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https://github.com/huggingface/peft

adapter diffusion fine-tuning llm lora parameter-efficient-learning peft python pytorch transformers

Score: 31.915006101224613

Last synced: about 5 hours ago
JSON representation

Repository metadata:

🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.

Owner metadata:

GitHub Events

Total
Last Year

Committers metadata

Last synced: 1 day ago

Total Commits: 1,935
Total Committers: 397
Avg Commits per committer: 4.874
Development Distribution Score (DDS): 0.698

Commits in past year: 552
Committers in past year: 147
Avg Commits per committer in past year: 3.755
Development Distribution Score (DDS) in past year: 0.683

Name Email Commits
Benjamin Bossan B****n 585
Sourab Mangrulkar 1****0 217
Younes Belkada 4****a 158
githubnemo g****o 91
Steven Liu 5****u 40
Daoyuan Li 9****6 37
Yao Matrix m****o@i****m 27
kaixuanliu k****u@i****m 27
Qingru Zhang q****g@g****u 21
Sergio Graciá 1****r 20
jiqing-feng j****g@i****m 19
Sayak Paul s****l@g****m 17
Wang, Yi y****g@i****m 16
dependabot[bot] 4****] 15
Alexander Kovalchuk k****l@g****m 12
Peft Jambot 2****t 11
Sumanth R Hegde 3****H 10
MKhalusova k****r@g****m 10
Fanli Lin f****n@i****m 10
Sergio Vizcaino Ferrer 1****F 9
Quentin Gallouédec 4****c 8
Saurav Maheshkar s****r@g****m 8
Michael Benayoun m****n@g****m 7
Sambhav Dixit 9****r 7
Sravan Jangam 1****m 7
Eric Buehler 6****r 6
Kashif Rasul k****l@g****m 6
Marc Sun 5****c 6
A Aswanth Raj a****7@g****m 5
Albert Villanova del Moral 8****a 5
and 367 more...

Issue and Pull Request metadata

Last synced: 1 day ago

Total issues: 1,234
Total pull requests: 2,304
Average time to close issues: about 1 month
Average time to close pull requests: 13 days
Total issue authors: 957
Total pull request authors: 487
Average comments per issue: 4.4
Average comments per pull request: 3.61
Merged pull request: 1,490
Bot issues: 0
Bot pull requests: 17

Past year issues: 239
Past year pull requests: 616
Past year average time to close issues: 23 days
Past year average time to close pull requests: 14 days
Past year issue authors: 147
Past year pull request authors: 220
Past year average comments per issue: 3.52
Past year average comments per pull request: 2.85
Past year merged pull request: 262
Past year bot issues: 0
Past year bot pull requests: 16

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/huggingface/peft

Top Issue Authors

  • BenjaminBossan (37)
  • DaoyuanLi2816 (19)
  • Sravanjangam (12)
  • Sergasgr (8)
  • imrankh46 (8)
  • aswanth-07 (6)
  • saeid93 (5)
  • LK-maker-007 (5)
  • hivaze (5)
  • eSVeeF (5)
  • AngledLuffa (5)
  • Minami-su (5)
  • amogh-nagri-11 (4)
  • BigDataMLexplorer (4)
  • EricLBuehler (4)

Top Pull Request Authors

  • BenjaminBossan (753)
  • younesbelkada (127)
  • githubnemo (90)
  • pacman100 (72)
  • DaoyuanLi2816 (56)
  • yao-matrix (38)
  • stevhliu (31)
  • jiqing-feng (21)
  • faaany (20)
  • peft-jambot (20)
  • kaixuanliu (17)
  • sywangyi (16)
  • dependabot[bot] (15)
  • Sergasgr (14)
  • sayakpaul (14)

Top Issue Labels

  • solved (23)
  • wip (16)
  • contributions-welcome (14)
  • bug (13)
  • PRs welcome to address this (10)
  • enhancement (6)
  • help wanted (5)
  • good first issue (4)
  • documentation (2)
  • good-second-pr (2)
  • invalid (1)
  • status: declined (1)
  • wontfix (1)

Top Pull Request Labels

  • triaged (130)
  • dependencies (15)
  • github_actions (14)
  • wip (9)
  • contributions-welcome (2)
  • qa/done (1)
  • PEFT v1 (1)
  • short review (1)
  • changelog/no-changelog (1)
  • bug (1)

