# 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.

Devtoolwatch Board, the week of Oct 25–31, 2026. Canonical page: https://index.devtoolwatch.com/tools/transformers/

## 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](https://index.devtoolwatch.com/categories/inference-and-training/)
- **Maintained by:** Hugging Face (organization, [@huggingface](https://github.com/huggingface) on GitHub)
- **License:** Apache 2.0
- **Language:** Python, Dockerfile, Shell
- **Repository:** [github.com/huggingface/transformers](https://github.com/huggingface/transformers)
- **Website:** [huggingface.co/transformers](https://huggingface.co/transformers)
- **PyPI package:** [transformers](https://pypi.org/project/transformers/) 5.20.2
- **Latest release:** [v5.20.2](https://github.com/huggingface/transformers/releases/tag/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 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

- 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.

## Usage data (the week of Oct 25–31, 2026)

- Board rank: #134 overall, #14 in Inference and training, momentum score 56
- Downloads that week: 27.8M (+11% vs 4-week average)
- New GitHub stars per week (last 12 weeks): Aug 9: 695; Aug 16: 288; Aug 23: 289; Aug 30: 248; Sep 6: 361; Sep 13: 1.2k; Sep 20: 309; Sep 27: 250; Oct 4: 655; Oct 11: 527; Oct 18: 358; Oct 25: 356

## Transformers alternatives

| Rank | Tool | Stars | Downloads/wk |
|---|---|---|---|
| #1 | [dsh-our-free-model](https://index.devtoolwatch.com/tools/ebony-vinyl-dsh-our-free-model/) | 54.3k | – |
| #2 | [magpie](https://index.devtoolwatch.com/tools/yetone-magpie/) | 26.9k | – |
| #3 | [Strata](https://index.devtoolwatch.com/tools/niko1221-strata/) | 37.1k | – |
| #4 | [edge0](https://index.devtoolwatch.com/tools/edge0-ai-edge0/) | 8.9k | – |
| #5 | [OmniRoute](https://index.devtoolwatch.com/tools/diegosouzapw-omniroute/) | 83.2k | 106k |
| #6 | [CLIProxyAPI](https://index.devtoolwatch.com/tools/router-for-me-cliproxyapi/) | 57.1k | – |

## 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](https://github.com/huggingface/transformers)
2. [Transformers README](https://github.com/huggingface/transformers/blob/main/README.md)
3. [transformers on PyPI](https://pypi.org/project/transformers/)
4. [Transformers website](https://huggingface.co/transformers)
5. [Release v5.20.2 (Oct 19, 2026)](https://github.com/huggingface/transformers/releases/tag/v5.20.2)
6. [Release v5.20.1 (Oct 16, 2026)](https://github.com/huggingface/transformers/releases/tag/v5.20.1)
7. [Release v5.20.0 (Oct 15, 2026)](https://github.com/huggingface/transformers/releases/tag/v5.20.0)
8. [Hacker News: Qwen 3.8 27B](https://news.ycombinator.com/item?id=49299605)
9. [Hacker News: Kimi-K3 on HuggingFace](https://news.ycombinator.com/item?id=49065752)
10. [Hacker News: DeepSeek-v3.2: Pushing the frontier of open large language models [pdf]](https://news.ycombinator.com/item?id=46108780)
11. [Devtoolwatch Board, the week of Oct 25–31, 2026](https://index.devtoolwatch.com/)