open-japanese-llm-leaderboard / requirements.txt
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# This file was autogenerated by uv via the following command:
# uv pip compile pyproject.toml -o requirements.txt
aiofiles==23.2.1
# via gradio
aiohappyeyeballs==2.4.3
# via aiohttp
aiohttp==3.10.10
# via
# datasets
# fsspec
aiosignal==1.3.1
# via aiohttp
annotated-types==0.7.0
# via pydantic
anyio==4.6.0
# via
# gradio
# httpx
# starlette
apscheduler==3.10.4
# via open-japanese-llm-leaderboard (pyproject.toml)
async-timeout==4.0.3
# via aiohttp
attrs==24.2.0
# via aiohttp
certifi==2024.8.30
# via
# httpcore
# httpx
# requests
charset-normalizer==3.4.0
# via requests
click==8.1.7
# via
# typer
# uvicorn
datasets==3.1.0
# via open-japanese-llm-leaderboard (pyproject.toml)
dill==0.3.8
# via
# datasets
# multiprocess
exceptiongroup==1.2.2
# via anyio
fastapi==0.115.2
# via gradio
ffmpy==0.4.0
# via gradio
filelock==3.16.1
# via
# datasets
# huggingface-hub
# torch
# transformers
# triton
frozenlist==1.4.1
# via
# aiohttp
# aiosignal
fsspec==2024.6.1
# via
# datasets
# gradio-client
# huggingface-hub
# torch
gradio==5.6.0
# via open-japanese-llm-leaderboard (pyproject.toml)
gradio-client==1.4.3
# via gradio
h11==0.14.0
# via
# httpcore
# uvicorn
hf-transfer==0.1.8
# via open-japanese-llm-leaderboard (pyproject.toml)
httpcore==1.0.6
# via httpx
httpx==0.27.2
# via
# gradio
# gradio-client
# safehttpx
huggingface-hub==0.25.2
# via
# datasets
# gradio
# gradio-client
# tokenizers
# transformers
idna==3.10
# via
# anyio
# httpx
# requests
# yarl
jinja2==3.1.4
# via
# gradio
# torch
markdown-it-py==3.0.0
# via rich
markupsafe==2.1.5
# via
# gradio
# jinja2
mdurl==0.1.2
# via markdown-it-py
mpmath==1.3.0
# via sympy
multidict==6.1.0
# via
# aiohttp
# yarl
multiprocess==0.70.16
# via datasets
networkx==3.4.2
# via torch
numpy==2.1.2
# via
# datasets
# gradio
# pandas
# pyarrow
# transformers
nvidia-cublas-cu12==12.4.5.8
# via
# nvidia-cudnn-cu12
# nvidia-cusolver-cu12
# torch
nvidia-cuda-cupti-cu12==12.4.127
# via torch
nvidia-cuda-nvrtc-cu12==12.4.127
# via torch
nvidia-cuda-runtime-cu12==12.4.127
# via torch
nvidia-cudnn-cu12==9.1.0.70
# via torch
nvidia-cufft-cu12==11.2.1.3
# via torch
nvidia-curand-cu12==10.3.5.147
# via torch
nvidia-cusolver-cu12==11.6.1.9
# via torch
nvidia-cusparse-cu12==12.3.1.170
# via
# nvidia-cusolver-cu12
# torch
nvidia-nccl-cu12==2.21.5
# via torch
nvidia-nvjitlink-cu12==12.4.127
# via
# nvidia-cusolver-cu12
# nvidia-cusparse-cu12
# torch
nvidia-nvtx-cu12==12.4.127
# via torch
orjson==3.10.7
# via gradio
packaging==24.1
# via
# datasets
# gradio
# gradio-client
# huggingface-hub
# plotly
# transformers
pandas==2.2.3
# via
# datasets
# gradio
pillow==10.4.0
# via gradio
plotly==5.24.1
# via open-japanese-llm-leaderboard (pyproject.toml)
propcache==0.2.0
# via yarl
pyarrow==17.0.0
# via datasets
pydantic==2.9.2
# via
# fastapi
# gradio
pydantic-core==2.23.4
# via pydantic
pydub==0.25.1
# via gradio
pygments==2.18.0
# via rich
python-dateutil==2.9.0.post0
# via pandas
python-multipart==0.0.12
# via gradio
pytz==2024.2
# via
# apscheduler
# pandas
pyyaml==6.0.2
# via
# datasets
# gradio
# huggingface-hub
# transformers
regex==2024.9.11
# via transformers
requests==2.32.3
# via
# datasets
# huggingface-hub
# transformers
rich==13.9.2
# via typer
ruff==0.6.9
# via gradio
safehttpx==0.1.1
# via gradio
safetensors==0.4.5
# via transformers
semantic-version==2.10.0
# via gradio
shellingham==1.5.4
# via typer
six==1.16.0
# via
# apscheduler
# python-dateutil
sniffio==1.3.1
# via
# anyio
# httpx
starlette==0.40.0
# via
# fastapi
# gradio
sympy==1.13.1
# via torch
tenacity==9.0.0
# via plotly
tokenizers==0.20.1
# via transformers
tomlkit==0.12.0
# via gradio
torch==2.5.1
# via open-japanese-llm-leaderboard (pyproject.toml)
tqdm==4.66.5
# via
# datasets
# huggingface-hub
# transformers
transformers==4.46.2
# via open-japanese-llm-leaderboard (pyproject.toml)
triton==3.1.0
# via torch
typer==0.12.5
# via gradio
typing-extensions==4.12.2
# via
# anyio
# fastapi
# gradio
# gradio-client
# huggingface-hub
# multidict
# pydantic
# pydantic-core
# rich
# torch
# typer
# uvicorn
tzdata==2024.2
# via pandas
tzlocal==5.2
# via apscheduler
urllib3==2.2.3
# via requests
uvicorn==0.31.1
# via gradio
websockets==12.0
# via gradio-client
xxhash==3.5.0
# via datasets
yarl==1.15.0
# via aiohttp