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LLaMA-Factory-Mirror
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4
.dockerignore
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@ -4,10 +4,10 @@
.venv
cache
data
docker
saves
hf_cache
output
examples
.dockerignore
.gitattributes
.gitignore
Dockerfile

2
.github/ISSUE_TEMPLATE/bug-report.yml vendored
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@ -38,7 +38,9 @@ body:
请合理使用 Markdown 标签来格式化您的文本。
placeholder: |
```bash
llamafactory-cli train ...
```
- type: textarea
id: expected-behavior

1
.github/PULL_REQUEST_TEMPLATE.md vendored
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@ -5,3 +5,4 @@ Fixes # (issue)
## Before submitting
- [ ] Did you read the [contributor guideline](https://github.com/hiyouga/LLaMA-Factory/blob/main/.github/CONTRIBUTING.md)?
- [ ] Did you write any new necessary tests?

27
.github/workflows/label_issue.yml vendored Normal file
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@ -0,0 +1,27 @@
name: label_issue
on:
issues:
types:
- opened
jobs:
label_issue:
runs-on: ubuntu-latest
steps:
- env:
GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
ISSUE_URL: ${{ github.event.issue.html_url }}
ISSUE_TITLE: ${{ github.event.issue.title }}
run: |
LABEL=pending
NPU_KEYWORDS=(npu ascend huawei 华为 昇腾)
ISSUE_TITLE_LOWER=$(echo $ISSUE_TITLE | tr '[:upper:]' '[:lower:]')
for KEYWORD in ${NPU_KEYWORDS[@]}; do
if [[ $ISSUE_TITLE_LOWER == *$KEYWORD* ]] && [[ $ISSUE_TITLE_LOWER != *input* ]]; then
LABEL=pending,npu
break
fi
done
gh issue edit $ISSUE_URL --add-label $LABEL

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.github/workflows/publish.yml vendored Normal file
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@ -0,0 +1,40 @@
name: publish
on:
release:
types:
- published
jobs:
publish:
name: Upload release to PyPI
runs-on: ubuntu-latest
environment:
name: release
url: https://pypi.org/p/llamafactory
permissions:
id-token: write
steps:
- name: Checkout
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.8"
- name: Install dependencies
run: |
python -m pip install --upgrade pip
python -m pip install build
- name: Build package
run: |
python -m build
- name: Publish package
uses: pypa/gh-action-pypi-publish@release/v1

10
.github/workflows/tests.yml vendored
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@ -19,21 +19,27 @@ on:
jobs:
tests:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Checkout
uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.8"
cache: "pip"
cache-dependency-path: "setup.py"
- name: Install dependencies
run: |
python -m pip install --upgrade pip
python -m pip install .[torch,dev]
python -m pip install ".[torch,dev]"
- name: Check quality
run: |
make style && make quality
- name: Test with pytest
run: |
make test

6
.gitignore vendored
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@ -160,6 +160,8 @@ cython_debug/
.idea/
# custom .gitignore
user.config
saves/
cache/
config/
saves/
output/
wandb/

11
CITATION.cff
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@ -12,12 +12,16 @@ authors:
given-names: "Yanhan"
- family-names: "Luo"
given-names: "Zheyan"
- family-names: "Feng"
given-names: "Zhangchi"
- family-names: "Ma"
given-names: "Yongqiang"
title: "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models"
url: "https://arxiv.org/abs/2403.13372"
preferred-citation:
type: article
type: conference-paper
conference:
name: "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)"
authors:
- family-names: "Zheng"
given-names: "Yaowei"
@ -29,9 +33,12 @@ preferred-citation:
given-names: "Yanhan"
- family-names: "Luo"
given-names: "Zheyan"
- family-names: "Feng"
given-names: "Zhangchi"
- family-names: "Ma"
given-names: "Yongqiang"
journal: "arXiv preprint arXiv:2403.13372"
title: "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models"
url: "https://arxiv.org/abs/2403.13372"
year: 2024
publisher: "Association for Computational Linguistics"
address: "Bangkok, Thailand"

14
Dockerfile
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@ -1,14 +0,0 @@
FROM nvcr.io/nvidia/pytorch:24.01-py3
WORKDIR /app
COPY requirements.txt /app/
RUN pip install -r requirements.txt
COPY . /app/
RUN pip install -e .[metrics,bitsandbytes,qwen]
VOLUME [ "/root/.cache/huggingface/", "/app/data", "/app/output" ]
EXPOSE 7860
CMD [ "llamafactory-cli", "webui" ]

1
MANIFEST.in Normal file
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@ -0,0 +1 @@
include LICENSE requirements.txt

2
Makefile
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@ -11,4 +11,4 @@ style:
ruff format $(check_dirs)
test:
pytest tests/
CUDA_VISIBLE_DEVICES= pytest tests/

227
README.md
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@ -4,7 +4,7 @@
[![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/llamafactory)](https://pypi.org/project/llamafactory/)
[![Citation](https://img.shields.io/badge/citation-44-green)](#projects-using-llama-factory)
[![Citation](https://img.shields.io/badge/citation-71-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)
@ -15,7 +15,7 @@
[![GitHub Tread](https://trendshift.io/api/badge/repositories/4535)](https://trendshift.io/repositories/4535)
👋 Join our [WeChat](assets/wechat.jpg).
👋 Join our [WeChat](assets/wechat.jpg) or [NPU user group](assets/wechat_npu.jpg).
\[ English | [中文](README_zh.md) \]
@ -48,8 +48,8 @@ Choose your path:
- **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, ORPO, etc.
- **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, BAdam, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ and Agent tuning.
- **Scalable resources**: 16-bit full-tuning, freeze-tuning, LoRA and 2/3/4/5/6/8-bit QLoRA via AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ.
- **Advanced algorithms**: GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, Mixture-of-Depths, LoRA+, LoftQ, PiSSA 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.
@ -71,9 +71,9 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
## Changelog
[24/06/07] We supported fine-tuning the **[Qwen-2](https://qwenlm.github.io/blog/qwen2/)** series models.
[24/06/16] We support **[PiSSA](https://arxiv.org/abs/2404.02948)** algorithm. See [examples](examples/README.md) for usage.
[24/06/05] We supported fine-tuning the **[GLM-4-9B/GLM-4-9B-Chat](https://github.com/THUDM/GLM-4)** models.
[24/06/07] We supported fine-tuning the **[Qwen2](https://qwenlm.github.io/blog/qwen2/)** and **[GLM-4](https://github.com/THUDM/GLM-4)** models.
[24/05/26] We supported **[SimPO](https://arxiv.org/abs/2405.14734)** algorithm for preference learning. See [examples](examples/README.md) for usage.
@ -151,35 +151,32 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
## Supported Models
| Model | Model size | Template |
| -------------------------------------------------------- | -------------------------------- | --------- |
| [Baichuan2](https://huggingface.co/baichuan-inc) | 7B/13B | baichuan2 |
| [BLOOM](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | - |
| [BLOOMZ](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | - |
| [ChatGLM3](https://huggingface.co/THUDM) | 6B | chatglm3 |
| [Command-R](https://huggingface.co/CohereForAI) | 35B/104B | cohere |
| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | deepseek |
| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |
| [Gemma/CodeGemma](https://huggingface.co/google) | 2B/7B | gemma |
| [GLM4](https://huggingface.co/THUDM) | 9B | glm4 |
| [InternLM2](https://huggingface.co/internlm) | 7B/20B | intern2 |
| [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
| [LLaMA-3](https://huggingface.co/meta-llama) | 8B/70B | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | vicuna |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | - |
| [PaliGemma](https://huggingface.co/google) | 3B | gemma |
| [Phi-1.5/2](https://huggingface.co/microsoft) | 1.3B/2.7B | - |
| [Phi-3](https://huggingface.co/microsoft) | 4B/7B/14B | phi |
| [Qwen](https://huggingface.co/Qwen) | 1.8B/7B/14B/72B | qwen |
| [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen) | 0.5B/1.8B/4B/7B/14B/32B/72B/110B | qwen |
| [Qwen2 (MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/7B/57B/72B | qwen |
| [StarCoder2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
| [Yi (1/1.5)](https://huggingface.co/01-ai) | 6B/9B/34B | yi |
| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | yi_vl |
| [Yuan](https://huggingface.co/IEITYuan) | 2B/51B/102B | yuan |
| Model | Model size | Template |
| ------------------------------------------------------------ | -------------------------------- | --------- |
| [Baichuan 2](https://huggingface.co/baichuan-inc) | 7B/13B | baichuan2 |
| [BLOOM/BLOOMZ](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | - |
| [ChatGLM3](https://huggingface.co/THUDM) | 6B | chatglm3 |
| [Command R](https://huggingface.co/CohereForAI) | 35B/104B | cohere |
| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | deepseek |
| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |
| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma |
| [GLM-4](https://huggingface.co/THUDM) | 9B | glm4 |
| [InternLM2](https://huggingface.co/internlm) | 7B/20B | intern2 |
| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
| [Llama 3](https://huggingface.co/meta-llama) | 8B/70B | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | vicuna |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | - |
| [PaliGemma](https://huggingface.co/google) | 3B | gemma |
| [Phi-1.5/Phi-2](https://huggingface.co/microsoft) | 1.3B/2.7B | - |
| [Phi-3](https://huggingface.co/microsoft) | 4B/7B/14B | phi |
| [Qwen/Qwen1.5/Qwen2 (Code/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/4B/7B/14B/32B/72B/110B | qwen |
| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
| [Yi/Yi-1.5](https://huggingface.co/01-ai) | 6B/9B/34B | yi |
| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | yi_vl |
| [Yuan 2](https://huggingface.co/IEITYuan) | 2B/51B/102B | yuan |
> [!NOTE]
> 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.
@ -259,6 +256,9 @@ You also can add a custom chat template to [template.py](src/llamafactory/data/t
- [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)
- [Neo-sft (zh)](https://huggingface.co/datasets/m-a-p/neo_sft_phase2)
- [WebInstructSub (en)](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub)
- [Magpie-Pro-300K-Filtered (en)](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-300K-Filtered)
- [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)
@ -335,10 +335,10 @@ huggingface-cli login
```bash
git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
pip install -e '.[torch,metrics]'
pip install -e ".[torch,metrics]"
```
Extra dependencies available: torch, torch_npu, metrics, deepspeed, bitsandbytes, vllm, galore, badam, gptq, awq, aqlm, qwen, modelscope, quality
Extra dependencies available: torch, torch-npu, metrics, deepspeed, bitsandbytes, hqq, eetq, gptq, awq, aqlm, vllm, galore, badam, qwen, modelscope, quality
> [!TIP]
> Use `pip install --no-deps -e .` to resolve package conflicts.
@ -357,9 +357,7 @@ To enable FlashAttention-2 on the Windows platform, you need to install the prec
<details><summary>For Ascend NPU users</summary>
Join [NPU user group](assets/wechat_npu.jpg).
To install LLaMA Factory on Ascend NPU devices, please specify extra dependencies: `pip install -e '.[torch-npu,metrics]'`. Additionally, you need to install the **[Ascend CANN Toolkit and Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**. Please follow the [installation tutorial](https://www.hiascend.com/document/detail/en/CANNCommunityEdition/600alphaX/softwareinstall/instg/atlasdeploy_03_0031.html) or use the following commands:
To install LLaMA Factory on Ascend NPU devices, please specify extra dependencies: `pip install -e ".[torch-npu,metrics]"`. Additionally, you need to install the **[Ascend CANN Toolkit and Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**. Please follow the [installation tutorial](https://www.hiascend.com/document/detail/en/CANNCommunityEdition/600alphaX/softwareinstall/instg/atlasdeploy_03_0031.html) or use the following commands:
```bash
# replace the url according to your CANN version and devices
@ -382,15 +380,12 @@ source /usr/local/Ascend/ascend-toolkit/set_env.sh
| torch-npu | 2.1.0 | 2.1.0.post3 |
| deepspeed | 0.13.2 | 0.13.2 |
Docker image:
- 32GB: [Download page](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html)
- 64GB: [Download page](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
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.
Download the pre-built Docker images: [32GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html) | [64GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
</details>
### Data Preparation
@ -405,9 +400,9 @@ Please refer to [data/README.md](data/README.md) for checking the details about
Use the following 3 commands to run LoRA **fine-tuning**, **inference** and **merging** of the Llama3-8B-Instruct model, respectively.
```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
llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
```
See [examples/README.md](examples/README.md) for advanced usage (including distributed training).
@ -417,34 +412,89 @@ See [examples/README.md](examples/README.md) for advanced usage (including distr
### Fine-Tuning with LLaMA Board GUI (powered by [Gradio](https://github.com/gradio-app/gradio))
#### Use local environment
```bash
CUDA_VISIBLE_DEVICES=0 GRADIO_SHARE=1 llamafactory-cli webui
llamafactory-cli webui
```
</details>
### Build Docker
#### Use Docker
For CUDA users:
```bash
docker build -f ./Dockerfile -t llama-factory:latest .
docker run --gpus=all \
-v ./hf_cache:/root/.cache/huggingface/ \
cd docker/docker-cuda/
docker-compose up -d
docker-compose exec llamafactory bash
```
For Ascend NPU users:
```bash
cd docker/docker-npu/
docker-compose up -d
docker-compose exec llamafactory bash
```
<details><summary>Build without Docker Compose</summary>
For CUDA users:
```bash
docker build -f ./docker/docker-cuda/Dockerfile \
--build-arg INSTALL_BNB=false \
--build-arg INSTALL_VLLM=false \
--build-arg INSTALL_DEEPSPEED=false \
--build-arg INSTALL_FLASHATTN=false \
--build-arg PIP_INDEX=https://pypi.org/simple \
-t llamafactory:latest .
docker run -dit --gpus=all \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./data:/app/data \
-v ./output:/app/output \
-p 7860:7860 \
-p 8000:8000 \
--shm-size 16G \
--name llama_factory \
-d llama-factory:latest
--name llamafactory \
llamafactory:latest
docker exec -it llamafactory bash
```
#### Use Docker Compose
For Ascend NPU users:
```bash
docker compose -f ./docker-compose.yml up -d
# Choose docker image upon your environment
docker build -f ./docker/docker-npu/Dockerfile \
--build-arg INSTALL_DEEPSPEED=false \
--build-arg PIP_INDEX=https://pypi.org/simple \
-t llamafactory:latest .
# Change `device` upon your resources
docker run -dit \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./data:/app/data \
-v ./output:/app/output \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-p 7860:7860 \
-p 8000:8000 \
--device /dev/davinci0 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
--shm-size 16G \
--name llamafactory \
llamafactory:latest
docker exec -it llamafactory bash
```
</details>
<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.
@ -456,7 +506,7 @@ docker compose -f ./docker-compose.yml up -d
### Deploy with OpenAI-style API and vLLM
```bash
CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
```
> [!TIP]
@ -474,7 +524,7 @@ Train the model by specifying a model ID of the ModelScope Hub as the `model_nam
### Use W&B Logger
To use [Weights & Biases](https://wandb.ai) for logging experimental results, you need to add the following arguments.
To use [Weights & Biases](https://wandb.ai) for logging experimental results, you need to add the following arguments to yaml files.
```yaml
report_to: wandb
@ -494,38 +544,63 @@ If you have a project that should be incorporated, please contact via email or c
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)
1. Luceri et al. Leveraging Large Language Models to Detect Influence Campaigns in Social Media. 2023. [[arxiv]](https://arxiv.org/abs/2311.07816)
1. Zhang et al. Alleviating Hallucinations of Large Language Models through Induced Hallucinations. 2023. [[arxiv]](https://arxiv.org/abs/2312.15710)
1. Wang et al. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2401.04319)
1. Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2401.07286)
1. Wang et al. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs. KDD 2024. [[arxiv]](https://arxiv.org/abs/2401.04319)
1. Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. ACL 2024. [[arxiv]](https://arxiv.org/abs/2401.07286)
1. Choi et al. FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2402.05904)
1. Zhang et al. AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts. 2024. [[arxiv]](https://arxiv.org/abs/2402.07625)
1. Lyu et al. KnowTuning: Knowledge-aware Fine-tuning for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11176)
1. Yang et al. LaCo: Large Language Model Pruning via Layer Collaps. 2024. [[arxiv]](https://arxiv.org/abs/2402.11187)
1. Bhardwaj et al. Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic. 2024. [[arxiv]](https://arxiv.org/abs/2402.11746)
1. Yang et al. Enhancing Empathetic Response Generation by Augmenting LLMs with Small-scale Empathetic Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11801)
1. Yi et al. Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding. 2024. [[arxiv]](https://arxiv.org/abs/2402.11809)
1. Yi et al. Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding. ACL 2024 Findings. [[arxiv]](https://arxiv.org/abs/2402.11809)
1. Cao et al. Head-wise Shareable Attention for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11819)
1. Zhang et al. Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages. 2024. [[arxiv]](https://arxiv.org/abs/2402.12204)
1. Kim et al. Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.14714)
1. Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.15043)
1. Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. ACL 2024. [[arxiv]](https://arxiv.org/abs/2402.15043)
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. Hongbin Na. CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering. COLING 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. Ma et al. Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation. ICML 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. Zhang et al. Small Language Models Need Strong Verifiers to Self-Correct Reasoning. ACL 2024 Findings. [[arxiv]](https://arxiv.org/abs/2404.17140)
1. Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. NAACL 2024. [[arxiv]](https://arxiv.org/abs/2404.18585)
1. Xu et al. Large Language Models for Cyber Security: A Systematic Literature Review. 2024. [[arxiv]](https://arxiv.org/abs/2405.04760)
1. Dammu et al. "They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversations. 2024. [[arxiv]](https://arxiv.org/abs/2405.05378)
1. Yi et al. A safety realignment framework via subspace-oriented model fusion for large language models. 2024. [[arxiv]](https://arxiv.org/abs/2405.09055)
1. Lou et al. SPO: Multi-Dimensional Preference Sequential Alignment With Implicit Reward Modeling. 2024. [[arxiv]](https://arxiv.org/abs/2405.12739)
1. Zhang et al. Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2405.13816)
1. Zhang et al. TS-Align: A Teacher-Student Collaborative Framework for Scalable Iterative Finetuning of Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2405.20215)
1. Zihong Chen. Sentence Segmentation and Sentence Punctuation Based on XunziALLM. 2024. [[paper]](https://aclanthology.org/2024.lt4hala-1.30)
1. Gao et al. The Best of Both Worlds: Toward an Honest and Helpful Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2406.00380)
1. Wang and Song. MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset. 2024. [[arxiv]](https://arxiv.org/abs/2406.02106)
1. Hu et al. Computational Limits of Low-Rank Adaptation (LoRA) for Transformer-Based Models. 2024. [[arxiv]](https://arxiv.org/abs/2406.03136)
1. Ge et al. Time Sensitive Knowledge Editing through Efficient Finetuning. ACL 2024. [[arxiv]](https://arxiv.org/abs/2406.04496)
1. Tan et al. Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions. 2024. [[arxiv]](https://arxiv.org/abs/2406.05688)
1. Song et al. Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters. 2024. [[arxiv]](https://arxiv.org/abs/2406.05955)
1. Gu et al. RWKV-CLIP: A Robust Vision-Language Representation Learner. 2024. [[arxiv]](https://arxiv.org/abs/2406.06973)
1. Chen et al. Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees. 2024. [[arxiv]](https://arxiv.org/abs/2406.07115)
1. Zhu et al. Are Large Language Models Good Statisticians?. 2024. [[arxiv]](https://arxiv.org/abs/2406.07815)
1. Li et al. Know the Unknown: An Uncertainty-Sensitive Method for LLM Instruction Tuning. 2024. [[arxiv]](https://arxiv.org/abs/2406.10099)
1. Ding et al. IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce. 2024. [[arxiv]](https://arxiv.org/abs/2406.10173)
1. He et al. COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities. 2024. [[arxiv]](https://arxiv.org/abs/2406.12074)
1. Lin et al. FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving. 2024. [[arxiv]](https://arxiv.org/abs/2406.14408)
1. Treutlein et al. Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. 2024. [[arxiv]](https://arxiv.org/abs/2406.14546)
1. Feng et al. SS-Bench: A Benchmark for Social Story Generation and Evaluation. 2024. [[arxiv]](https://arxiv.org/abs/2406.15695)
1. Feng et al. Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement. 2024. [[arxiv]](https://arxiv.org/abs/2406.17233)
1. Liu et al. Large Language Models for Cuffless Blood Pressure Measurement From Wearable Biosignals. 2024. [[arxiv]](https://arxiv.org/abs/2406.18069)
1. Iyer et al. Exploring Very Low-Resource Translation with LLMs: The University of Edinburgh’s Submission to AmericasNLP 2024 Translation Task. AmericasNLP 2024. [[paper]](https://aclanthology.org/2024.americasnlp-1.25)
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/X-D-Lab/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
@ -533,6 +608,8 @@ If you have a project that should be incorporated, please contact via email or c
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.
1. **[AutoRE](https://github.com/THUDM/AutoRE)**: A document-level relation extraction system based on large language models.
1. **[NVIDIA RTX AI Toolkit](https://github.com/NVIDIA/RTX-AI-Toolkit)**: SDKs for fine-tuning LLMs on Windows PC for NVIDIA RTX.
</details>
@ -540,17 +617,19 @@ If you have a project that should be incorporated, please contact via email or c
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) / [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) / [GLM4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [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)
Please follow the model licenses to use the corresponding model weights: [Baichuan 2](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) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [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/Phi-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) / [StarCoder 2](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 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
## Citation
If this work is helpful, please kindly cite as:
```bibtex
@article{zheng2024llamafactory,
@inproceedings{zheng2024llamafactory,
title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models},
author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Yongqiang Ma},
journal={arXiv preprint arXiv:2403.13372},
author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Zhangchi Feng and Yongqiang Ma},
booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)},
address={Bangkok, Thailand},
publisher={Association for Computational Linguistics},
year={2024},
url={http://arxiv.org/abs/2403.13372}
}

229
README_zh.md
View File

@ -4,7 +4,7 @@
[![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/llamafactory)](https://pypi.org/project/llamafactory/)
[![Citation](https://img.shields.io/badge/citation-44-green)](#使用了-llama-factory-的项目)
[![Citation](https://img.shields.io/badge/citation-71-green)](#使用了-llama-factory-的项目)
[![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)
@ -15,7 +15,7 @@
[![GitHub Tread](https://trendshift.io/api/badge/repositories/4535)](https://trendshift.io/repositories/4535)
👋 加入我们的[微信群](assets/wechat.jpg)。
👋 加入我们的[微信群](assets/wechat.jpg)或 [NPU 用户群](assets/wechat_npu.jpg)。
\[ [English](README.md) | 中文 \]
@ -48,8 +48,8 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
- **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、ORPO 训练等等。
- **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、BAdam、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ 和 Agent 微调。
- **多种精度**：16 比特全参数微调、冻结微调、LoRA 微调和基于 AQLM/AWQ/GPTQ/LLM.int8/HQQ/EETQ 的 2/3/4/5/6/8 比特 QLoRA 微调。
- **先进算法**：GaLore、BAdam、DoRA、LongLoRA、LLaMA Pro、Mixture-of-Depths、LoRA+、LoftQ、PiSSA 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。
@ -71,9 +71,9 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
## 更新日志
[24/06/07] 我们支持了 **[Qwen-2](https://qwenlm.github.io/blog/qwen2/)** 系列模型的微调。
[24/06/16] 我们支持了 **[PiSSA](https://arxiv.org/abs/2404.02948)** 算法。详细用法请参照 [examples](examples/README_zh.md)。
[24/06/05] 我们支持了 **[GLM-4-9B/GLM-4-9B-Chat](https://github.com/THUDM/GLM-4)** 模型的微调。
[24/06/07] 我们支持了 **[Qwen2](https://qwenlm.github.io/blog/qwen2/)** 和 **[GLM-4](https://github.com/THUDM/GLM-4)** 模型的微调。
[24/05/26] 我们支持了 **[SimPO](https://arxiv.org/abs/2405.14734)** 偏好对齐算法。详细用法请参照 [examples](examples/README_zh.md)。
@ -151,35 +151,32 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
## 模型
| 模型名 | 模型大小 | Template |
| -------------------------------------------------------- | -------------------------------- | --------- |
| [Baichuan2](https://huggingface.co/baichuan-inc) | 7B/13B | baichuan2 |
| [BLOOM](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | - |
| [BLOOMZ](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | - |
| [ChatGLM3](https://huggingface.co/THUDM) | 6B | chatglm3 |
| [Command-R](https://huggingface.co/CohereForAI) | 35B/104B | cohere |
| [DeepSeek (MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | deepseek |
| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |
| [Gemma/CodeGemma](https://huggingface.co/google) | 2B/7B | gemma |
| [GLM4](https://huggingface.co/THUDM) | 9B | glm4 |
| [InternLM2](https://huggingface.co/internlm) | 7B/20B | intern2 |
| [LLaMA](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
| [LLaMA-2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
| [LLaMA-3](https://huggingface.co/meta-llama) | 8B/70B | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | vicuna |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | - |
| [PaliGemma](https://huggingface.co/google) | 3B | gemma |
| [Phi-1.5/2](https://huggingface.co/microsoft) | 1.3B/2.7B | - |
| [Phi-3](https://huggingface.co/microsoft) | 4B/7B/14B | phi |
| [Qwen](https://huggingface.co/Qwen) | 1.8B/7B/14B/72B | qwen |
| [Qwen1.5 (Code/MoE)](https://huggingface.co/Qwen) | 0.5B/1.8B/4B/7B/14B/32B/72B/110B | qwen |
| [Qwen2 (MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/7B/57B/72B | qwen |
| [StarCoder2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
| [Yi (1/1.5)](https://huggingface.co/01-ai) | 6B/9B/34B | yi |
| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | yi_vl |
| [Yuan](https://huggingface.co/IEITYuan) | 2B/51B/102B | yuan |
| 模型名 | 模型大小 | Template |
| ------------------------------------------------------------ | -------------------------------- | --------- |
| [Baichuan 2](https://huggingface.co/baichuan-inc) | 7B/13B | baichuan2 |
| [BLOOM/BLOOMZ](https://huggingface.co/bigscience) | 560M/1.1B/1.7B/3B/7.1B/176B | - |
| [ChatGLM3](https://huggingface.co/THUDM) | 6B | chatglm3 |
| [Command R](https://huggingface.co/CohereForAI) | 35B/104B | cohere |
| [DeepSeek (Code/MoE)](https://huggingface.co/deepseek-ai) | 7B/16B/67B/236B | deepseek |
| [Falcon](https://huggingface.co/tiiuae) | 7B/11B/40B/180B | falcon |
| [Gemma/Gemma 2/CodeGemma](https://huggingface.co/google) | 2B/7B/9B/27B | gemma |
| [GLM-4](https://huggingface.co/THUDM) | 9B | glm4 |
| [InternLM2](https://huggingface.co/internlm) | 7B/20B | intern2 |
| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
| [Llama 2](https://huggingface.co/meta-llama) | 7B/13B/70B | llama2 |
| [Llama 3](https://huggingface.co/meta-llama) | 8B/70B | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | vicuna |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | - |
| [PaliGemma](https://huggingface.co/google) | 3B | gemma |
| [Phi-1.5/Phi-2](https://huggingface.co/microsoft) | 1.3B/2.7B | - |
| [Phi-3](https://huggingface.co/microsoft) | 4B/7B/14B | phi |
| [Qwen/Qwen1.5/Qwen2 (Code/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/4B/7B/14B/32B/72B/110B | qwen |
| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
| [Yi/Yi-1.5](https://huggingface.co/01-ai) | 6B/9B/34B | yi |
| [Yi-VL](https://huggingface.co/01-ai) | 6B/34B | yi_vl |
| [Yuan 2](https://huggingface.co/IEITYuan) | 2B/51B/102B | yuan |
> [!NOTE]
> 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**。
@ -259,6 +256,9 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
- [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)
- [Neo-sft (zh)](https://huggingface.co/datasets/m-a-p/neo_sft_phase2)
- [WebInstructSub (en)](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub)
- [Magpie-Pro-300K-Filtered (en)](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-300K-Filtered)
- [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)
@ -335,10 +335,10 @@ huggingface-cli login
```bash
git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
pip install -e '.[torch,metrics]'
pip install -e ".[torch,metrics]"
```
可选的额外依赖项：torch、torch_npu、metrics、deepspeed、bitsandbytes、vllm、galore、badam、gptq、awq、aqlm、qwen、modelscope、quality
可选的额外依赖项：torch、torch-npu、metrics、deepspeed、bitsandbytes、hqq、eetq、gptq、awq、aqlm、vllm、galore、badam、qwen、modelscope、quality
> [!TIP]
> 遇到包冲突时，可使用 `pip install --no-deps -e .` 解决。
@ -357,9 +357,7 @@ pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/downl
<details><summary>昇腾 NPU 用户指南</summary>
加入 [NPU 用户群](assets/wechat_npu.jpg)。
在昇腾 NPU 设备上安装 LLaMA Factory 时，需要指定额外依赖项，使用 `pip install -e '.[torch-npu,metrics]'` 命令安装。此外，还需要安装 **[Ascend CANN Toolkit and Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**，安装方法请参考[安装教程](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/80RC2alpha002/quickstart/quickstart/quickstart_18_0004.html)或使用以下命令：
在昇腾 NPU 设备上安装 LLaMA Factory 时，需要指定额外依赖项，使用 `pip install -e ".[torch-npu,metrics]"` 命令安装。此外，还需要安装 **[Ascend CANN Toolkit 与 Kernels](https://www.hiascend.com/developer/download/community/result?module=cann)**，安装方法请参考[安装教程](https://www.hiascend.com/document/detail/zh/CANNCommunityEdition/80RC2alpha002/quickstart/quickstart/quickstart_18_0004.html)或使用以下命令：
```bash
# 请替换 URL 为 CANN 版本和设备型号对应的 URL
@ -382,15 +380,12 @@ source /usr/local/Ascend/ascend-toolkit/set_env.sh
| torch-npu | 2.1.0 | 2.1.0.post3 |
| 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：[下载地址](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
请使用 `ASCEND_RT_VISIBLE_DEVICES` 而非 `CUDA_VISIBLE_DEVICES` 来指定运算设备。
如果遇到无法正常推理的情况，请尝试设置 `do_sample: false`。
下载预构建 Docker 镜像：[32GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/130.html) | [64GB](http://mirrors.cn-central-221.ovaijisuan.com/detail/131.html)
</details>
### 数据准备
@ -405,9 +400,9 @@ Docker 镜像：
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
llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
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）。
@ -417,32 +412,89 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli export examples/merge_lora/llama3_lora_s
### LLaMA Board 可视化微调（由 [Gradio](https://github.com/gradio-app/gradio) 驱动）
#### 使用本地环境
```bash
CUDA_VISIBLE_DEVICES=0 GRADIO_SHARE=1 llamafactory-cli webui
llamafactory-cli webui
```
#### 使用 Docker
### 构建 Docker
CUDA 用户：
```bash
docker build -f ./Dockerfile -t llama-factory:latest .
docker run --gpus=all \
-v ./hf_cache:/root/.cache/huggingface/ \
cd docker/docker-cuda/
docker-compose up -d
docker-compose exec llamafactory bash
```
昇腾 NPU 用户：
```bash
cd docker/docker-npu/
docker-compose up -d
docker-compose exec llamafactory bash
```
<details><summary>不使用 Docker Compose 构建</summary>
CUDA 用户：
```bash
docker build -f ./docker/docker-cuda/Dockerfile \
--build-arg INSTALL_BNB=false \
--build-arg INSTALL_VLLM=false \
--build-arg INSTALL_DEEPSPEED=false \
--build-arg INSTALL_FLASHATTN=false \
--build-arg PIP_INDEX=https://pypi.org/simple \
-t llamafactory:latest .
docker run -dit --gpus=all \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./data:/app/data \
-v ./output:/app/output \
-p 7860:7860 \
-p 8000:8000 \
--shm-size 16G \
--name llama_factory \
-d llama-factory:latest
--name llamafactory \
llamafactory:latest
docker exec -it llamafactory bash
```
#### 使用 Docker Compose
昇腾 NPU 用户：
```bash
docker compose -f ./docker-compose.yml up -d
# 根据您的环境选择镜像
docker build -f ./docker/docker-npu/Dockerfile \
--build-arg INSTALL_DEEPSPEED=false \
--build-arg PIP_INDEX=https://pypi.org/simple \
-t llamafactory:latest .
# 根据您的资源更改 `device`
docker run -dit \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./data:/app/data \
-v ./output:/app/output \
-v /usr/local/dcmi:/usr/local/dcmi \
-v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
-v /usr/local/Ascend/driver:/usr/local/Ascend/driver \
-v /etc/ascend_install.info:/etc/ascend_install.info \
-p 7860:7860 \
-p 8000:8000 \
--device /dev/davinci0 \
--device /dev/davinci_manager \
--device /dev/devmm_svm \
--device /dev/hisi_hdc \
--shm-size 16G \
--name llamafactory \
llamafactory:latest
docker exec -it llamafactory bash
```
</details>
<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。
@ -454,7 +506,7 @@ docker compose -f ./docker-compose.yml up -d
### 利用 vLLM 部署 OpenAI API
```bash
CUDA_VISIBLE_DEVICES=0,1 API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
```
> [!TIP]
@ -472,7 +524,7 @@ export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
### 使用 W&B 面板
若要使用 [Weights & Biases](https://wandb.ai) 记录实验数据，请添加下面的参数。
若要使用 [Weights & Biases](https://wandb.ai) 记录实验数据，请在 yaml 文件中添加下面的参数。
```yaml
report_to: wandb
@ -492,38 +544,63 @@ run_name: test_run # 可选
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)
1. Luceri et al. Leveraging Large Language Models to Detect Influence Campaigns in Social Media. 2023. [[arxiv]](https://arxiv.org/abs/2311.07816)
1. Zhang et al. Alleviating Hallucinations of Large Language Models through Induced Hallucinations. 2023. [[arxiv]](https://arxiv.org/abs/2312.15710)
1. Wang et al. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2401.04319)
1. Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. 2024. [[arxiv]](https://arxiv.org/abs/2401.07286)
1. Wang et al. Know Your Needs Better: Towards Structured Understanding of Marketer Demands with Analogical Reasoning Augmented LLMs. KDD 2024. [[arxiv]](https://arxiv.org/abs/2401.04319)
1. Wang et al. CANDLE: Iterative Conceptualization and Instantiation Distillation from Large Language Models for Commonsense Reasoning. ACL 2024. [[arxiv]](https://arxiv.org/abs/2401.07286)
1. Choi et al. FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMs. 2024. [[arxiv]](https://arxiv.org/abs/2402.05904)
1. Zhang et al. AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts. 2024. [[arxiv]](https://arxiv.org/abs/2402.07625)
1. Lyu et al. KnowTuning: Knowledge-aware Fine-tuning for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11176)
1. Yang et al. LaCo: Large Language Model Pruning via Layer Collaps. 2024. [[arxiv]](https://arxiv.org/abs/2402.11187)
1. Bhardwaj et al. Language Models are Homer Simpson! Safety Re-Alignment of Fine-tuned Language Models through Task Arithmetic. 2024. [[arxiv]](https://arxiv.org/abs/2402.11746)
1. Yang et al. Enhancing Empathetic Response Generation by Augmenting LLMs with Small-scale Empathetic Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11801)
1. Yi et al. Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding. 2024. [[arxiv]](https://arxiv.org/abs/2402.11809)
1. Yi et al. Generation Meets Verification: Accelerating Large Language Model Inference with Smart Parallel Auto-Correct Decoding. ACL 2024 Findings. [[arxiv]](https://arxiv.org/abs/2402.11809)
1. Cao et al. Head-wise Shareable Attention for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11819)
1. Zhang et al. Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages. 2024. [[arxiv]](https://arxiv.org/abs/2402.12204)
1. Kim et al. Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.14714)
1. Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.15043)
1. Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. ACL 2024. [[arxiv]](https://arxiv.org/abs/2402.15043)
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. Hongbin Na. CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering. COLING 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. Ma et al. Parameter Efficient Quasi-Orthogonal Fine-Tuning via Givens Rotation. ICML 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. Zhang et al. Small Language Models Need Strong Verifiers to Self-Correct Reasoning. ACL 2024 Findings. [[arxiv]](https://arxiv.org/abs/2404.17140)
1. Zhou et al. FREB-TQA: A Fine-Grained Robustness Evaluation Benchmark for Table Question Answering. NAACL 2024. [[arxiv]](https://arxiv.org/abs/2404.18585)
1. Xu et al. Large Language Models for Cyber Security: A Systematic Literature Review. 2024. [[arxiv]](https://arxiv.org/abs/2405.04760)
1. Dammu et al. "They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversations. 2024. [[arxiv]](https://arxiv.org/abs/2405.05378)
1. Yi et al. A safety realignment framework via subspace-oriented model fusion for large language models. 2024. [[arxiv]](https://arxiv.org/abs/2405.09055)
1. Lou et al. SPO: Multi-Dimensional Preference Sequential Alignment With Implicit Reward Modeling. 2024. [[arxiv]](https://arxiv.org/abs/2405.12739)
1. Zhang et al. Getting More from Less: Large Language Models are Good Spontaneous Multilingual Learners. 2024. [[arxiv]](https://arxiv.org/abs/2405.13816)
1. Zhang et al. TS-Align: A Teacher-Student Collaborative Framework for Scalable Iterative Finetuning of Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2405.20215)
1. Zihong Chen. Sentence Segmentation and Sentence Punctuation Based on XunziALLM. 2024. [[paper]](https://aclanthology.org/2024.lt4hala-1.30)
1. Gao et al. The Best of Both Worlds: Toward an Honest and Helpful Large Language Model. 2024. [[arxiv]](https://arxiv.org/abs/2406.00380)
1. Wang and Song. MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset. 2024. [[arxiv]](https://arxiv.org/abs/2406.02106)
1. Hu et al. Computational Limits of Low-Rank Adaptation (LoRA) for Transformer-Based Models. 2024. [[arxiv]](https://arxiv.org/abs/2406.03136)
1. Ge et al. Time Sensitive Knowledge Editing through Efficient Finetuning. ACL 2024. [[arxiv]](https://arxiv.org/abs/2406.04496)
1. Tan et al. Peer Review as A Multi-Turn and Long-Context Dialogue with Role-Based Interactions. 2024. [[arxiv]](https://arxiv.org/abs/2406.05688)
1. Song et al. Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters. 2024. [[arxiv]](https://arxiv.org/abs/2406.05955)
1. Gu et al. RWKV-CLIP: A Robust Vision-Language Representation Learner. 2024. [[arxiv]](https://arxiv.org/abs/2406.06973)
1. Chen et al. Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees. 2024. [[arxiv]](https://arxiv.org/abs/2406.07115)
1. Zhu et al. Are Large Language Models Good Statisticians?. 2024. [[arxiv]](https://arxiv.org/abs/2406.07815)
1. Li et al. Know the Unknown: An Uncertainty-Sensitive Method for LLM Instruction Tuning. 2024. [[arxiv]](https://arxiv.org/abs/2406.10099)
1. Ding et al. IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce. 2024. [[arxiv]](https://arxiv.org/abs/2406.10173)
1. He et al. COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities. 2024. [[arxiv]](https://arxiv.org/abs/2406.12074)
1. Lin et al. FVEL: Interactive Formal Verification Environment with Large Language Models via Theorem Proving. 2024. [[arxiv]](https://arxiv.org/abs/2406.14408)
1. Treutlein et al. Connecting the Dots: LLMs can Infer and Verbalize Latent Structure from Disparate Training Data. 2024. [[arxiv]](https://arxiv.org/abs/2406.14546)
1. Feng et al. SS-Bench: A Benchmark for Social Story Generation and Evaluation. 2024. [[arxiv]](https://arxiv.org/abs/2406.15695)
1. Feng et al. Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement. 2024. [[arxiv]](https://arxiv.org/abs/2406.17233)
1. Liu et al. Large Language Models for Cuffless Blood Pressure Measurement From Wearable Biosignals. 2024. [[arxiv]](https://arxiv.org/abs/2406.18069)
1. Iyer et al. Exploring Very Low-Resource Translation with LLMs: The University of Edinburgh’s Submission to AmericasNLP 2024 Translation Task. AmericasNLP 2024. [[paper]](https://aclanthology.org/2024.americasnlp-1.25)
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/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。
@ -531,6 +608,8 @@ run_name: test_run # 可选
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。
1. **[AutoRE](https://github.com/THUDM/AutoRE)**：基于大语言模型的文档级关系抽取系统。
1. **[NVIDIA RTX AI Toolkit](https://github.com/NVIDIA/RTX-AI-Toolkit)**: 在 Windows 主机上利用英伟达 RTX 设备进行大型语言模型微调的开发包。
</details>
@ -538,17 +617,19 @@ run_name: test_run # 可选
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) / [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) / [GLM4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [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)
使用模型权重时，请遵循对应的模型协议：[Baichuan 2](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) / [GLM-4](https://huggingface.co/THUDM/glm-4-9b/blob/main/LICENSE) / [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/Phi-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) / [StarCoder 2](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 2](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/LICENSE-Yuan)
## 引用
如果您觉得此项目有帮助，请考虑以下列格式引用
```bibtex
@article{zheng2024llamafactory,
title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models},
author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Yongqiang Ma},
journal={arXiv preprint arXiv:2403.13372},
@inproceedings{zheng2024llamafactory,
title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models},
author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Zhangchi Feng and Yongqiang Ma},
booktitle={Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)},
address={Bangkok, Thailand},
publisher={Association for Computational Linguistics},
year={2024},
url={http://arxiv.org/abs/2403.13372}
}

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data/dataset_info.json
View File

@ -248,6 +248,21 @@
"ruozhiba_gpt4": {
"hf_hub_url": "hfl/ruozhiba_gpt4_turbo"
},
"neo_sft": {
"hf_hub_url": "m-a-p/neo_sft_phase2",
"formatting": "sharegpt"
},
"magpie_pro_300k": {
"hf_hub_url": "Magpie-Align/Magpie-Pro-300K-Filtered",
"formatting": "sharegpt"
},
"web_instruct": {
"hf_hub_url": "TIGER-Lab/WebInstructSub",
"columns": {
"prompt": "question",
"response": "answer"
}
},
"llava_1k_en": {
"hf_hub_url": "BUAADreamer/llava-en-zh-2k",
"subset": "en",
@ -520,13 +535,13 @@
"prompt": "text"
}
},
"fileweb": {
"fineweb": {
"hf_hub_url": "HuggingFaceFW/fineweb",
"columns": {
"prompt": "text"
}
},
"fileweb_edu": {
"fineweb_edu": {
"hf_hub_url": "HuggingFaceFW/fineweb-edu",
"columns": {
"prompt": "text"

74
data/glaive_toolcall_en_demo.json
View File

@ -1,4 +1,41 @@
[
{
"conversations": [
{
"from": "human",
"value": "Hi, I have some ingredients and I want to cook something. Can you help me find a recipe?"
},
{
"from": "gpt",
"value": "Of course! I can help you with that. Please tell me what ingredients you have."
},
{
"from": "human",
"value": "I have chicken, bell peppers, and rice."
},
{
"from": "function_call",
"value": "{\"name\": \"search_recipes\", \"arguments\": {\"ingredients\": [\"chicken\", \"bell peppers\", \"rice\"]}}"
},
{
"from": "observation",
"value": "{\"recipes\": [{\"name\": \"Chicken and Bell Pepper Stir Fry\", \"ingredients\": [\"chicken\", \"bell peppers\", \"rice\"], \"instructions\": \"Cut the chicken into small pieces. Slice the bell peppers. Cook the rice. Stir fry the chicken and bell peppers. Serve over rice.\"}, {\"name\": \"Chicken and Rice Casserole\", \"ingredients\": [\"chicken\", \"bell peppers\", \"rice\"], \"instructions\": \"Cook the chicken and rice separately. Mix them together with the bell peppers in a casserole dish. Bake until golden brown.\"}]}"
},
{
"from": "gpt",
"value": "I found two recipes for you. The first one is \"Chicken and Bell Pepper Stir Fry\". The instructions are: Cut the chicken into small pieces. Slice the bell peppers. Cook the rice. Stir fry the chicken and bell peppers. Serve over rice. The second one is \"Chicken and Rice Casserole\". The instructions are: Cook the chicken and rice separately. Mix them together with the bell peppers in a casserole dish. Bake until golden brown. Which one would you like to try?"
},
{
"from": "human",
"value": "They both sound delicious, but I think I'll try the stir fry. Can you order the ingredients for me?"
},
{
"from": "gpt",
"value": "I'm sorry, but as an AI, I don't have the capability to perform external tasks such as ordering ingredients. However, I can help you find more recipes or provide cooking tips if you need."
}
],
"tools": "[{\"name\": \"search_recipes\", \"description\": \"Search for recipes based on ingredients\", \"parameters\": {\"type\": \"object\", \"properties\": {\"ingredients\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"The ingredients to search for\"}}, \"required\": [\"ingredients\"]}}]"
},
{
"conversations": [
{
@ -155,43 +192,6 @@
],
"tools": "[]"
},
{
"conversations": [
{
"from": "human",
"value": "Hi, I have some ingredients and I want to cook something. Can you help me find a recipe?"
},
{
"from": "gpt",
"value": "Of course! I can help you with that. Please tell me what ingredients you have."
},
{
"from": "human",
"value": "I have chicken, bell peppers, and rice."
},
{
"from": "function_call",
"value": "{\"name\": \"search_recipes\", \"arguments\": {\"ingredients\": [\"chicken\", \"bell peppers\", \"rice\"]}}"
},
{
"from": "observation",
"value": "{\"recipes\": [{\"name\": \"Chicken and Bell Pepper Stir Fry\", \"ingredients\": [\"chicken\", \"bell peppers\", \"rice\"], \"instructions\": \"Cut the chicken into small pieces. Slice the bell peppers. Cook the rice. Stir fry the chicken and bell peppers. Serve over rice.\"}, {\"name\": \"Chicken and Rice Casserole\", \"ingredients\": [\"chicken\", \"bell peppers\", \"rice\"], \"instructions\": \"Cook the chicken and rice separately. Mix them together with the bell peppers in a casserole dish. Bake until golden brown.\"}]}"
},
{
"from": "gpt",
"value": "I found two recipes for you. The first one is \"Chicken and Bell Pepper Stir Fry\". The instructions are: Cut the chicken into small pieces. Slice the bell peppers. Cook the rice. Stir fry the chicken and bell peppers. Serve over rice. The second one is \"Chicken and Rice Casserole\". The instructions are: Cook the chicken and rice separately. Mix them together with the bell peppers in a casserole dish. Bake until golden brown. Which one would you like to try?"
},
{
"from": "human",
"value": "They both sound delicious, but I think I'll try the stir fry. Can you order the ingredients for me?"
},
{
"from": "gpt",
"value": "I'm sorry, but as an AI, I don't have the capability to perform external tasks such as ordering ingredients. However, I can help you find more recipes or provide cooking tips if you need."
}
],
"tools": "[{\"name\": \"search_recipes\", \"description\": \"Search for recipes based on ingredients\", \"parameters\": {\"type\": \"object\", \"properties\": {\"ingredients\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"The ingredients to search for\"}}, \"required\": [\"ingredients\"]}}]"
},
{
"conversations": [
{

42
data/glaive_toolcall_zh_demo.json
View File

@ -1,25 +1,4 @@
[
{
"conversations": [
{
"from": "human",
"value": "假设你有一个需要随机数的Java程序，范围在0到10之间。你可以使用什么代码片段来生成这样的数字？\n\n下面是一个示例代码片段，它使用Math.random()方法来生成一个在0到1之间的随机数，然后缩放并四舍五入结果，得到一个在0到10之间的随机数：\n\n```java\nint randomNumber = (int) (Math.random() * 11);\n```\n\n在Java中是否有更有效的方法来生成随机数？你会如何修改代码以生成具有不同范围或分布的随机数？"
},
{
"from": "gpt",
"value": "您提供的代码片段是Java中生成随机数的一种常见且高效的方法。然而，如果您需要对随机数生成有更多的控制，您可以使用java.util.Random类，它提供了更多生成随机数的选项。\n\n要生成一个在不同范围内的随机数，您可以修改代码以调整缩放因子。例如，要生成一个在1到10之间的随机数，您可以使用以下代码：\n\nint randomNumber = (int) (Math.random() * 10) + 1;\n\n要生成具有不同分布的随机数，您可以使用各种技术，如正态分布、指数分布或自定义分布。生成正态分布的一种方法是使用Box-Muller变换。这里有一个代码示例，它生成具有正态分布的随机数：\n\nRandom random = new Random();\ndouble u1 = random.nextDouble();\ndouble u2 = random.nextDouble();\ndouble z = Math.sqrt(-2 * Math.log(u1)) * Math.cos(2 * Math.PI * u2);\ndouble mean = 5;\ndouble stdDev = 2;\nint randomNumber = (int) (z * stdDev + mean);\n\n这段代码生成一个具有均值5和标准差2的正态分布随机数。您可以调整均值和标准差来生成具有不同分布的随机数。"
},
{
"from": "human",
"value": "你能解释一下算法分析中的大O记法概念吗？"
},
{
"from": "gpt",
"value": "是的，大O表示法是一种描述算法性能随输入数据大小增加的方法。它用于估计算法在最坏情况下的时间复杂度或空间复杂度。大O中的“O”代表“数量级”，这意味着随着输入大小的增加，算法所需的操作数以一定的速率增长。\n\n例如，如果我们有一个需要n步才能解决大小为n的问题的算法，那么时间复杂度可以表示为O(n)，这意味着操作数随着输入大小线性增长。如果我们有一个需要n^2步才能解决大小为n的问题的算法，那么时间复杂度可以表示为O(n^2)，这意味着操作数随着输入大小二次方增长。\n\n还有其他几种大O表示法，如O(log n)、O(n log n)、O(2^n)等，它们代表了随着输入大小增加的不同操作数增长速率。通过分析算法的大O表示法，我们可以比较不同算法的性能，并选择给定问题最有效的算法。\n\n您是否有关于大O表示法或算法分析的特定问题？"
}
],
"tools": "[]"
},
{
"conversations": [
{
@ -41,6 +20,27 @@
],
"tools": "[{\"name\": \"generate_invoice\", \"description\": \"生成发票\", \"parameters\": {\"type\": \"object\", \"properties\": {\"customer_name\": {\"type\": \"string\", \"description\": \"客户名称\"}, \"items\": {\"type\": \"array\", \"items\": {\"type\": \"object\", \"properties\": {\"name\": {\"type\": \"string\", \"description\": \"The item name\"}, \"quantity\": {\"type\": \"integer\", \"description\": \"The quantity of the item\"}, \"price\": {\"type\": \"number\", \"description\": \"The price per unit\"}}, \"required\": [\"name\", \"quantity\", \"price\"]}}}, \"required\": [\"customer_name\", \"items\"]}}, {\"name\": \"generate_password\", \"description\": \"生成随机密码\", \"parameters\": {\"type\": \"object\", \"properties\": {\"length\": {\"type\": \"integer\", \"description\": \"密码的长度\"}}, \"required\": [\"length\"]}}]"
},
{
"conversations": [
{
"from": "human",
"value": "假设你有一个需要随机数的Java程序，范围在0到10之间。你可以使用什么代码片段来生成这样的数字？\n\n下面是一个示例代码片段，它使用Math.random()方法来生成一个在0到1之间的随机数，然后缩放并四舍五入结果，得到一个在0到10之间的随机数：\n\n```java\nint randomNumber = (int) (Math.random() * 11);\n```\n\n在Java中是否有更有效的方法来生成随机数？你会如何修改代码以生成具有不同范围或分布的随机数？"
},
{
"from": "gpt",
"value": "您提供的代码片段是Java中生成随机数的一种常见且高效的方法。然而，如果您需要对随机数生成有更多的控制，您可以使用java.util.Random类，它提供了更多生成随机数的选项。\n\n要生成一个在不同范围内的随机数，您可以修改代码以调整缩放因子。例如，要生成一个在1到10之间的随机数，您可以使用以下代码：\n\nint randomNumber = (int) (Math.random() * 10) + 1;\n\n要生成具有不同分布的随机数，您可以使用各种技术，如正态分布、指数分布或自定义分布。生成正态分布的一种方法是使用Box-Muller变换。这里有一个代码示例，它生成具有正态分布的随机数：\n\nRandom random = new Random();\ndouble u1 = random.nextDouble();\ndouble u2 = random.nextDouble();\ndouble z = Math.sqrt(-2 * Math.log(u1)) * Math.cos(2 * Math.PI * u2);\ndouble mean = 5;\ndouble stdDev = 2;\nint randomNumber = (int) (z * stdDev + mean);\n\n这段代码生成一个具有均值5和标准差2的正态分布随机数。您可以调整均值和标准差来生成具有不同分布的随机数。"
},
{
"from": "human",
"value": "你能解释一下算法分析中的大O记法概念吗？"
},
{
"from": "gpt",
"value": "是的，大O表示法是一种描述算法性能随输入数据大小增加的方法。它用于估计算法在最坏情况下的时间复杂度或空间复杂度。大O中的“O”代表“数量级”，这意味着随着输入大小的增加，算法所需的操作数以一定的速率增长。\n\n例如，如果我们有一个需要n步才能解决大小为n的问题的算法，那么时间复杂度可以表示为O(n)，这意味着操作数随着输入大小线性增长。如果我们有一个需要n^2步才能解决大小为n的问题的算法，那么时间复杂度可以表示为O(n^2)，这意味着操作数随着输入大小二次方增长。\n\n还有其他几种大O表示法，如O(log n)、O(n log n)、O(2^n)等，它们代表了随着输入大小增加的不同操作数增长速率。通过分析算法的大O表示法，我们可以比较不同算法的性能，并选择给定问题最有效的算法。\n\n您是否有关于大O表示法或算法分析的特定问题？"
}
],
"tools": "[]"
},
{
"conversations": [
{

23
docker-compose.yml
View File

@ -1,23 +0,0 @@
version: '3.8'
services:
llama-factory:
build:
dockerfile: Dockerfile
context: .
container_name: llama_factory
volumes:
- ./hf_cache:/root/.cache/huggingface/
- ./data:/app/data
- ./output:/app/output
ports:
- "7860:7860"
ipc: host
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: "all"
capabilities: [gpu]
restart: unless-stopped

58
docker/docker-cuda/Dockerfile Normal file
View File

@ -0,0 +1,58 @@
# Use the NVIDIA official image with PyTorch 2.3.0
# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-02.html
FROM nvcr.io/nvidia/pytorch:24.02-py3
# Define environments
ENV MAX_JOBS=4
ENV FLASH_ATTENTION_FORCE_BUILD=TRUE
# Define installation arguments
ARG INSTALL_BNB=false
ARG INSTALL_VLLM=false
ARG INSTALL_DEEPSPEED=false
ARG INSTALL_FLASHATTN=false
ARG PIP_INDEX=https://pypi.org/simple
# Set the working directory
WORKDIR /app
# Install the requirements
COPY requirements.txt /app
RUN pip config set global.index-url "$PIP_INDEX" && \
pip config set global.extra-index-url "$PIP_INDEX" && \
python -m pip install --upgrade pip && \
python -m pip install -r requirements.txt
# Rebuild flash attention
RUN pip uninstall -y transformer-engine flash-attn && \
if [ "$INSTALL_FLASHATTN" == "true" ]; then \
pip uninstall -y ninja && pip install ninja && \
pip install --no-cache-dir flash-attn --no-build-isolation; \
fi
# Copy the rest of the application into the image
COPY . /app
# Install the LLaMA Factory
RUN EXTRA_PACKAGES="metrics"; \
if [ "$INSTALL_BNB" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},bitsandbytes"; \
fi; \
if [ "$INSTALL_VLLM" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},vllm"; \
fi; \
if [ "$INSTALL_DEEPSPEED" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
fi; \
pip install -e ".[$EXTRA_PACKAGES]"
# Set up volumes
VOLUME [ "/root/.cache/huggingface", "/root/.cache/modelscope", "/app/data", "/app/output" ]
# Expose port 7860 for the LLaMA Board
ENV GRADIO_SERVER_PORT 7860
EXPOSE 7860
# Expose port 8000 for the API service
ENV API_PORT 8000
EXPOSE 8000

32
docker/docker-cuda/docker-compose.yml Normal file
View File

@ -0,0 +1,32 @@
services:
llamafactory:
build:
dockerfile: ./docker/docker-cuda/Dockerfile
context: ../..
args:
INSTALL_BNB: false
INSTALL_VLLM: false
INSTALL_DEEPSPEED: false
INSTALL_FLASHATTN: false
PIP_INDEX: https://pypi.org/simple
container_name: llamafactory
volumes:
- ../../hf_cache:/root/.cache/huggingface
- ../../ms_cache:/root/.cache/modelscope
- ../../data:/app/data
- ../../output:/app/output
ports:
- "7860:7860"
- "8000:8000"
ipc: host
tty: true
stdin_open: true
command: bash
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: "all"
capabilities: [gpu]
restart: unless-stopped

45
docker/docker-npu/Dockerfile Normal file
View File

@ -0,0 +1,45 @@
# Use the Ubuntu 22.04 image with CANN 8.0.rc1
# More versions can be found at https://hub.docker.com/r/cosdt/cann/tags
# FROM cosdt/cann:8.0.rc1-910-ubuntu22.04
FROM cosdt/cann:8.0.rc1-910b-ubuntu22.04
# FROM cosdt/cann:8.0.rc1-910-openeuler22.03
# FROM cosdt/cann:8.0.rc1-910b-openeuler22.03
# Define environments
ENV DEBIAN_FRONTEND=noninteractive
# Define installation arguments
ARG INSTALL_DEEPSPEED=false
ARG PIP_INDEX=https://pypi.org/simple
ARG TORCH_INDEX=https://download.pytorch.org/whl/cpu
# Set the working directory
WORKDIR /app
# Install the requirements
COPY requirements.txt /app
RUN pip config set global.index-url "$PIP_INDEX" && \
pip config set global.extra-index-url "$TORCH_INDEX" && \
python -m pip install --upgrade pip && \
python -m pip install -r requirements.txt
# Copy the rest of the application into the image
COPY . /app
# Install the LLaMA Factory
RUN EXTRA_PACKAGES="torch-npu,metrics"; \
if [ "$INSTALL_DEEPSPEED" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
fi; \
pip install -e ".[$EXTRA_PACKAGES]"
# Set up volumes
VOLUME [ "/root/.cache/huggingface", "/root/.cache/modelscope", "/app/data", "/app/output" ]
# Expose port 7860 for the LLaMA Board
ENV GRADIO_SERVER_PORT 7860
EXPOSE 7860
# Expose port 8000 for the API service
ENV API_PORT 8000
EXPOSE 8000

