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2
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@ -7,6 +7,8 @@ data
docker
saves
hf_cache
ms_cache
om_cache
output
.dockerignore
.gitattributes

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.env.local
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@ -1,32 +1,35 @@
# Note: actually we do not support .env, just for reference
# api
API_HOST=0.0.0.0
API_PORT=8000
API_HOST=
API_PORT=
API_KEY=
API_MODEL_NAME=gpt-3.5-turbo
API_MODEL_NAME=
FASTAPI_ROOT_PATH=
MAX_CONCURRENT=
# general
DISABLE_VERSION_CHECK=
FORCE_CHECK_IMPORTS=
LLAMAFACTORY_VERBOSITY=
USE_MODELSCOPE_HUB=
USE_OPENMIND_HUB=
RECORD_VRAM=
# torchrun
FORCE_TORCHRUN=
MASTER_ADDR=
MASTER_PORT=
NNODES=
RANK=
NODE_RANK=
NPROC_PER_NODE=
# wandb
WANDB_DISABLED=
WANDB_PROJECT=huggingface
WANDB_PROJECT=
WANDB_API_KEY=
# gradio ui
GRADIO_SHARE=False
GRADIO_SERVER_NAME=0.0.0.0
GRADIO_SHARE=
GRADIO_SERVER_NAME=
GRADIO_SERVER_PORT=
GRADIO_ROOT_PATH=
GRADIO_IPV6=
# setup
ENABLE_SHORT_CONSOLE=1
# reserved (do not use)

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.github/CONTRIBUTING.md vendored
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@ -19,3 +19,49 @@ There are several ways you can contribute to LLaMA Factory:
### Style guide
LLaMA Factory follows the [Google Python Style Guide](https://google.github.io/styleguide/pyguide.html), check it for details.
### Create a Pull Request
1. Fork the [repository](https://github.com/hiyouga/LLaMA-Factory) by clicking on the [Fork](https://github.com/hiyouga/LLaMA-Factory/fork) button on the repository's page. This creates a copy of the code under your GitHub user account.
2. Clone your fork to your local disk, and add the base repository as a remote:
```bash
git clone git@github.com:[username]/LLaMA-Factory.git
cd LLaMA-Factory
git remote add upstream https://github.com/hiyouga/LLaMA-Factory.git
```
3. Create a new branch to hold your development changes:
```bash
git checkout -b dev_your_branch
```
4. Set up a development environment by running the following command in a virtual environment:
```bash
pip install -e ".[dev]"
```
If LLaMA Factory was already installed in the virtual environment, remove it with `pip uninstall llamafactory` before reinstalling it in editable mode with the -e flag.
5. Check code before commit:
```bash
make commit
make style && make quality
make test
```
6. Submit changes:
```bash
git add .
git commit -m "commit message"
git fetch upstream
git rebase upstream/main
git push -u origin dev_your_branch
```
7. Create a merge request from your branch `dev_your_branch` at [origin repo](https://github.com/hiyouga/LLaMA-Factory).

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.github/workflows/publish.yml vendored
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@ -26,15 +26,15 @@ jobs:
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

3
.github/workflows/tests.yml vendored
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@ -22,7 +22,7 @@ jobs:
fail-fast: false
matrix:
python-version:
- "3.8"
- "3.8" # TODO: remove py38 in next transformers release
- "3.9"
- "3.10"
- "3.11"
@ -54,7 +54,6 @@ jobs:
- name: Install dependencies
run: |
python -m pip install --upgrade pip
python -m pip install git+https://github.com/huggingface/transformers.git
python -m pip install ".[torch,dev]"
- name: Check quality

1
.gitignore vendored
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@ -162,6 +162,7 @@ cython_debug/
# custom .gitignore
ms_cache/
hf_cache/
om_cache/
cache/
config/
saves/

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.pre-commit-config.yaml Normal file
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@ -0,0 +1,28 @@
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v5.0.0
hooks:
- id: check-ast
- id: check-added-large-files
args: ['--maxkb=25000']
- id: check-merge-conflict
- id: check-yaml
- id: debug-statements
- id: end-of-file-fixer
- id: trailing-whitespace
args: [--markdown-linebreak-ext=md]
- id: no-commit-to-branch
args: ['--branch', 'main']
- repo: https://github.com/asottile/pyupgrade
rev: v3.17.0
hooks:
- id: pyupgrade
args: [--py38-plus]
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.6.9
hooks:
- id: ruff
args: [--fix]
- id: ruff-format

11
Makefile
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@ -1,7 +1,14 @@
.PHONY: quality style test
.PHONY: build commit quality style test
check_dirs := scripts src tests setup.py
build:
pip install build && python -m build
commit:
pre-commit install
pre-commit run --all-files
quality:
ruff check $(check_dirs)
ruff format --check $(check_dirs)
@ -11,4 +18,4 @@ style:
ruff format $(check_dirs)
test:
CUDA_VISIBLE_DEVICES= pytest tests/
CUDA_VISIBLE_DEVICES= WANDB_DISABLED=true pytest -vv tests/

104
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-91-green)](#projects-using-llama-factory)
[![Citation](https://img.shields.io/badge/citation-92-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)
@ -26,10 +26,17 @@ https://github.com/user-attachments/assets/7c96b465-9df7-45f4-8053-bf03e58386d3
Choose your path:
- **Colab**: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
- **PAI-DSW**: https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
- **PAI-DSW**: [Llama3 Example](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory) | [Qwen2-VL Example](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl)
- **Local machine**: Please refer to [usage](#getting-started)
- **Documentation (WIP)**: https://llamafactory.readthedocs.io/zh-cn/latest/
Recent activities:
- **2024/10/18-2024/11/30**: Build a personal tour guide bot using PAI+LLaMA Factory. [[website]](https://developer.aliyun.com/topic/llamafactory2)
> [!NOTE]
> Except for the above links, all other websites are unauthorized third-party websites. Please carefully use them.
## Table of Contents
- [Features](#features)
@ -72,6 +79,8 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
## Changelog
[24/10/09] We supported downloading pre-trained models and datasets from the **[Modelers Hub](https://modelers.cn/models)**. See [this tutorial](#download-from-modelers-hub) for usage.
[24/09/19] We support fine-tuning the **[Qwen2.5](https://qwenlm.github.io/blog/qwen2.5/)** models.
[24/08/30] We support fine-tuning the **[Qwen2-VL](https://qwenlm.github.io/blog/qwen2-vl/)** models. Thank [@simonJJJ](https://github.com/simonJJJ)'s PR.
@ -130,7 +139,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
[23/12/12] We supported fine-tuning the latest MoE model **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)** in our framework. See hardware requirement [here](#hardware-requirement).
[23/12/01] We supported downloading pre-trained models and datasets from the **[ModelScope Hub](https://modelscope.cn/models)** for Chinese mainland users. See [this tutorial](#download-from-modelscope-hub) for usage.
[23/12/01] We supported downloading pre-trained models and datasets from the **[ModelScope Hub](https://modelscope.cn/models)**. See [this tutorial](#download-from-modelscope-hub) for usage.
[23/10/21] We supported **[NEFTune](https://arxiv.org/abs/2310.05914)** trick for fine-tuning. Try `neftune_noise_alpha: 5` argument to activate NEFTune.
@ -162,36 +171,39 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
## Supported Models
| 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/InternLM2.5](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/Llama 3.1](https://huggingface.co/meta-llama) | 8B/70B | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | llava |
| [MiniCPM](https://huggingface.co/openbmb) | 1B/2B/4B | cpm/cpm3 |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | - |
| [PaliGemma](https://huggingface.co/google) | 3B | paligemma |
| [Phi-1.5/Phi-2](https://huggingface.co/microsoft) | 1.3B/2.7B | - |
| [Phi-3](https://huggingface.co/microsoft) | 4B/14B | phi |
| [Phi-3-small](https://huggingface.co/microsoft) | 7B | phi-small |
| [Qwen/Qwen1.5/Qwen2 (Code/Math/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/4B/7B/14B/32B/72B/110B | qwen |
| [Qwen2.5 (Code/Math)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B | qwen |
| [Qwen2-VL](https://huggingface.co/Qwen) | 2B/7B/72B | qwen2_vl |
| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai) | 1.5B/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 |
| 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 |
| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
| [InternLM2/InternLM2.5](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 3-3.2](https://huggingface.co/meta-llama) | 1B/3B/8B/70B | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | llava |
| [LLaVA-NeXT](https://huggingface.co/llava-hf) | 7B/8B/13B/34B/72B/110B | llava_next |
| [LLaVA-NeXT-Video](https://huggingface.co/llava-hf) | 7B/34B | llava_next_video |
| [MiniCPM](https://huggingface.co/openbmb) | 1B/2B/4B | cpm/cpm3 |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | - |
| [PaliGemma](https://huggingface.co/google) | 3B | paligemma |
| [Phi-1.5/Phi-2](https://huggingface.co/microsoft) | 1.3B/2.7B | - |
| [Phi-3](https://huggingface.co/microsoft) | 4B/14B | phi |
| [Phi-3-small](https://huggingface.co/microsoft) | 7B | phi_small |
| [Pixtral](https://huggingface.co/mistralai) | 12B | pixtral |
| [Qwen (1-2.5) (Code/Math/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen |
| [Qwen2-VL](https://huggingface.co/Qwen) | 2B/7B/72B | qwen2_vl |
| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai) | 1.5B/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.
@ -360,7 +372,7 @@ cd LLaMA-Factory
pip install -e ".[torch,metrics]"
```
Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, awq, aqlm, vllm, galore, badam, adam-mini, qwen, modelscope, quality
Extra dependencies available: torch, torch-npu, metrics, deepspeed, liger-kernel, bitsandbytes, hqq, eetq, gptq, awq, aqlm, vllm, galore, badam, adam-mini, qwen, modelscope, openmind, quality
> [!TIP]
> Use `pip install --no-deps -e .` to resolve package conflicts.
@ -412,7 +424,7 @@ Download the pre-built Docker images: [32GB](http://mirrors.cn-central-221.ovaij
### Data Preparation
Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use datasets on HuggingFace / ModelScope hub or load the dataset in local disk.
Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use datasets on HuggingFace / ModelScope / Modelers hub or load the dataset in local disk.
> [!NOTE]
> Please update `data/dataset_info.json` to use your custom dataset.
@ -480,6 +492,7 @@ docker build -f ./docker/docker-cuda/Dockerfile \
docker run -dit --gpus=all \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./om_cache:/root/.cache/openmind \
-v ./data:/app/data \
-v ./output:/app/output \
-p 7860:7860 \
@ -504,6 +517,7 @@ docker build -f ./docker/docker-npu/Dockerfile \
docker run -dit \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./om_cache:/root/.cache/openmind \
-v ./data:/app/data \
-v ./output:/app/output \
-v /usr/local/dcmi:/usr/local/dcmi \
@ -537,6 +551,7 @@ docker build -f ./docker/docker-rocm/Dockerfile \
docker run -dit \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./om_cache:/root/.cache/openmind \
-v ./data:/app/data \
-v ./output:/app/output \
-v ./saves:/app/saves \
@ -557,6 +572,7 @@ docker exec -it llamafactory bash
- `hf_cache`: Utilize Hugging Face cache on the host machine. Reassignable if a cache already exists in a different directory.
- `ms_cache`: Similar to Hugging Face cache but for ModelScope users.
- `om_cache`: Similar to Hugging Face cache but for Modelers users.
- `data`: Place datasets on this dir of the host machine so that they can be selected on LLaMA Board GUI.
- `output`: Set export dir to this location so that the merged result can be accessed directly on the host machine.
@ -570,6 +586,8 @@ API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
> [!TIP]
> Visit [this page](https://platform.openai.com/docs/api-reference/chat/create) for API document.
>
> Examples: [Image understanding](scripts/test_image.py) | [Function calling](scripts/test_toolcall.py)
### Download from ModelScope Hub
@ -581,6 +599,16 @@ export USE_MODELSCOPE_HUB=1 # `set USE_MODELSCOPE_HUB=1` for Windows
Train the model by specifying a model ID of the ModelScope Hub as the `model_name_or_path`. You can find a full list of model IDs at [ModelScope Hub](https://modelscope.cn/models), e.g., `LLM-Research/Meta-Llama-3-8B-Instruct`.
### Download from Modelers Hub
You can also use Modelers Hub to download models and datasets.
```bash
export USE_OPENMIND_HUB=1 # `set USE_OPENMIND_HUB=1` for Windows
```
Train the model by specifying a model ID of the Modelers Hub as the `model_name_or_path`. You can find a full list of model IDs at [Modelers Hub](https://modelers.cn/models), e.g., `TeleAI/TeleChat-7B-pt`.
### Use W&B Logger
To use [Weights & Biases](https://wandb.ai) for logging experimental results, you need to add the following arguments to yaml files.
@ -684,11 +712,13 @@ If you have a project that should be incorporated, please contact via email or c
1. **[Sunsimiao](https://github.com/X-D-Lab/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
1. **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: A series of large language models for Chinese medical domain, based on LLaMA2-7B and Baichuan-13B.
1. **[MachineMindset](https://github.com/PKU-YuanGroup/Machine-Mindset/)**: A series of MBTI Personality large language models, capable of giving any LLM 16 different personality types based on different datasets and training methods.
1. **[Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-v3)**: A large language model specialized in generate metadata for stable diffusion. [[🤗Demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt)
1. **[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.
1. **[LazyLLM](https://github.com/LazyAGI/LazyLLM)**: An easy and lazy way for building multi-agent LLMs applications and supports model fine-tuning via LLaMA Factory.
1. **[RAG-Retrieval](https://github.com/NLPJCL/RAG-Retrieval)**: A full pipeline for RAG retrieval model fine-tuning, inference, and distillation. [[blog]](https://zhuanlan.zhihu.com/p/987727357)
</details>
@ -696,7 +726,7 @@ 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: [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/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [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)
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) / [Index](https://huggingface.co/IndexTeam/Index-1.9B/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/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral/Mixtral/Pixtral](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

99
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-91-green)](#使用了-llama-factory-的项目)
[![Citation](https://img.shields.io/badge/citation-92-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)
@ -26,11 +26,18 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
选择你的打开方式：
- **Colab**：https://colab.research.google.com/drive/1d5KQtbemerlSDSxZIfAaWXhKr30QypiK?usp=sharing
- **PAI-DSW**：https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory
- **PAI-DSW**：[Llama3 案例](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory) | [Qwen2-VL 案例](https://gallery.pai-ml.com/#/preview/deepLearning/nlp/llama_factory_qwen2vl)
- **本地机器**：请见[如何使用](#如何使用)
- **入门教程**：https://zhuanlan.zhihu.com/p/695287607
- **框架文档**：https://llamafactory.readthedocs.io/zh-cn/latest/
近期活动：
- **2024/10/18-2024/11/30**：使用 PAI+LLaMA Factory 构建个性化导游机器人。[[活动页面]](https://developer.aliyun.com/topic/llamafactory2)
> [!NOTE]
> 除上述链接以外的其他网站均为未经许可的第三方网站，请小心甄别。
## 目录
- [项目特色](#项目特色)
@ -73,6 +80,8 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
## 更新日志
[24/10/09] 我们支持了从 **[魔乐社区](https://modelers.cn/models)** 下载预训练模型和数据集。详细用法请参照 [此教程](#从魔乐社区下载)。
[24/09/19] 我们支持了 **[Qwen2.5](https://qwenlm.github.io/blog/qwen2.5/)** 模型的微调。
[24/08/30] 我们支持了 **[Qwen2-VL](https://qwenlm.github.io/blog/qwen2-vl/)** 模型的微调。感谢 [@simonJJJ](https://github.com/simonJJJ) 的 PR。
@ -163,35 +172,38 @@ https://github.com/user-attachments/assets/e6ce34b0-52d5-4f3e-a830-592106c4c272
## 模型
| 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/InternLM2.5](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/Llama 3.1](https://huggingface.co/meta-llama) | 8B/70B | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | llava |
| [MiniCPM](https://huggingface.co/openbmb) | 1B/2B/4B | cpm/cpm3 |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | - |
| [PaliGemma](https://huggingface.co/google) | 3B | paligemma |
| [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/Math/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/4B/7B/14B/32B/72B/110B | qwen |
| [Qwen2.5 (Code/Math)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B | qwen |
| [Qwen2-VL](https://huggingface.co/Qwen) | 2B/7B/72B | qwen2_vl |
| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai) | 1.5B/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 |
| 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 |
| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
| [InternLM2/InternLM2.5](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 3-3.2](https://huggingface.co/meta-llama) | 1B/3B/8B/70B | llama3 |
| [LLaVA-1.5](https://huggingface.co/llava-hf) | 7B/13B | llava |
| [LLaVA-NeXT](https://huggingface.co/llava-hf) | 7B/8B/13B/34B/72B/110B | llava_next |
| [LLaVA-NeXT-Video](https://huggingface.co/llava-hf) | 7B/34B | llava_next_video |
| [MiniCPM](https://huggingface.co/openbmb) | 1B/2B/4B | cpm/cpm3 |
| [Mistral/Mixtral](https://huggingface.co/mistralai) | 7B/8x7B/8x22B | mistral |
| [OLMo](https://huggingface.co/allenai) | 1B/7B | - |
| [PaliGemma](https://huggingface.co/google) | 3B | paligemma |
| [Phi-1.5/Phi-2](https://huggingface.co/microsoft) | 1.3B/2.7B | - |
| [Phi-3](https://huggingface.co/microsoft) | 4B/7B/14B | phi |
| [Pixtral](https://huggingface.co/mistralai) | 12B | pixtral |
| [Qwen (1-2.5) (Code/Math/MoE)](https://huggingface.co/Qwen) | 0.5B/1.5B/3B/7B/14B/32B/72B/110B | qwen |
| [Qwen2-VL](https://huggingface.co/Qwen) | 2B/7B/72B | qwen2_vl |
| [StarCoder 2](https://huggingface.co/bigcode) | 3B/7B/15B | - |
| [XVERSE](https://huggingface.co/xverse) | 7B/13B/65B | xverse |
| [Yi/Yi-1.5 (Code)](https://huggingface.co/01-ai) | 1.5B/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**。
@ -360,7 +372,7 @@ cd LLaMA-Factory
pip install -e ".[torch,metrics]"
```
可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、awq、aqlm、vllm、galore、badam、adam-mini、qwen、modelscope、quality
可选的额外依赖项：torch、torch-npu、metrics、deepspeed、liger-kernel、bitsandbytes、hqq、eetq、gptq、awq、aqlm、vllm、galore、badam、adam-mini、qwen、modelscope、openmind、quality
> [!TIP]
> 遇到包冲突时，可使用 `pip install --no-deps -e .` 解决。
@ -412,7 +424,7 @@ source /usr/local/Ascend/ascend-toolkit/set_env.sh
### 数据准备
关于数据集文件的格式，请参考 [data/README_zh.md](data/README_zh.md) 的内容。你可以使用 HuggingFace / ModelScope 上的数据集或加载本地数据集。
关于数据集文件的格式，请参考 [data/README_zh.md](data/README_zh.md) 的内容。你可以使用 HuggingFace / ModelScope / Modelers 上的数据集或加载本地数据集。
> [!NOTE]
> 使用自定义数据集时，请更新 `data/dataset_info.json` 文件。
@ -480,6 +492,7 @@ docker build -f ./docker/docker-cuda/Dockerfile \
docker run -dit --gpus=all \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./om_cache:/root/.cache/openmind \
-v ./data:/app/data \
-v ./output:/app/output \
-p 7860:7860 \
@ -504,6 +517,7 @@ docker build -f ./docker/docker-npu/Dockerfile \
docker run -dit \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./om_cache:/root/.cache/openmind \
-v ./data:/app/data \
-v ./output:/app/output \
-v /usr/local/dcmi:/usr/local/dcmi \
@ -537,6 +551,7 @@ docker build -f ./docker/docker-rocm/Dockerfile \
docker run -dit \
-v ./hf_cache:/root/.cache/huggingface \
-v ./ms_cache:/root/.cache/modelscope \
-v ./om_cache:/root/.cache/openmind \
-v ./data:/app/data \
-v ./output:/app/output \
-v ./saves:/app/saves \
@ -557,6 +572,7 @@ docker exec -it llamafactory bash
- `hf_cache`：使用宿主机的 Hugging Face 缓存文件夹，允许更改为新的目录。
- `ms_cache`：类似 Hugging Face 缓存文件夹，为 ModelScope 用户提供。
- `om_cache`：类似 Hugging Face 缓存文件夹，为 Modelers 用户提供。
- `data`：宿主机中存放数据集的文件夹路径。
- `output`：将导出目录设置为该路径后，即可在宿主机中访问导出后的模型。
@ -570,6 +586,8 @@ API_PORT=8000 llamafactory-cli api examples/inference/llama3_vllm.yaml
> [!TIP]
> API 文档请查阅[这里](https://platform.openai.com/docs/api-reference/chat/create)。
>
> 示例：[图像理解](scripts/test_image.py) | [工具调用](scripts/test_toolcall.py)
### 从魔搭社区下载
@ -581,6 +599,16 @@ export USE_MODELSCOPE_HUB=1 # Windows 使用 `set USE_MODELSCOPE_HUB=1`
将 `model_name_or_path` 设置为模型 ID 来加载对应的模型。在[魔搭社区](https://modelscope.cn/models)查看所有可用的模型，例如 `LLM-Research/Meta-Llama-3-8B-Instruct`。
### 从魔乐社区下载
您也可以通过下述方法，使用魔乐社区下载数据集和模型。
```bash
export USE_OPENMIND_HUB=1 # Windows 使用 `set USE_OPENMIND_HUB=1`
```
将 `model_name_or_path` 设置为模型 ID 来加载对应的模型。在[魔乐社区](https://modelers.cn/models)查看所有可用的模型，例如 `TeleAI/TeleChat-7B-pt`。
### 使用 W&B 面板
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。
@ -684,11 +712,12 @@ run_name: test_run # 可选
1. **[Sunsimiao](https://github.com/X-D-Lab/Sunsimiao)**: 孙思邈中文医疗大模型 Sumsimiao，基于 Baichuan-7B 和 ChatGLM-6B 在中文医疗数据上微调而得。
1. **[CareGPT](https://github.com/WangRongsheng/CareGPT)**: 医疗大模型项目 CareGPT，基于 LLaMA2-7B 和 Baichuan-13B 在中文医疗数据上微调而得。
1. **[MachineMindset](https://github.com/PKU-YuanGroup/Machine-Mindset/)**：MBTI性格大模型项目，根据数据集与训练方式让任意 LLM 拥有 16 个不同的性格类型。
1. **[Luminia-13B-v3](https://huggingface.co/Nekochu/Luminia-13B-v3)**：一个用于生成 Stable Diffusion 提示词的大型语言模型。[[🤗Demo]](https://huggingface.co/spaces/Nekochu/Luminia-13B_SD_Prompt)
1. **[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。
1. **[LazyLLM](https://github.com/LazyAGI/LazyLLM)**：一个低代码构建多 Agent 大模型应用的开发工具，支持基于 LLaMA Factory 的模型微调.
1. **[RAG-Retrieval](https://github.com/NLPJCL/RAG-Retrieval)**：一个全链路 RAG 检索模型微调、推理和蒸馏代码库。[[blog]](https://zhuanlan.zhihu.com/p/987727357)
</details>
@ -696,7 +725,7 @@ 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：[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/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [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)
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) / [Index](https://huggingface.co/IndexTeam/Index-1.9B/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/) / [MiniCPM](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md) / [Mistral/Mixtral/Pixtral](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

1630
assets/benchmark.svg
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2
data/alpaca_en_demo.json
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@ -4999,4 +4999,4 @@
"input": "Time waits for no one.",
"output": "No one can stop time from moving forward."
}
]
]

2
data/alpaca_zh_demo.json
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@ -4999,4 +4999,4 @@
"input": "",
"output": "安第斯山脉位于南美洲，横跨七个国家，包括委内瑞拉，哥伦比亚，厄瓜多尔，秘鲁，玻利维亚，智利和阿根廷。安第斯山脉是世界上最长的山脉之一，全长约7,000千米（4,350英里），其山脉沿着南美洲西海岸蜿蜒延伸，平均海拔约为4,000米（13,000英尺）。在其南部，安第斯山脉宽度达到700千米（430英里），在其北部宽度约为500千米（310英里）。"
}
]
]

