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. observed · 2026-08-28
Health v2 · maintenance only
95/100
- Activity 99
- Release rhythm 87
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 5.0
- age_days: 2865
- days_rel: 7
- days_push: 7
- n_releases_24m: 103
Adoption not part of the score
164475 stars · 34378 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
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
library · maturity stable
machine-learning deep-learning llm-inference llm-training nlp speech-recognition sdk machine-learning deep-learning large-language-models computer-vision speech-processing artificial-intelligence python cross-platform transformers pretrained-models model-hub pytorch hugging-face fine-tuning inference multimodal natural-language-processing gpu
10 sources
- readme: https://github.com/huggingface/transformers · fetched 2026-08-28 · 7a1c13598058
- homepage: https://huggingface.co/transformers · fetched 2026-08-28 · 15a63894ac07
- site_page: https://huggingface.co/docs · fetched 2026-08-28 · bdec26667b98
- site_page: https://huggingface.co/docs/transformers/installation · fetched 2026-08-28 · 931322347def
- site_page: https://huggingface.co/docs/transformers/quicktour · fetched 2026-08-28 · 7c48898ed00f
- site_page: https://huggingface.co/docs/transformers/peft · fetched 2026-08-28 · 73be7503a0f2
- site_page: https://huggingface.co/docs/transformers/models_timeline · fetched 2026-08-28 · e5e91997c7e1
- site_page: https://huggingface.co/docs/transformers/pipeline_tutorial · fetched 2026-08-28 · 3986bdb1c6e0
- site_page: https://huggingface.co/docs/transformers/trainer · fetched 2026-08-28 · b4e8b8e947c1
- registry_pypi: https://pypi.org/pypi/transformers/json · fetched 2026-08-28 · fb9e632382e8
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| huggingface/transformers | main | 95 |
For agents
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Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem