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Transformers review

Transformers is a Python model-definition framework for text, vision, audio, video, and multimodal machine learning models, used for both inference and training.

Updated Nov 1, 2026. Review written Oct 22, 2026 from the project's own sources; usage data refreshed every Monday.

TL;DR

Transformers is a Python library from Hugging Face that defines machine learning models for inference and training across text, vision, audio, video, and multimodal tasks. It is for researchers, engineers, and developers working with pretrained models. Its model definitions are compatible with many training frameworks, inference engines, and modeling libraries such as vLLM, DeepSpeed, and llama.cpp.

Transformers at a glance

Category
Inference and training
Maintained by
Hugging Face (organization, @huggingface on GitHub)
License
Apache 2.0
Language
Python, Dockerfile, Shell
Repository
github.com/huggingface/transformers
Website
huggingface.co/transformers
PyPI package
transformers 5.20.2
Latest release
v5.20.2 on GitHub, Oct 19, 2026
First published
Oct 29, 2018

What Transformers does

  • Transformers provides model definitions for text, computer vision, audio, video, and multimodal models, for both inference and training.
  • Transformers offers a high-level Pipeline API that handles input preprocessing and returns output for text, audio, vision, and multimodal tasks.
  • Transformers loads pretrained checkpoints from the Hugging Face Hub, then downloads and caches them for reuse.
  • Transformers lets you chat with a model from the command line while transformers serve is running.
  • Transformers model definitions work with training frameworks like Axolotl, Unsloth, DeepSpeed, and FSDP, and inference engines like vLLM, SGLang, and TGI.
  • Transformers provides examples for each architecture to reproduce results published by the original authors.
  • Transformers model files can be used independently of the library for quick experiments.

How it works

Transformers is a Python library you install into a virtual environment with PyTorch. You create a pipeline with a task and a Hub model ID, and the model is downloaded and cached. You then pass text, audio URLs, or images to get output. The same model definitions are reused by training frameworks, inference engines like vLLM, and libraries like llama.cpp and mlx.

How to install and start using Transformers

With pip

pip install "transformers[torch]"

With uv

uv pip install "transformers[torch]"

From source with pip

pip install '.[torch]'

Requirements

  • Python 3.10 or later
  • PyTorch 2.6 or later

First steps

  1. Create and activate a virtual environment with venv or uv.
  2. Install Transformers with PyTorch support in the virtual environment.
  3. Import pipeline and instantiate it with a task, such as text-generation, and a model ID from the Hugging Face Hub.
  4. Pass text or a chat history to the pipeline to get generated output.

Who Transformers is for

  • Developers running inference with pretrained text, vision, audio, or multimodal models from the Hugging Face Hub.
  • Researchers who want to iterate quickly on model code without extra abstraction layers.
  • Teams training or fine-tuning PyTorch models provided by Transformers.

Limitations to know

  • Transformers is not a modular toolbox of neural network building blocks. Model files are intentionally not refactored into shared abstractions.
  • Transformers' training API is optimized for its own PyTorch models. The README recommends a library like Accelerate for generic machine learning loops.
  • Transformers installed from source gets the latest changes, but the README notes the latest version may not be stable.

Is Transformers growing? Usage data

In the week of Oct 25–31, 2026, Transformers ranked #134 on the Devtoolwatch Board and #14 in Inference and training, with a momentum score of 56 out of 100. Its main package had 27.8M downloads that week, +11% against its own 4-week average.

New GitHub stars per week for Transformers12 weeks from Aug 9, 2026: 695, 288, 289, 248, 361, 1.2k, 309, 250, 655, 527, 358, 356 new stars, an average of 459 a week.Aug 9week of Oct 25
New GitHub stars per week for the last 12 weeks, from GitHub's stargazer history (today's stargazers by the week they starred).
Show the weekly numbers
New GitHub stars per week for Transformers
Week ofNew stars
Aug 9, 2026695
Aug 16, 2026288
Aug 23, 2026289
Aug 30, 2026248
Sep 6, 2026361
Sep 13, 20261.2k
Sep 20, 2026309
Sep 27, 2026250
Oct 4, 2026655
Oct 11, 2026527
Oct 18, 2026358
Oct 25, 2026356

On Hacker News

The Board ranks tools by four signals each week: new stars, new stars for the repo's size, added downloads and download growth beyond the market. How the ranking works.