6
data/belle_multiturn/belle_multiturn.py
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@ -17,9 +17,9 @@ _CITATION = """\
}
"""
_HOMEPAGE = "{}/datasets/BelleGroup/multiturn_chat_0.8M".format(_HF_ENDPOINT)
_HOMEPAGE = f"{_HF_ENDPOINT}/datasets/BelleGroup/multiturn_chat_0.8M"
_LICENSE = "gpl-3.0"
_URL = "{}/datasets/BelleGroup/multiturn_chat_0.8M/resolve/main/multiturn_chat_0.8M.json".format(_HF_ENDPOINT)
_URL = f"{_HF_ENDPOINT}/datasets/BelleGroup/multiturn_chat_0.8M/resolve/main/multiturn_chat_0.8M.json"
class BelleMultiturn(datasets.GeneratorBasedBuilder):
@ -38,7 +38,7 @@ class BelleMultiturn(datasets.GeneratorBasedBuilder):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": file_path})]
def _generate_examples(self, filepath: str):
with open(filepath, "r", encoding="utf-8") as f:
with open(filepath, encoding="utf-8") as f:
for key, row in enumerate(f):
data = json.loads(row)
conversations = []

2
data/c4_demo.json
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8
data/dataset_info.json
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@ -54,7 +54,8 @@
},
"alpaca_en": {
"hf_hub_url": "llamafactory/alpaca_en",
"ms_hub_url": "llamafactory/alpaca_en"
"ms_hub_url": "llamafactory/alpaca_en",
"om_hub_url": "HaM/alpaca_en"
},
"alpaca_zh": {
"hf_hub_url": "llamafactory/alpaca_zh",
@ -66,7 +67,8 @@
},
"alpaca_gpt4_zh": {
"hf_hub_url": "llamafactory/alpaca_gpt4_zh",
"ms_hub_url": "llamafactory/alpaca_gpt4_zh"
"ms_hub_url": "llamafactory/alpaca_gpt4_zh",
"om_hub_url": "State_Cloud/alpaca-gpt4-data-zh"
},
"glaive_toolcall_en": {
"hf_hub_url": "llamafactory/glaive_toolcall_en",
@ -623,4 +625,4 @@
},
"folder": "python"
}
}
}

2
data/dpo_en_demo.json
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@ -7223,4 +7223,4 @@
"value": "Abstraction is a principle in object-oriented programming that refers to the process of focusing on the essential features of an object or concept without emphasizing the details that are not important for its functionality. It enables programmers to create classes that represent the objects they want to work with, and specifies only the behavior of those objects, leaving out unnecessary details. Abstraction helps to make the code more maintainable, modular, and scalable. It also improves the productivity of developers by reducing the amount of code they need to write."
}
}
]
]

2
data/dpo_zh_demo.json
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@ -5055,4 +5055,4 @@
"value": "C. 参与讨论"
}
}
]
]

2
data/glaive_toolcall_en_demo.json
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@ -9155,4 +9155,4 @@
],
"tools": "[]"
}
]
]

2
data/glaive_toolcall_zh_demo.json
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@ -9019,4 +9019,4 @@
],
"tools": "[]"
}
]
]

6
data/hh_rlhf_en/hh_rlhf_en.py
View File

@ -8,9 +8,9 @@ import datasets
_HF_ENDPOINT = os.getenv("HF_ENDPOINT", "https://huggingface.co")
_DESCRIPTION = "Human preference data about helpfulness and harmlessness."
_CITATION = ""
_HOMEPAGE = "{}/datasets/Anthropic/hh-rlhf".format(_HF_ENDPOINT)
_HOMEPAGE = f"{_HF_ENDPOINT}/datasets/Anthropic/hh-rlhf"
_LICENSE = "mit"
_URL = "{}/datasets/Anthropic/hh-rlhf/resolve/main/".format(_HF_ENDPOINT)
_URL = f"{_HF_ENDPOINT}/datasets/Anthropic/hh-rlhf/resolve/main/"
_URLS = {
"train": [
_URL + "harmless-base/train.jsonl.gz",
@ -53,7 +53,7 @@ class HhRlhfEn(datasets.GeneratorBasedBuilder):
def _generate_examples(self, filepaths: List[str]):
key = 0
for filepath in filepaths:
with open(filepath, "r", encoding="utf-8") as f:
with open(filepath, encoding="utf-8") as f:
for row in f:
data = json.loads(row)
chosen = data["chosen"]

2
data/identity.json
View File

@ -454,4 +454,4 @@
"input": "",
"output": "抱歉，我不是 OpenAI 开发的 ChatGPT，我是 {{author}} 开发的 {{name}}，旨在为用户提供智能化的回答和帮助。"
}
]
]

2
data/kto_en_demo.json
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@ -5395,4 +5395,4 @@
],
"label": false
}
]
]

2
data/mllm_demo.json
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@ -137,4 +137,4 @@
"mllm_demo_data/3.jpg"
]
}
]
]

2
data/mllm_video_demo.json
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@ -44,4 +44,4 @@
"mllm_demo_data/3.mp4"
]
}
]
]

6
data/ultra_chat/ultra_chat.py
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@ -20,9 +20,9 @@ _CITATION = """\
}
"""
_HOMEPAGE = "{}/datasets/stingning/ultrachat".format(_HF_ENDPOINT)
_HOMEPAGE = f"{_HF_ENDPOINT}/datasets/stingning/ultrachat"
_LICENSE = "cc-by-nc-4.0"
_BASE_DATA_URL = "{}/datasets/stingning/ultrachat/resolve/main/train_{{idx}}.jsonl".format(_HF_ENDPOINT)
_BASE_DATA_URL = f"{_HF_ENDPOINT}/datasets/stingning/ultrachat/resolve/main/train_{{idx}}.jsonl"
class UltraChat(datasets.GeneratorBasedBuilder):
@ -42,7 +42,7 @@ class UltraChat(datasets.GeneratorBasedBuilder):
def _generate_examples(self, filepaths: List[str]):
for filepath in filepaths:
with open(filepath, "r", encoding="utf-8") as f:
with open(filepath, encoding="utf-8") as f:
for row in f:
try:
data = json.loads(row)

2
data/wiki_demo.txt
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19
docker/docker-cuda/Dockerfile
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@ -1,6 +1,7 @@
# 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
# Default use the NVIDIA official image with PyTorch 2.3.0
# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html
ARG BASE_IMAGE=nvcr.io/nvidia/pytorch:24.02-py3
FROM ${BASE_IMAGE}
# Define environments
ENV MAX_JOBS=4
@ -12,6 +13,9 @@ ARG INSTALL_BNB=false
ARG INSTALL_VLLM=false
ARG INSTALL_DEEPSPEED=false
ARG INSTALL_FLASHATTN=false
ARG INSTALL_LIGER_KERNEL=false
ARG INSTALL_HQQ=false
ARG INSTALL_EETQ=false
ARG PIP_INDEX=https://pypi.org/simple
# Set the working directory
@ -38,6 +42,15 @@ RUN EXTRA_PACKAGES="metrics"; \
if [ "$INSTALL_DEEPSPEED" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
fi; \
if [ "$INSTALL_LIGER_KERNEL" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},liger-kernel"; \
fi; \
if [ "$INSTALL_HQQ" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},hqq"; \
fi; \
if [ "$INSTALL_EETQ" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},eetq"; \
fi; \
pip install -e ".[$EXTRA_PACKAGES]"
# Rebuild flash attention

4
docker/docker-cuda/docker-compose.yml
View File

@ -8,11 +8,15 @@ services:
INSTALL_VLLM: false
INSTALL_DEEPSPEED: false
INSTALL_FLASHATTN: false
INSTALL_LIGER_KERNEL: false
INSTALL_HQQ: false
INSTALL_EETQ: false
PIP_INDEX: https://pypi.org/simple
container_name: llamafactory
volumes:
- ../../hf_cache:/root/.cache/huggingface
- ../../ms_cache:/root/.cache/modelscope
- ../../om_cache:/root/.cache/openmind
- ../../data:/app/data
- ../../output:/app/output
ports:

1
docker/docker-npu/docker-compose.yml
View File

@ -10,6 +10,7 @@ services:
volumes:
- ../../hf_cache:/root/.cache/huggingface
- ../../ms_cache:/root/.cache/modelscope
- ../../om_cache:/root/.cache/openmind
- ../../data:/app/data
- ../../output:/app/output
- /usr/local/dcmi:/usr/local/dcmi

8
docker/docker-rocm/Dockerfile
View File

@ -10,6 +10,8 @@ ARG INSTALL_BNB=false
ARG INSTALL_VLLM=false
ARG INSTALL_DEEPSPEED=false
ARG INSTALL_FLASHATTN=false
ARG INSTALL_LIGER_KERNEL=false
ARG INSTALL_HQQ=false
ARG PIP_INDEX=https://pypi.org/simple
# Set the working directory
@ -36,6 +38,12 @@ RUN EXTRA_PACKAGES="metrics"; \
if [ "$INSTALL_DEEPSPEED" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
fi; \
if [ "$INSTALL_LIGER_KERNEL" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},liger-kernel"; \
fi; \
if [ "$INSTALL_HQQ" == "true" ]; then \
EXTRA_PACKAGES="${EXTRA_PACKAGES},hqq"; \
fi; \
pip install -e ".[$EXTRA_PACKAGES]"
# Rebuild flash attention

3
docker/docker-rocm/docker-compose.yml
View File

@ -8,11 +8,14 @@ services:
INSTALL_VLLM: false
INSTALL_DEEPSPEED: false
INSTALL_FLASHATTN: false
INSTALL_LIGER_KERNEL: false
INSTALL_HQQ: false
PIP_INDEX: https://pypi.org/simple
container_name: llamafactory
volumes:
- ../../hf_cache:/root/.cache/huggingface
- ../../ms_cache:/root/.cache/modelscope
- ../../om_cache:/root/.cache/openmind
- ../../data:/app/data
- ../../output:/app/output
- ../../saves:/app/saves

2
evaluation/ceval/mapping.json
View File

@ -207,4 +207,4 @@
"name": "兽医学",
"category": "STEM"
}
}
}

2
evaluation/cmmlu/mapping.json
View File

@ -267,4 +267,4 @@
"name": "世界宗教",
"category": "Humanities"
}
}
}

2
evaluation/mmlu/mapping.json
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@ -227,4 +227,4 @@
"name": "world religions",
"category": "Humanities"
}
}
}

3
evaluation/mmlu/mmlu.py
View File

@ -158,5 +158,4 @@ class MMLU(datasets.GeneratorBasedBuilder):
df = pd.read_csv(filepath, header=None)
df.columns = ["question", "A", "B", "C", "D", "answer"]
for i, instance in enumerate(df.to_dict(orient="records")):
yield i, instance
yield from enumerate(df.to_dict(orient="records"))

4
examples/README.md
View File

@ -89,8 +89,8 @@ llamafactory-cli train examples/train_lora/llama3_lora_predict.yaml
#### Supervised Fine-Tuning on Multiple Nodes
```bash
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
FORCE_TORCHRUN=1 NNODES=2 NODE_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 NODE_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)

4
examples/README_zh.md
View File

@ -89,8 +89,8 @@ llamafactory-cli train examples/train_lora/llama3_lora_predict.yaml
#### 多机指令监督微调
```bash
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
FORCE_TORCHRUN=1 NNODES=2 NODE_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 NODE_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

2
examples/deepspeed/ds_z0_config.json
View File

@ -25,4 +25,4 @@
"contiguous_gradients": true,
"round_robin_gradients": true
}
}
}

2
examples/deepspeed/ds_z2_config.json
View File

@ -25,4 +25,4 @@
"contiguous_gradients": true,
"round_robin_gradients": true
}
}
}

2
examples/deepspeed/ds_z2_offload_config.json
View File

@ -29,4 +29,4 @@
"contiguous_gradients": true,
"round_robin_gradients": true
}
}
}

2
examples/deepspeed/ds_z3_config.json
View File

@ -27,4 +27,4 @@
"stage3_max_reuse_distance": 1e9,
"stage3_gather_16bit_weights_on_model_save": true
}
}
}

2
examples/deepspeed/ds_z3_offload_config.json
View File

@ -35,4 +35,4 @@
"stage3_max_reuse_distance": 1e9,
"stage3_gather_16bit_weights_on_model_save": true
}
}
}

9
requirements.txt
View File

@ -1,9 +1,9 @@
transformers>=4.41.2,<=4.45.0
datasets>=2.16.0,<=2.21.0
accelerate>=0.30.1,<=0.34.2
transformers>=4.41.2,<=4.46.1
datasets>=2.16.0,<=3.0.2
accelerate>=0.34.0,<=1.0.1
peft>=0.11.1,<=0.12.0
trl>=0.8.6,<=0.9.6
gradio>=4.0.0
gradio>=4.0.0,<5.0.0
pandas>=2.0.0
scipy
einops
@ -19,3 +19,4 @@ fire
packaging
pyyaml
numpy<2.0.0
av

1
scripts/cal_flops.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 Microsoft Corporation and the LlamaFactory team.
#
# This code is inspired by the Microsoft's DeepSpeed library.

3
scripts/cal_lr.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 imoneoi and the LlamaFactory team.
#
# This code is inspired by the imoneoi's OpenChat library.
@ -74,7 +73,7 @@ def calculate_lr(
elif stage == "sft":
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, label_pad_token_id=IGNORE_INDEX)
else:
raise NotImplementedError("Stage does not supported: {}.".format(stage))
raise NotImplementedError(f"Stage does not supported: {stage}.")
dataloader = DataLoader(trainset, batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True)
valid_tokens, total_tokens = 0, 0

9
scripts/cal_mfu.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
@ -100,7 +99,7 @@ def compute_device_flops(world_size: int) -> float:
elif "4090" in device_name:
return 98 * 1e12 * world_size
else:
raise NotImplementedError("Device not supported: {}.".format(device_name))
raise NotImplementedError(f"Device not supported: {device_name}.")
def calculate_mfu(
@ -140,10 +139,10 @@ def calculate_mfu(
"bf16": True,
}
if deepspeed_stage in [2, 3]:
args["deepspeed"] = "examples/deepspeed/ds_z{}_config.json".format(deepspeed_stage)
args["deepspeed"] = f"examples/deepspeed/ds_z{deepspeed_stage}_config.json"
run_exp(args)
with open(os.path.join("saves", "test_mfu", "all_results.json"), "r", encoding="utf-8") as f:
with open(os.path.join("saves", "test_mfu", "all_results.json"), encoding="utf-8") as f:
result = json.load(f)
if dist.is_initialized():
@ -157,7 +156,7 @@ def calculate_mfu(
* compute_model_flops(model_name_or_path, total_batch_size, seq_length)
/ compute_device_flops(world_size)
)
print("MFU: {:.2f}%".format(mfu_value * 100))
print(f"MFU: {mfu_value * 100:.2f}%")
if __name__ == "__main__":

7
scripts/cal_ppl.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
@ -100,7 +99,7 @@ def calculate_ppl(
tokenizer=tokenizer, label_pad_token_id=IGNORE_INDEX, train_on_prompt=train_on_prompt
)
else:
raise NotImplementedError("Stage does not supported: {}.".format(stage))
raise NotImplementedError(f"Stage does not supported: {stage}.")
dataloader = DataLoader(trainset, batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True)
criterion = torch.nn.CrossEntropyLoss(reduction="none")
@ -125,8 +124,8 @@ def calculate_ppl(
with open(save_name, "w", encoding="utf-8") as f:
json.dump(perplexities, f, indent=2)
print("Average perplexity is {:.2f}".format(total_ppl / len(perplexities)))
print("Perplexities have been saved at {}.".format(save_name))
print(f"Average perplexity is {total_ppl / len(perplexities):.2f}")
print(f"Perplexities have been saved at {save_name}.")
if __name__ == "__main__":

3
scripts/length_cdf.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
@ -61,7 +60,7 @@ def length_cdf(
for length, count in length_tuples:
count_accu += count
prob_accu += count / total_num * 100
print("{:d} ({:.2f}%) samples have length < {}.".format(count_accu, prob_accu, length + interval))
print(f"{count_accu:d} ({prob_accu:.2f}%) samples have length < {length + interval}.")
if __name__ == "__main__":

21
scripts/llama_pro.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 Tencent Inc. and the LlamaFactory team.
#
# This code is inspired by the Tencent's LLaMA-Pro library.
@ -40,7 +39,7 @@ if TYPE_CHECKING:
def change_name(name: str, old_index: int, new_index: int) -> str:
return name.replace(".{:d}.".format(old_index), ".{:d}.".format(new_index))
return name.replace(f".{old_index:d}.", f".{new_index:d}.")
def block_expansion(
@ -76,27 +75,27 @@ def block_expansion(
state_dict = model.state_dict()
if num_layers % num_expand != 0:
raise ValueError("`num_layers` {} should be divisible by `num_expand` {}.".format(num_layers, num_expand))
raise ValueError(f"`num_layers` {num_layers} should be divisible by `num_expand` {num_expand}.")
split = num_layers // num_expand
layer_cnt = 0
output_state_dict = OrderedDict()
for i in range(num_layers):
for key, value in state_dict.items():
if ".{:d}.".format(i) in key:
if f".{i:d}." in key:
output_state_dict[change_name(key, i, layer_cnt)] = value
print("Add layer {} copied from layer {}".format(layer_cnt, i))
print(f"Add layer {layer_cnt} copied from layer {i}")
layer_cnt += 1
if (i + 1) % split == 0:
for key, value in state_dict.items():
if ".{:d}.".format(i) in key:
if f".{i:d}." in key:
if "down_proj" in key or "o_proj" in key:
output_state_dict[change_name(key, i, layer_cnt)] = torch.zeros_like(value)
else:
output_state_dict[change_name(key, i, layer_cnt)] = torch.clone(value)
print("Add layer {} expanded from layer {}".format(layer_cnt, i))
print(f"Add layer {layer_cnt} expanded from layer {i}")
layer_cnt += 1
for key, value in state_dict.items():
@ -113,17 +112,17 @@ def block_expansion(
torch.save(shard, os.path.join(output_dir, shard_file))
if index is None:
print("Model weights saved in {}".format(os.path.join(output_dir, weights_name)))
print(f"Model weights saved in {os.path.join(output_dir, weights_name)}")
else:
index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
with open(os.path.join(output_dir, index_name), "w", encoding="utf-8") as f:
json.dump(index, f, indent=2, sort_keys=True)
print("Model weights saved in {}".format(output_dir))
print(f"Model weights saved in {output_dir}")
print("- Fine-tune this model with:")
print("model_name_or_path: {}".format(output_dir))
print(f"model_name_or_path: {output_dir}")
print("finetuning_type: freeze")
print("freeze_trainable_layers: {}".format(num_expand))
print(f"freeze_trainable_layers: {num_expand}")
print("use_llama_pro: true")

9
scripts/llamafy_baichuan2.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
@ -63,16 +62,16 @@ def save_weight(input_dir: str, output_dir: str, shard_size: str, save_safetenso
torch.save(shard, os.path.join(output_dir, shard_file))
if index is None:
print("Model weights saved in {}".format(os.path.join(output_dir, WEIGHTS_NAME)))
print(f"Model weights saved in {os.path.join(output_dir, WEIGHTS_NAME)}")
else:
index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
with open(os.path.join(output_dir, index_name), "w", encoding="utf-8") as f:
json.dump(index, f, indent=2, sort_keys=True)
print("Model weights saved in {}".format(output_dir))
print(f"Model weights saved in {output_dir}")
def save_config(input_dir: str, output_dir: str):
with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f:
with open(os.path.join(input_dir, CONFIG_NAME), encoding="utf-8") as f:
llama2_config_dict: Dict[str, Any] = json.load(f)
llama2_config_dict["architectures"] = ["LlamaForCausalLM"]
@ -82,7 +81,7 @@ def save_config(input_dir: str, output_dir: str):
with open(os.path.join(output_dir, CONFIG_NAME), "w", encoding="utf-8") as f:
json.dump(llama2_config_dict, f, indent=2)
print("Model config saved in {}".format(os.path.join(output_dir, CONFIG_NAME)))
print(f"Model config saved in {os.path.join(output_dir, CONFIG_NAME)}")
def llamafy_baichuan2(

11
scripts/llamafy_qwen.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
@ -86,7 +85,7 @@ def save_weight(input_dir: str, output_dir: str, shard_size: str, save_safetenso
elif "lm_head" in key:
llama2_state_dict[key] = value
else:
raise KeyError("Unable to process key {}".format(key))
raise KeyError(f"Unable to process key {key}")
weights_name = SAFE_WEIGHTS_NAME if save_safetensors else WEIGHTS_NAME
shards, index = shard_checkpoint(llama2_state_dict, max_shard_size=shard_size, weights_name=weights_name)
@ -98,18 +97,18 @@ def save_weight(input_dir: str, output_dir: str, shard_size: str, save_safetenso
torch.save(shard, os.path.join(output_dir, shard_file))
if index is None:
print("Model weights saved in {}".format(os.path.join(output_dir, weights_name)))
print(f"Model weights saved in {os.path.join(output_dir, weights_name)}")
else:
index_name = SAFE_WEIGHTS_INDEX_NAME if save_safetensors else WEIGHTS_INDEX_NAME
with open(os.path.join(output_dir, index_name), "w", encoding="utf-8") as f:
json.dump(index, f, indent=2, sort_keys=True)
print("Model weights saved in {}".format(output_dir))
print(f"Model weights saved in {output_dir}")
return str(torch_dtype).replace("torch.", "")
def save_config(input_dir: str, output_dir: str, torch_dtype: str):
with open(os.path.join(input_dir, CONFIG_NAME), "r", encoding="utf-8") as f:
with open(os.path.join(input_dir, CONFIG_NAME), encoding="utf-8") as f:
qwen_config_dict: Dict[str, Any] = json.load(f)
llama2_config_dict: Dict[str, Any] = OrderedDict()
@ -135,7 +134,7 @@ def save_config(input_dir: str, output_dir: str, torch_dtype: str):
with open(os.path.join(output_dir, CONFIG_NAME), "w", encoding="utf-8") as f:
json.dump(llama2_config_dict, f, indent=2)
print("Model config saved in {}".format(os.path.join(output_dir, CONFIG_NAME)))
print(f"Model config saved in {os.path.join(output_dir, CONFIG_NAME)}")
def llamafy_qwen(

11
scripts/loftq_init.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
#
# This code is based on the HuggingFace's PEFT library.
@ -70,19 +69,19 @@ def quantize_loftq(
setattr(peft_model.peft_config["default"], "base_model_name_or_path", os.path.abspath(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))
print(f"Adapter weights saved in {loftq_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(f"Model weights saved in {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(f"model_name_or_path: {output_dir}")
print(f"adapter_name_or_path: {loftq_dir}")
print("finetuning_type: lora")
print("quantization_bit: {}".format(loftq_bits))
print(f"quantization_bit: {loftq_bits}")
if __name__ == "__main__":

11
scripts/pissa_init.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 HuggingFace Inc. and the LlamaFactory team.
#
# This code is based on the HuggingFace's PEFT library.
@ -54,7 +53,7 @@ def quantize_pissa(
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_lora_weights="pissa" if pissa_iter == -1 else f"pissa_niter_{pissa_iter}",
)
# Init PiSSA model
@ -65,17 +64,17 @@ def quantize_pissa(
setattr(peft_model.peft_config["default"], "base_model_name_or_path", os.path.abspath(output_dir))
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))
print(f"Adapter weights saved in {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(f"Model weights saved in {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(f"model_name_or_path: {output_dir}")
print(f"adapter_name_or_path: {pissa_dir}")
print("finetuning_type: lora")
print("pissa_init: false")
print("pissa_convert: true")

65
scripts/test_image.py Normal file
View File

@ -0,0 +1,65 @@
# 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 openai import OpenAI
from transformers.utils.versions import require_version
require_version("openai>=1.5.0", "To fix: pip install openai>=1.5.0")
def main():
client = OpenAI(
api_key="{}".format(os.environ.get("API_KEY", "0")),
base_url="http://localhost:{}/v1".format(os.environ.get("API_PORT", 8000)),
)
messages = []
messages.append(
{
"role": "user",
"content": [
{"type": "text", "text": "Output the color and number of each box."},
{
"type": "image_url",
"image_url": {"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/boxes.png"},
},
],
}
)
result = client.chat.completions.create(messages=messages, model="test")
messages.append(result.choices[0].message)
print("Round 1:", result.choices[0].message.content)
# The image shows a pyramid of colored blocks with numbers on them. Here are the colors and numbers of ...
messages.append(
{
"role": "user",
"content": [
{"type": "text", "text": "What kind of flower is this?"},
{
"type": "image_url",
"image_url": {"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/flowers.jpg"},
},
],
}
)
result = client.chat.completions.create(messages=messages, model="test")
messages.append(result.choices[0].message)
print("Round 2:", result.choices[0].message.content)
# The image shows a cluster of forget-me-not flowers. Forget-me-nots are small ...
if __name__ == "__main__":
main()