31
docker/docker-npu/docker-compose.yml Normal file
View File

@ -0,0 +1,31 @@
services:
llamafactory:
build:
dockerfile: ./docker/docker-npu/Dockerfile
context: ../..
args:
INSTALL_DEEPSPEED: false
PIP_INDEX: https://pypi.org/simple
container_name: llamafactory
volumes:
- ../../hf_cache:/root/.cache/huggingface
- ../../ms_cache:/root/.cache/modelscope
- ../../data:/app/data
- ../../output:/app/output
- /usr/local/dcmi:/usr/local/dcmi
- /usr/local/bin/npu-smi:/usr/local/bin/npu-smi
- /usr/local/Ascend/driver:/usr/local/Ascend/driver
- /etc/ascend_install.info:/etc/ascend_install.info
ports:
- "7860:7860"
- "8000:8000"
ipc: host
tty: true
stdin_open: true
command: bash
devices:
- /dev/davinci0
- /dev/davinci_manager
- /dev/devmm_svm
- /dev/hisi_hdc
restart: unless-stopped

1
evaluation/ceval/ceval.py
View File

@ -11,6 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import datasets

1
evaluation/cmmlu/cmmlu.py
View File

@ -11,6 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import datasets

1
evaluation/mmlu/mmlu.py
View File

@ -11,6 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import datasets

136
examples/README.md
View File

@ -4,59 +4,59 @@ 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)
- [LoRA Fine-Tuning](#lora-fine-tuning)
- [QLoRA Fine-Tuning](#qlora-fine-tuning)
- [Full-Parameter Fine-Tuning](#full-parameter-fine-tuning)
- [Merging LoRA Adapters and Quantization](#merging-lora-adapters-and-quantization)
- [Inferring LoRA Fine-Tuned Models](#inferring-lora-fine-tuned-models)
- [Extras](#extras)
Use `CUDA_VISIBLE_DEVICES` (GPU) or `ASCEND_RT_VISIBLE_DEVICES` (NPU) to choose computing devices.
## Examples
### LoRA Fine-Tuning on A Single GPU
### LoRA Fine-Tuning
#### (Continuous) Pre-Training
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_pretrain.yaml
llamafactory-cli train examples/train_lora/llama3_lora_pretrain.yaml
```
#### Supervised Fine-Tuning
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_sft.yaml
llamafactory-cli train examples/train_lora/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
llamafactory-cli train examples/train_lora/llava1_5_lora_sft.yaml
```
#### Reward Modeling
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_reward.yaml
llamafactory-cli train examples/train_lora/llama3_lora_reward.yaml
```
#### PPO Training
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_ppo.yaml
llamafactory-cli train examples/train_lora/llama3_lora_ppo.yaml
```
#### DPO/ORPO/SimPO Training
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_dpo.yaml
llamafactory-cli train examples/train_lora/llama3_lora_dpo.yaml
```
#### KTO Training
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_kto.yaml
llamafactory-cli train examples/train_lora/llama3_lora_kto.yaml
```
#### Preprocess Dataset
@ -64,95 +64,79 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lo
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
llamafactory-cli train examples/train_lora/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
llamafactory-cli eval examples/train_lora/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 on Single Node
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
llamafactory-cli train examples/train_lora/llama3_lora_predict.yaml
```
#### Supervised Fine-Tuning on Multiple Nodes
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
```
#### Supervised Fine-Tuning with DeepSpeed ZeRO-3 (Weight Sharding)
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft_ds.yaml
FORCE_TORCHRUN=1 llamafactory-cli train examples/train_lora/llama3_lora_sft_ds3.yaml
```
### LoRA Fine-Tuning on Multiple NPUs
### QLoRA Fine-Tuning
#### Supervised Fine-Tuning with DeepSpeed ZeRO-0
#### Supervised Fine-Tuning with 4/8-bit Bitsandbytes/HQQ/EETQ Quantization (Recommended)
```bash
ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_npu/llama3_lora_sft_ds.yaml
llamafactory-cli train examples/train_qlora/llama3_lora_sft_otfq.yaml
```
### Full-Parameter Fine-Tuning on Multiple GPUs
#### Supervised Fine-Tuning with 4/8-bit GPTQ Quantization
```bash
llamafactory-cli train examples/train_qlora/llama3_lora_sft_gptq.yaml
```
#### Supervised Fine-Tuning with 4-bit AWQ Quantization
```bash
llamafactory-cli train examples/train_qlora/llama3_lora_sft_awq.yaml
```
#### Supervised Fine-Tuning with 2-bit AQLM Quantization
```bash
llamafactory-cli train examples/train_qlora/llama3_lora_sft_aqlm.yaml
```
### Full-Parameter Fine-Tuning
#### Supervised Fine-Tuning on Single Node
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/llama3_full_sft_ds3.yaml
```
#### Supervised Fine-Tuning on Multiple Nodes
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft_ds3.yaml
FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft_ds3.yaml
```
#### Batch Predicting and Computing BLEU and ROUGE Scores
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llama3_full_predict.yaml
llamafactory-cli train examples/train_full/llama3_full_predict.yaml
```
### Merging LoRA Adapters and Quantization
@ -162,35 +146,33 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llam
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
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
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
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
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
llamafactory-cli api examples/inference/llama3_lora_sft.yaml
```
### Extras
@ -198,36 +180,42 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli api examples/inference/llama3_lora_sft.y
#### Full-Parameter Fine-Tuning using GaLore
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/galore/llama3_full_sft.yaml
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
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
llamafactory-cli train examples/extras/loraplus/llama3_lora_sft.yaml
```
#### PiSSA Fine-Tuning
```bash
llamafactory-cli train examples/extras/pissa/llama3_lora_sft.yaml
```
#### Mixture-of-Depths Fine-Tuning
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/extras/mod/llama3_full_sft.yaml
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
llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
```
#### FSDP+QLoRA Fine-Tuning
```bash
bash examples/extras/fsdp_qlora/single_node.sh
bash examples/extras/fsdp_qlora/train.sh
```

138
examples/README_zh.md
View File

@ -4,59 +4,59 @@
## 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)
- [LoRA 微调](#lora-微调)
- [QLoRA 微调](#qlora-微调)
- [全参数微调](#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)
使用 `CUDA_VISIBLE_DEVICES`（GPU）或 `ASCEND_RT_VISIBLE_DEVICES`（NPU）选择计算设备。
## 示例
### 单 GPU LoRA 微调
### LoRA 微调
#### （增量）预训练
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_pretrain.yaml
llamafactory-cli train examples/train_lora/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
llamafactory-cli train examples/train_lora/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
llamafactory-cli train examples/train_lora/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
llamafactory-cli train examples/train_lora/llama3_lora_reward.yaml
```
#### PPO 训练
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_ppo.yaml
llamafactory-cli train examples/train_lora/llama3_lora_ppo.yaml
```
#### DPO/ORPO/SimPO 训练
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_dpo.yaml
llamafactory-cli train examples/train_lora/llama3_lora_dpo.yaml
```
#### KTO 训练
```bash
CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lora_kto.yaml
llamafactory-cli train examples/train_lora/llama3_lora_kto.yaml
```
#### 预处理数据集
@ -64,95 +64,79 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli train examples/lora_single_gpu/llama3_lo
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
llamafactory-cli train examples/train_lora/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
llamafactory-cli eval examples/train_lora/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
llamafactory-cli train examples/train_lora/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）
#### 多机指令监督微调
```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
#### 在单机上进行指令监督微调
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
```
#### 在多机上进行指令监督微调
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft.yaml
FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
```
#### 使用 DeepSpeed ZeRO-3 平均分配显存
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_gpu/llama3_lora_sft_ds.yaml
FORCE_TORCHRUN=1 llamafactory-cli train examples/train_lora/llama3_lora_sft_ds3.yaml
```
### 多 NPU LoRA 微调
### QLoRA 微调
#### 使用 DeepSpeed ZeRO-0 进行指令监督微调
#### 基于 4/8 比特 Bitsandbytes/HQQ/EETQ 量化进行指令监督微调（推荐）
```bash
ASCEND_RT_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/lora_multi_npu/llama3_lora_sft_ds.yaml
llamafactory-cli train examples/train_qlora/llama3_lora_sft_otfq.yaml
```
### 多 GPU 全参数微调
#### 基于 4/8 比特 GPTQ 量化进行指令监督微调
```bash
llamafactory-cli train examples/train_qlora/llama3_lora_sft_gptq.yaml
```
#### 基于 4 比特 AWQ 量化进行指令监督微调
```bash
llamafactory-cli train examples/train_qlora/llama3_lora_sft_awq.yaml
```
#### 基于 2 比特 AQLM 量化进行指令监督微调
```bash
llamafactory-cli train examples/train_qlora/llama3_lora_sft_aqlm.yaml
```
### 全参数微调
#### 在单机上进行指令监督微调
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
FORCE_TORCHRUN=1 llamafactory-cli train examples/train_full/llama3_full_sft_ds3.yaml
```
#### 在多机上进行指令监督微调
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
CUDA_VISIBLE_DEVICES=0,1,2,3 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/full_multi_gpu/llama3_full_sft.yaml
FORCE_TORCHRUN=1 NNODES=2 RANK=0 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft_ds3.yaml
FORCE_TORCHRUN=1 NNODES=2 RANK=1 MASTER_ADDR=192.168.0.1 MASTER_PORT=29500 llamafactory-cli train examples/train_full/llama3_full_sft_ds3.yaml
```
#### 批量预测并计算 BLEU 和 ROUGE 分数
```bash
CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llama3_full_predict.yaml
llamafactory-cli train examples/train_full/llama3_full_predict.yaml
```
### 合并 LoRA 适配器与模型量化
@ -162,35 +146,33 @@ CUDA_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/full_multi_gpu/llam
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
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
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
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
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
llamafactory-cli api examples/inference/llama3_lora_sft.yaml
```
### 杂项
@ -198,36 +180,42 @@ CUDA_VISIBLE_DEVICES=0 llamafactory-cli api examples/inference/llama3_lora_sft.y
#### 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
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
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
llamafactory-cli train examples/extras/loraplus/llama3_lora_sft.yaml
```
#### PiSSA 微调
```bash
llamafactory-cli train examples/extras/pissa/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
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
llamafactory-cli train examples/extras/llama_pro/llama3_freeze_sft.yaml
```
#### FSDP+QLoRA 微调
```bash
bash examples/extras/fsdp_qlora/single_node.sh
bash examples/extras/fsdp_qlora/train.sh
```

12
examples/full_multi_gpu/llama3_full_sft.yaml → examples/extras/badam/llama3_full_sft.yaml
View File

@ -5,10 +5,11 @@ model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
stage: sft
do_train: true
finetuning_type: full
### ddp
ddp_timeout: 180000000
deepspeed: examples/deepspeed/ds_z3_config.json
use_badam: true
badam_mode: layer
badam_switch_mode: ascending
badam_switch_interval: 50
badam_verbose: 2
### dataset
dataset: identity,alpaca_en_demo
@ -27,12 +28,11 @@ overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 2
gradient_accumulation_steps: 8
learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
### eval
val_size: 0.1

3
examples/extras/badam/llama3_lora_sft.yaml → examples/extras/badam/llama3_full_sft_ds3.yaml
View File

@ -6,9 +6,11 @@ stage: sft
do_train: true
finetuning_type: full
use_badam: true
badam_mode: layer
badam_switch_mode: ascending
badam_switch_interval: 50
badam_verbose: 2
deepspeed: examples/deepspeed/ds_z3_config.json
### dataset
dataset: identity,alpaca_en_demo
@ -32,7 +34,6 @@ learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
pure_bf16: true
### eval
val_size: 0.1

6
examples/extras/fsdp_qlora/llama3_lora_sft.yaml
View File

@ -8,9 +8,6 @@ do_train: true
finetuning_type: lora
lora_target: all
### ddp
ddp_timeout: 180000000
### dataset
dataset: identity,alpaca_en_demo
template: llama3
@ -33,7 +30,8 @@ learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1

0
examples/extras/fsdp_qlora/single_node.sh → examples/extras/fsdp_qlora/train.sh
View File

3
examples/extras/llama_pro/llama3_freeze_sft.yaml
View File

@ -31,7 +31,8 @@ learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1

3
examples/extras/loraplus/llama3_lora_sft.yaml
View File

@ -30,7 +30,8 @@ learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1

1
examples/extras/mod/llama3_full_sft.yaml
View File

@ -31,6 +31,7 @@ num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
pure_bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1

7
examples/qlora_single_gpu/llama3_lora_sft_bitsandbytes.yaml → examples/extras/pissa/llama3_lora_sft.yaml
View File

@ -1,12 +1,14 @@
### 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: all
pissa_init: true
pissa_iter: 4
pissa_convert: true
### dataset
dataset: identity,alpaca_en_demo
@ -30,7 +32,8 @@ learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1

0
examples/full_multi_gpu/llama3_full_predict.yaml → examples/train_full/llama3_full_predict.yaml
View File

12
examples/lora_multi_gpu/llama3_lora_sft.yaml → examples/train_full/llama3_full_sft_ds3.yaml
View File

@ -4,11 +4,8 @@ model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: all
### ddp
ddp_timeout: 180000000
finetuning_type: full
deepspeed: examples/deepspeed/ds_z3_config.json
### dataset
dataset: identity,alpaca_en_demo
@ -19,7 +16,7 @@ overwrite_cache: true
preprocessing_num_workers: 16
### output
output_dir: saves/llama3-8b/lora/sft
output_dir: saves/llama3-8b/full/sft
logging_steps: 10
save_steps: 500
plot_loss: true
@ -32,7 +29,8 @@ learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1

5
examples/lora_single_gpu/llama3_lora_dpo.yaml → examples/train_lora/llama3_lora_dpo.yaml
View File

@ -7,7 +7,7 @@ do_train: true
finetuning_type: lora
lora_target: all
pref_beta: 0.1
pref_loss: sigmoid # [sigmoid (dpo), orpo, simpo]
pref_loss: sigmoid # choices: [sigmoid (dpo), orpo, simpo]
### dataset
dataset: dpo_en_demo
@ -31,7 +31,8 @@ learning_rate: 5.0e-6
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1

0
examples/lora_single_gpu/llama3_lora_eval.yaml → examples/train_lora/llama3_lora_eval.yaml
View File

4
examples/lora_single_gpu/llama3_lora_kto.yaml → examples/train_lora/llama3_lora_kto.yaml
View File

@ -6,6 +6,7 @@ stage: kto
do_train: true
finetuning_type: lora
lora_target: all
pref_beta: 0.1
### dataset
dataset: kto_en_demo
@ -29,7 +30,8 @@ learning_rate: 5.0e-6
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1

3
examples/lora_single_gpu/llama3_lora_ppo.yaml → examples/train_lora/llama3_lora_ppo.yaml
View File

@ -30,7 +30,8 @@ learning_rate: 1.0e-5
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
bf16: true
ddp_timeout: 180000000
### generate
max_new_tokens: 512

1
examples/lora_single_gpu/llama3_lora_predict.yaml → examples/train_lora/llama3_lora_predict.yaml
View File

@ -22,3 +22,4 @@ overwrite_output_dir: true
### eval
per_device_eval_batch_size: 1
predict_with_generate: true
ddp_timeout: 180000000

3
examples/lora_single_gpu/llama3_lora_pretrain.yaml → examples/train_lora/llama3_lora_pretrain.yaml
View File

@ -28,7 +28,8 @@ learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1

5
examples/lora_single_gpu/llama3_lora_reward.yaml → examples/train_lora/llama3_lora_reward.yaml
View File

@ -25,11 +25,12 @@ overwrite_output_dir: true
### train
per_device_train_batch_size: 1
gradient_accumulation_steps: 8
learning_rate: 1.0e-5
learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1

3
examples/lora_single_gpu/llama3_lora_sft.yaml → examples/train_lora/llama3_lora_sft.yaml
View File

@ -29,7 +29,8 @@ learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1

6
examples/lora_multi_npu/llama3_lora_sft_ds.yaml → examples/train_lora/llama3_lora_sft_ds0.yaml
View File

@ -6,9 +6,6 @@ stage: sft
do_train: true
finetuning_type: lora
lora_target: all
### ddp
ddp_timeout: 180000000
deepspeed: examples/deepspeed/ds_z0_config.json
### dataset
@ -33,7 +30,8 @@ learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1

6
examples/lora_multi_gpu/llama3_lora_sft_ds.yaml → examples/train_lora/llama3_lora_sft_ds3.yaml
View File

@ -6,9 +6,6 @@ stage: sft
do_train: true
finetuning_type: lora
lora_target: all
### ddp
ddp_timeout: 180000000
deepspeed: examples/deepspeed/ds_z3_config.json
### dataset
@ -33,7 +30,8 @@ learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1

0
examples/lora_single_gpu/llama3_preprocess.yaml → examples/train_lora/llama3_preprocess.yaml
View File

3
examples/lora_single_gpu/llava1_5_lora_sft.yaml → examples/train_lora/llava1_5_lora_sft.yaml
View File

@ -30,7 +30,8 @@ learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1

3
examples/qlora_single_gpu/llama3_lora_sft_aqlm.yaml → examples/train_qlora/llama3_lora_sft_aqlm.yaml
View File

@ -29,7 +29,8 @@ learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1

3
examples/qlora_single_gpu/llama3_lora_sft_awq.yaml → examples/train_qlora/llama3_lora_sft_awq.yaml
View File

@ -29,7 +29,8 @@ learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1

3
examples/qlora_single_gpu/llama3_lora_sft_gptq.yaml → examples/train_qlora/llama3_lora_sft_gptq.yaml
View File

@ -29,7 +29,8 @@ learning_rate: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
fp16: true
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1

41
examples/train_qlora/llama3_lora_sft_otfq.yaml Normal file
View File

@ -0,0 +1,41 @@
### model
model_name_or_path: meta-llama/Meta-Llama-3-8B-Instruct
quantization_bit: 4
quantization_method: bitsandbytes # choices: [bitsandbytes (4/8), hqq (2/3/4/5/6/8), eetq (8)]
### method
stage: sft
do_train: true
finetuning_type: lora
lora_target: all
### 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: 1.0e-4
num_train_epochs: 3.0
lr_scheduler_type: cosine
warmup_ratio: 0.1
bf16: true
ddp_timeout: 180000000
### eval
val_size: 0.1
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 500

2
requirements.txt
View File

@ -4,6 +4,7 @@ accelerate>=0.30.1
peft>=0.11.1
trl>=0.8.6
gradio>=4.0.0
pandas>=2.0.0
scipy
einops
sentencepiece
@ -17,3 +18,4 @@ matplotlib>=3.7.0
fire
packaging
pyyaml
numpy<2.0.0

23
scripts/cal_flops.py
View File

@ -1,7 +1,20 @@
# coding=utf-8
# Calculates the flops of pre-trained models.
# 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/
# Copyright 2024 Microsoft Corporation and the LlamaFactory team.
#
# This code is inspired by the Microsoft's DeepSpeed library.
# https://www.deepspeed.ai/tutorials/flops-profiler/
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import fire
import torch
@ -17,6 +30,10 @@ def calculate_flops(
seq_length: int = 256,
flash_attn: str = "auto",
):
r"""
Calculates the flops of pre-trained models.
Usage: python cal_flops.py --model_name_or_path path_to_model --batch_size 1 --seq_length 512
"""
with get_accelerator().device(0):
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)