Package metadata

nixpkgs-23.11: python310Packages.peft

State-of-the art parameter-efficient fine tuning

nixpkgs-24.11: python311Packages.peft

State-of-the art parameter-efficient fine tuning

nixpkgs-unstable: python314Packages.peft

State-of-the art parameter-efficient fine tuning

nixpkgs-unstable: python313Packages.peft

State-of-the art parameter-efficient fine tuning

pypi.org: peft

Parameter-Efficient Fine-Tuning (PEFT)

  • Homepage: https://github.com/huggingface/peft
  • Documentation: https://peft.readthedocs.io/
  • Licenses: Apache
  • Latest release: 0.21.2 (published about 1 month ago)
  • Last Synced: 2026-11-06T01:13:41.694Z (1 day ago)
  • Versions: 39
  • Dependent Packages: 192
  • Dependent Repositories: 527
  • Downloads: 8,262,388 Last month
  • Docker Downloads: 47,941
  • Rankings:
    • Stargazers count: 0.25%
    • Downloads: 0.383%
    • Dependent repos count: 0.625%
    • Average: 1.194%
    • Forks count: 1.476%
    • Dependent packages count: 1.611%
    • Docker downloads count: 2.819%
  • Maintainers (4)
proxy.golang.org: github.com/huggingface/peft

  • Homepage:
  • Documentation: https://pkg.go.dev/github.com/huggingface/peft#section-documentation
  • Licenses: apache-2.0
  • Latest release: v0.21.2 (published about 1 month ago)
  • Last Synced: 2026-11-06T01:13:42.948Z (1 day ago)
  • Versions: 38
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Stargazers count: 0.627%
    • Forks count: 0.946%
    • Average: 5.151%
    • Dependent packages count: 8.384%
    • Dependent repos count: 10.647%
pypi.org: peft-machinify

Parameter-Efficient Fine-Tuning (PEFT)

  • Homepage: https://github.com/huggingface/peft
  • Documentation: https://peft-machinify.readthedocs.io/
  • Licenses: Apache
  • Latest release: 0.3.0.dev0 (published over 3 years ago)
  • Last Synced: 2026-11-06T01:21:23.306Z (1 day ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 11 Last month
  • Rankings:
    • Dependent packages count: 6.924%
    • Average: 18.733%
    • Dependent repos count: 30.542%
  • Maintainers (1)
spack.io: py-peft

Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model's parameters.

  • Homepage: https://github.com/huggingface/peft
  • Licenses: []
  • Latest release: 0.17.1 (published 7 months ago)
  • Last Synced: 2026-11-06T01:13:54.970Z (1 day ago)
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 0.0%
    • Average: 26.033%
    • Dependent packages count: 52.065%
anaconda.org: peft

Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model's parameters. Fine-tuning large-scale PLMs is often prohibitively costly. In this regard, PEFT methods only fine-tune a small number of (extra) model parameters, thereby greatly decreasing the computational and storage costs. Recent State-of-the-Art PEFT techniques achieve performance comparable to that of full fine-tuning.

  • Homepage: https://github.com/huggingface/peft
  • Licenses: Apache-2.0
  • Latest release: 0.19.1 (published 5 months ago)
  • Last Synced: 2026-10-26T07:29:10.852Z (12 days ago)
  • Versions: 2
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 173 Total
  • Rankings:
    • Downloads: 13.37%
    • Average: 31.125%
    • Dependent packages count: 38.273%
    • Dependent repos count: 41.731%
pypi.org: peft-cheneydon

Parameter-Efficient Fine-Tuning (PEFT)

  • Homepage: https://github.com/huggingface/peft
  • Documentation: https://peft-cheneydon.readthedocs.io/
  • Licenses: Apache
  • Latest release: 0.5.0.dev0 (published over 3 years ago)
  • Last Synced: 2026-11-06T01:13:54.258Z (1 day ago)
  • Versions: 1
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent packages count: 7.519%
    • Average: 31.408%
    • Dependent repos count: 55.297%
  • Maintainers (1)
nixpkgs-23.11: python311Packages.peft