1
scripts/test_toolcall.py
View File

@ -1,4 +1,3 @@
# coding=utf-8
# Copyright 2024 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");

11
setup.py
View File

@ -20,7 +20,7 @@ from setuptools import find_packages, setup
def get_version() -> str:
with open(os.path.join("src", "llamafactory", "extras", "env.py"), "r", encoding="utf-8") as f:
with open(os.path.join("src", "llamafactory", "extras", "env.py"), encoding="utf-8") as f:
file_content = f.read()
pattern = r"{}\W*=\W*\"([^\"]+)\"".format("VERSION")
(version,) = re.findall(pattern, file_content)
@ -28,7 +28,7 @@ def get_version() -> str:
def get_requires() -> List[str]:
with open("requirements.txt", "r", encoding="utf-8") as f:
with open("requirements.txt", encoding="utf-8") as f:
file_content = f.read()
lines = [line.strip() for line in file_content.strip().split("\n") if not line.startswith("#")]
return lines
@ -54,13 +54,14 @@ extra_require = {
"gptq": ["optimum>=1.17.0", "auto-gptq>=0.5.0"],
"awq": ["autoawq"],
"aqlm": ["aqlm[gpu]>=1.1.0"],
"vllm": ["vllm>=0.4.3,<=0.6.0"],
"vllm": ["vllm>=0.4.3,<=0.6.3"],
"galore": ["galore-torch"],
"badam": ["badam>=1.2.1"],
"adam-mini": ["adam-mini"],
"qwen": ["transformers_stream_generator"],
"modelscope": ["modelscope"],
"dev": ["ruff", "pytest"],
"openmind": ["openmind"],
"dev": ["pre-commit", "ruff", "pytest"],
}
@ -71,7 +72,7 @@ def main():
author="hiyouga",
author_email="hiyouga" "@" "buaa.edu.cn",
description="Easy-to-use LLM fine-tuning framework",
long_description=open("README.md", "r", encoding="utf-8").read(),
long_description=open("README.md", encoding="utf-8").read(),
long_description_content_type="text/markdown",
keywords=["LLaMA", "BLOOM", "Falcon", "LLM", "ChatGPT", "transformer", "pytorch", "deep learning"],
license="Apache 2.0 License",

6
src/api.py
View File

@ -23,9 +23,9 @@ from llamafactory.chat import ChatModel
def main():
chat_model = ChatModel()
app = create_app(chat_model)
api_host = os.environ.get("API_HOST", "0.0.0.0")
api_port = int(os.environ.get("API_PORT", "8000"))
print("Visit http://localhost:{}/docs for API document.".format(api_port))
api_host = os.getenv("API_HOST", "0.0.0.0")
api_port = int(os.getenv("API_PORT", "8000"))
print(f"Visit http://localhost:{api_port}/docs for API document.")
uvicorn.run(app, host=api_host, port=api_port)

11
src/llamafactory/__init__.py
View File

@ -20,17 +20,17 @@ Level:
Dependency graph:
main:
transformers>=4.41.2,<=4.45.0
datasets>=2.16.0,<=2.21.0
accelerate>=0.30.1,<=0.34.2
transformers>=4.41.2,<=4.46.1
datasets>=2.16.0,<=3.0.2
accelerate>=0.34.0,<=1.0.1
peft>=0.11.1,<=0.12.0
trl>=0.8.6,<=0.9.6
attention:
transformers>=4.42.4 (gemma+fa2)
longlora:
transformers>=4.41.2,<=4.45.0
transformers>=4.41.2,<=4.46.1
packing:
transformers>=4.41.2,<=4.45.0
transformers>=4.41.2,<=4.46.1
Disable version checking: DISABLE_VERSION_CHECK=1
Enable VRAM recording: RECORD_VRAM=1
@ -38,6 +38,7 @@ Force check imports: FORCE_CHECK_IMPORTS=1
Force using torchrun: FORCE_TORCHRUN=1
Set logging verbosity: LLAMAFACTORY_VERBOSITY=WARN
Use modelscope: USE_MODELSCOPE_HUB=1
Use openmind: USE_OPENMIND_HUB=1
"""
from .extras.env import VERSION

12
src/llamafactory/api/app.py
View File

@ -68,7 +68,7 @@ async def lifespan(app: "FastAPI", chat_model: "ChatModel"): # collects GPU mem
def create_app(chat_model: "ChatModel") -> "FastAPI":
root_path = os.environ.get("FASTAPI_ROOT_PATH", "")
root_path = os.getenv("FASTAPI_ROOT_PATH", "")
app = FastAPI(lifespan=partial(lifespan, chat_model=chat_model), root_path=root_path)
app.add_middleware(
CORSMiddleware,
@ -77,7 +77,7 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
allow_methods=["*"],
allow_headers=["*"],
)
api_key = os.environ.get("API_KEY", None)
api_key = os.getenv("API_KEY")
security = HTTPBearer(auto_error=False)
async def verify_api_key(auth: Annotated[Optional[HTTPAuthorizationCredentials], Depends(security)]):
@ -91,7 +91,7 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
dependencies=[Depends(verify_api_key)],
)
async def list_models():
model_card = ModelCard(id=os.environ.get("API_MODEL_NAME", "gpt-3.5-turbo"))
model_card = ModelCard(id=os.getenv("API_MODEL_NAME", "gpt-3.5-turbo"))
return ModelList(data=[model_card])
@app.post(
@ -128,7 +128,7 @@ def create_app(chat_model: "ChatModel") -> "FastAPI":
def run_api() -> None:
chat_model = ChatModel()
app = create_app(chat_model)
api_host = os.environ.get("API_HOST", "0.0.0.0")
api_port = int(os.environ.get("API_PORT", "8000"))
print("Visit http://localhost:{}/docs for API document.".format(api_port))
api_host = os.getenv("API_HOST", "0.0.0.0")
api_port = int(os.getenv("API_PORT", "8000"))
print(f"Visit http://localhost:{api_port}/docs for API document.")
uvicorn.run(app, host=api_host, port=api_port)

30
src/llamafactory/api/chat.py
View File

@ -21,7 +21,7 @@ import uuid
from typing import TYPE_CHECKING, AsyncGenerator, Dict, List, Optional, Tuple
from ..data import Role as DataRole
from ..extras.logging import get_logger
from ..extras import logging
from ..extras.packages import is_fastapi_available, is_pillow_available, is_requests_available
from .common import dictify, jsonify
from .protocol import (
@ -57,7 +57,7 @@ if TYPE_CHECKING:
from .protocol import ChatCompletionRequest, ScoreEvaluationRequest
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
ROLE_MAPPING = {
Role.USER: DataRole.USER.value,
Role.ASSISTANT: DataRole.ASSISTANT.value,
@ -69,8 +69,8 @@ ROLE_MAPPING = {
def _process_request(
request: "ChatCompletionRequest",
) -> Tuple[List[Dict[str, str]], Optional[str], Optional[str], Optional["ImageInput"]]:
logger.info("==== request ====\n{}".format(json.dumps(dictify(request), indent=2, ensure_ascii=False)))
) -> Tuple[List[Dict[str, str]], Optional[str], Optional[str], Optional[List["ImageInput"]]]:
logger.info_rank0(f"==== request ====\n{json.dumps(dictify(request), indent=2, ensure_ascii=False)}")
if len(request.messages) == 0:
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid length")
@ -84,7 +84,7 @@ def _process_request(
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Only supports u/a/u/a/u...")
input_messages = []
image = None
images = []
for i, message in enumerate(request.messages):
if i % 2 == 0 and message.role not in [Role.USER, Role.TOOL]:
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid role")
@ -111,7 +111,7 @@ def _process_request(
else: # web uri
image_stream = requests.get(image_url, stream=True).raw
image = Image.open(image_stream).convert("RGB")
images.append(Image.open(image_stream).convert("RGB"))
else:
input_messages.append({"role": ROLE_MAPPING[message.role], "content": message.content})
@ -124,7 +124,7 @@ def _process_request(
else:
tools = None
return input_messages, system, tools, image
return input_messages, system, tools, images or None
def _create_stream_chat_completion_chunk(
@ -142,13 +142,13 @@ def _create_stream_chat_completion_chunk(
async def create_chat_completion_response(
request: "ChatCompletionRequest", chat_model: "ChatModel"
) -> "ChatCompletionResponse":
completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
input_messages, system, tools, image = _process_request(request)
completion_id = f"chatcmpl-{uuid.uuid4().hex}"
input_messages, system, tools, images = _process_request(request)
responses = await chat_model.achat(
input_messages,
system,
tools,
image,
images,
do_sample=request.do_sample,
temperature=request.temperature,
top_p=request.top_p,
@ -169,7 +169,7 @@ async def create_chat_completion_response(
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))
tool_calls.append(FunctionCall(id=f"call_{uuid.uuid4().hex}", function=function))
response_message = ChatCompletionMessage(role=Role.ASSISTANT, tool_calls=tool_calls)
finish_reason = Finish.TOOL
@ -193,8 +193,8 @@ async def create_chat_completion_response(
async def create_stream_chat_completion_response(
request: "ChatCompletionRequest", chat_model: "ChatModel"
) -> AsyncGenerator[str, None]:
completion_id = "chatcmpl-{}".format(uuid.uuid4().hex)
input_messages, system, tools, image = _process_request(request)
completion_id = f"chatcmpl-{uuid.uuid4().hex}"
input_messages, system, tools, images = _process_request(request)
if tools:
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Cannot stream function calls.")
@ -208,7 +208,7 @@ async def create_stream_chat_completion_response(
input_messages,
system,
tools,
image,
images,
do_sample=request.do_sample,
temperature=request.temperature,
top_p=request.top_p,
@ -229,7 +229,7 @@ async def create_stream_chat_completion_response(
async def create_score_evaluation_response(
request: "ScoreEvaluationRequest", chat_model: "ChatModel"
) -> "ScoreEvaluationResponse":
score_id = "scoreval-{}".format(uuid.uuid4().hex)
score_id = f"scoreval-{uuid.uuid4().hex}"
if len(request.messages) == 0:
raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid request")

8
src/llamafactory/chat/base_engine.py
View File

@ -66,8 +66,8 @@ class BaseEngine(ABC):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> List["Response"]:
r"""
@ -81,8 +81,8 @@ class BaseEngine(ABC):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
r"""

26
src/llamafactory/chat/chat_model.py
View File

@ -53,7 +53,7 @@ class ChatModel:
elif model_args.infer_backend == "vllm":
self.engine: "BaseEngine" = VllmEngine(model_args, data_args, finetuning_args, generating_args)
else:
raise NotImplementedError("Unknown backend: {}".format(model_args.infer_backend))
raise NotImplementedError(f"Unknown backend: {model_args.infer_backend}")
self._loop = asyncio.new_event_loop()
self._thread = Thread(target=_start_background_loop, args=(self._loop,), daemon=True)
@ -64,15 +64,15 @@ class ChatModel:
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> List["Response"]:
r"""
Gets a list of responses of the chat model.
"""
task = asyncio.run_coroutine_threadsafe(
self.achat(messages, system, tools, image, video, **input_kwargs), self._loop
self.achat(messages, system, tools, images, videos, **input_kwargs), self._loop
)
return task.result()
@ -81,28 +81,28 @@ class ChatModel:
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> List["Response"]:
r"""
Asynchronously gets a list of responses of the chat model.
"""
return await self.engine.chat(messages, system, tools, image, video, **input_kwargs)
return await self.engine.chat(messages, system, tools, images, videos, **input_kwargs)
def stream_chat(
self,
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> Generator[str, None, None]:
r"""
Gets the response token-by-token of the chat model.
"""
generator = self.astream_chat(messages, system, tools, image, video, **input_kwargs)
generator = self.astream_chat(messages, system, tools, images, videos, **input_kwargs)
while True:
try:
task = asyncio.run_coroutine_threadsafe(generator.__anext__(), self._loop)
@ -115,14 +115,14 @@ class ChatModel:
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
r"""
Asynchronously gets the response token-by-token of the chat model.
"""
async for new_token in self.engine.stream_chat(messages, system, tools, image, video, **input_kwargs):
async for new_token in self.engine.stream_chat(messages, system, tools, images, videos, **input_kwargs):
yield new_token
def get_scores(

84
src/llamafactory/chat/hf_engine.py
View File

@ -23,8 +23,8 @@ from transformers import GenerationConfig, TextIteratorStreamer
from typing_extensions import override
from ..data import get_template_and_fix_tokenizer
from ..extras import logging
from ..extras.constants import IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
from ..extras.logging import get_logger
from ..extras.misc import get_logits_processor
from ..model import load_model, load_tokenizer
from .base_engine import BaseEngine, Response
@ -39,7 +39,7 @@ if TYPE_CHECKING:
from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class HuggingfaceEngine(BaseEngine):
@ -63,11 +63,11 @@ class HuggingfaceEngine(BaseEngine):
try:
asyncio.get_event_loop()
except RuntimeError:
logger.warning("There is no current event loop, creating a new one.")
logger.warning_once("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")))
self.semaphore = asyncio.Semaphore(int(os.getenv("MAX_CONCURRENT", "1")))
@staticmethod
def _process_args(
@ -79,20 +79,20 @@ class HuggingfaceEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
input_kwargs: Optional[Dict[str, Any]] = {},
) -> Tuple[Dict[str, Any], int]:
mm_input_dict = {"images": [], "videos": [], "imglens": [0], "vidlens": [0]}
if image is not None:
mm_input_dict.update({"images": [image], "imglens": [1]})
if IMAGE_PLACEHOLDER not in messages[0]["content"]:
messages[0]["content"] = IMAGE_PLACEHOLDER + messages[0]["content"]
if images is not None:
mm_input_dict.update({"images": images, "imglens": [len(images)]})
if not any(IMAGE_PLACEHOLDER not in message["content"] for message in messages):
messages[0]["content"] = IMAGE_PLACEHOLDER * len(images) + messages[0]["content"]
if video is not None:
mm_input_dict.update({"videos": [video], "vidlens": [1]})
if VIDEO_PLACEHOLDER not in messages[0]["content"]:
messages[0]["content"] = VIDEO_PLACEHOLDER + messages[0]["content"]
if videos is not None:
mm_input_dict.update({"videos": videos, "vidlens": [len(videos)]})
if not any(VIDEO_PLACEHOLDER not in message["content"] for message in messages):
messages[0]["content"] = VIDEO_PLACEHOLDER * len(videos) + messages[0]["content"]
messages = template.mm_plugin.process_messages(
messages, mm_input_dict["images"], mm_input_dict["videos"], processor
@ -119,7 +119,7 @@ class HuggingfaceEngine(BaseEngine):
stop: Optional[Union[str, List[str]]] = input_kwargs.pop("stop", None)
if stop is not None:
logger.warning("Stop parameter is not supported by the huggingface engine yet.")
logger.warning_rank0("Stop parameter is not supported by the huggingface engine yet.")
generating_args = generating_args.copy()
generating_args.update(
@ -166,7 +166,11 @@ class HuggingfaceEngine(BaseEngine):
mm_inputs = template.mm_plugin.get_mm_inputs(**mm_input_dict, seqlens=[prompt_length], processor=processor)
for key, value in mm_inputs.items():
value = value if isinstance(value, torch.Tensor) else torch.tensor(value)
if isinstance(value, list) and all(isinstance(v, torch.Tensor) for v in value): # for pixtral inputs
value = torch.stack(value) # assume they have same sizes
elif not isinstance(value, torch.Tensor):
value = torch.tensor(value)
gen_kwargs[key] = value.to(model.device)
return gen_kwargs, prompt_length
@ -182,12 +186,22 @@ class HuggingfaceEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
input_kwargs: Optional[Dict[str, Any]] = {},
) -> List["Response"]:
gen_kwargs, prompt_length = HuggingfaceEngine._process_args(
model, tokenizer, processor, template, generating_args, messages, system, tools, image, video, input_kwargs
model,
tokenizer,
processor,
template,
generating_args,
messages,
system,
tools,
images,
videos,
input_kwargs,
)
generate_output = model.generate(**gen_kwargs)
response_ids = generate_output[:, prompt_length:]
@ -218,12 +232,22 @@ class HuggingfaceEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
input_kwargs: Optional[Dict[str, Any]] = {},
) -> Callable[[], str]:
gen_kwargs, _ = HuggingfaceEngine._process_args(
model, tokenizer, processor, template, generating_args, messages, system, tools, image, video, input_kwargs
model,
tokenizer,
processor,
template,
generating_args,
messages,
system,
tools,
images,
videos,
input_kwargs,
)
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
gen_kwargs["streamer"] = streamer
@ -266,8 +290,8 @@ class HuggingfaceEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> List["Response"]:
if not self.can_generate:
@ -283,8 +307,8 @@ class HuggingfaceEngine(BaseEngine):
messages,
system,
tools,
image,
video,
images,
videos,
input_kwargs,
)
async with self.semaphore:
@ -297,8 +321,8 @@ class HuggingfaceEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
if not self.can_generate:
@ -314,8 +338,8 @@ class HuggingfaceEngine(BaseEngine):
messages,
system,
tools,
image,
video,
images,
videos,
input_kwargs,
)
async with self.semaphore:

46
src/llamafactory/chat/vllm_engine.py
View File

@ -18,8 +18,8 @@ from typing import TYPE_CHECKING, Any, AsyncGenerator, AsyncIterator, Dict, List
from typing_extensions import override
from ..data import get_template_and_fix_tokenizer
from ..extras import logging
from ..extras.constants import IMAGE_PLACEHOLDER
from ..extras.logging import get_logger
from ..extras.misc import get_device_count
from ..extras.packages import is_pillow_available, is_vllm_available
from ..model import load_config, load_tokenizer
@ -43,7 +43,7 @@ if TYPE_CHECKING:
from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class VllmEngine(BaseEngine):
@ -87,7 +87,7 @@ class VllmEngine(BaseEngine):
if getattr(config, "is_yi_vl_derived_model", None):
import vllm.model_executor.models.llava
logger.info("Detected Yi-VL model, applying projector patch.")
logger.info_rank0("Detected Yi-VL model, applying projector patch.")
vllm.model_executor.models.llava.LlavaMultiModalProjector = LlavaMultiModalProjectorForYiVLForVLLM
self.model = AsyncLLMEngine.from_engine_args(AsyncEngineArgs(**engine_args))
@ -101,14 +101,14 @@ class VllmEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> AsyncIterator["RequestOutput"]:
request_id = "chatcmpl-{}".format(uuid.uuid4().hex)
if image is not None:
if IMAGE_PLACEHOLDER not in messages[0]["content"]:
messages[0]["content"] = IMAGE_PLACEHOLDER + messages[0]["content"]
request_id = f"chatcmpl-{uuid.uuid4().hex}"
if images is not None:
if not any(IMAGE_PLACEHOLDER not in message["content"] for message in messages):
messages[0]["content"] = IMAGE_PLACEHOLDER * len(images) + messages[0]["content"]
paired_messages = messages + [{"role": "assistant", "content": ""}]
system = system or self.generating_args["default_system"]
@ -157,14 +157,18 @@ class VllmEngine(BaseEngine):
skip_special_tokens=True,
)
if image is not None: # add image features
if not isinstance(image, (str, ImageObject)):
raise ValueError("Expected image input is a path or PIL.Image, but got {}.".format(type(image)))
if images is not None: # add image features
image_data = []
for image in images:
if not isinstance(image, (str, ImageObject)):
raise ValueError(f"Expected image input is a path or PIL.Image, but got {type(image)}.")
if isinstance(image, str):
image = Image.open(image).convert("RGB")
if isinstance(image, str):
image = Image.open(image).convert("RGB")
multi_modal_data = {"image": image}
image_data.append(image)
multi_modal_data = {"image": image_data}
else:
multi_modal_data = None
@ -182,12 +186,12 @@ class VllmEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> List["Response"]:
final_output = None
generator = await self._generate(messages, system, tools, image, video, **input_kwargs)
generator = await self._generate(messages, system, tools, images, videos, **input_kwargs)
async for request_output in generator:
final_output = request_output
@ -210,12 +214,12 @@ class VllmEngine(BaseEngine):
messages: Sequence[Dict[str, str]],
system: Optional[str] = None,
tools: Optional[str] = None,
image: Optional["ImageInput"] = None,
video: Optional["VideoInput"] = None,
images: Optional[Sequence["ImageInput"]] = None,
videos: Optional[Sequence["VideoInput"]] = None,
**input_kwargs,
) -> AsyncGenerator[str, None]:
generated_text = ""
generator = await self._generate(messages, system, tools, image, video, **input_kwargs)
generator = await self._generate(messages, system, tools, images, videos, **input_kwargs)
async for result in generator:
delta_text = result.outputs[0].text[len(generated_text) :]
generated_text = result.outputs[0].text

22
src/llamafactory/cli.py
View File

@ -22,8 +22,8 @@ from . import launcher
from .api.app import run_api
from .chat.chat_model import run_chat
from .eval.evaluator import run_eval
from .extras import logging
from .extras.env import VERSION, print_env
from .extras.logging import get_logger
from .extras.misc import get_device_count
from .train.tuner import export_model, run_exp
from .webui.interface import run_web_demo, run_web_ui
@ -47,7 +47,7 @@ USAGE = (
WELCOME = (
"-" * 58
+ "\n"
+ "| Welcome to LLaMA Factory, version {}".format(VERSION)
+ f"| Welcome to LLaMA Factory, version {VERSION}"
+ " " * (21 - len(VERSION))
+ "|\n|"
+ " " * 56
@ -56,7 +56,7 @@ WELCOME = (
+ "-" * 58
)
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
@unique
@ -86,19 +86,19 @@ def main():
elif command == Command.EXPORT:
export_model()
elif command == Command.TRAIN:
force_torchrun = os.environ.get("FORCE_TORCHRUN", "0").lower() in ["true", "1"]
force_torchrun = os.getenv("FORCE_TORCHRUN", "0").lower() in ["true", "1"]
if force_torchrun or get_device_count() > 1:
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))
master_addr = os.getenv("MASTER_ADDR", "127.0.0.1")
master_port = os.getenv("MASTER_PORT", str(random.randint(20001, 29999)))
logger.info_rank0(f"Initializing distributed tasks at: {master_addr}:{master_port}")
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}"
).format(
nnodes=os.environ.get("NNODES", "1"),
node_rank=os.environ.get("RANK", "0"),
nproc_per_node=os.environ.get("NPROC_PER_NODE", str(get_device_count())),
nnodes=os.getenv("NNODES", "1"),
node_rank=os.getenv("NODE_RANK", "0"),
nproc_per_node=os.getenv("NPROC_PER_NODE", str(get_device_count())),
master_addr=master_addr,
master_port=master_port,
file_name=launcher.__file__,
@ -118,4 +118,4 @@ def main():
elif command == Command.HELP:
print(USAGE)
else:
raise NotImplementedError("Unknown command: {}.".format(command))
raise NotImplementedError(f"Unknown command: {command}.")