23
scripts/cal_lr.py
View File

@ -1,7 +1,20 @@
# coding=utf-8
# Calculates the optimal learning rate for 7B/13B models using LLaMA's hyper-parameters.
# Usage: python cal_lr.py --model_name_or_path path_to_model --dataset alpaca_en --cutoff_len 1024 --batch_size 16
# Inspired by: https://github.com/imoneoi/openchat/blob/master/ochat/training_deepspeed/train.py
# Copyright 2024 imoneoi and the LlamaFactory team.
#
# This code is inspired by the imoneoi's OpenChat library.
# https://github.com/imoneoi/openchat/blob/3.6.0/ochat/training_deepspeed/train.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import Literal
@ -32,6 +45,10 @@ def calculate_lr(
cutoff_len: int = 1024, # i.e. maximum input length during training
is_mistral: bool = False, # mistral model uses a smaller learning rate,
):
r"""
Calculates the optimal learning rate for 7B/13B models using LLaMA's hyper-parameters.
Usage: python cal_lr.py --model_name_or_path path_to_model --dataset alpaca_en --cutoff_len 1024 --batch_size 16
"""
model_args, data_args, training_args, _, _ = get_train_args(
dict(
stage=stage,

19
scripts/cal_ppl.py
View File

@ -1,6 +1,17 @@
# 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
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
from dataclasses import dataclass
@ -56,6 +67,10 @@ def cal_ppl(
max_samples: Optional[int] = None,
train_on_prompt: bool = False,
):
r"""
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
"""
model_args, data_args, training_args, finetuning_args, _ = get_train_args(
dict(
stage=stage,

19
scripts/length_cdf.py
View File

@ -1,6 +1,17 @@
# coding=utf-8
# Calculates the distribution of the input lengths in the dataset.
# Usage: python length_cdf.py --model_name_or_path path_to_model --dataset alpaca_en --template default
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import defaultdict
@ -19,6 +30,10 @@ def length_cdf(
template: str = "default",
interval: int = 1000,
):
r"""
Calculates the distribution of the input lengths in the dataset.
Usage: python length_cdf.py --model_name_or_path path_to_model --dataset alpaca_en --template default
"""
model_args, data_args, training_args, _, _ = get_train_args(
dict(
stage="sft",

25
scripts/llama_pro.py
View File

@ -1,7 +1,20 @@
# coding=utf-8
# Performs block expansion for LLaMA, Mistral, Qwen1.5 or Yi models.
# Usage: python llama_pro.py --model_name_or_path meta-llama/Llama-2-7b-hf --output_dir llama2_pro --num_expand 8
# Inspired by: https://github.com/TencentARC/LLaMA-Pro/blob/main/scripts/block_expansion.py
# Copyright 2024 Tencent Inc. and the LlamaFactory team.
#
# This code is inspired by the Tencent's LLaMA-Pro library.
# https://github.com/TencentARC/LLaMA-Pro/blob/main/scripts/block_expansion.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
@ -37,6 +50,10 @@ def block_expansion(
shard_size: Optional[str] = "2GB",
save_safetensors: Optional[bool] = False,
):
r"""
Performs block expansion for LLaMA, Mistral, Qwen1.5 or Yi models.
Usage: python llama_pro.py --model_name_or_path meta-llama/Llama-2-7b-hf --output_dir llama2_pro --num_expand 8
"""
config: "PretrainedConfig" = AutoConfig.from_pretrained(model_name_or_path)
num_layers = getattr(config, "num_hidden_layers")
setattr(config, "num_hidden_layers", num_layers + num_expand)
@ -103,7 +120,7 @@ def block_expansion(
json.dump(index, f, indent=2, sort_keys=True)
print("Model weights saved in {}".format(output_dir))
print("Fine-tune this model with:")
print("- Fine-tune this model with:")
print("model_name_or_path: {}".format(output_dir))
print("finetuning_type: freeze")
print("freeze_trainable_layers: {}".format(num_expand))

22
scripts/llamafy_baichuan2.py
View File

@ -1,8 +1,17 @@
# coding=utf-8
# Converts the Baichuan2-7B model in the same format as LLaMA2-7B.
# Usage: python llamafy_baichuan2.py --input_dir input --output_dir output
# Inspired by: https://huggingface.co/fireballoon/baichuan-llama-7b/blob/main/convert_baichuan_to_llama.py
# Converted model: https://huggingface.co/hiyouga/Baichuan2-7B-Base-LLaMAfied
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
@ -79,6 +88,11 @@ def save_config(input_dir: str, output_dir: str):
def llamafy_baichuan2(
input_dir: str, output_dir: str, shard_size: Optional[str] = "2GB", save_safetensors: Optional[bool] = False
):
r"""
Converts the Baichuan2-7B model in the same format as LLaMA2-7B.
Usage: python llamafy_baichuan2.py --input_dir input --output_dir output
Converted model: https://huggingface.co/hiyouga/Baichuan2-7B-Base-LLaMAfied
"""
try:
os.makedirs(output_dir, exist_ok=False)
except Exception as e:

21
scripts/llamafy_qwen.py
View File

@ -1,7 +1,17 @@
# coding=utf-8
# Converts the Qwen models in the same format as LLaMA2.
# Usage: python llamafy_qwen.py --input_dir input --output_dir output
# Converted model: https://huggingface.co/hiyouga/Qwen-14B-Chat-LLaMAfied
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
@ -131,6 +141,11 @@ def save_config(input_dir: str, output_dir: str, torch_dtype: str):
def llamafy_qwen(
input_dir: str, output_dir: str, shard_size: Optional[str] = "2GB", save_safetensors: Optional[bool] = False
):
r"""
Converts the Qwen models in the same format as LLaMA2.
Usage: python llamafy_qwen.py --input_dir input --output_dir output
Converted model: https://huggingface.co/hiyouga/Qwen-14B-Chat-LLaMAfied
"""
try:
os.makedirs(output_dir, exist_ok=False)
except Exception as e:

97
scripts/loftq_init.py
View File

@ -1,14 +1,25 @@
# coding=utf-8
# Initializes LoRA weights with LoRA-fine-tuning-aware Quantization (LoftQ)
# Usage: python loftq_init.py --model_name_or_path path_to_model --save_dir output_dir
# Inspired by: https://github.com/huggingface/peft/blob/main/examples/loftq_finetuning/quantize_save_load.py
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
#
# This code is based on the HuggingFace's PEFT library.
# https://github.com/huggingface/peft/blob/v0.10.0/examples/loftq_finetuning/quantize_save_load.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import TYPE_CHECKING, Optional
from typing import TYPE_CHECKING
import fire
import torch
import torch.nn as nn
from peft import LoftQConfig, LoraConfig, TaskType, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer
@ -17,65 +28,61 @@ if TYPE_CHECKING:
from transformers import PreTrainedModel
class Shell(nn.Module):
def __init__(self, weight: torch.Tensor, bias: Optional[torch.Tensor] = None):
super().__init__()
self.weight = nn.Parameter(weight, requires_grad=False)
if bias is not None:
self.bias = nn.Parameter(bias, requires_grad=False)
def unwrap_model(model: nn.Module, pattern=".base_layer") -> None:
for name in {k.split(pattern)[0] for k, _ in model.named_modules() if pattern in k}:
parent_name = ".".join(name.split(".")[:-1])
child_name = name.split(".")[-1]
parent_module = model.get_submodule(parent_name)
child_module = getattr(parent_module, child_name)
base_layer = getattr(child_module, "base_layer")
weight = getattr(base_layer, "weight", None)
bias = getattr(base_layer, "bias", None)
setattr(parent_module, child_name, Shell(weight, bias))
print("Model unwrapped.")
def quantize_loftq(
model_name_or_path: str,
save_dir: str,
loftq_bits: Optional[int] = 4,
loftq_iter: Optional[int] = 1,
lora_alpha: Optional[int] = None,
lora_rank: Optional[int] = 16,
lora_target: Optional[str] = "q_proj,v_proj",
save_safetensors: Optional[bool] = False,
output_dir: str,
loftq_bits: int = 4,
loftq_iter: int = 4,
lora_alpha: int = None,
lora_rank: int = 16,
lora_dropout: float = 0,
lora_target: tuple = ("q_proj", "v_proj"),
save_safetensors: bool = True,
):
r"""
Initializes LoRA weights with LoRA-fine-tuning-aware Quantization (LoftQ)
Usage: python loftq_init.py --model_name_or_path path_to_model --output_dir output_dir
"""
if isinstance(lora_target, str):
lora_target = [name.strip() for name in lora_target.split(",")]
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype="auto")
loftq_config = LoftQConfig(loftq_bits=loftq_bits, loftq_iter=loftq_iter)
lora_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
inference_mode=True,
r=lora_rank,
lora_alpha=lora_alpha if lora_alpha is not None else lora_rank * 2,
lora_dropout=0.1,
target_modules=[name.strip() for name in lora_target.split(",")],
lora_dropout=lora_dropout,
target_modules=lora_target,
init_lora_weights="loftq",
loftq_config=loftq_config,
)
# Init LoftQ model
lora_model = get_peft_model(model, lora_config)
base_model: "PreTrainedModel" = lora_model.get_base_model()
print("Initializing LoftQ weights, it may be take several minutes, wait patiently.")
peft_model = get_peft_model(model, lora_config)
loftq_dir = os.path.join(output_dir, "loftq_init")
# Save LoftQ model
setattr(lora_model.base_model.peft_config["default"], "base_model_name_or_path", save_dir)
setattr(lora_model.base_model.peft_config["default"], "init_lora_weights", True)
lora_model.save_pretrained(os.path.join(save_dir, "adapters"), safe_serialization=save_safetensors)
setattr(peft_model.peft_config["default"], "base_model_name_or_path", output_dir)
setattr(peft_model.peft_config["default"], "init_lora_weights", True) # don't apply loftq again
peft_model.save_pretrained(loftq_dir, safe_serialization=save_safetensors)
print("Adapter weights saved in {}".format(loftq_dir))
# Save base model
unwrap_model(base_model)
base_model.save_pretrained(save_dir, safe_serialization=save_safetensors)
tokenizer.save_pretrained(save_dir)
base_model: "PreTrainedModel" = peft_model.unload()
base_model.save_pretrained(output_dir, safe_serialization=save_safetensors)
tokenizer.save_pretrained(output_dir)
print("Model weights saved in {}".format(output_dir))
print("- Fine-tune this model with:")
print("model_name_or_path: {}".format(output_dir))
print("adapter_name_or_path: {}".format(loftq_dir))
print("finetuning_type: lora")
print("quantization_bit: {}".format(loftq_bits))
if __name__ == "__main__":

86
scripts/pissa_init.py Normal file
View File

@ -0,0 +1,86 @@
# coding=utf-8
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
#
# This code is based on the HuggingFace's PEFT library.
# https://github.com/huggingface/peft/blob/v0.11.0/examples/pissa_finetuning/preprocess.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import TYPE_CHECKING
import fire
from peft import LoraConfig, TaskType, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer
if TYPE_CHECKING:
from transformers import PreTrainedModel
def quantize_pissa(
model_name_or_path: str,
output_dir: str,
pissa_iter: int = 4,
lora_alpha: int = None,
lora_rank: int = 16,
lora_dropout: float = 0,
lora_target: tuple = ("q_proj", "v_proj"),
save_safetensors: bool = True,
):
r"""
Initializes LoRA weights with Principal Singular values and Singular vectors Adaptation (PiSSA)
Usage: python pissa_init.py --model_name_or_path path_to_model --output_dir output_dir
"""
if isinstance(lora_target, str):
lora_target = [name.strip() for name in lora_target.split(",")]
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype="auto")
lora_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
r=lora_rank,
lora_alpha=lora_alpha if lora_alpha is not None else lora_rank * 2,
lora_dropout=lora_dropout,
target_modules=lora_target,
init_lora_weights="pissa" if pissa_iter == -1 else "pissa_niter_{}".format(pissa_iter),
)
# Init PiSSA model
peft_model = get_peft_model(model, lora_config)
pissa_dir = os.path.join(output_dir, "pissa_init")
# Save PiSSA model
setattr(peft_model.peft_config["default"], "init_lora_weights", True) # don't apply pissa again
peft_model.save_pretrained(pissa_dir, safe_serialization=save_safetensors)
print("Adapter weights saved in {}".format(pissa_dir))
# Save base model
base_model: "PreTrainedModel" = peft_model.unload()
base_model.save_pretrained(output_dir, safe_serialization=save_safetensors)
tokenizer.save_pretrained(output_dir)
print("Model weights saved in {}".format(output_dir))
print("- Fine-tune this model with:")
print("model_name_or_path: {}".format(output_dir))
print("adapter_name_or_path: {}".format(pissa_dir))
print("finetuning_type: lora")
print("pissa_init: false")
print("pissa_convert: true")
print("- and optionally with:")
print("quantization_bit: 4")
if __name__ == "__main__":
fire.Fire(quantize_pissa)

15
scripts/test_toolcall.py
View File

@ -1,3 +1,18 @@
# coding=utf-8
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from typing import Sequence

26
setup.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import re
@ -23,14 +37,16 @@ extra_require = {
"torch": ["torch>=1.13.1"],
"torch-npu": ["torch==2.1.0", "torch-npu==2.1.0.post3", "decorator"],
"metrics": ["nltk", "jieba", "rouge-chinese"],
"deepspeed": ["deepspeed>=0.10.0,<=0.14.0"],
"deepspeed": ["deepspeed>=0.10.0"],
"bitsandbytes": ["bitsandbytes>=0.39.0"],
"vllm": ["vllm>=0.4.3"],
"galore": ["galore-torch"],
"badam": ["badam"],
"gptq": ["optimum>=1.16.0", "auto-gptq>=0.5.0"],
"hqq": ["hqq"],
"eetq": ["eetq"],
"gptq": ["optimum>=1.17.0", "auto-gptq>=0.5.0"],
"awq": ["autoawq"],
"aqlm": ["aqlm[gpu]>=1.1.0"],
"vllm": ["vllm>=0.4.3"],
"galore": ["galore-torch"],
"badam": ["badam>=1.2.1"],
"qwen": ["transformers_stream_generator"],
"modelscope": ["modelscope"],
"dev": ["ruff", "pytest"],

14
src/api.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import uvicorn

14
src/llamafactory/__init__.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Level: api, webui > chat, eval, train > data, model > hparams > extras
from .cli import VERSION

14
src/llamafactory/api/app.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from contextlib import asynccontextmanager
from typing import Optional

38
src/llamafactory/api/chat.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import base64
import io
import json
@ -78,9 +92,11 @@ def _process_request(
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
if message.role == Role.ASSISTANT and isinstance(message.tool_calls, list) and len(message.tool_calls):
name = message.tool_calls[0].function.name
arguments = message.tool_calls[0].function.arguments
content = json.dumps({"name": name, "argument": arguments}, ensure_ascii=False)
tool_calls = [
{"name": tool_call.function.name, "arguments": tool_call.function.arguments}
for tool_call in message.tool_calls
]
content = json.dumps(tool_calls, ensure_ascii=False)
input_messages.append({"role": ROLE_MAPPING[Role.FUNCTION], "content": content})
elif isinstance(message.content, list):
for input_item in message.content:
@ -104,7 +120,7 @@ def _process_request(
if isinstance(tool_list, list) and len(tool_list):
try:
tools = json.dumps([dictify(tool.function) for tool in tool_list], ensure_ascii=False)
except Exception:
except json.JSONDecodeError:
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid tools")
else:
tools = None
@ -146,15 +162,17 @@ async def create_chat_completion_response(
choices = []
for i, response in enumerate(responses):
if tools:
result = chat_model.engine.template.format_tools.extract(response.response_text)
result = chat_model.engine.template.extract_tool(response.response_text)
else:
result = response.response_text
if isinstance(result, tuple):
name, arguments = result
function = Function(name=name, arguments=arguments)
tool_call = FunctionCall(id="call_{}".format(uuid.uuid4().hex), function=function)
response_message = ChatCompletionMessage(role=Role.ASSISTANT, tool_calls=[tool_call])
if isinstance(result, list):
tool_calls = []
for tool in result:
function = Function(name=tool[0], arguments=tool[1])
tool_calls.append(FunctionCall(id="call_{}".format(uuid.uuid4().hex), function=function))
response_message = ChatCompletionMessage(role=Role.ASSISTANT, tool_calls=tool_calls)
finish_reason = Finish.TOOL
else:
response_message = ChatCompletionMessage(role=Role.ASSISTANT, content=result)

14
src/llamafactory/api/common.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
from typing import TYPE_CHECKING, Any, Dict

14
src/llamafactory/api/protocol.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import time
from enum import Enum, unique
from typing import Any, Dict, List, Optional, Union

14
src/llamafactory/chat/__init__.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .base_engine import BaseEngine
from .chat_model import ChatModel

19
src/llamafactory/chat/base_engine.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, AsyncGenerator, Dict, List, Literal, Optional, Sequence, Union
@ -36,11 +50,6 @@ class BaseEngine(ABC):
generating_args: "GeneratingArguments",
) -> None: ...
@abstractmethod
async def start(
self,
) -> None: ...
@abstractmethod
async def chat(
self,

20
src/llamafactory/chat/chat_model.py
View File

@ -1,3 +1,20 @@
# Copyright 2024 THUDM and the LlamaFactory team.
#
# This code is inspired by the THUDM's ChatGLM implementation.
# https://github.com/THUDM/ChatGLM-6B/blob/main/cli_demo.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import asyncio
from threading import Thread
from typing import TYPE_CHECKING, Any, AsyncGenerator, Dict, Generator, List, Optional, Sequence
@ -14,7 +31,7 @@ if TYPE_CHECKING:
from .base_engine import BaseEngine, Response
def _start_background_loop(loop: asyncio.AbstractEventLoop) -> None:
def _start_background_loop(loop: "asyncio.AbstractEventLoop") -> None:
asyncio.set_event_loop(loop)
loop.run_forever()
@ -32,7 +49,6 @@ class ChatModel:
self._loop = asyncio.new_event_loop()
self._thread = Thread(target=_start_background_loop, args=(self._loop,), daemon=True)
self._thread.start()
asyncio.run_coroutine_threadsafe(self.engine.start(), self._loop)
def chat(
self,

33
src/llamafactory/chat/hf_engine.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import asyncio
import concurrent.futures
import os
@ -40,11 +54,19 @@ class HuggingfaceEngine(BaseEngine):
self.tokenizer = tokenizer_module["tokenizer"]
self.processor = tokenizer_module["processor"]
self.tokenizer.padding_side = "left" if self.can_generate else "right"
self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template, data_args.tool_format)
self.model = load_model(
self.tokenizer, model_args, finetuning_args, is_trainable=False, add_valuehead=(not self.can_generate)
) # must after fixing tokenizer to resize vocab
self.generating_args = generating_args.to_dict()
try:
asyncio.get_event_loop()
except RuntimeError:
logger.warning("There is no current event loop, creating a new one.")
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
self.semaphore = asyncio.Semaphore(int(os.environ.get("MAX_CONCURRENT", "1")))
@staticmethod
def _process_args(
@ -245,9 +267,6 @@ class HuggingfaceEngine(BaseEngine):
return scores
async def start(self) -> None:
self._semaphore = asyncio.Semaphore(int(os.environ.get("MAX_CONCURRENT", 1)))
async def chat(
self,
messages: Sequence[Dict[str, str]],
@ -272,7 +291,7 @@ class HuggingfaceEngine(BaseEngine):
image,
input_kwargs,
)
async with self._semaphore:
async with self.semaphore:
with concurrent.futures.ThreadPoolExecutor() as pool:
return await loop.run_in_executor(pool, self._chat, *input_args)
@ -300,7 +319,7 @@ class HuggingfaceEngine(BaseEngine):
image,
input_kwargs,
)
async with self._semaphore:
async with self.semaphore:
with concurrent.futures.ThreadPoolExecutor() as pool:
stream = self._stream_chat(*input_args)
while True:
@ -319,6 +338,6 @@ class HuggingfaceEngine(BaseEngine):
loop = asyncio.get_running_loop()
input_args = (self.model, self.tokenizer, batch_input, input_kwargs)
async with self._semaphore:
async with self.semaphore:
with concurrent.futures.ThreadPoolExecutor() as pool:
return await loop.run_in_executor(pool, self._get_scores, *input_args)