Transformers alternatives

Other LLM inference and training tools on the Board in the week of Oct 25–31, 2026, by momentum:

Inference and training ranked by momentum, the week of Oct 25–31, 2026
RankToolStarsDownloads/wk
#1dsh-our-free-modeldsh-our-free-model is a dsh plugin that provides DeepSeek V4.1 Flash, Kimi K3 and other models without login, sign-up or API key.54.3k–
#2magpiemagpie is a local gateway and app that lets coding agents like Claude Code, Codex and Gemini CLI use any provider's model.26.9k–
#3StrataStrata is a local inference engine and app that runs large open models like Qwen3.8-Flash-Next on consumer hardware with…37.1k–
#4edge0edge0 is an open-source streaming inference framework that runs large sparse mixture-of-experts models on consumer hardware by streaming…8.9k–
#5OmniRouteOmniRoute is an open-source AI gateway that routes requests across many model providers through one OpenAI-compatible endpoint, with…83.2k106k
#6CLIProxyAPICLIProxyAPI is a Go proxy server that exposes CLI coding accounts like Codex, Claude Code and Gemini CLI through OpenAI, Gemini and…57.1k–

All LLM inference and training tools, ranked

Recent Transformers releases

Transformers FAQ

Is Transformers free and open source?

Yes. Transformers's source code is on GitHub (huggingface/transformers) under the Apache 2.0 license, so you can use it for free. Its website also links to a pricing page (https://huggingface.co/pricing) for any paid plans.

How do I install Transformers?

Run pip install "transformers[torch]". The transformers package needs Python 3.10.0 or newer. The install section above has the other options from the project's README.

Is Transformers still maintained?

Yes. The huggingface/transformers repository was last updated on Oct 22, 2026, and the latest release, v5.20.2 on GitHub, came out on Oct 19, 2026.

How popular is Transformers?

Transformers has 168.4k GitHub stars as of Nov 1, 2026. It gained 356 new stars in the week of Oct 25–31, 2026, against a 12-week average of 459 a week. Its main package had 27.8M downloads that week, +11% against its own 4-week average. That made it #14 among LLM inference and training tools on the Devtoolwatch Board.

What are the best alternatives to Transformers?

Other LLM inference and training tools on the Devtoolwatch Board in the week of Oct 25–31, 2026, by momentum: dsh-our-free-model (#1), magpie (#2), Strata (#3), edge0 (#4), OmniRoute (#5). The alternatives table above has their numbers.

How do I use the Transformers pipeline?

Transformers pipelines are created with pipeline(task=..., model=...), using a task such as text-generation and a Hub model ID. You then call the pipeline with your input, such as a text prompt or an audio URL. The pipeline handles preprocessing and returns the output.

How do I use the Hugging Face Transformers library in Python?

Transformers is used in Python by installing it into a virtual environment with PyTorch, then importing it with from transformers import pipeline. The quickstart instantiates a text-generation pipeline with a Hub model and passes it a prompt.

Can I chat with a model using Transformers?

Transformers supports chat through the same Pipeline pattern, with a chat history of system and user messages as input. You can also chat from the command line with transformers chat, as long as transformers serve is running.

What tasks does Transformers support?

Transformers supports text, computer vision, audio, video, and multimodal tasks. README examples include text generation, automatic speech recognition, image classification, visual question answering, summarization, translation, object detection, and text to speech.

Does Transformers work with vLLM or llama.cpp?

Transformers model definitions are compatible with inference engines such as vLLM, SGLang, and TGI, and with libraries such as llama.cpp and mlx, according to the README. Supported models also work with training frameworks like DeepSpeed and FSDP.

Sources

  1. huggingface/transformers on GitHub
  2. Transformers README
  3. transformers on PyPI
  4. Transformers website
  5. Release v5.20.2 (Oct 19, 2026)
  6. Release v5.20.1 (Oct 16, 2026)
  7. Release v5.20.0 (Oct 15, 2026)
  8. Hacker News: Qwen 3.8 27B
  9. Hacker News: Kimi-K3 on HuggingFace
  10. Hacker News: DeepSeek-v3.2: Pushing the frontier of open large language models [pdf]
  11. Devtoolwatch Board, the week of Oct 25–31, 2026

Transformers README badge

Maintainers can show the tool's weekly Board rank in their README:

Devtoolwatch badge for Transformers

[![Devtoolwatch](https://index.devtoolwatch.com/badge/transformers.svg)](https://index.devtoolwatch.com/tools/transformers/)

How this page is made: Devtoolwatch writes each review from the project's README, releases, package metadata and website, with an AI model, then checks every claim against the source it cites and drops what the sources don't support. The numbers come from Devtoolwatch's daily GitHub snapshots, GitHub's stargazer history, and the npm and PyPI download APIs. Spotted an error? Email [email protected].