38
src/llamafactory/data/aligner.py
View File

@ -16,7 +16,7 @@ import os
from functools import partial
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Union
from ..extras.logging import get_logger
from ..extras import logging
from .data_utils import Role
@ -29,45 +29,51 @@ if TYPE_CHECKING:
from .parser import DatasetAttr
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _convert_images(
images: Sequence["ImageInput"],
images: Union["ImageInput", Sequence["ImageInput"]],
dataset_attr: "DatasetAttr",
data_args: "DataArguments",
) -> Optional[List["ImageInput"]]:
r"""
Optionally concatenates image path to dataset dir when loading from local disk.
"""
if len(images) == 0:
if not isinstance(images, list):
images = [images]
elif len(images) == 0:
return None
else:
images = images[:]
images = images[:]
if dataset_attr.load_from in ["script", "file"]:
for i in range(len(images)):
if isinstance(images[i], str) and os.path.isfile(os.path.join(data_args.dataset_dir, images[i])):
images[i] = os.path.join(data_args.dataset_dir, images[i])
if isinstance(images[i], str) and os.path.isfile(os.path.join(data_args.image_dir, images[i])):
images[i] = os.path.join(data_args.image_dir, images[i])
return images
def _convert_videos(
videos: Sequence["VideoInput"],
videos: Union["VideoInput", Sequence["VideoInput"]],
dataset_attr: "DatasetAttr",
data_args: "DataArguments",
) -> Optional[List["VideoInput"]]:
r"""
Optionally concatenates video path to dataset dir when loading from local disk.
"""
if len(videos) == 0:
if not isinstance(videos, list):
videos = [videos]
elif len(videos) == 0:
return None
else:
videos = videos[:]
videos = videos[:]
if dataset_attr.load_from in ["script", "file"]:
for i in range(len(videos)):
if isinstance(videos[i], str) and os.path.isfile(os.path.join(data_args.dataset_dir, videos[i])):
videos[i] = os.path.join(data_args.dataset_dir, videos[i])
if isinstance(videos[i], str) and os.path.isfile(os.path.join(data_args.image_dir, videos[i])):
videos[i] = os.path.join(data_args.image_dir, videos[i])
return videos
@ -161,7 +167,7 @@ def convert_sharegpt(
broken_data = False
for turn_idx, message in enumerate(messages):
if message[dataset_attr.role_tag] not in accept_tags[turn_idx % 2]:
logger.warning("Invalid role tag in {}.".format(messages))
logger.warning_rank0(f"Invalid role tag in {messages}.")
broken_data = True
aligned_messages.append(
@ -171,7 +177,7 @@ def convert_sharegpt(
if (not dataset_attr.ranking and len(aligned_messages) % 2 != 0) or (
dataset_attr.ranking and len(aligned_messages) % 2 == 0
):
logger.warning("Invalid message count in {}.".format(messages))
logger.warning_rank0(f"Invalid message count in {messages}.")
broken_data = True
if dataset_attr.kto_tag and isinstance(example[dataset_attr.kto_tag], bool): # kto example
@ -192,7 +198,7 @@ def convert_sharegpt(
chosen[dataset_attr.role_tag] not in accept_tags[-1]
or rejected[dataset_attr.role_tag] not in accept_tags[-1]
):
logger.warning("Invalid role tag in {}.".format([chosen, rejected]))
logger.warning_rank0(f"Invalid role tag in {[chosen, rejected]}.")
broken_data = True
prompt = aligned_messages
@ -205,7 +211,7 @@ def convert_sharegpt(
response = aligned_messages[-1:]
if broken_data:
logger.warning("Skipping this abnormal example.")
logger.warning_rank0("Skipping this abnormal example.")
prompt, response = [], []
convert_images = partial(_convert_images, dataset_attr=dataset_attr, data_args=data_args)

9
src/llamafactory/data/collator.py
View File

@ -99,6 +99,9 @@ class MultiModalDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):
features: Dict[str, "torch.Tensor"] = super().__call__(features)
features.update(mm_inputs)
if isinstance(features.get("pixel_values"), list): # for pixtral inputs
features = features.data # use default_collate() instead of BatchEncoding.to()
return features
@ -137,9 +140,9 @@ class PairwiseDataCollatorWithPadding(MultiModalDataCollatorForSeq2Seq):
for key in ("chosen", "rejected"):
for feature in features:
target_feature = {
"input_ids": feature["{}_input_ids".format(key)],
"attention_mask": feature["{}_attention_mask".format(key)],
"labels": feature["{}_labels".format(key)],
"input_ids": feature[f"{key}_input_ids"],
"attention_mask": feature[f"{key}_attention_mask"],
"labels": feature[f"{key}_labels"],
"images": feature["images"],
"videos": feature["videos"],
}

10
src/llamafactory/data/data_utils.py
View File

@ -17,7 +17,7 @@ from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Set, TypedDict
from datasets import DatasetDict, concatenate_datasets, interleave_datasets
from ..extras.logging import get_logger
from ..extras import logging
if TYPE_CHECKING:
@ -26,7 +26,7 @@ if TYPE_CHECKING:
from ..hparams import DataArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
SLOTS = Sequence[Union[str, Set[str], Dict[str, str]]]
@ -56,12 +56,12 @@ def merge_dataset(
return all_datasets[0]
elif data_args.mix_strategy == "concat":
if data_args.streaming:
logger.warning("The samples between different datasets will not be mixed in streaming mode.")
logger.warning_once("The samples between different datasets will not be mixed in streaming mode.")
return concatenate_datasets(all_datasets)
elif data_args.mix_strategy.startswith("interleave"):
if not data_args.streaming:
logger.warning("We recommend using `mix_strategy=concat` in non-streaming mode.")
logger.warning_once("We recommend using `mix_strategy=concat` in non-streaming mode.")
return interleave_datasets(
datasets=all_datasets,
@ -70,7 +70,7 @@ def merge_dataset(
stopping_strategy="first_exhausted" if data_args.mix_strategy.endswith("under") else "all_exhausted",
)
else:
raise ValueError("Unknown mixing strategy: {}.".format(data_args.mix_strategy))
raise ValueError(f"Unknown mixing strategy: {data_args.mix_strategy}.")
def split_dataset(

10
src/llamafactory/data/formatter.py
View File

@ -83,14 +83,14 @@ class StringFormatter(Formatter):
if isinstance(slot, str):
for name, value in kwargs.items():
if not isinstance(value, str):
raise RuntimeError("Expected a string, got {}".format(value))
raise RuntimeError(f"Expected a string, got {value}")
slot = slot.replace("{{" + name + "}}", value, 1)
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)))
raise RuntimeError(f"Input must be string, set[str] or dict[str, str], got {type(slot)}")
return elements
@ -113,7 +113,7 @@ class FunctionFormatter(Formatter):
functions.append((tool_call["name"], json.dumps(tool_call["arguments"], ensure_ascii=False)))
except json.JSONDecodeError:
functions = []
raise RuntimeError(f"Invalid JSON format in function message: {str([content])}") # flat string
elements = []
for name, arguments in functions:
@ -124,7 +124,7 @@ class FunctionFormatter(Formatter):
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)))
raise RuntimeError(f"Input must be string, set[str] or dict[str, str], got {type(slot)}")
return elements
@ -141,7 +141,7 @@ class ToolFormatter(Formatter):
tools = json.loads(content)
return [self.tool_utils.tool_formatter(tools) if len(tools) != 0 else ""]
except json.JSONDecodeError:
return [""]
raise RuntimeError(f"Invalid JSON format in tool description: {str([content])}") # flat string
@override
def extract(self, content: str) -> Union[str, List["FunctionCall"]]:

59
src/llamafactory/data/loader.py
View File

@ -20,8 +20,8 @@ import numpy as np
from datasets import DatasetDict, load_dataset, load_from_disk
from transformers.utils.versions import require_version
from ..extras import logging
from ..extras.constants import FILEEXT2TYPE
from ..extras.logging import get_logger
from ..extras.misc import has_tokenized_data
from .aligner import align_dataset
from .data_utils import merge_dataset, split_dataset
@ -39,7 +39,7 @@ if TYPE_CHECKING:
from .template import Template
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _load_single_dataset(
@ -51,9 +51,9 @@ def _load_single_dataset(
r"""
Loads a single dataset and aligns it to the standard format.
"""
logger.info("Loading dataset {}...".format(dataset_attr))
logger.info_rank0(f"Loading dataset {dataset_attr}...")
data_path, data_name, data_dir, data_files = None, None, None, None
if dataset_attr.load_from in ["hf_hub", "ms_hub"]:
if dataset_attr.load_from in ["hf_hub", "ms_hub", "om_hub"]:
data_path = dataset_attr.dataset_name
data_name = dataset_attr.subset
data_dir = dataset_attr.folder
@ -69,25 +69,24 @@ def _load_single_dataset(
if os.path.isdir(local_path): # is directory
for file_name in os.listdir(local_path):
data_files.append(os.path.join(local_path, file_name))
if data_path is None:
data_path = FILEEXT2TYPE.get(file_name.split(".")[-1], None)
elif data_path != FILEEXT2TYPE.get(file_name.split(".")[-1], None):
raise ValueError("File types should be identical.")
elif os.path.isfile(local_path): # is file
data_files.append(local_path)
data_path = FILEEXT2TYPE.get(local_path.split(".")[-1], None)
else:
raise ValueError("File {} not found.".format(local_path))
raise ValueError(f"File {local_path} not found.")
data_path = FILEEXT2TYPE.get(os.path.splitext(data_files[0])[-1][1:], None)
if data_path is None:
raise ValueError("Allowed file types: {}.".format(",".join(FILEEXT2TYPE.keys())))
if any(data_path != FILEEXT2TYPE.get(os.path.splitext(data_file)[-1][1:], None) for data_file in data_files):
raise ValueError("File types should be identical.")
else:
raise NotImplementedError("Unknown load type: {}.".format(dataset_attr.load_from))
raise NotImplementedError(f"Unknown load type: {dataset_attr.load_from}.")
if dataset_attr.load_from == "ms_hub":
require_version("modelscope>=1.11.0", "To fix: pip install modelscope>=1.11.0")
from modelscope import MsDataset
from modelscope.utils.config_ds import MS_DATASETS_CACHE
from modelscope import MsDataset # type: ignore
from modelscope.utils.config_ds import MS_DATASETS_CACHE # type: ignore
cache_dir = model_args.cache_dir or MS_DATASETS_CACHE
dataset = MsDataset.load(
@ -98,10 +97,27 @@ def _load_single_dataset(
split=dataset_attr.split,
cache_dir=cache_dir,
token=model_args.ms_hub_token,
use_streaming=(data_args.streaming and (dataset_attr.load_from != "file")),
use_streaming=data_args.streaming,
)
if isinstance(dataset, MsDataset):
dataset = dataset.to_hf_dataset()
elif dataset_attr.load_from == "om_hub":
require_version("openmind>=0.8.0", "To fix: pip install openmind>=0.8.0")
from openmind import OmDataset # type: ignore
from openmind.utils.hub import OM_DATASETS_CACHE # type: ignore
cache_dir = model_args.cache_dir or OM_DATASETS_CACHE
dataset = OmDataset.load_dataset(
path=data_path,
name=data_name,
data_dir=data_dir,
data_files=data_files,
split=dataset_attr.split,
cache_dir=cache_dir,
token=model_args.om_hub_token,
streaming=data_args.streaming,
)
else:
dataset = load_dataset(
path=data_path,
@ -111,13 +127,10 @@ def _load_single_dataset(
split=dataset_attr.split,
cache_dir=model_args.cache_dir,
token=model_args.hf_hub_token,
streaming=(data_args.streaming and (dataset_attr.load_from != "file")),
streaming=data_args.streaming,
trust_remote_code=True,
)
if data_args.streaming and (dataset_attr.load_from == "file"): # faster than specifying streaming=True
dataset = dataset.to_iterable_dataset() # TODO: add num shards parameter
if dataset_attr.num_samples is not None and not data_args.streaming:
target_num = dataset_attr.num_samples
indexes = np.random.permutation(len(dataset))[:target_num] # all samples should be included
@ -128,7 +141,7 @@ def _load_single_dataset(
assert len(indexes) == dataset_attr.num_samples, "Sample num mismatched."
dataset = dataset.select(indexes)
logger.info("Sampled {} examples from dataset {}.".format(dataset_attr.num_samples, dataset_attr))
logger.info_rank0(f"Sampled {dataset_attr.num_samples} examples from dataset {dataset_attr}.")
if data_args.max_samples is not None: # truncate dataset
max_samples = min(data_args.max_samples, len(dataset))
@ -224,9 +237,9 @@ def get_dataset(
# Load tokenized dataset
if data_args.tokenized_path is not None:
if has_tokenized_data(data_args.tokenized_path):
logger.warning("Loading dataset from disk will ignore other data arguments.")
logger.warning_rank0("Loading dataset from disk will ignore other data arguments.")
dataset_dict: "DatasetDict" = load_from_disk(data_args.tokenized_path)
logger.info("Loaded tokenized dataset from {}.".format(data_args.tokenized_path))
logger.info_rank0(f"Loaded tokenized dataset from {data_args.tokenized_path}.")
dataset_module: Dict[str, "Dataset"] = {}
if "train" in dataset_dict:
@ -277,8 +290,8 @@ def get_dataset(
if data_args.tokenized_path is not None:
if training_args.should_save:
dataset_dict.save_to_disk(data_args.tokenized_path)
logger.info("Tokenized dataset saved at {}.".format(data_args.tokenized_path))
logger.info("Please restart the training with `tokenized_path: {}`.".format(data_args.tokenized_path))
logger.info_rank0(f"Tokenized dataset saved at {data_args.tokenized_path}.")
logger.info_rank0(f"Please restart the training with `tokenized_path: {data_args.tokenized_path}`.")
sys.exit(0)

285
src/llamafactory/data/mm_plugin.py
View File

@ -4,6 +4,7 @@ from io import BytesIO
from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, TypedDict, Union
import numpy as np
from transformers.image_utils import get_image_size, to_numpy_array
from typing_extensions import override
from ..extras.constants import IGNORE_INDEX, IMAGE_PLACEHOLDER, VIDEO_PLACEHOLDER
@ -110,7 +111,7 @@ class BasePlugin:
image = Image.open(image["path"])
if not isinstance(image, ImageObject):
raise ValueError("Expect input is a list of Images, but got {}.".format(type(image)))
raise ValueError(f"Expect input is a list of Images, but got {type(image)}.")
results.append(self._preprocess_image(image, **kwargs))
@ -157,6 +158,7 @@ class BasePlugin:
It holds num_patches == torch.prod(image_grid_thw)
"""
image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
video_processor: "BaseImageProcessor" = getattr(processor, "video_processor", image_processor)
input_dict = {"images": None} # default key
if len(images) != 0:
images = self._regularize_images(
@ -174,10 +176,16 @@ class BasePlugin:
)
input_dict["videos"] = videos
if input_dict.get("images", None) is not None or input_dict.get("videos", None) is not None:
return image_processor(**input_dict, return_tensors="pt")
else:
return {}
mm_inputs = {}
if image_processor != video_processor:
if input_dict.get("images") is not None:
mm_inputs.update(image_processor(input_dict["images"], return_tensors="pt"))
if input_dict.get("videos") is not None:
mm_inputs.update(video_processor(input_dict["videos"], return_tensors="pt"))
elif input_dict.get("images") is not None or input_dict.get("videos") is not None: # same processor (qwen2-vl)
mm_inputs.update(image_processor(**input_dict, return_tensors="pt"))
return mm_inputs
def process_messages(
self,
@ -218,6 +226,14 @@ class BasePlugin:
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
r"""
Builds batched multimodal inputs for VLMs.
Arguments:
images: a list of image inputs, shape (num_images,)
videos: a list of video inputs, shape (num_videos,)
imglens: number of images in each sample, shape (batch_size,)
vidlens: number of videos in each sample, shape (batch_size,)
seqlens: number of tokens in each sample, shape (batch_size,)
processor: a processor for pre-processing images and videos
"""
self._validate_input(images, videos)
return {}
@ -245,7 +261,123 @@ class LlavaPlugin(BasePlugin):
message["content"] = content.replace("{{image}}", self.image_token * image_seqlen)
if len(images) != num_image_tokens:
raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
return messages
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
seqlens: Sequence[int],
processor: Optional["ProcessorMixin"],
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
self._validate_input(images, videos)
return self._get_mm_inputs(images, videos, processor)
class LlavaNextPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[Dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
processor: Optional["ProcessorMixin"],
) -> List[Dict[str, str]]:
self._validate_input(images, videos)
num_image_tokens = 0
messages = deepcopy(messages)
mm_inputs = self._get_mm_inputs(images, videos, processor)
if "image_sizes" in mm_inputs:
image_sizes = iter(mm_inputs["image_sizes"])
if "pixel_values" in mm_inputs:
height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values"][0][0]))
for message in messages:
content = message["content"]
while self.image_token in content:
image_size = next(image_sizes)
orig_height, orig_width = image_size
image_seqlen = processor._get_number_of_features(orig_height, orig_width, height, width)
if processor.vision_feature_select_strategy == "default":
image_seqlen -= 1
num_image_tokens += 1
content = content.replace(self.image_token, "{{image}}" * image_seqlen, 1)
message["content"] = content.replace("{{image}}", self.image_token)
if len(images) != num_image_tokens:
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
return messages
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
seqlens: Sequence[int],
processor: Optional["ProcessorMixin"],
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
self._validate_input(images, videos)
res = self._get_mm_inputs(images, videos, processor)
return res
class LlavaNextVideoPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[Dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
processor: Optional["ProcessorMixin"],
) -> List[Dict[str, str]]:
self._validate_input(images, videos)
num_image_tokens = 0
num_video_tokens = 0
messages = deepcopy(messages)
mm_inputs = self._get_mm_inputs(images, videos, processor)
if "pixel_values" in mm_inputs:
image_sizes = iter(mm_inputs["image_sizes"])
height, width = get_image_size(to_numpy_array(mm_inputs["pixel_values"][0][0]))
for message in messages:
content = message["content"]
while self.image_token in content:
image_size = next(image_sizes)
orig_height, orig_width = image_size
image_seqlen = processor._get_number_of_features(orig_height, orig_width, height, width)
if processor.vision_feature_select_strategy == "default":
image_seqlen -= 1
num_image_tokens += 1
content = content.replace(self.image_token, "{{image}}" * image_seqlen, 1)
message["content"] = content.replace("{{image}}", self.image_token)
if "pixel_values_videos" in mm_inputs:
pixel_values_video = to_numpy_array(mm_inputs.get("pixel_values_videos")[0])
height, width = get_image_size(pixel_values_video[0])
num_frames = pixel_values_video.shape[0] # frame dim is always after batch dim
image_seqlen = (height // processor.patch_size) * (width // processor.patch_size)
video_seqlen = image_seqlen // 4 * num_frames # divide by 4 needed for avg pooling layer
for message in messages:
content = message["content"]
while self.video_token in content:
num_video_tokens += 1
content = content.replace(self.video_token, "{{video}}", 1)
message["content"] = content.replace("{{video}}", self.video_token * video_seqlen)
if len(images) != num_image_tokens:
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
if len(videos) != num_video_tokens:
raise ValueError(f"The number of videos does not match the number of {IMAGE_PLACEHOLDER} tokens")
return messages
@ -284,7 +416,7 @@ class PaliGemmaPlugin(BasePlugin):
message["content"] = content.replace("{{image}}", "")
if len(images) != num_image_tokens:
raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
return messages
@ -324,6 +456,68 @@ class PaliGemmaPlugin(BasePlugin):
return mm_inputs
class PixtralPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[Dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
processor: Optional["ProcessorMixin"],
) -> List[Dict[str, str]]:
self._validate_input(images, videos)
patch_size = getattr(processor, "patch_size")
image_token = getattr(processor, "image_token")
image_break_token = getattr(processor, "image_break_token")
image_end_token = getattr(processor, "image_end_token")
num_image_tokens = 0
messages = deepcopy(messages)
mm_inputs = self._get_mm_inputs(images, videos, processor)
image_input_sizes = mm_inputs.get("image_sizes", None)
for message in messages:
content = message["content"]
while IMAGE_PLACEHOLDER in content:
if image_input_sizes is None:
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
image_size = image_input_sizes[0][num_image_tokens]
height, width = image_size
num_height_tokens = height // patch_size
num_width_tokens = width // patch_size
replace_tokens = [[image_token] * num_width_tokens + [image_break_token]] * num_height_tokens
replace_tokens = [item for sublist in replace_tokens for item in sublist] # flatten list
replace_tokens[-1] = image_end_token
replace_str = "".join(replace_tokens)
content = content.replace(IMAGE_PLACEHOLDER, replace_str, 1)
num_image_tokens += 1
message["content"] = content
if len(images) != num_image_tokens:
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
return messages
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
seqlens: Sequence[int],
processor: Optional["ProcessorMixin"],
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
self._validate_input(images, videos)
mm_inputs = self._get_mm_inputs(images, videos, processor)
if mm_inputs.get("pixel_values"):
mm_inputs["pixel_values"] = mm_inputs["pixel_values"][0]
mm_inputs.pop("image_sizes", None)
return mm_inputs
class Qwen2vlPlugin(BasePlugin):
@override
def _preprocess_image(self, image: "ImageObject", **kwargs) -> "ImageObject":
@ -369,7 +563,7 @@ class Qwen2vlPlugin(BasePlugin):
content = message["content"]
while IMAGE_PLACEHOLDER in content:
if num_image_tokens >= len(image_grid_thw):
raise ValueError("`len(images)` is less than the number of {} tokens.".format(IMAGE_PLACEHOLDER))
raise ValueError(f"`len(images)` is less than the number of {IMAGE_PLACEHOLDER} tokens.")
content = content.replace(
IMAGE_PLACEHOLDER,
@ -382,7 +576,7 @@ class Qwen2vlPlugin(BasePlugin):
while VIDEO_PLACEHOLDER in content:
if num_video_tokens >= len(video_grid_thw):
raise ValueError("`len(videos)` is less than the number of {} tokens.".format(VIDEO_PLACEHOLDER))
raise ValueError(f"`len(videos)` is less than the number of {VIDEO_PLACEHOLDER} tokens.")
content = content.replace(
VIDEO_PLACEHOLDER,
@ -396,10 +590,73 @@ class Qwen2vlPlugin(BasePlugin):
message["content"] = content
if len(images) != num_image_tokens:
raise ValueError("The number of images does not match the number of {} tokens".format(IMAGE_PLACEHOLDER))
raise ValueError(f"The number of images does not match the number of {IMAGE_PLACEHOLDER} tokens")
if len(videos) != num_video_tokens:
raise ValueError("The number of videos does not match the number of {} tokens".format(VIDEO_PLACEHOLDER))
raise ValueError(f"The number of videos does not match the number of {VIDEO_PLACEHOLDER} tokens")
return messages
@override
def get_mm_inputs(
self,
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
imglens: Sequence[int],
vidlens: Sequence[int],
seqlens: Sequence[int],
processor: Optional["ProcessorMixin"],
) -> Dict[str, Union[List[int], "torch.Tensor"]]:
self._validate_input(images, videos)
return self._get_mm_inputs(images, videos, processor)
class VideoLlavaPlugin(BasePlugin):
@override
def process_messages(
self,
messages: Sequence[Dict[str, str]],
images: Sequence["ImageInput"],
videos: Sequence["VideoInput"],
processor: Optional["ProcessorMixin"],
) -> List[Dict[str, str]]:
self._validate_input(images, videos)
num_image_tokens = 0
num_video_tokens = 0
messages = deepcopy(messages)
mm_inputs = self._get_mm_inputs(images, videos, processor)
num_frames = 0
exist_images = "pixel_values_images" in mm_inputs
exist_videos = "pixel_values_videos" in mm_inputs
if exist_videos or exist_images:
if exist_images:
height, width = get_image_size(to_numpy_array(mm_inputs.get("pixel_values_images")[0]))
num_frames = 1
if exist_videos:
pixel_values_video = to_numpy_array(mm_inputs.get("pixel_values_videos")[0])
height, width = get_image_size(pixel_values_video[0])
num_frames = pixel_values_video.shape[0] # frame dim is always after batch dim
image_seqlen = (height // processor.patch_size) * (width // processor.patch_size) + 1
video_seqlen = image_seqlen * num_frames
if processor.vision_feature_select_strategy == "default":
image_seqlen -= 1
for message in messages:
content = message["content"]
while self.image_token in content:
num_image_tokens += 1
content = content.replace(self.image_token, "{{image}}", 1)
while self.video_token in content:
num_video_tokens += 1
content = content.replace(self.video_token, "{{video}}", 1)
content = content.replace("{{image}}", self.image_token * image_seqlen)
message["content"] = content.replace("{{video}}", self.video_token * video_seqlen)
if len(images) != num_image_tokens:
raise ValueError(f"The number of images does not match the number of {self.image_token} tokens")
if len(videos) != num_video_tokens:
raise ValueError(f"The number of videos does not match the number of {self.video_token} tokens")
return messages
@ -420,8 +677,12 @@ class Qwen2vlPlugin(BasePlugin):
PLUGINS = {
"base": BasePlugin,
"llava": LlavaPlugin,
"llava_next": LlavaNextPlugin,
"llava_next_video": LlavaNextVideoPlugin,
"paligemma": PaliGemmaPlugin,
"pixtral": PixtralPlugin,
"qwen2_vl": Qwen2vlPlugin,
"video_llava": VideoLlavaPlugin,
}
@ -432,6 +693,6 @@ def get_mm_plugin(
) -> "BasePlugin":
plugin_class = PLUGINS.get(name, None)
if plugin_class is None:
raise ValueError("Multimodal plugin `{}` not found.".format(name))
raise ValueError(f"Multimodal plugin `{name}` not found.")
return plugin_class(image_token, video_token)