34
src/llamafactory/chat/vllm_engine.py
View File

@ -1,10 +1,24 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import uuid
from typing import TYPE_CHECKING, AsyncGenerator, AsyncIterator, Dict, List, Optional, Sequence, Union
from ..data import get_template_and_fix_tokenizer
from ..extras.logging import get_logger
from ..extras.misc import get_device_count
from ..extras.packages import is_vllm_available
from ..extras.packages import is_vllm_available, is_vllm_version_greater_than_0_5
from ..model import load_config, load_tokenizer
from ..model.model_utils.visual import LlavaMultiModalProjectorForYiVLForVLLM
from .base_engine import BaseEngine, Response
@ -13,7 +27,11 @@ from .base_engine import BaseEngine, Response
if is_vllm_available():
from vllm import AsyncEngineArgs, AsyncLLMEngine, RequestOutput, SamplingParams
from vllm.lora.request import LoRARequest
from vllm.sequence import MultiModalData
if is_vllm_version_greater_than_0_5():
from vllm.multimodal.image import ImagePixelData
else:
from vllm.sequence import MultiModalData
if TYPE_CHECKING:
@ -41,14 +59,14 @@ class VllmEngine(BaseEngine):
self.tokenizer = tokenizer_module["tokenizer"]
self.processor = tokenizer_module["processor"]
self.tokenizer.padding_side = "left"
self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template, data_args.tool_format)
self.generating_args = generating_args.to_dict()
engine_args = {
"model": model_args.model_name_or_path,
"trust_remote_code": True,
"download_dir": model_args.cache_dir,
"dtype": model_args.vllm_dtype,
"dtype": model_args.infer_dtype,
"max_model_len": model_args.vllm_maxlen,
"tensor_parallel_size": get_device_count() or 1,
"gpu_memory_utilization": model_args.vllm_gpu_util,
@ -106,7 +124,10 @@ class VllmEngine(BaseEngine):
if self.processor is not None and image is not None: # add image features
image_processor: "BaseImageProcessor" = getattr(self.processor, "image_processor")
pixel_values = image_processor(image, return_tensors="pt")["pixel_values"]
multi_modal_data = MultiModalData(type=MultiModalData.Type.IMAGE, data=pixel_values)
if is_vllm_version_greater_than_0_5():
multi_modal_data = ImagePixelData(image=pixel_values)
else: # TODO: remove vllm 0.4.3 support
multi_modal_data = MultiModalData(type=MultiModalData.Type.IMAGE, data=pixel_values)
else:
multi_modal_data = None
@ -162,9 +183,6 @@ class VllmEngine(BaseEngine):
)
return result_generator
async def start(self) -> None:
pass
async def chat(
self,
messages: Sequence[Dict[str, str]],

19
src/llamafactory/cli.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import random
import subprocess
@ -60,7 +74,7 @@ class Command(str, Enum):
def main():
command = sys.argv.pop(1)
command = sys.argv.pop(1) if len(sys.argv) != 1 else Command.HELP
if command == Command.API:
run_api()
elif command == Command.CHAT:
@ -77,7 +91,7 @@ def main():
master_addr = os.environ.get("MASTER_ADDR", "127.0.0.1")
master_port = os.environ.get("MASTER_PORT", str(random.randint(20001, 29999)))
logger.info("Initializing distributed tasks at: {}:{}".format(master_addr, master_port))
subprocess.run(
process = subprocess.run(
(
"torchrun --nnodes {nnodes} --node_rank {node_rank} --nproc_per_node {nproc_per_node} "
"--master_addr {master_addr} --master_port {master_port} {file_name} {args}"
@ -92,6 +106,7 @@ def main():
),
shell=True,
)
sys.exit(process.returncode)
else:
run_exp()
elif command == Command.WEBDEMO:

14
src/llamafactory/data/__init__.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .collator import KTODataCollatorWithPadding, PairwiseDataCollatorWithPadding
from .data_utils import Role, split_dataset
from .loader import get_dataset

22
src/llamafactory/data/aligner.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from functools import partial
from typing import TYPE_CHECKING, Any, Dict, List, Union
@ -10,6 +24,7 @@ from .data_utils import Role
if TYPE_CHECKING:
from datasets import Dataset, IterableDataset
from transformers import Seq2SeqTrainingArguments
from ..hparams import DataArguments
from .parser import DatasetAttr
@ -175,7 +190,10 @@ def convert_sharegpt(
def align_dataset(
dataset: Union["Dataset", "IterableDataset"], dataset_attr: "DatasetAttr", data_args: "DataArguments"
dataset: Union["Dataset", "IterableDataset"],
dataset_attr: "DatasetAttr",
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",
) -> Union["Dataset", "IterableDataset"]:
r"""
Aligned dataset:
@ -208,7 +226,7 @@ def align_dataset(
if not data_args.streaming:
kwargs = dict(
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=(not data_args.overwrite_cache),
load_from_cache_file=(not data_args.overwrite_cache) or (training_args.local_process_index != 0),
desc="Converting format of dataset",
)

14
src/llamafactory/data/collator.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import Any, Dict, Sequence

26
src/llamafactory/data/data_utils.py
View File

@ -1,5 +1,19 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from enum import Enum, unique
from typing import TYPE_CHECKING, Dict, List, Tuple, Union
from typing import TYPE_CHECKING, Dict, List, Sequence, Set, Union
from datasets import concatenate_datasets, interleave_datasets
@ -16,6 +30,9 @@ if TYPE_CHECKING:
logger = get_logger(__name__)
SLOTS = Sequence[Union[str, Set[str], Dict[str, str]]]
@unique
class Role(str, Enum):
USER = "user"
@ -25,13 +42,6 @@ class Role(str, Enum):
OBSERVATION = "observation"
def infer_max_len(source_len: int, target_len: int, max_len: int, reserved_label_len: int) -> Tuple[int, int]:
max_target_len = int(max_len * (target_len / (source_len + target_len)))
max_target_len = max(max_target_len, reserved_label_len)
max_source_len = max_len - min(max_target_len, target_len)
return max_source_len, max_target_len
def merge_dataset(
all_datasets: List[Union["Dataset", "IterableDataset"]],
data_args: "DataArguments",

159
src/llamafactory/data/formatter.py
View File

@ -1,83 +1,36 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import re
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from typing import Any, Dict, List, Literal, Optional, Sequence, Set, Tuple, Union
from typing import List, Literal, Optional, Tuple, Union
SLOTS = Sequence[Union[str, Set[str], Dict[str, str]]]
JSON_FORMAT_PROMPT = (
""", in a JSON format representing the kwargs (e.g. ```{"input": "hello world", "num_beams": 5}```)"""
)
TOOL_SYSTEM_PROMPT = (
"You have access to the following tools:\n{tool_text}"
"Use the following format if using a tool:\n"
"```\n"
"Action: tool name (one of [{tool_names}]).\n"
"Action Input: the input to the tool{format_prompt}.\n"
"```\n"
)
def default_tool_formatter(tools: List[Dict[str, Any]]) -> str:
tool_text = ""
tool_names = []
for tool in tools:
param_text = ""
for name, param in tool["parameters"]["properties"].items():
required = ", required" if name in tool["parameters"].get("required", []) else ""
enum = ", should be one of [{}]".format(", ".join(param["enum"])) if param.get("enum", None) else ""
items = (
", where each item should be {}".format(param["items"].get("type", "")) if param.get("items") else ""
)
param_text += " - {name} ({type}{required}): {desc}{enum}{items}\n".format(
name=name,
type=param.get("type", ""),
required=required,
desc=param.get("description", ""),
enum=enum,
items=items,
)
tool_text += "> Tool Name: {name}\nTool Description: {desc}\nTool Args:\n{args}\n".format(
name=tool["name"], desc=tool.get("description", ""), args=param_text
)
tool_names.append(tool["name"])
return TOOL_SYSTEM_PROMPT.format(
tool_text=tool_text, tool_names=", ".join(tool_names), format_prompt=JSON_FORMAT_PROMPT
)
def default_tool_extractor(content: str) -> Union[str, Tuple[str, str]]:
regex = re.compile(r"Action:\s*([a-zA-Z0-9_]+).*?Action Input:\s*(.*)", re.DOTALL)
action_match = re.search(regex, content)
if not action_match:
return content
tool_name = action_match.group(1).strip()
tool_input = action_match.group(2).strip().strip('"').strip("```")
try:
arguments = json.loads(tool_input)
except json.JSONDecodeError:
return content
return tool_name, json.dumps(arguments, ensure_ascii=False)
from .data_utils import SLOTS
from .tool_utils import DefaultToolUtils, GLM4ToolUtils
@dataclass
class Formatter(ABC):
slots: SLOTS = field(default_factory=list)
tool_format: Optional[Literal["default"]] = None
tool_format: Optional[Literal["default", "glm4"]] = None
@abstractmethod
def apply(self, **kwargs) -> SLOTS: ...
def extract(self, content: str) -> Union[str, Tuple[str, str]]:
def extract(self, content: str) -> Union[str, List[Tuple[str, str]]]:
raise NotImplementedError
@ -128,34 +81,37 @@ class StringFormatter(Formatter):
@dataclass
class FunctionFormatter(Formatter):
def __post_init__(self):
has_name, has_args = False, False
for slot in filter(lambda s: isinstance(s, str), self.slots):
if "{{name}}" in slot:
has_name = True
if "{{arguments}}" in slot:
has_args = True
if not has_name or not has_args:
raise ValueError("Name and arguments placeholders are required in the function formatter.")
if self.tool_format == "default":
self.slots = DefaultToolUtils.get_function_slots() + self.slots
elif self.tool_format == "glm4":
self.slots = GLM4ToolUtils.get_function_slots() + self.slots
else:
raise NotImplementedError("Tool format {} was not found.".format(self.tool_format))
def apply(self, **kwargs) -> SLOTS:
content = kwargs.pop("content")
functions: List[Tuple[str, str]] = []
try:
function = json.loads(content)
name = function["name"]
arguments = json.dumps(function["arguments"], ensure_ascii=False)
except Exception:
name, arguments = "", ""
tool_calls = json.loads(content)
if not isinstance(tool_calls, list): # parallel function call
tool_calls = [tool_calls]
for tool_call in tool_calls:
functions.append((tool_call["name"], json.dumps(tool_call["arguments"], ensure_ascii=False)))
except json.JSONDecodeError:
functions = []
elements = []
for slot in self.slots:
if isinstance(slot, str):
slot = slot.replace("{{name}}", name).replace("{{arguments}}", arguments)
elements.append(slot)
elif isinstance(slot, (dict, set)):
elements.append(slot)
else:
raise RuntimeError("Input must be string, set[str] or dict[str, str], got {}".format(type(slot)))
for name, arguments in functions:
for slot in self.slots:
if isinstance(slot, str):
slot = slot.replace("{{name}}", name).replace("{{arguments}}", arguments)
elements.append(slot)
elif isinstance(slot, (dict, set)):
elements.append(slot)
else:
raise RuntimeError("Input must be string, set[str] or dict[str, str], got {}".format(type(slot)))
return elements
@ -163,25 +119,22 @@ class FunctionFormatter(Formatter):
@dataclass
class ToolFormatter(Formatter):
def __post_init__(self):
if self.tool_format is None:
raise ValueError("Tool format was not found.")
if self.tool_format == "default":
self._tool_formatter = DefaultToolUtils.tool_formatter
self._tool_extractor = DefaultToolUtils.tool_extractor
elif self.tool_format == "glm4":
self._tool_formatter = GLM4ToolUtils.tool_formatter
self._tool_extractor = GLM4ToolUtils.tool_extractor
else:
raise NotImplementedError("Tool format {} was not found.".format(self.tool_format))
def apply(self, **kwargs) -> SLOTS:
content = kwargs.pop("content")
try:
tools = json.loads(content)
if not len(tools):
return [""]
if self.tool_format == "default":
return [default_tool_formatter(tools)]
else:
raise NotImplementedError
except Exception:
return [self._tool_formatter(tools) if len(tools) != 0 else ""]
except json.JSONDecodeError:
return [""]
def extract(self, content: str) -> Union[str, Tuple[str, str]]:
if self.tool_format == "default":
return default_tool_extractor(content)
else:
raise NotImplementedError
def extract(self, content: str) -> Union[str, List[Tuple[str, str]]]:
return self._tool_extractor(content)

27
src/llamafactory/data/loader.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import os
import sys
@ -18,8 +32,7 @@ from .template import get_template_and_fix_tokenizer
if TYPE_CHECKING:
from datasets import Dataset, IterableDataset
from transformers import ProcessorMixin, Seq2SeqTrainingArguments
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers import PreTrainedTokenizer, ProcessorMixin, Seq2SeqTrainingArguments
from ..hparams import DataArguments, ModelArguments
from .parser import DatasetAttr
@ -32,6 +45,7 @@ def load_single_dataset(
dataset_attr: "DatasetAttr",
model_args: "ModelArguments",
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",
) -> Union["Dataset", "IterableDataset"]:
logger.info("Loading dataset {}...".format(dataset_attr))
data_path, data_name, data_dir, data_files = None, None, None, None
@ -123,7 +137,7 @@ def load_single_dataset(
max_samples = min(data_args.max_samples, len(dataset))
dataset = dataset.select(range(max_samples))
return align_dataset(dataset, dataset_attr, data_args)
return align_dataset(dataset, dataset_attr, data_args, training_args)
def get_dataset(
@ -134,7 +148,7 @@ def get_dataset(
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"] = None,
) -> Union["Dataset", "IterableDataset"]:
template = get_template_and_fix_tokenizer(tokenizer, data_args.template)
template = get_template_and_fix_tokenizer(tokenizer, data_args.template, data_args.tool_format)
if data_args.train_on_prompt and template.efficient_eos:
raise ValueError("Current template does not support `train_on_prompt`.")
@ -157,7 +171,8 @@ def get_dataset(
if (stage == "rm" and dataset_attr.ranking is False) or (stage != "rm" and dataset_attr.ranking is True):
raise ValueError("The dataset is not applicable in the current training stage.")
all_datasets.append(load_single_dataset(dataset_attr, model_args, data_args))
all_datasets.append(load_single_dataset(dataset_attr, model_args, data_args, training_args))
dataset = merge_dataset(all_datasets, data_args, training_args)
with training_args.main_process_first(desc="pre-process dataset"):
@ -169,7 +184,7 @@ def get_dataset(
if not data_args.streaming:
kwargs = dict(
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=(not data_args.overwrite_cache),
load_from_cache_file=(not data_args.overwrite_cache) or (training_args.local_process_index != 0),
desc="Running tokenizer on dataset",
)

14
src/llamafactory/data/parser.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from dataclasses import dataclass

17
src/llamafactory/data/preprocess.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from functools import partial
from typing import TYPE_CHECKING, Callable, Literal, Optional, Tuple
@ -13,8 +27,7 @@ from .processors.unsupervised import preprocess_unsupervised_dataset, print_unsu
if TYPE_CHECKING:
from transformers import ProcessorMixin, Seq2SeqTrainingArguments
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers import PreTrainedTokenizer, ProcessorMixin, Seq2SeqTrainingArguments
from ..hparams import DataArguments
from .template import Template

33
src/llamafactory/data/processors/feedback.py
View File

@ -1,13 +1,26 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from .processor_utils import get_paligemma_token_type_ids, get_pixel_values
from .processor_utils import get_paligemma_token_type_ids, get_pixel_values, infer_seqlen
if TYPE_CHECKING:
from transformers import ProcessorMixin
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers import PreTrainedTokenizer, ProcessorMixin
from ...hparams import DataArguments
from ..template import Template
@ -42,12 +55,8 @@ def _encode_feedback_example(
else:
kl_messages = prompt + [kl_response[1]]
prompt_ids, response_ids = template.encode_oneturn(
tokenizer, messages, system, tools, data_args.cutoff_len, data_args.reserved_label_len
)
_, kl_response_ids = template.encode_oneturn(
tokenizer, kl_messages, system, tools, data_args.cutoff_len, data_args.reserved_label_len
)
prompt_ids, response_ids = template.encode_oneturn(tokenizer, messages, system, tools)
_, kl_response_ids = template.encode_oneturn(tokenizer, kl_messages, system, tools)
if template.efficient_eos:
response_ids += [tokenizer.eos_token_id]
@ -57,6 +66,12 @@ def _encode_feedback_example(
image_token_id = tokenizer.convert_tokens_to_ids(template.image_token)
prompt_ids = [image_token_id] * getattr(processor, "image_seq_length") + prompt_ids
# do not consider the kl_response
source_len, target_len = infer_seqlen(len(prompt_ids), len(response_ids), data_args.cutoff_len)
prompt_ids = prompt_ids[:source_len]
response_ids = response_ids[:target_len]
kl_response_ids = kl_response_ids[:target_len]
input_ids = prompt_ids + response_ids
labels = [IGNORE_INDEX] * len(prompt_ids) + response_ids
kl_input_ids = prompt_ids + kl_response_ids

34
src/llamafactory/data/processors/pairwise.py
View File

@ -1,13 +1,26 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from .processor_utils import get_paligemma_token_type_ids, get_pixel_values
from .processor_utils import get_paligemma_token_type_ids, get_pixel_values, infer_seqlen
if TYPE_CHECKING:
from transformers import ProcessorMixin
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers import PreTrainedTokenizer, ProcessorMixin
from ...hparams import DataArguments
from ..template import Template
@ -31,12 +44,8 @@ def _encode_pairwise_example(
chosen_messages = prompt + [response[0]]
rejected_messages = prompt + [response[1]]
prompt_ids, chosen_ids = template.encode_oneturn(
tokenizer, chosen_messages, system, tools, data_args.cutoff_len, data_args.reserved_label_len
)
_, rejected_ids = template.encode_oneturn(
tokenizer, rejected_messages, system, tools, data_args.cutoff_len, data_args.reserved_label_len
)
prompt_ids, chosen_ids = template.encode_oneturn(tokenizer, chosen_messages, system, tools)
_, rejected_ids = template.encode_oneturn(tokenizer, rejected_messages, system, tools)
if template.efficient_eos:
chosen_ids += [tokenizer.eos_token_id]
@ -46,6 +55,13 @@ def _encode_pairwise_example(
image_token_id = tokenizer.convert_tokens_to_ids(template.image_token)
prompt_ids = [image_token_id] * getattr(processor, "image_seq_length") + prompt_ids
source_len, target_len = infer_seqlen(
len(prompt_ids), max(len(chosen_ids), len(rejected_ids)), data_args.cutoff_len
) # consider the response is more important
prompt_ids = prompt_ids[:source_len]
chosen_ids = chosen_ids[:target_len]
rejected_ids = rejected_ids[:target_len]
chosen_input_ids = prompt_ids + chosen_ids
chosen_labels = [IGNORE_INDEX] * len(prompt_ids) + chosen_ids
rejected_input_ids = prompt_ids + rejected_ids

