llama-factory 微调 Qwen2.5-3B-Instruct

0、资源链接

官方 readme:  https://github.com/hiyouga/LLaMA-Factory/blob/v0.9.1/README_zh.md

官方文档:  https://llamafactory.readthedocs.io/zh-cn/latest/

官方推荐的知乎教程：https://zhuanlan.zhihu.com/p/695287607

1、安装 LLaMA Factory

git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
pip install -e ".[torch,metrics]"

 笔者conda环境 Python 包如下：

accelerate==1.4.0
aiofiles==23.2.1
aiohappyeyeballs==2.6.1
aiohttp==3.11.13
aiosignal==1.3.2
airportsdata==20250224
annotated-types==0.7.0
anyio==4.8.0
astor==0.8.1
async-timeout==5.0.1
attrs==25.2.0
audioread==3.0.1
av==14.2.0
blake3==1.0.4
certifi==2025.1.31
cffi==1.17.1
charset-normalizer==3.4.1
click==8.1.8
cloudpickle==3.1.1
compressed-tensors==0.9.1
contourpy==1.3.1
cupy-cuda12x==13.4.0
cycler==0.12.1
datasets==3.3.2
decorator==5.2.1
depyf==0.18.0
dill==0.3.8
diskcache==5.6.3
distro==1.9.0
dnspython==2.7.0
docstring_parser==0.16
einops==0.8.1
email_validator==2.2.0
exceptiongroup==1.2.2
fastapi==0.115.11
fastapi-cli==0.0.7
fastrlock==0.8.3
ffmpy==0.5.0
filelock==3.17.0
fire==0.7.0
fonttools==4.56.0
frozenlist==1.5.0
fsspec==2024.12.0
gguf==0.10.0
gradio==5.21.0
gradio_client==1.7.2
groovy==0.1.2
h11==0.14.0
httpcore==1.0.7
httptools==0.6.4
httpx==0.28.1
huggingface-hub==0.29.3
idna==3.10
importlib_metadata==8.6.1
iniconfig==2.0.0
interegular==0.3.3
jieba==0.42.1
Jinja2==3.1.6
jiter==0.9.0
joblib==1.4.2
jsonschema==4.23.0
jsonschema-specifications==2024.10.1
kiwisolver==1.4.8
lark==1.2.2
lazy_loader==0.4
librosa==0.11.0
-e git+https://github.com/hiyouga/LLaMA-Factory.git@30038d9ce701b421bd521c72073fcc08909fe25a#egg=llamafactory
llvmlite==0.43.0
lm-format-enforcer==0.10.11
markdown-it-py==3.0.0
MarkupSafe==2.1.5
matplotlib==3.10.1
mdurl==0.1.2
mistral_common==1.5.3
mpmath==1.3.0
msgpack==1.1.0
msgspec==0.19.0
multidict==6.1.0
multiprocess==0.70.16
nest-asyncio==1.6.0
networkx==3.4.2
nltk==3.9.1
numba==0.60.0
numpy==1.26.4
nvidia-cublas-cu12==12.4.5.8
nvidia-cuda-cupti-cu12==12.4.127
nvidia-cuda-nvrtc-cu12==12.4.127
nvidia-cuda-runtime-cu12==12.4.127
nvidia-cudnn-cu12==9.1.0.70
nvidia-cufft-cu12==11.2.1.3
nvidia-curand-cu12==10.3.5.147
nvidia-cusolver-cu12==11.6.1.9
nvidia-cusparse-cu12==12.3.1.170
nvidia-cusparselt-cu12==0.6.2
nvidia-nccl-cu12==2.21.5
nvidia-nvjitlink-cu12==12.4.127
nvidia-nvtx-cu12==12.4.127
openai==1.66.3
opencv-python-headless==4.11.0.86
orjson==3.10.15
outlines==0.1.11
outlines_core==0.1.26
packaging==24.2
pandas==2.2.3
partial-json-parser==0.2.1.1.post5
peft==0.12.0
pillow==11.1.0
platformdirs==4.3.6
pluggy==1.5.0
pooch==1.8.2
prometheus-fastapi-instrumentator==7.0.2
prometheus_client==0.21.1
propcache==0.3.0
protobuf==6.30.0
psutil==7.0.0
py-cpuinfo==9.0.0
pyarrow==19.0.1
pybind11==2.13.6
pycountry==24.6.1
pycparser==2.22
pydantic==2.10.6
pydantic_core==2.27.2
pydub==0.25.1
Pygments==2.19.1
pyparsing==3.2.1
pytest==8.3.5
python-dateutil==2.9.0.post0
python-dotenv==1.0.1
python-multipart==0.0.20
pytz==2025.1
PyYAML==6.0.2
pyzmq==26.3.0
qwen-vl-utils==0.0.10
ray==2.40.0
referencing==0.36.2
regex==2024.11.6
requests==2.32.3
rich==13.9.4
rich-toolkit==0.13.2
rouge-chinese==1.0.3
rpds-py==0.23.1
ruff==0.11.0
safehttpx==0.1.6
safetensors==0.5.3
scikit-learn==1.6.1
scipy==1.15.2
semantic-version==2.10.0
sentencepiece==0.2.0
shellingham==1.5.4
shtab==1.7.1
six==1.17.0
sniffio==1.3.1
soundfile==0.13.1
soxr==0.5.0.post1
sse-starlette==2.2.1
starlette==0.46.1
sympy==1.13.1
termcolor==2.5.0
threadpoolctl==3.6.0
tiktoken==0.9.0
tokenizers==0.21.0
tomli==2.2.1
tomlkit==0.13.2
torch==2.5.1
torchaudio==2.5.1
torchvision==0.20.1
tqdm==4.67.1
transformers @ file:///home/coco/my_project/transformers
triton==3.1.0
trl==0.9.6
typer==0.15.2
typing_extensions==4.12.2
tyro==0.8.14
tzdata==2025.1
urllib3==2.3.0
uvicorn==0.34.0
uvloop==0.21.0
vllm==0.7.3
watchfiles==1.0.4
websockets==15.0.1
xformers==0.0.28.post3
xgrammar==0.1.11
xxhash==3.5.0
yarl==1.18.3
zipp==3.21.0

2、可视化微调UI页面

LLaMA Board 可视化微调（由 Gradio 驱动）

命令行输入：

llamafactory-cli webui

 执行截图：

3、finetune 数据准备

关于数据集文件的格式，请参考 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。

注册数据集

需要在/home/coco/my_project/LLaMA-Factory/data/dataset_info.json文件中注册自定义数据集。笔者使用的数据集定义如下：

  "eye_amd": {
    "file_name": "short_ds_vl2_AMD_DR_train.json",
    "formatting": "sharegpt",
    "columns": {
 &n