24
src/llamafactory/data/parser.py
View File

@ -20,7 +20,7 @@ from typing import Any, Dict, List, Literal, Optional, Sequence
from transformers.utils import cached_file
from ..extras.constants import DATA_CONFIG
from ..extras.misc import use_modelscope
from ..extras.misc import use_modelscope, use_openmind
@dataclass
@ -30,7 +30,7 @@ class DatasetAttr:
"""
# basic configs
load_from: Literal["hf_hub", "ms_hub", "script", "file"]
load_from: Literal["hf_hub", "ms_hub", "om_hub", "script", "file"]
dataset_name: str
formatting: Literal["alpaca", "sharegpt"] = "alpaca"
ranking: bool = False
@ -87,31 +87,39 @@ def get_dataset_list(dataset_names: Optional[Sequence[str]], dataset_dir: str) -
config_path = os.path.join(dataset_dir, DATA_CONFIG)
try:
with open(config_path, "r") as f:
with open(config_path) as f:
dataset_info = json.load(f)
except Exception as err:
if len(dataset_names) != 0:
raise ValueError("Cannot open {} due to {}.".format(config_path, str(err)))
raise ValueError(f"Cannot open {config_path} due to {str(err)}.")
dataset_info = None
dataset_list: List["DatasetAttr"] = []
for name in dataset_names:
if dataset_info is None: # dataset_dir is ONLINE
load_from = "ms_hub" if use_modelscope() else "hf_hub"
if use_modelscope():
load_from = "ms_hub"
elif use_openmind():
load_from = "om_hub"
else:
load_from = "hf_hub"
dataset_attr = DatasetAttr(load_from, dataset_name=name)
dataset_list.append(dataset_attr)
continue
if name not in dataset_info:
raise ValueError("Undefined dataset {} in {}.".format(name, DATA_CONFIG))
raise ValueError(f"Undefined dataset {name} in {DATA_CONFIG}.")
has_hf_url = "hf_hub_url" in dataset_info[name]
has_ms_url = "ms_hub_url" in dataset_info[name]
has_om_url = "om_hub_url" in dataset_info[name]
if has_hf_url or has_ms_url:
if (use_modelscope() and has_ms_url) or (not has_hf_url):
if has_hf_url or has_ms_url or has_om_url:
if has_ms_url and (use_modelscope() or not has_hf_url):
dataset_attr = DatasetAttr("ms_hub", dataset_name=dataset_info[name]["ms_hub_url"])
elif has_om_url and (use_openmind() or not has_hf_url):
dataset_attr = DatasetAttr("om_hub", dataset_name=dataset_info[name]["om_hub_url"])
else:
dataset_attr = DatasetAttr("hf_hub", dataset_name=dataset_info[name]["hf_hub_url"])
elif "script_url" in dataset_info[name]:

10
src/llamafactory/data/processors/feedback.py
View File

@ -15,8 +15,8 @@
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
from ...extras import logging
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from .processor_utils import infer_seqlen
@ -28,7 +28,7 @@ if TYPE_CHECKING:
from ..template import Template
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _encode_feedback_example(
@ -94,7 +94,9 @@ def preprocess_feedback_dataset(
model_inputs = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) < 2:
logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
input_ids, labels, kl_input_ids, kl_labels, kto_tag = _encode_feedback_example(
@ -123,6 +125,6 @@ def preprocess_feedback_dataset(
desirable_num = sum([1 for tag in model_inputs["kto_tags"] if tag])
undesirable_num = len(model_inputs["kto_tags"]) - desirable_num
if desirable_num == 0 or undesirable_num == 0:
logger.warning("Your dataset only has one preference type.")
logger.warning_rank0("Your dataset only has one preference type.")
return model_inputs

12
src/llamafactory/data/processors/pairwise.py
View File

@ -15,8 +15,8 @@
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
from ...extras import logging
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from .processor_utils import infer_seqlen
@ -28,7 +28,7 @@ if TYPE_CHECKING:
from ..template import Template
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _encode_pairwise_example(
@ -77,7 +77,9 @@ def preprocess_pairwise_dataset(
model_inputs = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) < 2:
logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
chosen_input_ids, chosen_labels, rejected_input_ids, rejected_labels = _encode_pairwise_example(
@ -110,8 +112,8 @@ def print_pairwise_dataset_example(example: Dict[str, List[int]], tokenizer: "Pr
print("chosen_input_ids:\n{}".format(example["chosen_input_ids"]))
print("chosen_inputs:\n{}".format(tokenizer.decode(example["chosen_input_ids"], skip_special_tokens=False)))
print("chosen_label_ids:\n{}".format(example["chosen_labels"]))
print("chosen_labels:\n{}".format(tokenizer.decode(valid_chosen_labels, skip_special_tokens=False)))
print(f"chosen_labels:\n{tokenizer.decode(valid_chosen_labels, skip_special_tokens=False)}")
print("rejected_input_ids:\n{}".format(example["rejected_input_ids"]))
print("rejected_inputs:\n{}".format(tokenizer.decode(example["rejected_input_ids"], skip_special_tokens=False)))
print("rejected_label_ids:\n{}".format(example["rejected_labels"]))
print("rejected_labels:\n{}".format(tokenizer.decode(valid_rejected_labels, skip_special_tokens=False)))
print(f"rejected_labels:\n{tokenizer.decode(valid_rejected_labels, skip_special_tokens=False)}")

16
src/llamafactory/data/processors/supervised.py
View File

@ -15,8 +15,8 @@
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
from ...extras import logging
from ...extras.constants import IGNORE_INDEX
from ...extras.logging import get_logger
from .processor_utils import greedy_knapsack, infer_seqlen
@ -28,7 +28,7 @@ if TYPE_CHECKING:
from ..template import Template
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _encode_supervised_example(
@ -99,7 +99,9 @@ def preprocess_supervised_dataset(
model_inputs = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) != 1:
logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
input_ids, labels = _encode_supervised_example(
@ -141,7 +143,9 @@ def preprocess_packed_supervised_dataset(
length2indexes = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) != 1:
logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
input_ids, labels = _encode_supervised_example(
@ -160,7 +164,7 @@ def preprocess_packed_supervised_dataset(
)
length = len(input_ids)
if length > data_args.cutoff_len:
logger.warning("Dropped lengthy example with length {} > {}.".format(length, data_args.cutoff_len))
logger.warning_rank0(f"Dropped lengthy example with length {length} > {data_args.cutoff_len}.")
else:
lengths.append(length)
length2indexes[length].append(valid_num)
@ -212,4 +216,4 @@ def print_supervised_dataset_example(example: Dict[str, List[int]], tokenizer: "
print("input_ids:\n{}".format(example["input_ids"]))
print("inputs:\n{}".format(tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
print("label_ids:\n{}".format(example["labels"]))
print("labels:\n{}".format(tokenizer.decode(valid_labels, skip_special_tokens=False)))
print(f"labels:\n{tokenizer.decode(valid_labels, skip_special_tokens=False)}")

8
src/llamafactory/data/processors/unsupervised.py
View File

@ -15,7 +15,7 @@
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence, Tuple
from ...extras.logging import get_logger
from ...extras import logging
from ..data_utils import Role
from .processor_utils import infer_seqlen
@ -28,7 +28,7 @@ if TYPE_CHECKING:
from ..template import Template
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _encode_unsupervised_example(
@ -71,7 +71,9 @@ def preprocess_unsupervised_dataset(
model_inputs = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1:
logger.warning("Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i]))
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
input_ids, labels = _encode_unsupervised_example(

183
src/llamafactory/data/template.py
View File

@ -18,7 +18,7 @@ from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
from transformers.utils.versions import require_version
from typing_extensions import override
from ..extras.logging import get_logger
from ..extras import logging
from .data_utils import Role
from .formatter import EmptyFormatter, FunctionFormatter, StringFormatter, ToolFormatter
from .mm_plugin import get_mm_plugin
@ -32,7 +32,7 @@ if TYPE_CHECKING:
from .mm_plugin import BasePlugin
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
@dataclass
@ -49,6 +49,7 @@ class Template:
stop_words: List[str]
efficient_eos: bool
replace_eos: bool
replace_jinja_template: bool
mm_plugin: "BasePlugin"
def encode_oneturn(
@ -146,7 +147,7 @@ class Template:
elif "eos_token" in elem and tokenizer.eos_token_id is not None:
token_ids += [tokenizer.eos_token_id]
else:
raise ValueError("Input must be string, set[str] or dict[str, str], got {}".format(type(elem)))
raise ValueError(f"Input must be string, set[str] or dict[str, str], got {type(elem)}")
return token_ids
@ -214,6 +215,7 @@ def _register_template(
stop_words: Sequence[str] = [],
efficient_eos: bool = False,
replace_eos: bool = False,
replace_jinja_template: bool = True,
mm_plugin: "BasePlugin" = get_mm_plugin(name="base"),
) -> None:
r"""
@ -263,6 +265,7 @@ def _register_template(
stop_words=stop_words,
efficient_eos=efficient_eos,
replace_eos=replace_eos,
replace_jinja_template=replace_jinja_template,
mm_plugin=mm_plugin,
)
@ -272,12 +275,12 @@ def _add_or_replace_eos_token(tokenizer: "PreTrainedTokenizer", eos_token: str)
num_added_tokens = tokenizer.add_special_tokens({"eos_token": eos_token})
if is_added:
logger.info("Add eos token: {}".format(tokenizer.eos_token))
logger.info_rank0(f"Add eos token: {tokenizer.eos_token}")
else:
logger.info("Replace eos token: {}".format(tokenizer.eos_token))
logger.info_rank0(f"Replace eos token: {tokenizer.eos_token}")
if num_added_tokens > 0:
logger.warning("New tokens have been added, make sure `resize_vocab` is True.")
logger.warning_rank0("New tokens have been added, make sure `resize_vocab` is True.")
def _jinja_escape(content: str) -> str:
@ -353,24 +356,21 @@ def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args:
r"""
Gets chat template and fixes the tokenizer.
"""
if data_args.template in ["llava", "paligemma", "qwen2_vl"]:
require_version(
"transformers>=4.45.0.dev0", "To fix: pip install git+https://github.com/huggingface/transformers.git"
)
require_version("accelerate>=0.34.0", "To fix: pip install accelerate>=0.34.0")
if data_args.template is None:
template = TEMPLATES["empty"] # placeholder
else:
template = TEMPLATES.get(data_args.template, None)
if template is None:
raise ValueError("Template {} does not exist.".format(data_args.template))
raise ValueError(f"Template {data_args.template} does not exist.")
if template.mm_plugin.__class__.__name__ != "BasePlugin":
require_version("transformers>=4.45.0", "To fix: pip install transformers>=4.45.0")
if data_args.train_on_prompt and template.efficient_eos:
raise ValueError("Current template does not support `train_on_prompt`.")
if data_args.tool_format is not None:
logger.info("Using tool format: {}.".format(data_args.tool_format))
logger.info_rank0(f"Using tool format: {data_args.tool_format}.")
eos_slots = [] if template.efficient_eos else [{"eos_token"}]
template.format_function = FunctionFormatter(slots=eos_slots, tool_format=data_args.tool_format)
template.format_tools = ToolFormatter(tool_format=data_args.tool_format)
@ -388,20 +388,21 @@ def get_template_and_fix_tokenizer(tokenizer: "PreTrainedTokenizer", data_args:
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
logger.info("Add pad token: {}".format(tokenizer.pad_token))
logger.info_rank0(f"Add pad token: {tokenizer.pad_token}")
if stop_words:
num_added_tokens = tokenizer.add_special_tokens(
dict(additional_special_tokens=stop_words), replace_additional_special_tokens=False
)
logger.info("Add {} to stop words.".format(",".join(stop_words)))
logger.info_rank0("Add {} to stop words.".format(",".join(stop_words)))
if num_added_tokens > 0:
logger.warning("New tokens have been added, make sure `resize_vocab` is True.")
logger.warning_rank0("New tokens have been added, make sure `resize_vocab` is True.")
try:
tokenizer.chat_template = _get_jinja_template(template, tokenizer)
except ValueError:
logger.info("Cannot add this chat template to tokenizer.")
if tokenizer.chat_template is None or template.replace_jinja_template:
try:
tokenizer.chat_template = _get_jinja_template(template, tokenizer)
except ValueError as e:
logger.info_rank0(f"Cannot add this chat template to tokenizer: {e}.")
return template
@ -640,6 +641,14 @@ _register_template(
)
_register_template(
name="exaone",
format_user=StringFormatter(slots=["[|user|]{{content}}\n[|assistant|]"]),
format_system=StringFormatter(slots=["[|system|]{{content}}[|endofturn|]\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
)
_register_template(
name="falcon",
format_user=StringFormatter(slots=["User: {{content}}\nFalcon:"]),
@ -664,6 +673,7 @@ _register_template(
format_separator=EmptyFormatter(slots=["<end_of_turn>\n"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
efficient_eos=True,
replace_jinja_template=False,
)
@ -681,6 +691,14 @@ _register_template(
)
_register_template(
name="index",
format_user=StringFormatter(slots=["reserved_0{{content}}reserved_1"]),
format_system=StringFormatter(slots=["<unk>{{content}}"]),
efficient_eos=True,
)
_register_template(
name="intern",
format_user=StringFormatter(slots=["<|User|>:{{content}}\n<|Bot|>:"]),
@ -740,6 +758,7 @@ _register_template(
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
stop_words=["<|eot_id|>"],
replace_eos=True,
replace_jinja_template=False,
)
@ -754,6 +773,107 @@ _register_template(
)
_register_template(
name="llava_next",
format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
default_system=(
"A chat between a curious user and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the user's questions."
),
mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)
_register_template(
name="llava_next_llama3",
format_user=StringFormatter(
slots=[
(
"<|start_header_id|>user<|end_header_id|>\n\n{{content}}<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
)
]
),
format_system=StringFormatter(slots=["<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]),
format_observation=StringFormatter(
slots=[
(
"<|start_header_id|>tool<|end_header_id|>\n\n{{content}}<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
)
]
),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
stop_words=["<|eot_id|>"],
replace_eos=True,
replace_jinja_template=False,
mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)
_register_template(
name="llava_next_mistral",
format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)
_register_template(
name="llava_next_qwen",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_observation=StringFormatter(slots=["<|im_start|>tool\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
default_system="You are a helpful assistant.",
stop_words=["<|im_end|>"],
replace_eos=True,
replace_jinja_template=False,
mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)
_register_template(
name="llava_next_yi",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
stop_words=["<|im_end|>"],
replace_eos=True,
mm_plugin=get_mm_plugin(name="llava_next", image_token="<image>"),
)
_register_template(
name="llava_next_video",
format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
default_system=(
"A chat between a curious user and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the user's questions."
),
mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
)
_register_template(
name="llava_next_video_mistral",
format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
)
_register_template(
name="llava_next_video_yi",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
format_system=StringFormatter(slots=["<|im_start|>system\n{{content}}<|im_end|>\n"]),
format_separator=EmptyFormatter(slots=["\n"]),
stop_words=["<|im_end|>"],
replace_eos=True,
mm_plugin=get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>"),
)
_register_template(
name="mistral",
format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
@ -831,6 +951,14 @@ _register_template(
replace_eos=True,
)
_register_template(
name="pixtral",
format_user=StringFormatter(slots=["[INST] {{content}} [/INST]"]),
format_prefix=EmptyFormatter(slots=[{"bos_token"}]),
mm_plugin=get_mm_plugin(name="pixtral", image_token="[IMG]"),
)
_register_template(
name="qwen",
format_user=StringFormatter(slots=["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]),
@ -840,6 +968,7 @@ _register_template(
default_system="You are a helpful assistant.",
stop_words=["<|im_end|>"],
replace_eos=True,
replace_jinja_template=False,
)
@ -852,6 +981,7 @@ _register_template(
default_system="You are a helpful assistant.",
stop_words=["<|im_end|>"],
replace_eos=True,
replace_jinja_template=False,
mm_plugin=get_mm_plugin(name="qwen2_vl", image_token="<|image_pad|>", video_token="<|video_pad|>"),
)
@ -907,6 +1037,17 @@ _register_template(
)
_register_template(
name="video_llava",
format_user=StringFormatter(slots=["USER: {{content}} ASSISTANT:"]),
default_system=(
"A chat between a curious user and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the user's questions."
),
mm_plugin=get_mm_plugin(name="video_llava", image_token="<image>", video_token="<video>"),
)
_register_template(
name="xuanyuan",
format_user=StringFormatter(slots=["Human: {{content}} Assistant:"]),

2
src/llamafactory/data/tool_utils.py
View File

@ -177,6 +177,6 @@ TOOLS = {
def get_tool_utils(name: str) -> "ToolUtils":
tool_utils = TOOLS.get(name, None)
if tool_utils is None:
raise ValueError("Tool utils `{}` not found.".format(name))
raise ValueError(f"Tool utils `{name}` not found.")
return tool_utils

4
src/llamafactory/eval/evaluator.py
View File

@ -87,7 +87,7 @@ class Evaluator:
token=self.model_args.hf_hub_token,
)
with open(mapping, "r", encoding="utf-8") as f:
with open(mapping, encoding="utf-8") as f:
categorys: Dict[str, Dict[str, str]] = json.load(f)
category_corrects = {subj: np.array([], dtype="bool") for subj in SUBJECTS}
@ -139,7 +139,7 @@ class Evaluator:
def _save_results(self, category_corrects: Dict[str, "NDArray"], results: Dict[str, Dict[int, str]]) -> None:
score_info = "\n".join(
[
"{:>15}: {:.2f}".format(category_name, 100 * np.mean(category_correct))
f"{category_name:>15}: {100 * np.mean(category_correct):.2f}"
for category_name, category_correct in category_corrects.items()
if len(category_correct)
]

2
src/llamafactory/eval/template.py
View File

@ -61,7 +61,7 @@ def _register_eval_template(name: str, system: str, choice: str, answer: str) ->
def get_eval_template(name: str) -> "EvalTemplate":
eval_template = eval_templates.get(name, None)
assert eval_template is not None, "Template {} does not exist.".format(name)
assert eval_template is not None, f"Template {name} does not exist."
return eval_template