22
src/llamafactory/data/processors/pretrain.py
View File

@ -1,9 +1,26 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from itertools import chain
from typing import TYPE_CHECKING, Any, Dict, List
if TYPE_CHECKING:
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers import PreTrainedTokenizer
from ...hparams import DataArguments
@ -12,7 +29,8 @@ def preprocess_pretrain_dataset(
examples: Dict[str, List[Any]], tokenizer: "PreTrainedTokenizer", data_args: "DataArguments"
) -> Dict[str, List[List[int]]]:
# build grouped texts with format `X1 X2 X3 ...` if packing is enabled
text_examples = [messages[0]["content"] + tokenizer.eos_token for messages in examples["prompt"]]
eos_token = "<|end_of_text|>" if data_args.template == "llama3" else tokenizer.eos_token
text_examples = [messages[0]["content"] + eos_token for messages in examples["prompt"]]
if not data_args.packing:
if data_args.template == "gemma":

29
src/llamafactory/data/processors/processor_utils.py
View File

@ -1,5 +1,19 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import bisect
from typing import TYPE_CHECKING, List, Sequence
from typing import TYPE_CHECKING, List, Sequence, Tuple
from ...extras.packages import is_pillow_available
@ -62,3 +76,16 @@ def get_paligemma_token_type_ids(input_len: int, processor: "ProcessorMixin") ->
"""
image_seq_length = getattr(processor, "image_seq_length")
return [0] * image_seq_length + [1] * (input_len - image_seq_length)
def infer_seqlen(source_len: int, target_len: int, cutoff_len: int) -> Tuple[int, int]:
if target_len * 2 < cutoff_len: # truncate source
max_target_len = cutoff_len
elif source_len * 2 < cutoff_len: # truncate target
max_target_len = cutoff_len - source_len
else: # truncate both
max_target_len = int(cutoff_len * (target_len / (source_len + target_len)))
new_target_len = min(max_target_len, target_len)
new_source_len = max(cutoff_len - new_target_len, 0)
return new_source_len, new_target_len

32
src/llamafactory/data/processors/supervised.py
View File

@ -1,14 +1,27 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from .processor_utils import get_paligemma_token_type_ids, get_pixel_values, greedy_knapsack
from .processor_utils import get_paligemma_token_type_ids, get_pixel_values, greedy_knapsack, infer_seqlen
if TYPE_CHECKING:
from transformers import ProcessorMixin
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers import PreTrainedTokenizer, ProcessorMixin
from ...hparams import DataArguments
from ..template import Template
@ -38,10 +51,17 @@ def _encode_supervised_example(
input_ids += [image_token_id] * getattr(processor, "image_seq_length")
labels += [IGNORE_INDEX] * getattr(processor, "image_seq_length")
encoded_pairs = template.encode_multiturn(
tokenizer, messages, system, tools, data_args.cutoff_len, data_args.reserved_label_len
)
encoded_pairs = template.encode_multiturn(tokenizer, messages, system, tools)
total_length = 1 if template.efficient_eos else 0
for turn_idx, (source_ids, target_ids) in enumerate(encoded_pairs):
if total_length >= data_args.cutoff_len:
break
source_len, target_len = infer_seqlen(len(source_ids), len(target_ids), data_args.cutoff_len - total_length)
source_ids = source_ids[:source_len]
target_ids = target_ids[:target_len]
total_length += source_len + target_len
if data_args.train_on_prompt:
source_mask = source_ids
elif turn_idx != 0 and template.efficient_eos:

26
src/llamafactory/data/processors/unsupervised.py
View File

@ -1,13 +1,26 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
from ...extras.logging import get_logger
from ..data_utils import Role
from .processor_utils import get_paligemma_token_type_ids, get_pixel_values
from .processor_utils import get_paligemma_token_type_ids, get_pixel_values, infer_seqlen
if TYPE_CHECKING:
from transformers import ProcessorMixin
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers import PreTrainedTokenizer, ProcessorMixin
from ...hparams import DataArguments
from ..template import Template
@ -34,9 +47,7 @@ def _encode_unsupervised_example(
else:
messages = prompt + [{"role": Role.ASSISTANT.value, "content": ""}]
input_ids, labels = template.encode_oneturn(
tokenizer, messages, system, tools, data_args.cutoff_len, data_args.reserved_label_len
)
input_ids, labels = template.encode_oneturn(tokenizer, messages, system, tools)
if template.efficient_eos:
labels += [tokenizer.eos_token_id]
@ -44,6 +55,9 @@ def _encode_unsupervised_example(
image_token_id = tokenizer.convert_tokens_to_ids(template.image_token)
input_ids = [image_token_id] * getattr(processor, "image_seq_length") + input_ids
source_len, target_len = infer_seqlen(len(input_ids), len(labels), data_args.cutoff_len)
input_ids = input_ids[:source_len]
labels = labels[:target_len]
return input_ids, labels

277
src/llamafactory/data/template.py
View File

@ -1,8 +1,22 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
from ..extras.logging import get_logger
from .data_utils import Role, infer_max_len
from .data_utils import Role
from .formatter import EmptyFormatter, FunctionFormatter, StringFormatter, ToolFormatter
@ -24,69 +38,74 @@ class Template:
format_observation: "Formatter"
format_tools: "Formatter"
format_separator: "Formatter"
format_prefix: "Formatter"
default_system: str
stop_words: List[str]
image_token: str
efficient_eos: bool
replace_eos: bool
force_system: bool
def encode_oneturn(
self,
tokenizer: "PreTrainedTokenizer",
messages: List[Dict[str, str]],
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
cutoff_len: int = 1_000_000,
reserved_label_len: int = 1,
) -> Tuple[List[int], List[int]]:
r"""
Returns a single pair of token ids representing prompt and response respectively.
"""
encoded_pairs = self._encode(tokenizer, messages, system, tools, cutoff_len, reserved_label_len)
encoded_messages = self._encode(tokenizer, messages, system, tools)
prompt_ids = []
for query_ids, resp_ids in encoded_pairs[:-1]:
prompt_ids += query_ids + resp_ids
prompt_ids = prompt_ids + encoded_pairs[-1][0]
answer_ids = encoded_pairs[-1][1]
for encoded_ids in encoded_messages[:-1]:
prompt_ids += encoded_ids
answer_ids = encoded_messages[-1]
return prompt_ids, answer_ids
def encode_multiturn(
self,
tokenizer: "PreTrainedTokenizer",
messages: List[Dict[str, str]],
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
cutoff_len: int = 1_000_000,
reserved_label_len: int = 1,
) -> Sequence[Tuple[List[int], List[int]]]:
) -> List[Tuple[List[int], List[int]]]:
r"""
Returns multiple pairs of token ids representing prompts and responses respectively.
"""
return self._encode(tokenizer, messages, system, tools, cutoff_len, reserved_label_len)
encoded_messages = self._encode(tokenizer, messages, system, tools)
return [(encoded_messages[i], encoded_messages[i + 1]) for i in range(0, len(encoded_messages), 2)]
def extract_tool(self, content: str) -> Union[str, List[Tuple[str, str]]]:
r"""
Extracts tool message.
"""
return self.format_tools.extract(content)
def _encode(
self,
tokenizer: "PreTrainedTokenizer",
messages: List[Dict[str, str]],
messages: Sequence[Dict[str, str]],
system: Optional[str],
tools: Optional[str],
cutoff_len: int,
reserved_label_len: int,
) -> Sequence[Tuple[List[int], List[int]]]:
) -> List[List[int]]:
r"""
Encodes formatted inputs to pairs of token ids.
Turn 0: system + query resp
Turn t: sep + query resp
Turn 0: prefix + system + query resp
Turn t: sep + query resp
"""
system = system or self.default_system
encoded_messages = []
for i, message in enumerate(messages):
elements = []
if i == 0 and (system or tools or self.force_system):
tool_text = self.format_tools.apply(content=tools)[0] if tools else ""
elements += self.format_system.apply(content=(system + tool_text))
elif i > 0 and i % 2 == 0:
if i == 0:
elements += self.format_prefix.apply()
if system or tools:
tool_text = self.format_tools.apply(content=tools)[0] if tools else ""
elements += self.format_system.apply(content=(system + tool_text))
if i > 0 and i % 2 == 0:
elements += self.format_separator.apply()
if message["role"] == Role.USER.value:
@ -102,11 +121,9 @@ class Template:
encoded_messages.append(self._convert_elements_to_ids(tokenizer, elements))
return self._make_pairs(encoded_messages, cutoff_len, reserved_label_len)
return encoded_messages
def _convert_elements_to_ids(
self, tokenizer: "PreTrainedTokenizer", elements: List[Union[str, Dict[str, str]]]
) -> List[int]:
def _convert_elements_to_ids(self, tokenizer: "PreTrainedTokenizer", elements: "SLOTS") -> List[int]:
r"""
Converts elements to token ids.
"""
@ -127,57 +144,34 @@ class Template:
return token_ids
def _make_pairs(
self,
encoded_messages: Sequence[List[int]],
cutoff_len: int,
reserved_label_len: int,
) -> Sequence[Tuple[List[int], List[int]]]:
encoded_pairs = []
total_length = 0
for i in range(0, len(encoded_messages), 2):
if total_length >= cutoff_len:
break
max_source_len, max_target_len = infer_max_len(
source_len=len(encoded_messages[i]),
target_len=len(encoded_messages[i + 1]),
max_len=(cutoff_len - total_length),
reserved_label_len=reserved_label_len,
)
source_ids = encoded_messages[i][:max_source_len]
target_ids = encoded_messages[i + 1][:max_target_len]
total_length += len(source_ids) + len(target_ids)
encoded_pairs.append((source_ids, target_ids))
return encoded_pairs
@dataclass
class Llama2Template(Template):
def _encode(
self,
tokenizer: "PreTrainedTokenizer",
messages: List[Dict[str, str]],
messages: Sequence[Dict[str, str]],
system: str,
tools: str,
cutoff_len: int,
reserved_label_len: int,
) -> Sequence[Tuple[List[int], List[int]]]:
) -> List[List[int]]:
r"""
Encodes formatted inputs to pairs of token ids.
Turn 0: system + query resp
Turn t: sep + query resp
Turn 0: prefix + system + query resp
Turn t: sep + query resp
"""
system = system or self.default_system
encoded_messages = []
for i, message in enumerate(messages):
elements = []
system_text = ""
if i == 0 and (system or tools or self.force_system):
tool_text = self.format_tools.apply(content=tools)[0] if tools else ""
system_text = self.format_system.apply(content=(system + tool_text))[0]
elif i > 0 and i % 2 == 0:
if i == 0:
elements += self.format_prefix.apply()
if system or tools:
tool_text = self.format_tools.apply(content=tools)[0] if tools else ""
system_text = self.format_system.apply(content=(system + tool_text))[0]
if i > 0 and i % 2 == 0:
elements += self.format_separator.apply()
if message["role"] == Role.USER.value:
@ -193,7 +187,7 @@ class Llama2Template(Template):
encoded_messages.append(self._convert_elements_to_ids(tokenizer, elements))
return self._make_pairs(encoded_messages, cutoff_len, reserved_label_len)
return encoded_messages
TEMPLATES: Dict[str, Template] = {}
@ -208,12 +202,12 @@ def _register_template(
format_observation: Optional["Formatter"] = None,
format_tools: Optional["Formatter"] = None,
format_separator: Optional["Formatter"] = None,
format_prefix: Optional["Formatter"] = None,
default_system: str = "",
stop_words: List[str] = [],
stop_words: Sequence[str] = [],
image_token: str = "<image>",
efficient_eos: bool = False,
replace_eos: bool = False,
force_system: bool = False,
) -> None:
r"""
Registers a chat template.
@ -245,9 +239,10 @@ def _register_template(
template_class = Llama2Template if name.startswith("llama2") else Template
default_user_formatter = StringFormatter(slots=["{{content}}"])
default_assistant_formatter = StringFormatter(slots=["{{content}}"] + eos_slots)
default_function_formatter = FunctionFormatter(slots=["Action: {{name}}\nAction Input: {{arguments}}"] + eos_slots)
default_function_formatter = FunctionFormatter(slots=eos_slots, tool_format="default")
default_tool_formatter = ToolFormatter(tool_format="default")
default_separator_formatter = EmptyFormatter()
default_prefix_formatter = EmptyFormatter()
TEMPLATES[name] = template_class(
format_user=format_user or default_user_formatter,
format_assistant=format_assistant or default_assistant_formatter,
@ -256,12 +251,12 @@ def _register_template(
format_observation=format_observation or format_user or default_user_formatter,
format_tools=format_tools or default_tool_formatter,
format_separator=format_separator or default_separator_formatter,
format_prefix=format_prefix or default_prefix_formatter,
default_system=default_system,
stop_words=stop_words,
image_token=image_token,
efficient_eos=efficient_eos,
replace_eos=replace_eos,
force_system=force_system,
)
@ -307,6 +302,10 @@ def _convert_slots_to_jinja(slots: "SLOTS", tokenizer: "PreTrainedTokenizer", pl
def _get_jinja_template(template: "Template", tokenizer: "PreTrainedTokenizer") -> str:
jinja_template = ""
prefix = _convert_slots_to_jinja(template.format_prefix.apply(), tokenizer)
if prefix:
jinja_template += "{{ " + prefix + " }}"
if template.default_system:
jinja_template += "{% set system_message = '" + _jinja_escape(template.default_system) + "' %}"
@ -315,11 +314,7 @@ def _get_jinja_template(template: "Template", tokenizer: "PreTrainedTokenizer")
)
system_message = _convert_slots_to_jinja(template.format_system.apply(), tokenizer, placeholder="system_message")
if isinstance(template, Llama2Template):
pass
elif template.force_system:
jinja_template += "{{ " + system_message + " }}"
else:
if not isinstance(template, Llama2Template):
jinja_template += "{% if system_message is defined %}{{ " + system_message + " }}{% endif %}"
jinja_template += "{% for message in messages %}"
@ -346,6 +341,7 @@ def _get_jinja_template(template: "Template", tokenizer: "PreTrainedTokenizer")
def get_template_and_fix_tokenizer(
tokenizer: "PreTrainedTokenizer",
name: Optional[str] = None,
tool_format: Optional[str] = None,
) -> Template:
if name is None:
template = TEMPLATES["empty"] # placeholder
@ -354,6 +350,12 @@ def get_template_and_fix_tokenizer(
if template is None:
raise ValueError("Template {} does not exist.".format(name))
if tool_format is not None:
logger.info("Using tool format: {}.".format(tool_format))
eos_slots = [] if template.efficient_eos else [{"eos_token"}]
template.format_tools = ToolFormatter(tool_format=tool_format)
template.format_function = FunctionFormatter(slots=eos_slots, tool_format=tool_format)
stop_words = template.stop_words
if template.replace_eos:
if not stop_words:
@ -435,9 +437,8 @@ _register_template(
_register_template(
name="belle",
format_user=StringFormatter(slots=["Human: {{content}}\n\nBelle: "]),
format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
format_separator=EmptyFormatter(slots=["\n\n"]),
force_system=True,
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
)
@ -450,11 +451,7 @@ _register_template(
_register_template(
name="breeze",
format_user=StringFormatter(slots=["[INST] {{content}} [/INST] "]),
format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
default_system=(
"You are a helpful AI assistant built by MediaTek Research. "
"The user you are helping speaks Traditional Chinese and comes from Taiwan."
),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
efficient_eos=True,
)
@ -462,10 +459,9 @@ _register_template(
_register_template(
name="chatglm2",
format_user=StringFormatter(slots=["[Round {{idx}}]\n\n问：{{content}}\n\n答："]),
format_system=StringFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}, "{{content}}"]),
format_separator=EmptyFormatter(slots=["\n\n"]),
format_prefix=EmptyFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}]),
efficient_eos=True,
force_system=True,
)
@ -473,32 +469,13 @@ _register_template(
name="chatglm3",
format_user=StringFormatter(slots=[{"token": "<|user|>"}, "\n", "{{content}}", {"token": "<|assistant|>"}]),
format_assistant=StringFormatter(slots=["\n", "{{content}}"]),
format_system=StringFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}, "{{content}}"]),
format_function=FunctionFormatter(slots=["{{name}}\n{{arguments}}"]),
format_system=StringFormatter(slots=[{"token": "<|system|>"}, "\n", "{{content}}"]),
format_function=FunctionFormatter(slots=[], tool_format="glm4"),
format_observation=StringFormatter(
slots=[{"token": "<|observation|>"}, "\n", "{{content}}", {"token": "<|assistant|>"}]
),
stop_words=["<|user|>", "<|observation|>"],
efficient_eos=True,
force_system=True,
)
_register_template(
name="chatglm3_system",
format_user=StringFormatter(slots=[{"token": "<|user|>"}, "\n", "{{content}}", {"token": "<|assistant|>"}]),
format_assistant=StringFormatter(slots=["\n", "{{content}}"]),
format_system=StringFormatter(
slots=[{"token": "[gMASK]"}, {"token": "sop"}, {"token": "<|system|>"}, "\n", "{{content}}"]
),
format_function=FunctionFormatter(slots=["{{name}}\n{{arguments}}"]),
format_observation=StringFormatter(
slots=[{"token": "<|observation|>"}, "\n", "{{content}}", {"token": "<|assistant|>"}]
),
default_system=(
"You are ChatGLM3, a large language model trained by Zhipu.AI. "
"Follow the user's instructions carefully. Respond using markdown."
),
format_tools=ToolFormatter(tool_format="glm4"),
format_prefix=EmptyFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}]),
stop_words=["<|user|>", "<|observation|>"],
efficient_eos=True,
)
@ -529,8 +506,7 @@ _register_template(
_register_template(
name="codegeex2",
format_system=StringFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}, "{{content}}"]),
force_system=True,
format_prefix=EmptyFormatter(slots=[{"token": "[gMASK]"}, {"token": "sop"}]),
)
@ -544,21 +520,15 @@ _register_template(
)
]
),
format_system=StringFormatter(
slots=[{"bos_token"}, "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{content}}<|END_OF_TURN_TOKEN|>"]
),
default_system=(
"You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users "
"by providing thorough responses. You are trained by Cohere."
),
format_system=StringFormatter(slots=["<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{content}}<|END_OF_TURN_TOKEN|>"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
)
_register_template(
name="cpm",
format_user=StringFormatter(slots=["<用户>{{content}}<AI>"]),
format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
force_system=True,
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
)
@ -591,30 +561,28 @@ _register_template(
_register_template(
name="deepseek",
format_user=StringFormatter(slots=["User: {{content}}\n\nAssistant:"]),
format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
force_system=True,
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
)
_register_template(
name="deepseekcoder",
format_user=StringFormatter(slots=["### Instruction:\n{{content}}\n### Response:"]),
format_assistant=StringFormatter(slots=["\n", "{{content}}"]),
format_separator=EmptyFormatter(slots=["\n<|EOT|>\n"]),
format_assistant=StringFormatter(slots=["\n{{content}}\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
default_system=(
"You are an AI programming assistant, utilizing the Deepseek Coder model, "
"developed by Deepseek Company, and you only answer questions related to computer science. "
"For politically sensitive questions, security and privacy issues, "
"and other non-computer science questions, you will refuse to answer\n"
),
stop_words=["<|EOT|>"],
efficient_eos=True,
)
_register_template(
name="default",
format_user=StringFormatter(slots=["Human: {{content}}\nAssistant: "]),
format_user=StringFormatter(slots=["Human: {{content}}\nAssistant:"]),
format_system=StringFormatter(slots=["{{content}}\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
)
@ -622,11 +590,7 @@ _register_template(
_register_template(
name="empty",
format_user=StringFormatter(slots=["{{content}}"]),
format_assistant=StringFormatter(slots=["{{content}}"]),
format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
efficient_eos=True,
force_system=True,
)
@ -648,13 +612,12 @@ _register_template(
_register_template(
name="gemma",
format_user=StringFormatter(slots=["<start_of_turn>user\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]),
format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
format_observation=StringFormatter(
slots=["<start_of_turn>tool\n{{content}}<end_of_turn>\n<start_of_turn>model\n"]
),
format_separator=EmptyFormatter(slots=["<end_of_turn>\n"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
efficient_eos=True,
force_system=True,
)
@ -662,36 +625,33 @@ _register_template(
name="glm4",
format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>"]),
format_assistant=StringFormatter(slots=["\n{{content}}"]),
format_system=StringFormatter(slots=["[gMASK]<sop>{{content}}"]),
format_function=FunctionFormatter(slots=["{{name}}\n{{arguments}}"]),
format_system=StringFormatter(slots=["<|system|>\n{{content}}"]),
format_function=FunctionFormatter(slots=[], tool_format="glm4"),
format_observation=StringFormatter(slots=["<|observation|>\n{{content}}<|assistant|>"]),
format_tools=ToolFormatter(tool_format="glm4"),
format_prefix=EmptyFormatter(slots=["[gMASK]<sop>"]),
stop_words=["<|user|>", "<|observation|>"],
efficient_eos=True,
force_system=True,
)
_register_template(
name="intern",
format_user=StringFormatter(slots=["<|User|>:{{content}}", {"token": "<eoh>"}, "\n<|Bot|>:"]),
format_separator=EmptyFormatter(slots=[{"token": "<eoa>"}, "\n"]),
format_user=StringFormatter(slots=["<|User|>:{{content}}\n<|Bot|>:"]),
format_system=StringFormatter(slots=["<|System|>:{{content}}\n"]),
format_separator=EmptyFormatter(slots=["<eoa>\n"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
stop_words=["<eoa>"],
efficient_eos=True,
efficient_eos=True, # internlm tokenizer cannot set eos_token_id
)
_register_template(
name="intern2",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_system=StringFormatter(slots=[{"bos_token"}, "<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
default_system=(
"You are an AI assistant whose name is InternLM (书生·浦语).\n"
"- InternLM (书生·浦语) is a conversational language model that is developed "
"by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.\n"
"- InternLM (书生·浦语) can understand and communicate fluently in the language chosen "
"by the user such as English and 中文."
),
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_separator=EmptyFormatter(slots=["<|im_end|>\n"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
stop_words=["<|im_end|>"],
efficient_eos=True, # internlm2 tokenizer cannot set eos_token_id
)
@ -700,7 +660,6 @@ _register_template(
_register_template(
name="llama2",
format_user=StringFormatter(slots=[{"bos_token"}, "[INST] {{content}} [/INST]"]),
format_assistant=StringFormatter(slots=[" {{content}} ", {"eos_token"}]),
format_system=StringFormatter(slots=["<<SYS>>\n{{content}}\n<</SYS>>\n\n"]),
)
@ -723,9 +682,7 @@ _register_template(
)
]
),
format_system=StringFormatter(
slots=[{"bos_token"}, "<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]
),
format_system=StringFormatter(slots=["<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]),
format_observation=StringFormatter(
slots=[
(
@ -734,7 +691,7 @@ _register_template(
)
]
),
default_system="You are a helpful assistant.",
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
stop_words=["<|eot_id|>"],
replace_eos=True,
)
@ -743,24 +700,21 @@ _register_template(
_register_template(
name="mistral",
format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
force_system=True,
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
)
_register_template(
name="olmo",
format_user=StringFormatter(slots=["<|user|>\n{{content}}<|assistant|>\n"]),
format_system=StringFormatter(slots=[{"eos_token"}, "{{content}}"]),
force_system=True,
format_prefix=EmptyFormatter(slots=[{"eos_token"}]),
)
_register_template(
name="openchat",
format_user=StringFormatter(slots=["GPT4 Correct User: {{content}}", {"eos_token"}, "GPT4 Correct Assistant:"]),
format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
force_system=True,
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
)
@ -774,27 +728,25 @@ _register_template(
)
]
),
format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
stop_words=["<|eot_id|>"],
replace_eos=True,
force_system=True,
)
_register_template(
name="orion",
format_user=StringFormatter(slots=["Human: {{content}}\n\nAssistant: ", {"eos_token"}]),
format_system=StringFormatter(slots=[{"bos_token"}, "{{content}}"]),
force_system=True,
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
)
_register_template(
name="phi",
format_user=StringFormatter(slots=["<|user|>\n{{content}}<|end|>\n<|assistant|>\n"]),
format_system=StringFormatter(slots=[{"bos_token"}, "<|system|>\n{{content}}<|end|>\n"]),
format_system=StringFormatter(slots=["<|system|>\n{{content}}<|end|>\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
default_system="You are a helpful AI assistant.",
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
stop_words=["<|end|>"],
replace_eos=True,
)
@ -827,7 +779,6 @@ _register_template(
format_separator=EmptyFormatter(slots=["\n"]),
stop_words=["<|end|>"],
replace_eos=True,
force_system=True,
)