State-of-the art parameter-efficient fine tuning

nixpkgs-24.05: python311Packages.peft

State-of-the art parameter-efficient fine tuning

nixpkgs-23.05: python311Packages.peft

State-of-the art parameter-efficient fine tuning

nixpkgs-24.05: python312Packages.peft

State-of-the art parameter-efficient fine tuning

nixpkgs-24.11: python312Packages.peft

State-of-the art parameter-efficient fine tuning

nixpkgs-23.05: python310Packages.peft

State-of-the art parameter-efficient fine tuning

Dependencies

.github/workflows/build_docker_images.yml actions
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docker/peft-cpu/Dockerfile docker
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docker/peft-gpu/Dockerfile docker
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examples/causal_language_modeling/requirements.txt pypi
  • accelerate *
  • datasets *
  • deepspeed *
  • evaluate *
  • tqdm *
  • transformers *
examples/conditional_generation/requirements.txt pypi
  • accelerate *
  • datasets *
  • deepspeed *
  • evaluate *
  • tqdm *
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examples/feature_extraction/requirements.txt pypi
  • datasets *
  • evaluate *
  • hnswlib *
  • huggingface_hub *
  • pandas *
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  • wandb *
examples/lora_dreambooth/requirements.txt pypi
  • Pillow *
  • accelerate *
  • datasets *
  • diffusers *
  • evaluate *
  • huggingface_hub *
  • safetensors *
  • torchvision *
  • tqdm *
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examples/sequence_classification/requirements.txt pypi
  • accelerate *
  • datasets *
  • evaluate *
  • tqdm *
  • transformers *
examples/token_classification/requirements.txt pypi
  • Pillow *
  • accelerate *
  • datasets *
  • evaluate *
  • torchvision *
  • tqdm *
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pyproject.toml pypi
setup.py pypi
  • accelerate >=0.21.0
  • numpy >=1.17
  • packaging >=20.0
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  • pyyaml *
  • safetensors *
  • torch >=1.13.0
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examples/sft/requirements.txt pypi
  • PyGithub *
  • bitsandbytes *
  • datasets *
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  • hf_transfer *
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  • matplotlib *
  • nltk *
  • numpy *
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  • scipy *
  • sentencepiece *
  • tensorboard *
  • tiktoken *
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examples/boft_dreambooth/requirements.txt pypi
  • Pillow *
  • accelerate ==1.9.0
  • datasets ==4.0.0
  • diffusers ==0.34.0
  • evaluate *
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  • ipykernel *
  • ipywidgets *
  • nb_conda_kernels *
  • safetensors *
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  • transformers ==4.54.0
  • wandb ==0.21.0
examples/cartridge_self_study/requirements.txt pypi
  • peft *
  • torch *
  • transformers *
  • vllm *
examples/sft/requirements_xpu.txt pypi
  • PyGithub *
  • bitsandbytes *
  • datasets *
  • deepspeed *
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  • numpy *
  • pandas *
  • scipy *
  • sentencepiece *
  • tensorboard *
  • tiktoken *
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method_comparison/MetaMathQA/requirements.txt pypi
  • bitsandbytes *
  • datasets *
  • numpy *
  • tqdm *
examples/lora_finetuning_transformer_engine/requirements.txt pypi
  • accelerate *
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  • numpy *
  • pandas *
  • peft *
  • pyarrow *
  • safetensors *
  • torch *
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examples/int8_training/requirements.txt pypi
  • accelerate *
  • datasets ==3.6.0
  • evaluate *
  • jiwer *
  • librosa *
  • soundfile *
  • transformers ==4.52.4
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requirements.txt pypi
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examples/arrow_multitask/requirements.txt pypi
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examples/lora_finetuning_transformer_engine/Dockerfile docker
  • nvcr.io/nvidia/pytorch 26.01-py3 build
examples/hra_dreambooth/requirements.txt pypi
  • Pillow *
  • accelerate ==1.9.0
  • datasets ==4.0.0
  • diffusers ==0.38.0
  • evaluate *
  • huggingface_hub *
  • ipykernel *
  • ipywidgets *
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  • transformers ==4.55.0
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method_comparison/image-gen/requirements.txt pypi
  • datasets *
  • diffusers >=0.37.0
  • numpy *
  • pillow *
  • torchvision *
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method_comparison/peft-shop/requirements.txt pypi
  • gradio >=6.2
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method_comparison/requirements-app.txt pypi
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examples/boft_controlnet/requirements.txt pypi
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  • numpy <2.0.0
  • opencv-python ==4.9.0.80
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examples/frod_finetuning/requirements.txt pypi
  • Pillow *
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examples/sft/requirements_colab.txt pypi
  • PyGithub *
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