444
src/llamafactory/extras/constants.py
View File

@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from collections import OrderedDict, defaultdict
from enum import Enum
from typing import Dict, Optional
@ -47,7 +48,7 @@ FILEEXT2TYPE = {
IGNORE_INDEX = -100
IMAGE_PLACEHOLDER = "<image>"
IMAGE_PLACEHOLDER = os.environ.get("IMAGE_PLACEHOLDER", "<image>")
LAYERNORM_NAMES = {"norm", "ln"}
@ -95,7 +96,7 @@ SUPPORTED_CLASS_FOR_BLOCK_DIAG_ATTN = {
SUPPORTED_CLASS_FOR_S2ATTN = {"llama"}
VIDEO_PLACEHOLDER = "<video>"
VIDEO_PLACEHOLDER = os.environ.get("VIDEO_PLACEHOLDER", "<video>")
V_HEAD_WEIGHTS_NAME = "value_head.bin"
@ -107,6 +108,7 @@ VISION_MODELS = set()
class DownloadSource(str, Enum):
DEFAULT = "hf"
MODELSCOPE = "ms"
OPENMIND = "om"
def register_model_group(
@ -114,17 +116,12 @@ def register_model_group(
template: Optional[str] = None,
vision: bool = False,
) -> None:
prefix = None
for name, path in models.items():
if prefix is None:
prefix = name.split("-")[0]
else:
assert prefix == name.split("-")[0], "prefix should be identical."
SUPPORTED_MODELS[name] = path
if template is not None:
DEFAULT_TEMPLATE[prefix] = template
if vision:
VISION_MODELS.add(prefix)
if template is not None and any(suffix in name for suffix in ("-Chat", "-Instruct")):
DEFAULT_TEMPLATE[name] = template
if vision:
VISION_MODELS.add(name)
register_model_group(
@ -168,14 +165,17 @@ register_model_group(
"Baichuan2-13B-Base": {
DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-13B-Base",
DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-13B-Base",
DownloadSource.OPENMIND: "Baichuan/Baichuan2_13b_base_pt",
},
"Baichuan2-7B-Chat": {
DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-7B-Chat",
DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-7B-Chat",
DownloadSource.OPENMIND: "Baichuan/Baichuan2_7b_chat_pt",
},
"Baichuan2-13B-Chat": {
DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-13B-Chat",
DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-13B-Chat",
DownloadSource.OPENMIND: "Baichuan/Baichuan2_13b_chat_pt",
},
},
template="baichuan2",
@ -274,27 +274,27 @@ register_model_group(
register_model_group(
models={
"ChineseLLaMA2-1.3B": {
"Chinese-Llama-2-1.3B": {
DownloadSource.DEFAULT: "hfl/chinese-llama-2-1.3b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-llama-2-1.3b",
},
"ChineseLLaMA2-7B": {
"Chinese-Llama-2-7B": {
DownloadSource.DEFAULT: "hfl/chinese-llama-2-7b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-llama-2-7b",
},
"ChineseLLaMA2-13B": {
"Chinese-Llama-2-13B": {
DownloadSource.DEFAULT: "hfl/chinese-llama-2-13b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-llama-2-13b",
},
"ChineseLLaMA2-1.3B-Chat": {
"Chinese-Alpaca-2-1.3B-Chat": {
DownloadSource.DEFAULT: "hfl/chinese-alpaca-2-1.3b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-alpaca-2-1.3b",
},
"ChineseLLaMA2-7B-Chat": {
"Chinese-Alpaca-2-7B-Chat": {
DownloadSource.DEFAULT: "hfl/chinese-alpaca-2-7b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-alpaca-2-7b",
},
"ChineseLLaMA2-13B-Chat": {
"Chinese-Alpaca-2-13B-Chat": {
DownloadSource.DEFAULT: "hfl/chinese-alpaca-2-13b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-alpaca-2-13b",
},
@ -450,25 +450,25 @@ register_model_group(
register_model_group(
models={
"DeepSeekCoder-6.7B-Base": {
"DeepSeek-Coder-6.7B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-6.7b-base",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-6.7b-base",
},
"DeepSeekCoder-7B-Base": {
"DeepSeek-Coder-7B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-7b-base-v1.5",
},
"DeepSeekCoder-33B-Base": {
"DeepSeek-Coder-33B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-33b-base",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-33b-base",
},
"DeepSeekCoder-6.7B-Instruct": {
"DeepSeek-Coder-6.7B-Instruct": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-6.7b-instruct",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-6.7b-instruct",
},
"DeepSeekCoder-7B-Instruct": {
"DeepSeek-Coder-7B-Instruct": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-7b-instruct-v1.5",
},
"DeepSeekCoder-33B-Instruct": {
"DeepSeek-Coder-33B-Instruct": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-33b-instruct",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-33b-instruct",
},
@ -477,6 +477,16 @@ register_model_group(
)
register_model_group(
models={
"EXAONE-3.0-7.8B-Instruct": {
DownloadSource.DEFAULT: "LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct",
},
},
template="exaone",
)
register_model_group(
models={
"Falcon-7B": {
@ -550,10 +560,12 @@ register_model_group(
"Gemma-2-2B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-2-2b-it",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-2b-it",
DownloadSource.OPENMIND: "LlamaFactory/gemma-2-2b-it",
},
"Gemma-2-9B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-2-9b-it",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-9b-it",
DownloadSource.OPENMIND: "LlamaFactory/gemma-2-9b-it",
},
"Gemma-2-27B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-2-27b-it",
@ -573,6 +585,7 @@ register_model_group(
"GLM-4-9B-Chat": {
DownloadSource.DEFAULT: "THUDM/glm-4-9b-chat",
DownloadSource.MODELSCOPE: "ZhipuAI/glm-4-9b-chat",
DownloadSource.OPENMIND: "LlamaFactory/glm-4-9b-chat",
},
"GLM-4-9B-1M-Chat": {
DownloadSource.DEFAULT: "THUDM/glm-4-9b-chat-1m",
@ -583,6 +596,33 @@ register_model_group(
)
register_model_group(
models={
"Index-1.9B-Chat": {
DownloadSource.DEFAULT: "IndexTeam/Index-1.9B-Chat",
DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B-Chat",
},
"Index-1.9B-Character-Chat": {
DownloadSource.DEFAULT: "IndexTeam/Index-1.9B-Character",
DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B-Character",
},
"Index-1.9B-Base": {
DownloadSource.DEFAULT: "IndexTeam/Index-1.9B",
DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B",
},
"Index-1.9B-Base-Pure": {
DownloadSource.DEFAULT: "IndexTeam/Index-1.9B-Pure",
DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B-Pure",
},
"Index-1.9B-Chat-32K": {
DownloadSource.DEFAULT: "IndexTeam/Index-1.9B-32K",
DownloadSource.MODELSCOPE: "IndexTeam/Index-1.9B-32K",
},
},
template="index",
)
register_model_group(
models={
"InternLM-7B": {
@ -624,16 +664,10 @@ register_model_group(
DownloadSource.DEFAULT: "internlm/internlm2-chat-20b",
DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2-chat-20b",
},
},
template="intern2",
)
register_model_group(
models={
"InternLM2.5-1.8B": {
DownloadSource.DEFAULT: "internlm/internlm2_5-1_8b",
DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-1_8b",
DownloadSource.OPENMIND: "Intern/internlm2_5-1_8b",
},
"InternLM2.5-7B": {
DownloadSource.DEFAULT: "internlm/internlm2_5-7b",
@ -642,22 +676,27 @@ register_model_group(
"InternLM2.5-20B": {
DownloadSource.DEFAULT: "internlm/internlm2_5-20b",
DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-20b",
DownloadSource.OPENMIND: "Intern/internlm2_5-20b",
},
"InternLM2.5-1.8B-Chat": {
DownloadSource.DEFAULT: "internlm/internlm2_5-1_8b-chat",
DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-1_8b-chat",
DownloadSource.OPENMIND: "Intern/internlm2_5-1_8b-chat",
},
"InternLM2.5-7B-Chat": {
DownloadSource.DEFAULT: "internlm/internlm2_5-7b-chat",
DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-7b-chat",
DownloadSource.OPENMIND: "Intern/internlm2_5-7b-chat",
},
"InternLM2.5-7B-1M-Chat": {
DownloadSource.DEFAULT: "internlm/internlm2_5-7b-chat-1m",
DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-7b-chat-1m",
DownloadSource.OPENMIND: "Intern/internlm2_5-7b-chat-1m",
},
"InternLM2.5-20B-Chat": {
DownloadSource.DEFAULT: "internlm/internlm2_5-20b-chat",
DownloadSource.MODELSCOPE: "Shanghai_AI_Laboratory/internlm2_5-20b-chat",
DownloadSource.OPENMIND: "Intern/internlm2_5-20b-chat",
},
},
template="intern2",
@ -686,19 +725,19 @@ register_model_group(
register_model_group(
models={
"LLaMA-7B": {
"Llama-7B": {
DownloadSource.DEFAULT: "huggyllama/llama-7b",
DownloadSource.MODELSCOPE: "skyline2006/llama-7b",
},
"LLaMA-13B": {
"Llama-13B": {
DownloadSource.DEFAULT: "huggyllama/llama-13b",
DownloadSource.MODELSCOPE: "skyline2006/llama-13b",
},
"LLaMA-30B": {
"Llama-30B": {
DownloadSource.DEFAULT: "huggyllama/llama-30b",
DownloadSource.MODELSCOPE: "skyline2006/llama-30b",
},
"LLaMA-65B": {
"Llama-65B": {
DownloadSource.DEFAULT: "huggyllama/llama-65b",
DownloadSource.MODELSCOPE: "skyline2006/llama-65b",
},
@ -708,27 +747,27 @@ register_model_group(
register_model_group(
models={
"LLaMA2-7B": {
"Llama-2-7B": {
DownloadSource.DEFAULT: "meta-llama/Llama-2-7b-hf",
DownloadSource.MODELSCOPE: "modelscope/Llama-2-7b-ms",
},
"LLaMA2-13B": {
"Llama-2-13B": {
DownloadSource.DEFAULT: "meta-llama/Llama-2-13b-hf",
DownloadSource.MODELSCOPE: "modelscope/Llama-2-13b-ms",
},
"LLaMA2-70B": {
"Llama-2-70B": {
DownloadSource.DEFAULT: "meta-llama/Llama-2-70b-hf",
DownloadSource.MODELSCOPE: "modelscope/Llama-2-70b-ms",
},
"LLaMA2-7B-Chat": {
"Llama-2-7B-Chat": {
DownloadSource.DEFAULT: "meta-llama/Llama-2-7b-chat-hf",
DownloadSource.MODELSCOPE: "modelscope/Llama-2-7b-chat-ms",
},
"LLaMA2-13B-Chat": {
"Llama-2-13B-Chat": {
DownloadSource.DEFAULT: "meta-llama/Llama-2-13b-chat-hf",
DownloadSource.MODELSCOPE: "modelscope/Llama-2-13b-chat-ms",
},
"LLaMA2-70B-Chat": {
"Llama-2-70B-Chat": {
DownloadSource.DEFAULT: "meta-llama/Llama-2-70b-chat-hf",
DownloadSource.MODELSCOPE: "modelscope/Llama-2-70b-chat-ms",
},
@ -739,60 +778,78 @@ register_model_group(
register_model_group(
models={
"LLaMA3-8B": {
"Llama-3-8B": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3-8B",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-8B",
},
"LLaMA3-70B": {
"Llama-3-70B": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3-70B",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-70B",
},
"LLaMA3-8B-Instruct": {
"Llama-3-8B-Instruct": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3-8B-Instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-8B-Instruct",
},
"LLaMA3-70B-Instruct": {
"Llama-3-70B-Instruct": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3-70B-Instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3-70B-Instruct",
},
"LLaMA3-8B-Chinese-Chat": {
"Llama-3-8B-Chinese-Chat": {
DownloadSource.DEFAULT: "shenzhi-wang/Llama3-8B-Chinese-Chat",
DownloadSource.MODELSCOPE: "LLM-Research/Llama3-8B-Chinese-Chat",
DownloadSource.OPENMIND: "LlamaFactory/Llama3-Chinese-8B-Instruct",
},
"LLaMA3-70B-Chinese-Chat": {
"Llama-3-70B-Chinese-Chat": {
DownloadSource.DEFAULT: "shenzhi-wang/Llama3-70B-Chinese-Chat",
},
},
template="llama3",
)
register_model_group(
models={
"LLaMA3.1-8B": {
"Llama-3.1-8B": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-8B",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-8B",
},
"LLaMA3.1-70B": {
"Llama-3.1-70B": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-70B",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-70B",
},
"LLaMA3.1-405B": {
"Llama-3.1-405B": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-405B",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-405B",
},
"LLaMA3.1-8B-Instruct": {
"Llama-3.1-8B-Instruct": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-8B-Instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-8B-Instruct",
},
"LLaMA3.1-70B-Instruct": {
"Llama-3.1-70B-Instruct": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-70B-Instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-70B-Instruct",
},
"LLaMA3.1-405B-Instruct": {
"Llama-3.1-405B-Instruct": {
DownloadSource.DEFAULT: "meta-llama/Meta-Llama-3.1-405B-Instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Meta-Llama-3.1-405B-Instruct",
},
"Llama-3.1-8B-Chinese-Chat": {
DownloadSource.DEFAULT: "shenzhi-wang/Llama3.1-8B-Chinese-Chat",
DownloadSource.MODELSCOPE: "XD_AI/Llama3.1-8B-Chinese-Chat",
},
"Llama-3.1-70B-Chinese-Chat": {
DownloadSource.DEFAULT: "shenzhi-wang/Llama3.1-70B-Chinese-Chat",
DownloadSource.MODELSCOPE: "XD_AI/Llama3.1-70B-Chinese-Chat",
},
"Llama-3.2-1B": {
DownloadSource.DEFAULT: "meta-llama/Llama-3.2-1B",
DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-1B",
},
"Llama-3.2-3B": {
DownloadSource.DEFAULT: "meta-llama/Llama-3.2-3B",
DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-3B",
},
"Llama-3.2-1B-Instruct": {
DownloadSource.DEFAULT: "meta-llama/Llama-3.2-1B-Instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-1B-Instruct",
},
"Llama-3.2-3B-Instruct": {
DownloadSource.DEFAULT: "meta-llama/Llama-3.2-3B-Instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Llama-3.2-3B-Instruct",
},
},
template="llama3",
)
@ -800,11 +857,13 @@ register_model_group(
register_model_group(
models={
"LLaVA1.5-7B-Chat": {
"LLaVA-1.5-7B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llava-1.5-7b-hf",
DownloadSource.MODELSCOPE: "swift/llava-1.5-7b-hf",
},
"LLaVA1.5-13B-Chat": {
"LLaVA-1.5-13B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llava-1.5-13b-hf",
DownloadSource.MODELSCOPE: "swift/llava-1.5-13b-hf",
},
},
template="llava",
@ -812,6 +871,117 @@ register_model_group(
)
register_model_group(
models={
"LLaVA-NeXT-7B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llava-v1.6-vicuna-7b-hf",
DownloadSource.MODELSCOPE: "swift/llava-v1.6-vicuna-7b-hf",
},
"LLaVA-NeXT-13B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llava-v1.6-vicuna-13b-hf",
DownloadSource.MODELSCOPE: "swift/llava-v1.6-vicuna-13b-hf",
},
},
template="llava_next",
vision=True,
)
register_model_group(
models={
"LLaVA-NeXT-Mistral-7B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llava-v1.6-mistral-7b-hf",
DownloadSource.MODELSCOPE: "swift/llava-v1.6-mistral-7b-hf",
},
},
template="llava_next_mistral",
vision=True,
)
register_model_group(
models={
"LLaVA-NeXT-Llama3-8B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llama3-llava-next-8b-hf",
DownloadSource.MODELSCOPE: "swift/llama3-llava-next-8b-hf",
},
},
template="llava_next_llama3",
vision=True,
)
register_model_group(
models={
"LLaVA-NeXT-34B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llava-v1.6-34b-hf",
DownloadSource.MODELSCOPE: "LLM-Research/llava-v1.6-34b-hf",
},
},
template="llava_next_yi",
vision=True,
)
register_model_group(
models={
"LLaVA-NeXT-72B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llava-next-72b-hf",
DownloadSource.MODELSCOPE: "AI-ModelScope/llava-next-72b-hf",
},
"LLaVA-NeXT-110B-Chat": {
DownloadSource.DEFAULT: "llava-hf/llava-next-110b-hf",
DownloadSource.MODELSCOPE: "AI-ModelScope/llava-next-110b-hf",
},
},
template="llava_next_qwen",
vision=True,
)
register_model_group(
models={
"LLaVA-NeXT-Video-7B-Chat": {
DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-7B-hf",
DownloadSource.MODELSCOPE: "swift/LLaVA-NeXT-Video-7B-hf",
},
"LLaVA-NeXT-Video-7B-DPO-Chat": {
DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-7B-DPO-hf",
DownloadSource.MODELSCOPE: "swift/LLaVA-NeXT-Video-7B-DPO-hf",
},
},
template="llava_next_video",
vision=True,
)
register_model_group(
models={
"LLaVA-NeXT-Video-7B-32k-Chat": {
DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-7B-32K-hf",
DownloadSource.MODELSCOPE: "swift/LLaVA-NeXT-Video-7B-32K-hf",
},
},
template="llava_next_video_mistral",
vision=True,
)
register_model_group(
models={
"LLaVA-NeXT-Video-34B-Chat": {
DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-34B-hf",
DownloadSource.MODELSCOPE: "swift/LLaVA-NeXT-Video-34B-hf",
},
"LLaVA-NeXT-Video-34B-DPO-Chat": {
DownloadSource.DEFAULT: "llava-hf/LLaVA-NeXT-Video-34B-DPO-hf",
},
},
template="llava_next_video_yi",
vision=True,
)
register_model_group(
models={
"MiniCPM-2B-SFT-Chat": {
@ -832,6 +1002,7 @@ register_model_group(
"MiniCPM3-4B-Chat": {
DownloadSource.DEFAULT: "openbmb/MiniCPM3-4B",
DownloadSource.MODELSCOPE: "OpenBMB/MiniCPM3-4B",
DownloadSource.OPENMIND: "LlamaFactory/MiniCPM3-4B",
},
},
template="cpm3",
@ -1005,27 +1176,27 @@ register_model_group(
register_model_group(
models={
"Phi3-4B-4k-Instruct": {
"Phi-3-4B-4k-Instruct": {
DownloadSource.DEFAULT: "microsoft/Phi-3-mini-4k-instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-mini-4k-instruct",
},
"Phi3-4B-128k-Instruct": {
"Phi-3-4B-128k-Instruct": {
DownloadSource.DEFAULT: "microsoft/Phi-3-mini-128k-instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-mini-128k-instruct",
},
"Phi3-7B-8k-Instruct": {
"Phi-3-7B-8k-Instruct": {
DownloadSource.DEFAULT: "microsoft/Phi-3-small-8k-instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-small-8k-instruct",
},
"Phi3-7B-128k-Instruct": {
"Phi-3-7B-128k-Instruct": {
DownloadSource.DEFAULT: "microsoft/Phi-3-small-128k-instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-small-128k-instruct",
},
"Phi3-14B-8k-Instruct": {
"Phi-3-14B-8k-Instruct": {
DownloadSource.DEFAULT: "microsoft/Phi-3-medium-4k-instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-medium-4k-instruct",
},
"Phi3-14B-128k-Instruct": {
"Phi-3-14B-128k-Instruct": {
DownloadSource.DEFAULT: "microsoft/Phi-3-medium-128k-instruct",
DownloadSource.MODELSCOPE: "LLM-Research/Phi-3-medium-128k-instruct",
},
@ -1034,6 +1205,18 @@ register_model_group(
)
register_model_group(
models={
"Pixtral-12B-Chat": {
DownloadSource.DEFAULT: "mistral-community/pixtral-12b",
DownloadSource.MODELSCOPE: "AI-ModelScope/pixtral-12b",
}
},
template="pixtral",
vision=True,
)
register_model_group(
models={
"Qwen-1.8B": {
@ -1068,35 +1251,35 @@ register_model_group(
DownloadSource.DEFAULT: "Qwen/Qwen-72B-Chat",
DownloadSource.MODELSCOPE: "qwen/Qwen-72B-Chat",
},
"Qwen-1.8B-int8-Chat": {
"Qwen-1.8B-Chat-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen-1_8B-Chat-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen-1_8B-Chat-Int8",
},
"Qwen-1.8B-int4-Chat": {
"Qwen-1.8B-Chat-Int4": {
DownloadSource.DEFAULT: "Qwen/Qwen-1_8B-Chat-Int4",
DownloadSource.MODELSCOPE: "qwen/Qwen-1_8B-Chat-Int4",
},
"Qwen-7B-int8-Chat": {
"Qwen-7B-Chat-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen-7B-Chat-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen-7B-Chat-Int8",
},
"Qwen-7B-int4-Chat": {
"Qwen-7B-Chat-Int4": {
DownloadSource.DEFAULT: "Qwen/Qwen-7B-Chat-Int4",
DownloadSource.MODELSCOPE: "qwen/Qwen-7B-Chat-Int4",
},
"Qwen-14B-int8-Chat": {
"Qwen-14B-Chat-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen-14B-Chat-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen-14B-Chat-Int8",
},
"Qwen-14B-int4-Chat": {
"Qwen-14B-Chat-Int4": {
DownloadSource.DEFAULT: "Qwen/Qwen-14B-Chat-Int4",
DownloadSource.MODELSCOPE: "qwen/Qwen-14B-Chat-Int4",
},
"Qwen-72B-int8-Chat": {
"Qwen-72B-Chat-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen-72B-Chat-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen-72B-Chat-Int8",
},
"Qwen-72B-int4-Chat": {
"Qwen-72B-Chat-Int4": {
DownloadSource.DEFAULT: "Qwen/Qwen-72B-Chat-Int4",
DownloadSource.MODELSCOPE: "qwen/Qwen-72B-Chat-Int4",
},
@ -1179,75 +1362,75 @@ register_model_group(
DownloadSource.DEFAULT: "Qwen/Qwen1.5-MoE-A2.7B-Chat",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-MoE-A2.7B-Chat",
},
"Qwen1.5-0.5B-int8-Chat": {
"Qwen1.5-0.5B-Chat-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-0.5B-Chat-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-0.5B-Chat-GPTQ-Int8",
},
"Qwen1.5-0.5B-int4-Chat": {
"Qwen1.5-0.5B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-0.5B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-0.5B-Chat-AWQ",
},
"Qwen1.5-1.8B-int8-Chat": {
"Qwen1.5-1.8B-Chat-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-1.8B-Chat-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-1.8B-Chat-GPTQ-Int8",
},
"Qwen1.5-1.8B-int4-Chat": {
"Qwen1.5-1.8B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-1.8B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-1.8B-Chat-AWQ",
},
"Qwen1.5-4B-int8-Chat": {
"Qwen1.5-4B-Chat-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-4B-Chat-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-4B-Chat-GPTQ-Int8",
},
"Qwen1.5-4B-int4-Chat": {
"Qwen1.5-4B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-4B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-4B-Chat-AWQ",
},
"Qwen1.5-7B-int8-Chat": {
"Qwen1.5-7B-Chat-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-7B-Chat-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-7B-Chat-GPTQ-Int8",
},
"Qwen1.5-7B-int4-Chat": {
"Qwen1.5-7B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-7B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-7B-Chat-AWQ",
},
"Qwen1.5-14B-int8-Chat": {
"Qwen1.5-14B-Chat-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-14B-Chat-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-14B-Chat-GPTQ-Int8",
},
"Qwen1.5-14B-int4-Chat": {
"Qwen1.5-14B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-14B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-14B-Chat-AWQ",
},
"Qwen1.5-32B-int4-Chat": {
"Qwen1.5-32B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-32B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-32B-Chat-AWQ",
},
"Qwen1.5-72B-int8-Chat": {
"Qwen1.5-72B-Chat-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-72B-Chat-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-72B-Chat-GPTQ-Int8",
},
"Qwen1.5-72B-int4-Chat": {
"Qwen1.5-72B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-72B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-72B-Chat-AWQ",
},
"Qwen1.5-110B-int4-Chat": {
"Qwen1.5-110B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-110B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-110B-Chat-AWQ",
},
"Qwen1.5-MoE-A2.7B-int4-Chat": {
"Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4": {
DownloadSource.DEFAULT: "Qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4",
DownloadSource.MODELSCOPE: "qwen/Qwen1.5-MoE-A2.7B-Chat-GPTQ-Int4",
},
"Qwen1.5-Code-7B": {
"CodeQwen1.5-7B": {
DownloadSource.DEFAULT: "Qwen/CodeQwen1.5-7B",
DownloadSource.MODELSCOPE: "qwen/CodeQwen1.5-7B",
},
"Qwen1.5-Code-7B-Chat": {
"CodeQwen1.5-7B-Chat": {
DownloadSource.DEFAULT: "Qwen/CodeQwen1.5-7B-Chat",
DownloadSource.MODELSCOPE: "qwen/CodeQwen1.5-7B-Chat",
},
"Qwen1.5-Code-7B-int4-Chat": {
"CodeQwen1.5-7B-Chat-AWQ": {
DownloadSource.DEFAULT: "Qwen/CodeQwen1.5-7B-Chat-AWQ",
DownloadSource.MODELSCOPE: "qwen/CodeQwen1.5-7B-Chat-AWQ",
},
@ -1281,14 +1464,17 @@ register_model_group(
"Qwen2-0.5B-Instruct": {
DownloadSource.DEFAULT: "Qwen/Qwen2-0.5B-Instruct",
DownloadSource.MODELSCOPE: "qwen/Qwen2-0.5B-Instruct",
DownloadSource.OPENMIND: "LlamaFactory/Qwen2-0.5B-Instruct",
},
"Qwen2-1.5B-Instruct": {
DownloadSource.DEFAULT: "Qwen/Qwen2-1.5B-Instruct",
DownloadSource.MODELSCOPE: "qwen/Qwen2-1.5B-Instruct",
DownloadSource.OPENMIND: "LlamaFactory/Qwen2-1.5B-Instruct",
},
"Qwen2-7B-Instruct": {
DownloadSource.DEFAULT: "Qwen/Qwen2-7B-Instruct",
DownloadSource.MODELSCOPE: "qwen/Qwen2-7B-Instruct",
DownloadSource.OPENMIND: "LlamaFactory/Qwen2-7B-Instruct",
},
"Qwen2-72B-Instruct": {
DownloadSource.DEFAULT: "Qwen/Qwen2-72B-Instruct",
@ -1568,51 +1754,53 @@ register_model_group(
register_model_group(
models={
"Qwen2VL-2B-Instruct": {
"Qwen2-VL-2B-Instruct": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B-Instruct",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-2B-Instruct",
DownloadSource.OPENMIND: "LlamaFactory/Qwen2-VL-2B-Instruct",
},
"Qwen2VL-7B-Instruct": {
"Qwen2-VL-7B-Instruct": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B-Instruct",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-7B-Instruct",
DownloadSource.OPENMIND: "LlamaFactory/Qwen2-VL-7B-Instruct",
},
"Qwen2VL-72B-Instruct": {
"Qwen2-VL-72B-Instruct": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B-Instruct",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-72B-Instruct",
},
"Qwen2VL-2B-Instruct-GPTQ-Int8": {
"Qwen2-VL-2B-Instruct-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-2B-Instruct-GPTQ-Int8",
},
"Qwen2VL-2B-Instruct-GPTQ-Int4": {
"Qwen2-VL-2B-Instruct-GPTQ-Int4": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4",
},
"Qwen2VL-2B-Instruct-AWQ": {
"Qwen2-VL-2B-Instruct-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-2B-Instruct-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-2B-Instruct-AWQ",
},
"Qwen2VL-7B-Instruct-GPTQ-Int8": {
"Qwen2-VL-7B-Instruct-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-7B-Instruct-GPTQ-Int8",
},
"Qwen2VL-7B-Instruct-GPTQ-Int4": {
"Qwen2-VL-7B-Instruct-GPTQ-Int4": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B-Instruct-GPTQ-Int4",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-7B-Instruct-GPTQ-Int4",
},
"Qwen2VL-7B-Instruct-AWQ": {
"Qwen2-VL-7B-Instruct-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-7B-Instruct-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-7B-Instruct-AWQ",
},
"Qwen2VL-72B-Instruct-GPTQ-Int8": {
"Qwen2-VL-72B-Instruct-GPTQ-Int8": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B-Instruct-GPTQ-Int8",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-72B-Instruct-GPTQ-Int8",
},
"Qwen2VL-72B-Instruct-GPTQ-Int4": {
"Qwen2-VL-72B-Instruct-GPTQ-Int4": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B-Instruct-GPTQ-Int4",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-72B-Instruct-GPTQ-Int4",
},
"Qwen2VL-72B-Instruct-AWQ": {
"Qwen2-VL-72B-Instruct-AWQ": {
DownloadSource.DEFAULT: "Qwen/Qwen2-VL-72B-Instruct-AWQ",
DownloadSource.MODELSCOPE: "qwen/Qwen2-VL-72B-Instruct-AWQ",
},
@ -1673,10 +1861,12 @@ register_model_group(
"TeleChat-7B-Chat": {
DownloadSource.DEFAULT: "Tele-AI/telechat-7B",
DownloadSource.MODELSCOPE: "TeleAI/telechat-7B",
DownloadSource.OPENMIND: "TeleAI/TeleChat-7B-pt",
},
"TeleChat-12B-Chat": {
DownloadSource.DEFAULT: "Tele-AI/TeleChat-12B",
DownloadSource.MODELSCOPE: "TeleAI/TeleChat-12B",
DownloadSource.OPENMIND: "TeleAI/TeleChat-12B-pt",
},
"TeleChat-12B-v2-Chat": {
DownloadSource.DEFAULT: "Tele-AI/TeleChat-12B-v2",
@ -1689,11 +1879,11 @@ register_model_group(
register_model_group(
models={
"Vicuna1.5-7B-Chat": {
"Vicuna-v1.5-7B-Chat": {
DownloadSource.DEFAULT: "lmsys/vicuna-7b-v1.5",
DownloadSource.MODELSCOPE: "Xorbits/vicuna-7b-v1.5",
},
"Vicuna1.5-13B-Chat": {
"Vicuna-v1.5-13B-Chat": {
DownloadSource.DEFAULT: "lmsys/vicuna-13b-v1.5",
DownloadSource.MODELSCOPE: "Xorbits/vicuna-13b-v1.5",
},
@ -1702,6 +1892,17 @@ register_model_group(
)
register_model_group(
models={
"Video-LLaVA-7B-Chat": {
DownloadSource.DEFAULT: "LanguageBind/Video-LLaVA-7B-hf",
},
},
template="video_llava",
vision=True,
)
register_model_group(
models={
"XuanYuan-6B": {
@ -1712,7 +1913,7 @@ register_model_group(
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B",
},
"XuanYuan-2-70B": {
"XuanYuan2-70B": {
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B",
},
@ -1724,31 +1925,31 @@ register_model_group(
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B-Chat",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B-Chat",
},
"XuanYuan-2-70B-Chat": {
"XuanYuan2-70B-Chat": {
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B-Chat",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B-Chat",
},
"XuanYuan-6B-int8-Chat": {
"XuanYuan-6B-Chat-8bit": {
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-6B-Chat-8bit",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-6B-Chat-8bit",
},
"XuanYuan-6B-int4-Chat": {
"XuanYuan-6B-Chat-4bit": {
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-6B-Chat-4bit",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-6B-Chat-4bit",
},
"XuanYuan-70B-int8-Chat": {
"XuanYuan-70B-Chat-8bit": {
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B-Chat-8bit",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B-Chat-8bit",
},
"XuanYuan-70B-int4-Chat": {
"XuanYuan-70B-Chat-4bit": {
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan-70B-Chat-4bit",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan-70B-Chat-4bit",
},
"XuanYuan-2-70B-int8-Chat": {
"XuanYuan2-70B-Chat-8bit": {
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B-Chat-8bit",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B-Chat-8bit",
},
"XuanYuan-2-70B-int4-Chat": {
"XuanYuan2-70B-Chat-4bit": {
DownloadSource.DEFAULT: "Duxiaoman-DI/XuanYuan2-70B-Chat-4bit",
DownloadSource.MODELSCOPE: "Duxiaoman-DI/XuanYuan2-70B-Chat-4bit",
},
@ -1853,19 +2054,19 @@ register_model_group(
DownloadSource.DEFAULT: "01-ai/Yi-34B-Chat",
DownloadSource.MODELSCOPE: "01ai/Yi-34B-Chat",
},
"Yi-6B-int8-Chat": {
"Yi-6B-Chat-8bits": {
DownloadSource.DEFAULT: "01-ai/Yi-6B-Chat-8bits",
DownloadSource.MODELSCOPE: "01ai/Yi-6B-Chat-8bits",
},
"Yi-6B-int4-Chat": {
"Yi-6B-Chat-4bits": {
DownloadSource.DEFAULT: "01-ai/Yi-6B-Chat-4bits",
DownloadSource.MODELSCOPE: "01ai/Yi-6B-Chat-4bits",
},
"Yi-34B-int8-Chat": {
"Yi-34B-Chat-8bits": {
DownloadSource.DEFAULT: "01-ai/Yi-34B-Chat-8bits",
DownloadSource.MODELSCOPE: "01ai/Yi-34B-Chat-8bits",
},
"Yi-34B-int4-Chat": {
"Yi-34B-Chat-4bits": {
DownloadSource.DEFAULT: "01-ai/Yi-34B-Chat-4bits",
DownloadSource.MODELSCOPE: "01ai/Yi-34B-Chat-4bits",
},
@ -1884,6 +2085,7 @@ register_model_group(
"Yi-1.5-6B-Chat": {
DownloadSource.DEFAULT: "01-ai/Yi-1.5-6B-Chat",
DownloadSource.MODELSCOPE: "01ai/Yi-1.5-6B-Chat",
DownloadSource.OPENMIND: "LlamaFactory/Yi-1.5-6B-Chat",
},
"Yi-1.5-9B-Chat": {
DownloadSource.DEFAULT: "01-ai/Yi-1.5-9B-Chat",
@ -1916,10 +2118,10 @@ register_model_group(
register_model_group(
models={
"YiVL-6B-Chat": {
"Yi-VL-6B-Chat": {
DownloadSource.DEFAULT: "BUAADreamer/Yi-VL-6B-hf",
},
"YiVL-34B-Chat": {
"Yi-VL-34B-Chat": {
DownloadSource.DEFAULT: "BUAADreamer/Yi-VL-34B-hf",
},
},

2
src/llamafactory/extras/env.py
View File

@ -72,4 +72,4 @@ def print_env() -> None:
except Exception:
pass
print("\n" + "\n".join(["- {}: {}".format(key, value) for key, value in info.items()]) + "\n")
print("\n" + "\n".join([f"- {key}: {value}" for key, value in info.items()]) + "\n")

74
src/llamafactory/extras/logging.py
View File

@ -20,6 +20,7 @@ import os
import sys
import threading
from concurrent.futures import ThreadPoolExecutor
from functools import lru_cache
from typing import Optional
from .constants import RUNNING_LOG
@ -37,12 +38,11 @@ class LoggerHandler(logging.Handler):
def __init__(self, output_dir: str) -> None:
super().__init__()
formatter = logging.Formatter(
fmt="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S"
self._formatter = logging.Formatter(
fmt="[%(levelname)s|%(asctime)s] %(filename)s:%(lineno)s >> %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
self.setLevel(logging.INFO)
self.setFormatter(formatter)
os.makedirs(output_dir, exist_ok=True)
self.running_log = os.path.join(output_dir, RUNNING_LOG)
if os.path.exists(self.running_log):
@ -58,7 +58,7 @@ class LoggerHandler(logging.Handler):
if record.name == "httpx":
return
log_entry = self.format(record)
log_entry = self._formatter.format(record)
self.thread_pool.submit(self._write_log, log_entry)
def close(self) -> None:
@ -66,6 +66,21 @@ class LoggerHandler(logging.Handler):
return super().close()
class _Logger(logging.Logger):
r"""
A logger that supports info_rank0 and warning_once.
"""
def info_rank0(self, *args, **kwargs) -> None:
self.info(*args, **kwargs)
def warning_rank0(self, *args, **kwargs) -> None:
self.warning(*args, **kwargs)
def warning_once(self, *args, **kwargs) -> None:
self.warning(*args, **kwargs)
def _get_default_logging_level() -> "logging._Level":
r"""
Returns the default logging level.
@ -75,7 +90,7 @@ def _get_default_logging_level() -> "logging._Level":
if env_level_str.upper() in logging._nameToLevel:
return logging._nameToLevel[env_level_str.upper()]
else:
raise ValueError("Unknown logging level: {}.".format(env_level_str))
raise ValueError(f"Unknown logging level: {env_level_str}.")
return _default_log_level
@ -84,7 +99,7 @@ def _get_library_name() -> str:
return __name__.split(".")[0]
def _get_library_root_logger() -> "logging.Logger":
def _get_library_root_logger() -> "_Logger":
return logging.getLogger(_get_library_name())
@ -95,12 +110,12 @@ def _configure_library_root_logger() -> None:
global _default_handler
with _thread_lock:
if _default_handler:
if _default_handler: # already configured
return
formatter = logging.Formatter(
fmt="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
fmt="[%(levelname)s|%(asctime)s] %(name)s:%(lineno)s >> %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
_default_handler = logging.StreamHandler(sys.stdout)
_default_handler.setFormatter(formatter)
@ -110,7 +125,7 @@ def _configure_library_root_logger() -> None:
library_root_logger.propagate = False
def get_logger(name: Optional[str] = None) -> "logging.Logger":
def get_logger(name: Optional[str] = None) -> "_Logger":
r"""
Returns a logger with the specified name. It it not supposed to be accessed externally.
"""
@ -119,3 +134,40 @@ def get_logger(name: Optional[str] = None) -> "logging.Logger":
_configure_library_root_logger()
return logging.getLogger(name)
def add_handler(handler: "logging.Handler") -> None:
r"""
Adds a handler to the root logger.
"""
_configure_library_root_logger()
_get_library_root_logger().addHandler(handler)
def remove_handler(handler: logging.Handler) -> None:
r"""
Removes a handler to the root logger.
"""
_configure_library_root_logger()
_get_library_root_logger().removeHandler(handler)
def info_rank0(self: "logging.Logger", *args, **kwargs) -> None:
if int(os.getenv("LOCAL_RANK", "0")) == 0:
self.info(*args, **kwargs)
def warning_rank0(self: "logging.Logger", *args, **kwargs) -> None:
if int(os.getenv("LOCAL_RANK", "0")) == 0:
self.warning(*args, **kwargs)
@lru_cache(None)
def warning_once(self: "logging.Logger", *args, **kwargs) -> None:
if int(os.getenv("LOCAL_RANK", "0")) == 0:
self.warning(*args, **kwargs)
logging.Logger.info_rank0 = info_rank0
logging.Logger.warning_rank0 = warning_rank0
logging.Logger.warning_once = warning_once

45
src/llamafactory/extras/misc.py
View File

@ -32,7 +32,7 @@ from transformers.utils import (
)
from transformers.utils.versions import require_version
from .logging import get_logger
from . import logging
_is_fp16_available = is_torch_npu_available() or is_torch_cuda_available()
@ -48,7 +48,7 @@ if TYPE_CHECKING:
from ..hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class AverageMeter:
@ -76,12 +76,12 @@ 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.")
if os.getenv("DISABLE_VERSION_CHECK", "0").lower() in ["true", "1"]:
logger.warning_once("Version checking has been disabled, may lead to unexpected behaviors.")
else:
require_version("transformers>=4.41.2,<=4.45.0", "To fix: pip install transformers>=4.41.2,<=4.45.0")
require_version("datasets>=2.16.0,<=2.21.0", "To fix: pip install datasets>=2.16.0,<=2.21.0")
require_version("accelerate>=0.30.1,<=0.34.2", "To fix: pip install accelerate>=0.30.1,<=0.34.2")
require_version("transformers>=4.41.2,<=4.46.1", "To fix: pip install transformers>=4.41.2,<=4.46.1")
require_version("datasets>=2.16.0,<=3.0.2", "To fix: pip install datasets>=2.16.0,<=3.0.2")
require_version("accelerate>=0.34.0,<=1.0.1", "To fix: pip install accelerate>=0.34.0,<=1.0.1")
require_version("peft>=0.11.1,<=0.12.0", "To fix: pip install peft>=0.11.1,<=0.12.0")
require_version("trl>=0.8.6,<=0.9.6", "To fix: pip install trl>=0.8.6,<=0.9.6")
@ -231,18 +231,35 @@ def torch_gc() -> None:
torch.cuda.empty_cache()
def try_download_model_from_ms(model_args: "ModelArguments") -> str:
if not use_modelscope() or os.path.exists(model_args.model_name_or_path):
def try_download_model_from_other_hub(model_args: "ModelArguments") -> str:
if (not use_modelscope() and not use_openmind()) or os.path.exists(model_args.model_name_or_path):
return model_args.model_name_or_path
try:
from modelscope import snapshot_download
if use_modelscope():
require_version("modelscope>=1.11.0", "To fix: pip install modelscope>=1.11.0")
from modelscope import snapshot_download # type: ignore
revision = "master" if model_args.model_revision == "main" else model_args.model_revision
return snapshot_download(model_args.model_name_or_path, revision=revision, cache_dir=model_args.cache_dir)
except ImportError:
raise ImportError("Please install modelscope via `pip install modelscope -U`")
return snapshot_download(
model_args.model_name_or_path,
revision=revision,
cache_dir=model_args.cache_dir,
)
if use_openmind():
require_version("openmind>=0.8.0", "To fix: pip install openmind>=0.8.0")
from openmind.utils.hub import snapshot_download # type: ignore
return snapshot_download(
model_args.model_name_or_path,
revision=model_args.model_revision,
cache_dir=model_args.cache_dir,
)
def use_modelscope() -> bool:
return os.environ.get("USE_MODELSCOPE_HUB", "0").lower() in ["true", "1"]
def use_openmind() -> bool:
return os.environ.get("USE_OPENMIND_HUB", "0").lower() in ["true", "1"]

5
src/llamafactory/extras/packages.py
View File

@ -79,6 +79,11 @@ def is_transformers_version_greater_than_4_43():
return _get_package_version("transformers") >= version.parse("4.43.0")
@lru_cache
def is_transformers_version_equal_to_4_46():
return version.parse("4.46.0") <= _get_package_version("transformers") <= version.parse("4.46.1")
def is_uvicorn_available():
return _is_package_available("uvicorn")

10
src/llamafactory/extras/ploting.py
View File

@ -19,7 +19,7 @@ from typing import Any, Dict, List
from transformers.trainer import TRAINER_STATE_NAME
from .logging import get_logger
from . import logging
from .packages import is_matplotlib_available
@ -28,7 +28,7 @@ if is_matplotlib_available():
import matplotlib.pyplot as plt
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def smooth(scalars: List[float]) -> List[float]:
@ -75,7 +75,7 @@ def plot_loss(save_dictionary: str, keys: List[str] = ["loss"]) -> None:
Plots loss curves and saves the image.
"""
plt.switch_backend("agg")
with open(os.path.join(save_dictionary, TRAINER_STATE_NAME), "r", encoding="utf-8") as f:
with open(os.path.join(save_dictionary, TRAINER_STATE_NAME), encoding="utf-8") as f:
data = json.load(f)
for key in keys:
@ -86,13 +86,13 @@ def plot_loss(save_dictionary: str, keys: List[str] = ["loss"]) -> None:
metrics.append(data["log_history"][i][key])
if len(metrics) == 0:
logger.warning(f"No metric {key} to plot.")
logger.warning_rank0(f"No metric {key} to plot.")
continue
plt.figure()
plt.plot(steps, metrics, color="#1f77b4", alpha=0.4, label="original")
plt.plot(steps, smooth(metrics), color="#1f77b4", label="smoothed")
plt.title("training {} of {}".format(key, save_dictionary))
plt.title(f"training {key} of {save_dictionary}")
plt.xlabel("step")
plt.ylabel(key)
plt.legend()

15
src/llamafactory/hparams/data_args.py
View File

@ -41,6 +41,10 @@ class DataArguments:
default="data",
metadata={"help": "Path to the folder containing the datasets."},
)
image_dir: Optional[str] = field(
default=None,
metadata={"help": "Path to the folder containing the images or videos. Defaults to `dataset_dir`."},
)
cutoff_len: int = field(
default=1024,
metadata={"help": "The cutoff length of the tokenized inputs in the dataset."},
@ -111,7 +115,13 @@ class DataArguments:
)
tokenized_path: Optional[str] = field(
default=None,
metadata={"help": "Path to save or load the tokenized datasets."},
metadata={
"help": (
"Path to save or load the tokenized datasets. "
"If tokenized_path not exists, it will save the tokenized datasets. "
"If tokenized_path exists, it will load the tokenized datasets."
)
},
)
def __post_init__(self):
@ -123,6 +133,9 @@ class DataArguments:
self.dataset = split_arg(self.dataset)
self.eval_dataset = split_arg(self.eval_dataset)
if self.image_dir is None:
self.image_dir = self.dataset_dir
if self.dataset is None and self.val_size > 1e-6:
raise ValueError("Cannot specify `val_size` if `dataset` is None.")

34
src/llamafactory/hparams/model_args.py
View File

@ -15,7 +15,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import asdict, dataclass, field, fields
from dataclasses import dataclass, field, fields
from typing import Any, Dict, Literal, Optional, Union
import torch
@ -267,6 +267,10 @@ class ModelArguments(QuantizationArguments, ProcessorArguments, ExportArguments,
default=None,
metadata={"help": "Auth token to log in with ModelScope Hub."},
)
om_hub_token: Optional[str] = field(
default=None,
metadata={"help": "Auth token to log in with Modelers Hub."},
)
print_param_status: bool = field(
default=False,
metadata={"help": "For debugging purposes, print the status of the parameters in the model."},
@ -308,20 +312,18 @@ class ModelArguments(QuantizationArguments, ProcessorArguments, ExportArguments,
if self.export_quantization_bit is not None and self.export_quantization_dataset is None:
raise ValueError("Quantization dataset is necessary for exporting.")
def to_dict(self) -> Dict[str, Any]:
return asdict(self)
@classmethod
def copyfrom(cls, old_arg: "Self", **kwargs) -> "Self":
arg_dict = old_arg.to_dict()
arg_dict.update(**kwargs)
for attr in fields(cls):
if not attr.init:
arg_dict.pop(attr.name)
def copyfrom(cls, source: "Self", **kwargs) -> "Self":
init_args, lazy_args = {}, {}
for attr in fields(source):
if attr.init:
init_args[attr.name] = getattr(source, attr.name)
else:
lazy_args[attr.name] = getattr(source, attr.name)
new_arg = cls(**arg_dict)
new_arg.compute_dtype = old_arg.compute_dtype
new_arg.device_map = old_arg.device_map
new_arg.model_max_length = old_arg.model_max_length
new_arg.block_diag_attn = old_arg.block_diag_attn
return new_arg
init_args.update(kwargs)
result = cls(**init_args)
for name, value in lazy_args.items():
setattr(result, name, value)
return result

47
src/llamafactory/hparams/parser.py
View File

@ -15,7 +15,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
import sys
from typing import Any, Dict, Optional, Tuple
@ -29,8 +28,8 @@ from transformers.training_args import ParallelMode
from transformers.utils import is_torch_bf16_gpu_available, is_torch_npu_available
from transformers.utils.versions import require_version
from ..extras import logging
from ..extras.constants import CHECKPOINT_NAMES
from ..extras.logging import get_logger
from ..extras.misc import check_dependencies, get_current_device
from .data_args import DataArguments
from .evaluation_args import EvaluationArguments
@ -39,7 +38,7 @@ from .generating_args import GeneratingArguments
from .model_args import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
check_dependencies()
@ -57,7 +56,7 @@ def _parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = Non
if args is not None:
return parser.parse_dict(args)
if len(sys.argv) == 2 and sys.argv[1].endswith(".yaml"):
if len(sys.argv) == 2 and (sys.argv[1].endswith(".yaml") or sys.argv[1].endswith(".yml")):
return parser.parse_yaml_file(os.path.abspath(sys.argv[1]))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
@ -67,14 +66,14 @@ def _parse_args(parser: "HfArgumentParser", args: Optional[Dict[str, Any]] = Non
if unknown_args:
print(parser.format_help())
print("Got unknown args, potentially deprecated arguments: {}".format(unknown_args))
raise ValueError("Some specified arguments are not used by the HfArgumentParser: {}".format(unknown_args))
print(f"Got unknown args, potentially deprecated arguments: {unknown_args}")
raise ValueError(f"Some specified arguments are not used by the HfArgumentParser: {unknown_args}")
return (*parsed_args,)
def _set_transformers_logging(log_level: Optional[int] = logging.INFO) -> None:
transformers.utils.logging.set_verbosity(log_level)
def _set_transformers_logging() -> None:
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
@ -104,7 +103,7 @@ def _verify_model_args(
raise ValueError("Quantized model only accepts a single adapter. Merge them first.")
if data_args.template == "yi" and model_args.use_fast_tokenizer:
logger.warning("We should use slow tokenizer for the Yi models. Change `use_fast_tokenizer` to False.")
logger.warning_rank0("We should use slow tokenizer for the Yi models. Change `use_fast_tokenizer` to False.")
model_args.use_fast_tokenizer = False
@ -123,7 +122,7 @@ def _check_extra_dependencies(
require_version("mixture-of-depth>=1.1.6", "To fix: pip install mixture-of-depth>=1.1.6")
if model_args.infer_backend == "vllm":
require_version("vllm>=0.4.3,<=0.6.0", "To fix: pip install vllm>=0.4.3,<=0.6.0")
require_version("vllm>=0.4.3,<=0.6.3", "To fix: pip install vllm>=0.4.3,<=0.6.3")
if finetuning_args.use_galore:
require_version("galore_torch", "To fix: pip install galore_torch")
@ -261,7 +260,7 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
raise ValueError("Unsloth is incompatible with DeepSpeed ZeRO-3.")
if data_args.neat_packing and not data_args.packing:
logger.warning("`neat_packing` requires `packing` is True. Change `packing` to True.")
logger.warning_rank0("`neat_packing` requires `packing` is True. Change `packing` to True.")
data_args.packing = True
_verify_model_args(model_args, data_args, finetuning_args)
@ -274,22 +273,26 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
and model_args.resize_vocab
and finetuning_args.additional_target is None
):
logger.warning("Remember to add embedding layers to `additional_target` to make the added tokens trainable.")
logger.warning_rank0(
"Remember to add embedding layers to `additional_target` to make the added tokens trainable."
)
if training_args.do_train and model_args.quantization_bit is not None and (not model_args.upcast_layernorm):
logger.warning("We recommend enable `upcast_layernorm` in quantized training.")
logger.warning_rank0("We recommend enable `upcast_layernorm` in quantized training.")
if training_args.do_train and (not training_args.fp16) and (not training_args.bf16):
logger.warning("We recommend enable mixed precision training.")
logger.warning_rank0("We recommend enable mixed precision training.")
if training_args.do_train and finetuning_args.use_galore and not finetuning_args.pure_bf16:
logger.warning("Using GaLore with mixed precision training may significantly increases GPU memory usage.")
logger.warning_rank0(
"Using GaLore with mixed precision training may significantly increases GPU memory usage."
)
if (not training_args.do_train) and model_args.quantization_bit is not None:
logger.warning("Evaluating model in 4/8-bit mode may cause lower scores.")
logger.warning_rank0("Evaluating model in 4/8-bit mode may cause lower scores.")
if (not training_args.do_train) and finetuning_args.stage == "dpo" and finetuning_args.ref_model is None:
logger.warning("Specify `ref_model` for computing rewards at evaluation.")
logger.warning_rank0("Specify `ref_model` for computing rewards at evaluation.")
# Post-process training arguments
if (
@ -297,13 +300,13 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
and training_args.ddp_find_unused_parameters is None
and finetuning_args.finetuning_type == "lora"
):
logger.warning("`ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training.")
logger.warning_rank0("`ddp_find_unused_parameters` needs to be set as False for LoRA in DDP training.")
training_args.ddp_find_unused_parameters = False
if finetuning_args.stage in ["rm", "ppo"] and finetuning_args.finetuning_type in ["full", "freeze"]:
can_resume_from_checkpoint = False
if training_args.resume_from_checkpoint is not None:
logger.warning("Cannot resume from checkpoint in current stage.")
logger.warning_rank0("Cannot resume from checkpoint in current stage.")
training_args.resume_from_checkpoint = None
else:
can_resume_from_checkpoint = True
@ -323,15 +326,15 @@ def get_train_args(args: Optional[Dict[str, Any]] = None) -> _TRAIN_CLS:
if last_checkpoint is not None:
training_args.resume_from_checkpoint = last_checkpoint
logger.info("Resuming training from {}.".format(training_args.resume_from_checkpoint))
logger.info("Change `output_dir` or use `overwrite_output_dir` to avoid.")
logger.info_rank0(f"Resuming training from {training_args.resume_from_checkpoint}.")
logger.info_rank0("Change `output_dir` or use `overwrite_output_dir` to avoid.")
if (
finetuning_args.stage in ["rm", "ppo"]
and finetuning_args.finetuning_type == "lora"
and training_args.resume_from_checkpoint is not None
):
logger.warning(
logger.warning_rank0(
"Add {} to `adapter_name_or_path` to resume training from checkpoint.".format(
training_args.resume_from_checkpoint
)

32
src/llamafactory/model/adapter.py
View File

@ -20,7 +20,7 @@ from peft import LoraConfig, LoraModel, PeftModel, TaskType, get_peft_model
from transformers.integrations import is_deepspeed_zero3_enabled
from transformers.modeling_utils import is_fsdp_enabled
from ..extras.logging import get_logger
from ..extras import logging
from .model_utils.misc import find_all_linear_modules, find_expanded_modules
from .model_utils.quantization import QuantizationMethod
from .model_utils.unsloth import get_unsloth_peft_model, load_unsloth_peft_model
@ -33,7 +33,7 @@ if TYPE_CHECKING:
from ..hparams import FinetuningArguments, ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _setup_full_tuning(
@ -45,7 +45,7 @@ def _setup_full_tuning(
if not is_trainable:
return
logger.info("Fine-tuning method: Full")
logger.info_rank0("Fine-tuning method: Full")
forbidden_modules = get_forbidden_modules(model.config, finetuning_args)
for name, param in model.named_parameters():
if not any(forbidden_module in name for forbidden_module in forbidden_modules):
@ -64,7 +64,7 @@ def _setup_freeze_tuning(
if not is_trainable:
return
logger.info("Fine-tuning method: Freeze")
logger.info_rank0("Fine-tuning method: Freeze")
if hasattr(model.config, "text_config"): # composite models
config = getattr(model.config, "text_config")
else:
@ -133,7 +133,7 @@ def _setup_freeze_tuning(
else:
param.requires_grad_(False)
logger.info("Set trainable layers: {}".format(",".join(trainable_layers)))
logger.info_rank0("Set trainable layers: {}".format(",".join(trainable_layers)))
def _setup_lora_tuning(
@ -145,7 +145,7 @@ def _setup_lora_tuning(
cast_trainable_params_to_fp32: bool,
) -> "PeftModel":
if is_trainable:
logger.info("Fine-tuning method: {}".format("DoRA" if finetuning_args.use_dora else "LoRA"))
logger.info_rank0("Fine-tuning method: {}".format("DoRA" if finetuning_args.use_dora else "LoRA"))
adapter_to_resume = None
@ -182,7 +182,7 @@ def _setup_lora_tuning(
model = model.merge_and_unload()
if len(adapter_to_merge) > 0:
logger.info("Merged {} adapter(s).".format(len(adapter_to_merge)))
logger.info_rank0(f"Merged {len(adapter_to_merge)} adapter(s).")
if adapter_to_resume is not None: # resume lora training
if model_args.use_unsloth:
@ -190,7 +190,7 @@ def _setup_lora_tuning(
else:
model = PeftModel.from_pretrained(model, adapter_to_resume, is_trainable=is_trainable, **init_kwargs)
logger.info("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path)))
logger.info_rank0("Loaded adapter(s): {}".format(",".join(model_args.adapter_name_or_path)))
if is_trainable and adapter_to_resume is None: # create new lora weights while training
if len(finetuning_args.lora_target) == 1 and finetuning_args.lora_target[0] == "all":
@ -219,7 +219,7 @@ def _setup_lora_tuning(
module_names.add(name.split(".")[-1])
finetuning_args.additional_target = module_names
logger.warning("Vocab has been resized, add {} to trainable params.".format(",".join(module_names)))
logger.warning_rank0("Vocab has been resized, add {} to trainable params.".format(",".join(module_names)))
peft_kwargs = {
"r": finetuning_args.lora_rank,
@ -236,11 +236,11 @@ def _setup_lora_tuning(
else:
if finetuning_args.pissa_init:
if finetuning_args.pissa_iter == -1:
logger.info("Using PiSSA initialization.")
logger.info_rank0("Using PiSSA initialization.")
peft_kwargs["init_lora_weights"] = "pissa"
else:
logger.info("Using PiSSA initialization with FSVD steps {}.".format(finetuning_args.pissa_iter))
peft_kwargs["init_lora_weights"] = "pissa_niter_{}".format(finetuning_args.pissa_iter)
logger.info_rank0(f"Using PiSSA initialization with FSVD steps {finetuning_args.pissa_iter}.")
peft_kwargs["init_lora_weights"] = f"pissa_niter_{finetuning_args.pissa_iter}"
lora_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
@ -284,11 +284,11 @@ def init_adapter(
if not is_trainable:
pass
elif finetuning_args.pure_bf16 or finetuning_args.use_badam:
logger.info("Pure bf16 / BAdam detected, remaining trainable params in half precision.")
logger.info_rank0("Pure bf16 / BAdam detected, remaining trainable params in half precision.")
elif model_args.quantization_bit is None and (is_deepspeed_zero3_enabled() or is_fsdp_enabled()):
logger.info("ZeRO3 / FSDP detected, remaining trainable params in float32.")
logger.info_rank0("ZeRO3 / FSDP detected, remaining trainable params in float32.")
else:
logger.info("Upcasting trainable params to float32.")
logger.info_rank0("Upcasting trainable params to float32.")
cast_trainable_params_to_fp32 = True
if finetuning_args.finetuning_type == "full":
@ -300,6 +300,6 @@ def init_adapter(
config, model, model_args, finetuning_args, is_trainable, cast_trainable_params_to_fp32
)
else:
raise NotImplementedError("Unknown finetuning type: {}.".format(finetuning_args.finetuning_type))
raise NotImplementedError(f"Unknown finetuning type: {finetuning_args.finetuning_type}.")
return model

42
src/llamafactory/model/loader.py
View File

@ -18,15 +18,15 @@ import torch
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForVision2Seq, AutoProcessor, AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
from ..extras.logging import get_logger
from ..extras.misc import count_parameters, skip_check_imports, try_download_model_from_ms
from ..extras import logging
from ..extras.misc import count_parameters, skip_check_imports, try_download_model_from_other_hub
from .adapter import init_adapter
from .model_utils.liger_kernel import apply_liger_kernel
from .model_utils.misc import register_autoclass
from .model_utils.mod import convert_pretrained_model_to_mod, load_mod_pretrained_model
from .model_utils.unsloth import load_unsloth_pretrained_model
from .model_utils.valuehead import load_valuehead_params
from .model_utils.visual import get_image_seqlen
from .patcher import patch_config, patch_model, patch_tokenizer, patch_valuehead_model
from .patcher import patch_config, patch_model, patch_processor, patch_tokenizer, patch_valuehead_model
if TYPE_CHECKING:
@ -35,7 +35,7 @@ if TYPE_CHECKING:
from ..hparams import FinetuningArguments, ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
class TokenizerModule(TypedDict):
@ -50,7 +50,7 @@ def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
Note: including inplace operation of model_args.
"""
skip_check_imports()
model_args.model_name_or_path = try_download_model_from_ms(model_args)
model_args.model_name_or_path = try_download_model_from_other_hub(model_args)
return {
"trust_remote_code": True,
"cache_dir": model_args.cache_dir,
@ -61,7 +61,7 @@ def _get_init_kwargs(model_args: "ModelArguments") -> Dict[str, Any]:
def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
r"""
Loads pretrained tokenizer.
Loads pretrained tokenizer and optionally loads processor.
Note: including inplace operation of model_args.
"""
@ -82,33 +82,30 @@ def load_tokenizer(model_args: "ModelArguments") -> "TokenizerModule":
padding_side="right",
**init_kwargs,
)
except Exception as e:
raise OSError("Failed to load tokenizer.") from e
if model_args.new_special_tokens is not None:
num_added_tokens = tokenizer.add_special_tokens(
dict(additional_special_tokens=model_args.new_special_tokens),
replace_additional_special_tokens=False,
)
logger.info("Add {} to special tokens.".format(",".join(model_args.new_special_tokens)))
logger.info_rank0("Add {} to special tokens.".format(",".join(model_args.new_special_tokens)))
if num_added_tokens > 0 and not model_args.resize_vocab:
model_args.resize_vocab = True
logger.warning("New tokens have been added, changed `resize_vocab` to True.")
logger.warning_rank0("New tokens have been added, changed `resize_vocab` to True.")
patch_tokenizer(tokenizer)
try:
processor = AutoProcessor.from_pretrained(model_args.model_name_or_path, **init_kwargs)
setattr(processor, "tokenizer", tokenizer)
setattr(processor, "image_seqlen", get_image_seqlen(config))
setattr(processor, "image_resolution", model_args.image_resolution)
setattr(processor, "video_resolution", model_args.video_resolution)
setattr(processor, "video_fps", model_args.video_fps)
setattr(processor, "video_maxlen", model_args.video_maxlen)
except Exception:
patch_processor(processor, config, tokenizer, model_args)
except Exception as e:
logger.debug(f"Processor was not found: {e}.")
processor = None
# Avoid load tokenizer, see:
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/models/auto/processing_auto.py#L324
if "Processor" not in processor.__class__.__name__:
if processor is not None and "Processor" not in processor.__class__.__name__:
processor = None
return {"tokenizer": tokenizer, "processor": processor}
@ -135,6 +132,7 @@ def load_model(
init_kwargs = _get_init_kwargs(model_args)
config = load_config(model_args)
patch_config(config, tokenizer, model_args, init_kwargs, is_trainable)
apply_liger_kernel(config, model_args, is_trainable, require_logits=(finetuning_args.stage not in ["pt", "sft"]))
model = None
lazy_load = False
@ -157,7 +155,7 @@ def load_model(
load_class = AutoModelForCausalLM
if model_args.train_from_scratch:
model = load_class.from_config(config)
model = load_class.from_config(config, trust_remote_code=True)
else:
model = load_class.from_pretrained(**init_kwargs)
@ -182,7 +180,7 @@ def load_model(
vhead_params = load_valuehead_params(vhead_path, model_args)
if vhead_params is not None:
model.load_state_dict(vhead_params, strict=False)
logger.info("Loaded valuehead from checkpoint: {}".format(vhead_path))
logger.info_rank0(f"Loaded valuehead from checkpoint: {vhead_path}")
if not is_trainable:
model.requires_grad_(False)
@ -200,9 +198,9 @@ def load_model(
trainable_params, all_param, 100 * trainable_params / all_param
)
else:
param_stats = "all params: {:,}".format(all_param)
param_stats = f"all params: {all_param:,}"
logger.info(param_stats)
logger.info_rank0(param_stats)
if model_args.print_param_status:
for name, param in model.named_parameters():

27
src/llamafactory/model/model_utils/attention.py
View File

@ -17,7 +17,7 @@ from typing import TYPE_CHECKING
from transformers.utils import is_flash_attn_2_available, is_torch_sdpa_available
from transformers.utils.versions import require_version
from ...extras.logging import get_logger
from ...extras import logging
if TYPE_CHECKING:
@ -26,7 +26,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def configure_attn_implementation(
@ -37,13 +37,16 @@ def configure_attn_implementation(
if is_flash_attn_2_available():
require_version("transformers>=4.42.4", "To fix: pip install transformers>=4.42.4")
require_version("flash_attn>=2.6.3", "To fix: pip install flash_attn>=2.6.3")
logger.warning("Gemma-2 should use flash attention 2, change `flash_attn` to fa2.")
model_args.flash_attn = "fa2"
if model_args.flash_attn != "fa2":
logger.warning_rank0("Gemma-2 should use flash attention 2, change `flash_attn` to fa2.")
model_args.flash_attn = "fa2"
else:
logger.warning("Gemma-2 should use eager attention, change `flash_attn` to disabled.")
logger.warning_rank0("FlashAttention-2 is not installed, use eager attention.")
model_args.flash_attn = "disabled"
elif model_args.flash_attn == "sdpa":
logger.warning("Gemma-2 should use soft-capping attention, while the SDPA attention does not support it.")
logger.warning_rank0(
"Gemma-2 should use soft-capping attention, while the SDPA attention does not support it."
)
if model_args.flash_attn == "auto":
return
@ -53,18 +56,18 @@ def configure_attn_implementation(
elif model_args.flash_attn == "sdpa":
if not is_torch_sdpa_available():
logger.warning("torch>=2.1.1 is required for SDPA attention.")
logger.warning_rank0("torch>=2.1.1 is required for SDPA attention.")
return
requested_attn_implementation = "sdpa"
elif model_args.flash_attn == "fa2":
if not is_flash_attn_2_available():
logger.warning("FlashAttention-2 is not installed.")
logger.warning_rank0("FlashAttention-2 is not installed.")
return
requested_attn_implementation = "flash_attention_2"
else:
raise NotImplementedError("Unknown attention type: {}".format(model_args.flash_attn))
raise NotImplementedError(f"Unknown attention type: {model_args.flash_attn}")
if getattr(config, "model_type", None) == "internlm2": # special case for custom models
setattr(config, "attn_implementation", requested_attn_implementation)
@ -79,8 +82,8 @@ def print_attn_implementation(config: "PretrainedConfig") -> None:
attn_implementation = getattr(config, "_attn_implementation", None)
if attn_implementation == "flash_attention_2":
logger.info("Using FlashAttention-2 for faster training and inference.")
logger.info_rank0("Using FlashAttention-2 for faster training and inference.")
elif attn_implementation == "sdpa":
logger.info("Using torch SDPA for faster training and inference.")
logger.info_rank0("Using torch SDPA for faster training and inference.")
else:
logger.info("Using vanilla attention implementation.")
logger.info_rank0("Using vanilla attention implementation.")

23
src/llamafactory/model/model_utils/checkpointing.py
View File

@ -19,14 +19,14 @@
# limitations under the License.
import inspect
from functools import partial, wraps
from functools import WRAPPER_ASSIGNMENTS, partial, wraps
from types import MethodType
from typing import TYPE_CHECKING, Any, Callable, Dict, Optional, Tuple, Union
import torch
from ...extras import logging
from ...extras.constants import LAYERNORM_NAMES
from ...extras.logging import get_logger
if TYPE_CHECKING:
@ -35,7 +35,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def get_unsloth_gradient_checkpointing_func() -> Callable:
@ -81,7 +81,7 @@ def get_custom_gradient_checkpointing_func(gradient_checkpointing_func: Callable
Only applies gradient checkpointing to trainable layers.
"""
@wraps(gradient_checkpointing_func)
@wraps(gradient_checkpointing_func, assigned=WRAPPER_ASSIGNMENTS + ("__self__",))
def custom_gradient_checkpointing_func(func: Callable, *args: Union["torch.Tensor", Any], **kwargs):
module: "torch.nn.Module" = func.__self__
@ -92,9 +92,6 @@ def get_custom_gradient_checkpointing_func(gradient_checkpointing_func: Callable
return gradient_checkpointing_func(func, *args, **kwargs)
if hasattr(gradient_checkpointing_func, "__self__"): # fix unsloth gc test case
custom_gradient_checkpointing_func.__self__ = gradient_checkpointing_func.__self__
return custom_gradient_checkpointing_func
@ -111,7 +108,7 @@ def _gradient_checkpointing_enable(
from torch.utils.checkpoint import checkpoint
if not self.supports_gradient_checkpointing:
raise ValueError("{} does not support gradient checkpointing.".format(self.__class__.__name__))
raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
if gradient_checkpointing_kwargs is None:
gradient_checkpointing_kwargs = {"use_reentrant": True}
@ -125,7 +122,7 @@ def _gradient_checkpointing_enable(
if "value" in inspect.signature(self._set_gradient_checkpointing).parameters: # old GC format
self.apply(partial(self._set_gradient_checkpointing, value=True))
self.enable_input_require_grads()
logger.warning("You are using the old GC format, some features (e.g. BAdam) will be invalid.")
logger.warning_once("You are using the old GC format, some features (e.g. BAdam) will be invalid.")
else: # have already enabled input require gradients
self._set_gradient_checkpointing(enable=True, gradient_checkpointing_func=gradient_checkpointing_func)
@ -144,14 +141,14 @@ def prepare_model_for_training(model: "PreTrainedModel", model_args: "ModelArgum
(3) add the upcasting of the lm_head in fp32
"""
if model_args.upcast_layernorm:
logger.info("Upcasting layernorm weights in float32.")
logger.info_rank0("Upcasting layernorm weights in float32.")
for name, param in model.named_parameters():
if param.ndim == 1 and any(ln_name in name for ln_name in LAYERNORM_NAMES):
param.data = param.data.to(torch.float32)
if not model_args.disable_gradient_checkpointing:
if not getattr(model, "supports_gradient_checkpointing", False):
logger.warning("Current model does not support gradient checkpointing.")
logger.warning_rank0("Current model does not support gradient checkpointing.")
else:
# use_reentrant=False might increase VRAM usage (have not been empirically verified yet)
# According to: https://github.com/huggingface/transformers/issues/28339
@ -161,10 +158,10 @@ def prepare_model_for_training(model: "PreTrainedModel", model_args: "ModelArgum
model.gradient_checkpointing_enable = MethodType(gradient_checkpointing_enable, model)
model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": True})
setattr(model.config, "use_cache", False) # turn off when gradient checkpointing is enabled
logger.info("Gradient checkpointing enabled.")
logger.info_rank0("Gradient checkpointing enabled.")
if model_args.upcast_lmhead_output:
output_layer = model.get_output_embeddings()
if isinstance(output_layer, torch.nn.Linear) and output_layer.weight.dtype != torch.float32:
logger.info("Upcasting lm_head outputs in float32.")
logger.info_rank0("Upcasting lm_head outputs in float32.")
output_layer.register_forward_hook(_fp32_forward_post_hook)

6
src/llamafactory/model/model_utils/embedding.py
View File

@ -19,14 +19,14 @@ from typing import TYPE_CHECKING
import torch
from transformers.integrations import is_deepspeed_zero3_enabled
from ...extras.logging import get_logger
from ...extras import logging
if TYPE_CHECKING:
from transformers import PreTrainedModel, PreTrainedTokenizer
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def _noisy_mean_initialization(embed_weight: "torch.Tensor", num_new_tokens: int) -> None:
@ -69,4 +69,4 @@ def resize_embedding_layer(model: "PreTrainedModel", tokenizer: "PreTrainedToken
_noisy_mean_initialization(model.get_input_embeddings().weight.data, num_new_tokens)
_noisy_mean_initialization(model.get_output_embeddings().weight.data, num_new_tokens)
logger.info("Resized token embeddings from {} to {}.".format(current_embedding_size, new_embedding_size))
logger.info_rank0(f"Resized token embeddings from {current_embedding_size} to {new_embedding_size}.")

24
src/llamafactory/model/model_utils/liger_kernel.py
View File

@ -12,9 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
from typing import TYPE_CHECKING
from ...extras.logging import get_logger
from ...extras import logging
if TYPE_CHECKING:
@ -23,10 +24,15 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def configure_liger_kernel(config: "PretrainedConfig", model_args: "ModelArguments", is_trainable: bool) -> None:
def apply_liger_kernel(
config: "PretrainedConfig",
model_args: "ModelArguments",
is_trainable: bool,
require_logits: bool,
) -> None:
if not is_trainable or not model_args.enable_liger_kernel:
return
@ -48,8 +54,14 @@ def configure_liger_kernel(config: "PretrainedConfig", model_args: "ModelArgumen
elif model_type == "qwen2_vl":
from liger_kernel.transformers import apply_liger_kernel_to_qwen2_vl as apply_liger_kernel
else:
logger.warning("Current model does not support liger kernel.")
logger.warning_rank0("Current model does not support liger kernel.")
return
apply_liger_kernel()
logger.info("Liger kernel has been applied to the model.")
if require_logits and "fused_linear_cross_entropy" in inspect.signature(apply_liger_kernel).parameters:
logger.info_rank0("Current training stage does not support chunked cross entropy.")
kwargs = {"fused_linear_cross_entropy": False}
else:
kwargs = {}
apply_liger_kernel(**kwargs)
logger.info_rank0("Liger kernel has been applied to the model.")

20
src/llamafactory/model/model_utils/longlora.py
View File

@ -22,6 +22,7 @@ from typing import TYPE_CHECKING, Optional, Tuple
import torch
import torch.nn as nn
import transformers
from transformers.models.llama.modeling_llama import (
Cache,
LlamaAttention,
@ -30,11 +31,10 @@ from transformers.models.llama.modeling_llama import (
apply_rotary_pos_emb,
repeat_kv,
)
from transformers.utils import logging
from transformers.utils.versions import require_version
from ...extras import logging
from ...extras.constants import SUPPORTED_CLASS_FOR_S2ATTN
from ...extras.logging import get_logger
from ...extras.packages import is_transformers_version_greater_than_4_43
@ -44,7 +44,7 @@ if TYPE_CHECKING:
from ...hparams import ModelArguments
transformers_logger = logging.get_logger(__name__)
transformers_logger = transformers.utils.logging.get_logger(__name__)
# Modified from:
@ -86,7 +86,7 @@ def llama_attention_forward(
if getattr(self.config, "group_size_ratio", None) and self.training: # shift
groupsz = int(q_len * getattr(self.config, "group_size_ratio"))
assert q_len % groupsz == 0, "q_len {} should be divisible by group size {}.".format(q_len, groupsz)
assert q_len % groupsz == 0, f"q_len {q_len} should be divisible by group size {groupsz}."
num_groups = q_len // groupsz
def shift(state: "torch.Tensor") -> "torch.Tensor":
@ -195,7 +195,7 @@ def llama_flash_attention_2_forward(
if getattr(self.config, "group_size_ratio", None) and self.training: # shift
groupsz = int(q_len * getattr(self.config, "group_size_ratio"))
assert q_len % groupsz == 0, "q_len {} should be divisible by group size {}.".format(q_len, groupsz)
assert q_len % groupsz == 0, f"q_len {q_len} should be divisible by group size {groupsz}."
num_groups = q_len // groupsz
def shift(state: "torch.Tensor") -> "torch.Tensor":
@ -301,7 +301,7 @@ def llama_sdpa_attention_forward(
if getattr(self.config, "group_size_ratio", None) and self.training: # shift
groupsz = int(q_len * getattr(self.config, "group_size_ratio"))
assert q_len % groupsz == 0, "q_len {} should be divisible by group size {}.".format(q_len, groupsz)
assert q_len % groupsz == 0, f"q_len {q_len} should be divisible by group size {groupsz}."
num_groups = q_len // groupsz
def shift(state: "torch.Tensor") -> "torch.Tensor":
@ -353,7 +353,7 @@ def llama_sdpa_attention_forward(
def _apply_llama_patch() -> None:
require_version("transformers>=4.41.2,<=4.45.0", "To fix: pip install transformers>=4.41.2,<=4.45.0")
require_version("transformers>=4.41.2,<=4.46.1", "To fix: pip install transformers>=4.41.2,<=4.46.1")
LlamaAttention.forward = llama_attention_forward
LlamaFlashAttention2.forward = llama_flash_attention_2_forward
LlamaSdpaAttention.forward = llama_sdpa_attention_forward
@ -363,11 +363,11 @@ def configure_longlora(config: "PretrainedConfig", model_args: "ModelArguments",
if not is_trainable or not model_args.shift_attn:
return
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
if getattr(config, "model_type", None) in SUPPORTED_CLASS_FOR_S2ATTN:
setattr(config, "group_size_ratio", 0.25)
_apply_llama_patch()
logger.info("Using shift short attention with group_size_ratio=1/4.")
logger.info_rank0("Using shift short attention with group_size_ratio=1/4.")
else:
logger.warning("Current model does not support shift short attention.")
logger.warning_rank0("Current model does not support shift short attention.")

14
src/llamafactory/model/model_utils/misc.py
View File

@ -14,14 +14,14 @@
from typing import TYPE_CHECKING, List
from ...extras.logging import get_logger
from ...extras import logging
if TYPE_CHECKING:
from transformers import PretrainedConfig, PreTrainedModel, PreTrainedTokenizer
logger = get_logger(__name__)
logger = logging.get_logger(__name__)
def find_all_linear_modules(model: "PreTrainedModel", freeze_vision_tower: bool) -> List[str]:
@ -34,7 +34,7 @@ def find_all_linear_modules(model: "PreTrainedModel", freeze_vision_tower: bool)
forbidden_modules.add("output_layer")
elif model_type == "internlm2":
forbidden_modules.add("output")
elif model_type in ["llava", "paligemma"]:
elif model_type in ["llava", "llava_next", "llava_next_video", "paligemma", "video_llava"]:
forbidden_modules.add("multi_modal_projector")
elif model_type == "qwen2_vl":
forbidden_modules.add("merger")
@ -53,7 +53,7 @@ def find_all_linear_modules(model: "PreTrainedModel", freeze_vision_tower: bool)
if "Linear" in module.__class__.__name__ and "Embedding" not in module.__class__.__name__:
module_names.add(name.split(".")[-1])
logger.info("Found linear modules: {}".format(",".join(module_names)))
logger.info_rank0("Found linear modules: {}".format(",".join(module_names)))
return list(module_names)
@ -67,12 +67,12 @@ def find_expanded_modules(model: "PreTrainedModel", target_modules: List[str], n
if num_layers % num_layer_trainable != 0:
raise ValueError(
"`num_layers` {} should be divisible by `num_layer_trainable` {}.".format(num_layers, num_layer_trainable)
f"`num_layers` {num_layers} should be divisible by `num_layer_trainable` {num_layer_trainable}."
)
stride = num_layers // num_layer_trainable
trainable_layer_ids = range(stride - 1, num_layers + stride - 1, stride)
trainable_layers = [".{:d}.".format(idx) for idx in trainable_layer_ids]
trainable_layers = [f".{idx:d}." for idx in trainable_layer_ids]
module_names = []
for name, _ in model.named_modules():
if any(target_module in name for target_module in target_modules) and any(
@ -80,7 +80,7 @@ def find_expanded_modules(model: "PreTrainedModel", target_modules: List[str], n
):
module_names.append(name)
logger.info("Apply lora to layers: {}".format(",".join(map(str, trainable_layer_ids))))
logger.info_rank0("Apply lora to layers: {}".format(",".join(map(str, trainable_layer_ids))))
return module_names

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