140
src/llamafactory/data/tool_utils.py Normal file
View File

@ -0,0 +1,140 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import re
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Any, Dict, List, Tuple, Union
from .data_utils import SLOTS
DEFAULT_TOOL_PROMPT = (
"You have access to the following tools:\n{tool_text}"
"Use the following format if using a tool:\n"
"```\n"
"Action: tool name (one of [{tool_names}]).\n"
"Action Input: the input to the tool, in a JSON format representing the kwargs "
"""(e.g. ```{{"input": "hello world", "num_beams": 5}}```).\n"""
"```\n"
)
GLM4_TOOL_PROMPT = (
"你是一个名为 ChatGLM 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的，"
"你的任务是针对用户的问题和要求提供适当的答复和支持。# 可用工具{tool_text}"
)
@dataclass
class ToolUtils(ABC):
@staticmethod
@abstractmethod
def get_function_slots() -> SLOTS: ...
@staticmethod
@abstractmethod
def tool_formatter(tools: List[Dict[str, Any]]) -> str: ...
@staticmethod
@abstractmethod
def tool_extractor(content: str) -> Union[str, List[Tuple[str, str]]]: ...
class DefaultToolUtils(ToolUtils):
@staticmethod
def get_function_slots() -> SLOTS:
return ["Action: {{name}}\nAction Input: {{arguments}}\n"]
@staticmethod
def tool_formatter(tools: List[Dict[str, Any]]) -> str:
tool_text = ""
tool_names = []
for tool in tools:
param_text = ""
for name, param in tool["parameters"]["properties"].items():
required, enum, items = "", "", ""
if name in tool["parameters"].get("required", []):
required = ", required"
if param.get("enum", None):
enum = ", should be one of [{}]".format(", ".join(param["enum"]))
if param.get("items", None):
items = ", where each item should be {}".format(param["items"].get("type", ""))
param_text += " - {name} ({type}{required}): {desc}{enum}{items}\n".format(
name=name,
type=param.get("type", ""),
required=required,
desc=param.get("description", ""),
enum=enum,
items=items,
)
tool_text += "> Tool Name: {name}\nTool Description: {desc}\nTool Args:\n{args}\n".format(
name=tool["name"], desc=tool.get("description", ""), args=param_text
)
tool_names.append(tool["name"])
return DEFAULT_TOOL_PROMPT.format(tool_text=tool_text, tool_names=", ".join(tool_names))
@staticmethod
def tool_extractor(content: str) -> Union[str, List[Tuple[str, str]]]:
regex = re.compile(r"Action:\s*([a-zA-Z0-9_]+)\s*Action Input:\s*(.+?)(?=\s*Action:|\s*$)", re.DOTALL)
action_match: List[Tuple[str, str]] = re.findall(regex, content)
if not action_match:
return content
results = []
for match in action_match:
tool_name = match[0].strip()
tool_input = match[1].strip().strip('"').strip("```")
try:
arguments = json.loads(tool_input)
results.append((tool_name, json.dumps(arguments, ensure_ascii=False)))
except json.JSONDecodeError:
return content
return results
class GLM4ToolUtils(ToolUtils):
@staticmethod
def get_function_slots() -> SLOTS:
return ["{{name}}\n{{arguments}}"]
@staticmethod
def tool_formatter(tools: List[Dict[str, Any]]) -> str:
tool_text = ""
for tool in tools:
tool_text += "\n\n## {name}\n\n{body}\n在调用上述函数时，请使用 Json 格式表示调用的参数。".format(
name=tool["name"], body=json.dumps(tool, indent=4, ensure_ascii=False)
)
return GLM4_TOOL_PROMPT.format(tool_text=tool_text)
@staticmethod
def tool_extractor(content: str) -> Union[str, List[Tuple[str, str]]]:
if "\n" not in content:
return content
tool_name, tool_input = content.split("\n", maxsplit=1)
try:
arguments = json.loads(tool_input)
except json.JSONDecodeError:
return content
return [(tool_name, json.dumps(arguments, ensure_ascii=False))]

43
src/llamafactory/eval/evaluator.py
View File

@ -1,4 +1,41 @@
# Inspired by: https://github.com/hendrycks/test/blob/master/evaluate_flan.py
# Copyright 2024 the LlamaFactory team.
#
# This code is inspired by the Dan's test library.
# https://github.com/hendrycks/test/blob/master/evaluate_flan.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# MIT License
#
# Copyright (c) 2020 Dan Hendrycks
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import inspect
import json
@ -26,9 +63,7 @@ class Evaluator:
self.template = get_template_and_fix_tokenizer(self.tokenizer, self.data_args.template)
self.model = load_model(self.tokenizer, self.model_args, finetuning_args)
self.eval_template = get_eval_template(self.eval_args.lang)
self.choice_inputs = [
self.tokenizer.encode(self.eval_template.prefix + ch, add_special_tokens=False)[-1] for ch in CHOICES
]
self.choice_inputs = [self.tokenizer.encode(ch, add_special_tokens=False)[-1] for ch in CHOICES]
@torch.inference_mode()
def batch_inference(self, batch_input: Dict[str, torch.Tensor]) -> List[str]:

23
src/llamafactory/eval/template.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import dataclass
from typing import Dict, List, Sequence, Tuple
@ -10,7 +24,6 @@ class EvalTemplate:
system: str
choice: str
answer: str
prefix: str
def _parse_example(self, example: Dict[str, str]) -> Tuple[str, str]:
r"""
@ -42,8 +55,8 @@ class EvalTemplate:
eval_templates: Dict[str, "EvalTemplate"] = {}
def _register_eval_template(name: str, system: str, choice: str, answer: str, prefix: str) -> None:
eval_templates[name] = EvalTemplate(system=system, choice=choice, answer=answer, prefix=prefix)
def _register_eval_template(name: str, system: str, choice: str, answer: str) -> None:
eval_templates[name] = EvalTemplate(system=system, choice=choice, answer=answer)
def get_eval_template(name: str) -> "EvalTemplate":
@ -56,8 +69,7 @@ _register_eval_template(
name="en",
system="The following are multiple choice questions (with answers) about {subject}.\n\n",
choice="\n{choice}. {content}",
answer="\nAnswer: ",
prefix=" ",
answer="\nAnswer:",
)
@ -66,5 +78,4 @@ _register_eval_template(
system="以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n",
choice="\n{choice}. {content}",
answer="\n答案：",
prefix=" ",
)

55
src/llamafactory/extras/constants.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import OrderedDict, defaultdict
from enum import Enum
from typing import Dict, Optional
@ -404,6 +418,18 @@ register_model_group(
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V2-Chat",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V2-Chat",
},
"DeepSeek-MoE-Coder-16B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-Coder-V2-Lite-Base",
},
"DeepSeek-MoE-Coder-236B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-Coder-V2-Base",
},
"DeepSeek-MoE-Coder-16B-Chat": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
},
"DeepSeek-MoE-Coder-236B-Chat": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-Coder-V2-Instruct",
},
},
template="deepseek",
)
@ -496,6 +522,18 @@ register_model_group(
"Gemma-1.1-7B-Chat": {
DownloadSource.DEFAULT: "google/gemma-1.1-7b-it",
},
"Gemma-2-9B": {
DownloadSource.DEFAULT: "google/gemma-2-9b",
},
"Gemma-2-27B": {
DownloadSource.DEFAULT: "google/gemma-2-27b",
},
"Gemma-2-9B-Chat": {
DownloadSource.DEFAULT: "google/gemma-2-9b-it",
},
"Gemma-2-27B-Chat": {
DownloadSource.DEFAULT: "google/gemma-2-27b-it",
},
},
template="gemma",
)
@ -568,7 +606,7 @@ register_model_group(
register_model_group(
models={
"Jambda-v0.1": {
"Jamba-v0.1": {
DownloadSource.DEFAULT: "ai21labs/Jamba-v0.1",
DownloadSource.MODELSCOPE: "AI-ModelScope/Jamba-v0.1",
}
@ -683,6 +721,21 @@ register_model_group(
)
register_model_group(
models={
"MiniCPM-2B-SFT-Chat": {
DownloadSource.DEFAULT: "openbmb/MiniCPM-2B-sft-bf16",
DownloadSource.MODELSCOPE: "OpenBMB/miniCPM-bf16",
},
"MiniCPM-2B-DPO-Chat": {
DownloadSource.DEFAULT: "openbmb/MiniCPM-2B-dpo-bf16",
DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM-2B-dpo-bf16",
},
},
template="cpm",
)
register_model_group(
models={
"Mistral-7B-v0.1": {

19
src/llamafactory/extras/env.py
View File

@ -1,3 +1,20 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/commands/env.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import platform
import accelerate
@ -9,7 +26,7 @@ import trl
from transformers.utils import is_torch_cuda_available, is_torch_npu_available
VERSION = "0.8.1.dev0"
VERSION = "0.8.3.dev0"
def print_env() -> None:

14
src/llamafactory/extras/logging.py
View File

@ -1,3 +1,17 @@
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
import sys

103
src/llamafactory/extras/misc.py
View File

@ -1,13 +1,29 @@
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's PEFT library.
# https://github.com/huggingface/peft/blob/v0.10.0/src/peft/peft_model.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import os
from typing import TYPE_CHECKING, Dict, Tuple
from typing import TYPE_CHECKING, Tuple
import torch
from peft import PeftModel
from transformers import InfNanRemoveLogitsProcessor, LogitsProcessorList, PreTrainedModel
import transformers.dynamic_module_utils
from transformers import InfNanRemoveLogitsProcessor, LogitsProcessorList
from transformers.dynamic_module_utils import get_relative_imports
from transformers.utils import (
SAFE_WEIGHTS_NAME,
WEIGHTS_NAME,
is_torch_bf16_gpu_available,
is_torch_cuda_available,
is_torch_mps_available,
@ -16,7 +32,6 @@ from transformers.utils import (
)
from transformers.utils.versions import require_version
from .constants import V_HEAD_SAFE_WEIGHTS_NAME, V_HEAD_WEIGHTS_NAME
from .logging import get_logger
@ -28,8 +43,6 @@ except Exception:
if TYPE_CHECKING:
from trl import AutoModelForCausalLMWithValueHead
from ..hparams import ModelArguments
@ -58,6 +71,9 @@ class AverageMeter:
def check_dependencies() -> None:
r"""
Checks the version of the required packages.
"""
if os.environ.get("DISABLE_VERSION_CHECK", "0").lower() in ["true", "1"]:
logger.warning("Version checking has been disabled, may lead to unexpected behaviors.")
else:
@ -68,7 +84,7 @@ def check_dependencies() -> None:
require_version("trl>=0.8.6", "To fix: pip install trl>=0.8.6")
def count_parameters(model: torch.nn.Module) -> Tuple[int, int]:
def count_parameters(model: "torch.nn.Module") -> Tuple[int, int]:
r"""
Returns the number of trainable parameters and number of all parameters in the model.
"""
@ -79,7 +95,7 @@ def count_parameters(model: torch.nn.Module) -> Tuple[int, int]:
if num_params == 0 and hasattr(param, "ds_numel"):
num_params = param.ds_numel
# Due to the design of 4bit linear layers from bitsandbytes, multiply the number of parameters by 2
# Due to the design of 4bit linear layers from bitsandbytes, multiply the number of parameters by itemsize
if param.__class__.__name__ == "Params4bit":
if hasattr(param, "quant_storage") and hasattr(param.quant_storage, "itemsize"):
num_bytes = param.quant_storage.itemsize
@ -97,55 +113,7 @@ def count_parameters(model: torch.nn.Module) -> Tuple[int, int]:
return trainable_params, all_param
def fix_valuehead_checkpoint(
model: "AutoModelForCausalLMWithValueHead", output_dir: str, safe_serialization: bool
) -> None:
r"""
The model is already unwrapped.
There are three cases:
1. full tuning without ds_zero3: state_dict = {"model.layers.*": ..., "v_head.summary.*": ...}
2. lora tuning without ds_zero3: state_dict = {"v_head.summary.*": ...}
3. under deepspeed zero3: state_dict = {"pretrained_model.model.layers.*": ..., "v_head.summary.*": ...}
We assume `stage3_gather_16bit_weights_on_model_save=true`.
"""
if not isinstance(model.pretrained_model, (PreTrainedModel, PeftModel)):
return
if safe_serialization:
from safetensors import safe_open
from safetensors.torch import save_file
path_to_checkpoint = os.path.join(output_dir, SAFE_WEIGHTS_NAME)
with safe_open(path_to_checkpoint, framework="pt", device="cpu") as f:
state_dict: Dict[str, torch.Tensor] = {key: f.get_tensor(key) for key in f.keys()}
else:
path_to_checkpoint = os.path.join(output_dir, WEIGHTS_NAME)
state_dict: Dict[str, torch.Tensor] = torch.load(path_to_checkpoint, map_location="cpu")
decoder_state_dict = {}
v_head_state_dict = {}
for name, param in state_dict.items():
if name.startswith("v_head."):
v_head_state_dict[name] = param
else:
decoder_state_dict[name.replace("pretrained_model.", "")] = param
os.remove(path_to_checkpoint)
model.pretrained_model.save_pretrained(
output_dir, state_dict=decoder_state_dict or None, safe_serialization=safe_serialization
)
if safe_serialization:
save_file(v_head_state_dict, os.path.join(output_dir, V_HEAD_SAFE_WEIGHTS_NAME), metadata={"format": "pt"})
else:
torch.save(v_head_state_dict, os.path.join(output_dir, V_HEAD_WEIGHTS_NAME))
logger.info("Value head model saved at: {}".format(output_dir))
def get_current_device() -> torch.device:
def get_current_device() -> "torch.device":
r"""
Gets the current available device.
"""
@ -184,7 +152,14 @@ def get_logits_processor() -> "LogitsProcessorList":
return logits_processor
def infer_optim_dtype(model_dtype: torch.dtype) -> torch.dtype:
def has_tokenized_data(path: "os.PathLike") -> bool:
r"""
Checks if the path has a tokenized dataset.
"""
return os.path.isdir(path) and len(os.listdir(path)) > 0
def infer_optim_dtype(model_dtype: "torch.dtype") -> "torch.dtype":
r"""
Infers the optimal dtype according to the model_dtype and device compatibility.
"""
@ -203,11 +178,9 @@ def is_gpu_or_npu_available() -> bool:
return is_torch_npu_available() or is_torch_cuda_available()
def has_tokenized_data(path: os.PathLike) -> bool:
r"""
Checks if the path has a tokenized dataset.
"""
return os.path.isdir(path) and len(os.listdir(path)) > 0
def skip_check_imports() -> None:
if os.environ.get("FORCE_CHECK_IMPORTS", "0").lower() not in ["true", "1"]:
transformers.dynamic_module_utils.check_imports = get_relative_imports
def torch_gc() -> None:

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