# huggingface/transformers

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

Repository: https://github.com/huggingface/transformers
Canonical: https://ross.abutalabs.com/products/transformers
Homepage: https://huggingface.co/transformers
Language: Python
License: Apache-2.0
License Family: permissive
Topics: nlp, natural-language-processing, pytorch, pytorch-transformers, transformer, model-hub, pretrained-models, speech-recognition, hacktoberfest, python, machine-learning, deep-learning, audio, deepseek, gemma, glm, llm, qwen, vlm
Last push: 2026-08-27T00:36:24+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 87, longevity 100
- inputs: {"age_days": 2865, "days_push": 7, "days_rel": 7, "gap_med": 5.0, "n_releases_24m": 103}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 164475, forks 34378 (observed 2026-08-28T04:12:24.415170+00:00)

## What it is
Hugging Face Transformers is a Python library that serves as the model-definition framework for state-of-the-art machine learning models across text, vision, audio, video, and multimodal modalities. It provides pretrained model checkpoints, a simple Pipeline inference API, and a Trainer for training and fine-tuning, integrating tightly with the Hugging Face Hub ecosystem.

## Use cases
- run inference with pretrained transformer models
- fine-tune an LLM on my own dataset
- transcribe audio with speech recognition models
- classify text with BERT-style models
- load a model from the Hugging Face Hub in Python
- fine-tune a vision model for image classification
- run a text generation pipeline locally
- parameter-efficient fine-tuning with PEFT adapters

## When to choose
- you want quick access to thousands of pretrained models for text, vision, audio, or multimodal tasks
- you need a standardized model definition compatible with the Hugging Face ecosystem (PEFT, Accelerate, TRL, Datasets)
- you want high-level APIs (Pipeline, Trainer) for inference and training with minimal boilerplate
- you are doing research or production work with transformer architectures in PyTorch

## When to avoid
- you need maximum inference performance for serving LLMs at scale (consider TGI, vLLM, or llama.cpp)
- you work outside PyTorch or need lightweight browser-only inference (consider Transformers.js or ONNX runtimes)
- you need a minimal dependency footprint, as the library is large and heavyweight
- you want to train models from scratch with custom architectures unrelated to the Hub ecosystem

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, deep-learning, llm-inference, llm-training, nlp, speech-recognition, sdk
- domain: machine-learning, deep-learning, large-language-models, computer-vision, speech-processing, artificial-intelligence
- platform: python, cross-platform
- tags: transformers, pretrained-models, model-hub, pytorch, hugging-face, fine-tuning, inference, multimodal, natural-language-processing, gpu

## Member repositories
- huggingface/transformers (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:12:24.415170+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T16:10:55.342287+00:00, confidence not recorded.
  - readme: https://github.com/huggingface/transformers (fetched 2026-08-28T04:12:24.415170+00:00, sha 7a1c13598058)
  - homepage: https://huggingface.co/transformers (fetched 2026-08-28T17:24:30.887704+00:00, sha 15a63894ac07)
  - site_page: https://huggingface.co/docs (fetched 2026-08-28T17:24:30.896342+00:00, sha bdec26667b98)
  - site_page: https://huggingface.co/docs/transformers/installation (fetched 2026-08-28T17:24:30.900076+00:00, sha 931322347def)
  - site_page: https://huggingface.co/docs/transformers/quicktour (fetched 2026-08-28T17:24:30.901821+00:00, sha 7c48898ed00f)
  - site_page: https://huggingface.co/docs/transformers/peft (fetched 2026-08-28T17:24:30.903603+00:00, sha 73be7503a0f2)
  - site_page: https://huggingface.co/docs/transformers/models_timeline (fetched 2026-08-28T17:24:30.905372+00:00, sha e5e91997c7e1)
  - site_page: https://huggingface.co/docs/transformers/pipeline_tutorial (fetched 2026-08-28T17:24:30.906921+00:00, sha 3986bdb1c6e0)
  - site_page: https://huggingface.co/docs/transformers/trainer (fetched 2026-08-28T17:24:30.908877+00:00, sha b4e8b8e947c1)
  - registry_pypi: https://pypi.org/pypi/transformers/json (fetched 2026-08-28T17:24:30.910507+00:00, sha fb9e632382e8)
- Data as of 2026-08-30T08:39:29.467469+00:00.