---
title: "transformers vs FastChat"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/huggingface-transformers-vs-lm-sys-fastchat"
tools: ["huggingface-transformers", "lm-sys-fastchat"]
---

# transformers vs FastChat

*GraphCanon updated Jul 11, 2026*

## Verdict

Pick transformers if transformers is a versatile library for training and deploying state-of-the-art models across various domains such as NLP, computer vision, speech recognition, and multi-modal tasks. It supports PyTorch 2.4+ and Python 3; pick FastChat if fastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models.

[transformers](https://huggingface.co/transformers) reports 162k GitHub stars, 34k forks, and 2.5k open issues, last pushed Jul 11, 2026. [FastChat](https://github.com/lm-sys/FastChat) has 39k stars, 4.8k forks, and 1.0k open issues, last pushed May 1, 2026. Figures are from public GitHub metadata via [transformers's repository](https://github.com/huggingface/transformers) and [FastChat's repository](https://github.com/lm-sys/FastChat).

| | [transformers](/tools/huggingface-transformers.md) | [FastChat](/tools/lm-sys-fastchat.md) |
| --- | --- | --- |
| Tagline | Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models | An open platform for training, serving, and evaluating large language models |
| Stars | 162,482 | 39,490 |
| Forks | 33,865 | 4,788 |
| Open issues | 2,475 | 1,027 |
| Language | Python | Python |
| Adopt for | Transformers is a versatile library for training and deploying state-of-the-art models across various domains such as NLP, computer vision, speech recognition, and multi-modal tasks. It supports PyTorch 2.4+ and Python 3 | FastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB |
| Persona | - | - |
| Runtime | - | - |
| License | Transformers is distributed under the Apache-2.0 license, ensuring wide permissions for use in both open-source and proprietary systems. | Apache-2.0 |
| Categories | LLM Frameworks, Model Training, Inference & Serving, Speech & Audio, