finetuning_type: lora
### dataset
task: mmlu
split: test
template: fewshot
lang: en
n_shot: 5
### output
save_dir: saves/llama3-8b/lora/eval
### eval
batch_size: 4

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examples/lora_single_gpu/llama3_lora_kto.yaml Normal file
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### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
### method
stage: kto
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
kto_ftx: 0.1
### dataset
dataset: kto_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/kto
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.000005
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

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examples/lora_single_gpu/llama3_lora_orpo.yaml Normal file
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### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
### method
stage: orpo
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
### dataset
dataset: dpo_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/orpo
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.000005
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

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### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
reward_model: saves/llama3-8b/lora/reward
### method
stage: ppo
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/ppo
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.00001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### generate
max_new_tokens: 512
top_k: 0
top_p: 0.9

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examples/lora_single_gpu/llama3_lora_predict.yaml Normal file
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### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
adapter_name_or_path: saves/llama3-8b/lora/sft
### method
stage: sft
do_predict: true
finetuning_type: lora
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 50
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/predict
overwrite_output_dir: true
### eval
per_device_eval_batch_size: 1
predict_with_generate: true

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### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
### method
stage: pt
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
### dataset
dataset: c4_demo
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

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examples/lora_single_gpu/llama3_lora_reward.yaml Normal file
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### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
### method
stage: rm
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
### dataset
dataset: dpo_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/reward
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.00001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

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examples/lora_single_gpu/llama3_lora_sft.yaml Normal file
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### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

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examples/lora_single_gpu/llama3_preprocess.yaml Normal file
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### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
tokenized_path: saves/llama3-8b/dataset/sft
### output
output_dir: saves/llama3-8b/lora/sft
overwrite_output_dir: true

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examples/lora_single_gpu/llava1_5_lora_sft.yaml Normal file
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### model
model_name_or_path: llava-hf/llava-1.5-7b-hf
visual_inputs: true
### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
### dataset
dataset: mllm_demo
template: vicuna
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llava1_5-7b/lora/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

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examples/lora_single_gpu/ppo.sh
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#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
--stage ppo \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
--create_new_adapter \
--dataset alpaca_gpt4_en \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--reward_model ../../saves/LLaMA2-7B/lora/reward \
--output_dir ../../saves/LLaMA2-7B/lora/ppo \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 512 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 100 \
--learning_rate 1e-5 \
--num_train_epochs 1.0 \
--max_samples 1000 \
--top_k 0 \
--top_p 0.9 \
--max_new_tokens 256 \
--plot_loss \
--fp16

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examples/lora_single_gpu/predict.sh
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#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
--stage sft \
--do_predict \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft,../../saves/LLaMA2-7B/lora/dpo \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--output_dir ../../saves/LLaMA2-7B/lora/predict \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_eval_batch_size 1 \
--max_samples 20 \
--predict_with_generate

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examples/lora_single_gpu/pretrain.sh
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#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
--stage pt \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--dataset c4_demo \
--dataset_dir ../../data \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/pretrain \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 10000 \
--val_size 0.1 \
--plot_loss \
--fp16

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examples/lora_single_gpu/reward.sh
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#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
--stage rm \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
--create_new_adapter \
--dataset comparison_gpt4_en \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/reward \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--learning_rate 1e-5 \
--num_train_epochs 1.0 \
--max_samples 5000 \
--val_size 0.1 \
--plot_loss \
--fp16

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examples/lora_single_gpu/sft.sh
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#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
--stage sft \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--preprocessing_num_workers 16 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--warmup_steps 20 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--plot_loss \
--fp16

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examples/merge_lora/README.md
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Usage:
- `merge.sh`: merge the lora weights
- `quantize.sh`: quantize the model with AutoGPTQ (must after merge.sh, optional)

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examples/merge_lora/llama3_gptq.yaml Normal file
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@ -0,0 +1,11 @@
### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
template: llama3
### export
export_dir: models/llama3_gptq
export_quantization_bit: 4
export_quantization_dataset: data/c4_demo.json
export_size: 2
export_device: cpu
export_legacy_format: false

13
examples/merge_lora/llama3_lora_sft.yaml Normal file
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@ -0,0 +1,13 @@
### Note: DO NOT use quantized model or quantization_bit when merging lora adapters
### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
adapter_name_or_path: saves/llama3-8b/lora/sft
template: llama3
finetuning_type: lora
### export
export_dir: models/llama3_lora_sft
export_size: 2
export_device: cpu
export_legacy_format: false

10
examples/merge_lora/merge.sh
View File

@ -1,10 +0,0 @@
#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../src/export_model.py \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--adapter_name_or_path ../../saves/LLaMA2-7B/lora/sft \
--template default \
--finetuning_type lora \
--export_dir ../../models/llama2-7b-sft \
--export_size 2 \
--export_legacy_format False

10
examples/merge_lora/quantize.sh
View File

@ -1,10 +0,0 @@
#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../src/export_model.py \
--model_name_or_path ../../models/llama2-7b-sft \
--template default \
--export_dir ../../models/llama2-7b-sft-int4 \
--export_quantization_bit 4 \
--export_quantization_dataset ../../data/c4_demo.json \
--export_size 2 \
--export_legacy_format False

30
examples/qlora_single_gpu/aqlm.sh
View File

@ -1,30 +0,0 @@
#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
--stage sft \
--do_train \
--model_name_or_path BlackSamorez/Llama-2-7b-AQLM-2Bit-1x16-hf \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--plot_loss \
--fp16

30
examples/qlora_single_gpu/awq.sh
View File

@ -1,30 +0,0 @@
#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
--stage sft \
--do_train \
--model_name_or_path TheBloke/Llama-2-7B-AWQ \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--plot_loss \
--fp16

31
examples/qlora_single_gpu/bitsandbytes.sh
View File

@ -1,31 +0,0 @@
#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
--stage sft \
--do_train \
--model_name_or_path meta-llama/Llama-2-7b-hf \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--quantization_bit 4 \
--plot_loss \
--fp16

30
examples/qlora_single_gpu/gptq.sh
View File

@ -1,30 +0,0 @@
#!/bin/bash
CUDA_VISIBLE_DEVICES=0 python ../../src/train_bash.py \
--stage sft \
--do_train \
--model_name_or_path TheBloke/Llama-2-7B-GPTQ \
--dataset alpaca_gpt4_en,glaive_toolcall \
--dataset_dir ../../data \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir ../../saves/LLaMA2-7B/lora/sft \
--overwrite_cache \
--overwrite_output_dir \
--cutoff_len 1024 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 100 \
--eval_steps 100 \
--evaluation_strategy steps \
--load_best_model_at_end \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--max_samples 3000 \
--val_size 0.1 \
--plot_loss \
--fp16

38
examples/qlora_single_gpu/llama3_lora_sft_aqlm.yaml Normal file
View File

@ -0,0 +1,38 @@
### model
model_name_or_path: ISTA-DASLab/Meta-Llama-3-8B-Instruct-AQLM-2Bit-1x16
### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

38
examples/qlora_single_gpu/llama3_lora_sft_awq.yaml Normal file
View File

@ -0,0 +1,38 @@
### model
model_name_or_path: TechxGenus/Meta-Llama-3-8B-Instruct-AWQ
### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

39
examples/qlora_single_gpu/llama3_lora_sft_bitsandbytes.yaml Normal file
View File

@ -0,0 +1,39 @@
### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
quantization_bit: 4
### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

38
examples/qlora_single_gpu/llama3_lora_sft_gptq.yaml Normal file
View File

@ -0,0 +1,38 @@
### model
model_name_or_path: TechxGenus/Meta-Llama-3-8B-Instruct-GPTQ
### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: q_proj,v_proj
### dataset
dataset: identity,alpaca_en_demo
template: llama3
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/sft
logging_steps: 10
save_steps: 500
plot_loss: true
overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 0.0001
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_steps: 0.1
fp16: true
### eval
val_size: 0.1
per_device_eval_batch_size: 1
evaluation_strategy: steps
eval_steps: 500

2
pyproject.toml
View File

@ -13,7 +13,7 @@ select = ["C", "E", "F", "I", "W"]
[tool.ruff.lint.isort]
lines-after-imports = 2
known-first-party = ["llmtuner"]
known-first-party = ["llamafactory"]
known-third-party = [
"accelerate",
"datasets",

10
requirements.txt
View File

@ -1,10 +1,9 @@
torch>=1.13.1
transformers>=4.37.2
datasets>=2.14.3
accelerate>=0.27.2
peft>=0.9.0
peft>=0.10.0
trl>=0.8.1
gradio>=3.38.0,<4.0.0
gradio>=4.0.0
scipy
einops
sentencepiece
@ -13,6 +12,7 @@ uvicorn
pydantic
fastapi
sse-starlette
matplotlib
matplotlib>=3.7.0
fire
galore-torch
packaging
pyyaml

12
scripts/cal_flops.py
View File

@ -3,24 +3,22 @@
# Usage: python cal_flops.py --model_name_or_path path_to_model --batch_size 1 --seq_length 512
# Inspired by: https://www.deepspeed.ai/tutorials/flops-profiler/
from typing import Optional
import fire
import torch
from deepspeed.accelerator import get_accelerator # type: ignore
from deepspeed.profiling.flops_profiler import get_model_profile # type: ignore
from llmtuner import ChatModel
from llamafactory.chat import ChatModel
def calculate_flops(
model_name_or_path: str,
batch_size: Optional[int] = 1,
seq_length: Optional[int] = 256,
flash_attn: Optional[bool] = False,
batch_size: int = 1,
seq_length: int = 256,
flash_attn: str = "auto",
):
with get_accelerator().device(0):
chat_model = ChatModel(dict(model_name_or_path=model_name_or_path, template="vanilla", flash_attn=flash_attn))
chat_model = ChatModel(dict(model_name_or_path=model_name_or_path, template="empty", flash_attn=flash_attn))
fake_input = torch.ones((batch_size, seq_length), dtype=torch.long, device=chat_model.model.device)
input_dict = {"input_ids": fake_input, "labels": fake_input.clone()}
flops, macs, params = get_model_profile(chat_model.model, kwargs=input_dict, print_profile=True, detailed=True)

33
scripts/cal_lr.py
View File

@ -4,7 +4,7 @@
# Inspired by: https://github.com/imoneoi/openchat/blob/master/ochat/training_deepspeed/train.py
import math
from typing import Optional
from typing import Literal
import fire
import torch
@ -12,10 +12,10 @@ from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import DataCollatorForLanguageModeling, DataCollatorForSeq2Seq
from llmtuner.data import get_dataset
from llmtuner.extras.constants import IGNORE_INDEX
from llmtuner.hparams import get_train_args
from llmtuner.model import load_model_and_tokenizer
from llamafactory.data import get_dataset
from llamafactory.extras.constants import IGNORE_INDEX
from llamafactory.hparams import get_train_args
from llamafactory.model import load_tokenizer
BASE_LR = 3e-4 # 1.5e-4 for 30B-70B models
@ -25,14 +25,14 @@ BASE_BS = 4_000_000 # from llama paper
def calculate_lr(
model_name_or_path: str,
batch_size: int, # total batch size, namely (batch size * gradient accumulation * world size)
stage: Optional[str] = "sft",
dataset: Optional[str] = "alpaca_en",
dataset_dir: Optional[str] = "data",
template: Optional[str] = "default",
cutoff_len: Optional[int] = 1024, # i.e. maximum input length during training
is_mistral: Optional[bool] = False, # mistral model uses a smaller learning rate,
stage: Literal["pt", "sft"] = "sft",
dataset: str = "alpaca_en",
dataset_dir: str = "data",
template: str = "default",
cutoff_len: int = 1024, # i.e. maximum input length during training
is_mistral: bool = False, # mistral model uses a smaller learning rate,
):
model_args, data_args, training_args, finetuning_args, _ = get_train_args(
model_args, data_args, training_args, _, _ = get_train_args(
dict(
stage=stage,
model_name_or_path=model_name_or_path,
@ -44,8 +44,9 @@ def calculate_lr(
overwrite_cache=True,
)
)
_, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, is_trainable=False, add_valuehead=False)
trainset = get_dataset(tokenizer, model_args, data_args, training_args, stage=stage)
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
trainset = get_dataset(model_args, data_args, training_args, stage, **tokenizer_module)
if stage == "pt":
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
elif stage == "sft":
@ -53,9 +54,7 @@ def calculate_lr(
else:
raise NotImplementedError
dataloader = DataLoader(
dataset=trainset, batch_size=batch_size, shuffle=True, collate_fn=data_collator, pin_memory=True
)
dataloader = DataLoader(trainset, batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True)
valid_tokens, total_tokens = 0, 0
for batch in tqdm(dataloader):
valid_tokens += torch.sum(batch["labels"] != IGNORE_INDEX).item()

116
scripts/cal_ppl.py Normal file
View File

@ -0,0 +1,116 @@
# coding=utf-8
# Calculates the ppl on the dataset of the pre-trained models.
# Usage: python cal_ppl.py --model_name_or_path path_to_model --save_name ppl.json
import json
from dataclasses import dataclass
from typing import Any, Dict, Literal, Optional, Sequence
import fire
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import DataCollatorForLanguageModeling, DataCollatorForSeq2Seq
from llamafactory.data import get_dataset
from llamafactory.extras.constants import IGNORE_INDEX
from llamafactory.hparams import get_train_args
from llamafactory.model import load_model, load_tokenizer
@dataclass
class PairwiseDataCollatorWithPadding(DataCollatorForSeq2Seq):
r"""
Data collator for pairwise data.
"""
train_on_prompt: bool = False
def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, torch.Tensor]:
r"""
Pads batched data to the longest sequence in the batch.
We generate 2 * n examples where the first n examples represent chosen examples and
the last n examples represent rejected examples.
"""
chosen_features = []
for feature in features:
prompt_len, answer_len = len(feature["prompt_ids"]), len(feature["chosen_ids"])
input_ids = feature["prompt_ids"] + feature["chosen_ids"]
attention_mask = [1] * (prompt_len + answer_len)
labels = input_ids if self.train_on_prompt else [IGNORE_INDEX] * prompt_len + feature["chosen_ids"]
chosen_features.append({"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels})
return super().__call__(chosen_features)
def cal_ppl(
model_name_or_path: str,
save_name: str,
batch_size: int = 4,
stage: Literal["pt", "sft", "rm"] = "sft",
dataset: str = "alpaca_en",
dataset_dir: str = "data",
template: str = "default",
cutoff_len: int = 1024,
max_samples: Optional[int] = None,
train_on_prompt: bool = False,
):
model_args, data_args, training_args, finetuning_args, _ = get_train_args(
dict(
stage=stage,
model_name_or_path=model_name_or_path,
dataset=dataset,
dataset_dir=dataset_dir,
template=template,
cutoff_len=cutoff_len,
max_samples=max_samples,
train_on_prompt=train_on_prompt,
output_dir="dummy_dir",
overwrite_cache=True,
)
)
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
trainset = get_dataset(model_args, data_args, training_args, stage, **tokenizer_module)
model = load_model(tokenizer, model_args, finetuning_args, is_trainable=False)
if stage == "pt":
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
elif stage == "sft":
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, label_pad_token_id=IGNORE_INDEX)
elif stage == "rm":
data_collator = PairwiseDataCollatorWithPadding(
tokenizer=tokenizer, label_pad_token_id=IGNORE_INDEX, train_on_prompt=train_on_prompt
)
else:
raise NotImplementedError
dataloader = DataLoader(trainset, batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True)
criterion = torch.nn.CrossEntropyLoss(reduction="none")
total_ppl = 0
perplexities = []
batch: Dict[str, "torch.Tensor"]
with torch.no_grad():
for batch in tqdm(dataloader):
batch = batch.to(model.device)
outputs = model(**batch)
shift_logits: "torch.Tensor" = outputs["logits"][..., :-1, :]
shift_labels: "torch.Tensor" = batch["labels"][..., 1:]
loss_mask = shift_labels != IGNORE_INDEX
flatten_logits = shift_logits.contiguous().view(shift_labels.size(0) * shift_labels.size(1), -1)
flatten_labels = shift_labels.contiguous().view(-1)
token_logps: "torch.Tensor" = criterion(flatten_logits, flatten_labels)
token_logps = token_logps.contiguous().view(shift_logits.size(0), -1)
sentence_logps = (token_logps * loss_mask).sum(-1) / loss_mask.sum(-1)
total_ppl += sentence_logps.exp().sum().item()
perplexities.extend(sentence_logps.exp().tolist())
with open(save_name, "w", encoding="utf-8") as f:
json.dump(perplexities, f, indent=2)
print("Average perplexity is {:.2f}".format(total_ppl / len(perplexities)))
print("Perplexities have been saved at {}.".format(save_name))
if __name__ == "__main__":
fire.Fire(cal_ppl)

21
scripts/length_cdf.py
View File

@ -3,24 +3,23 @@
# Usage: python length_cdf.py --model_name_or_path path_to_model --dataset alpaca_en --template default
from collections import defaultdict
from typing import Optional
import fire
from tqdm import tqdm
from llmtuner.data import get_dataset
from llmtuner.hparams import get_train_args
from llmtuner.model import load_model_and_tokenizer
from llamafactory.data import get_dataset
from llamafactory.hparams import get_train_args
from llamafactory.model import load_tokenizer
def length_cdf(
model_name_or_path: str,
dataset: Optional[str] = "alpaca_en",
dataset_dir: Optional[str] = "data",
template: Optional[str] = "default",
interval: Optional[int] = 1000,
dataset: str = "alpaca_en",
dataset_dir: str = "data",
template: str = "default",
interval: int = 1000,
):
model_args, data_args, training_args, finetuning_args, _ = get_train_args(
model_args, data_args, training_args, _, _ = get_train_args(
dict(
stage="sft",
model_name_or_path=model_name_or_path,
@ -32,8 +31,8 @@ def length_cdf(
overwrite_cache=True,
)
)
_, tokenizer = load_model_and_tokenizer(model_args, finetuning_args, is_trainable=False, add_valuehead=False)
trainset = get_dataset(tokenizer, model_args, data_args, training_args, stage="sft")
tokenizer_module = load_tokenizer(model_args)
trainset = get_dataset(model_args, data_args, training_args, stage="sft", **tokenizer_module)
total_num = len(trainset)
length_dict = defaultdict(int)
for sample in tqdm(trainset["input_ids"]):

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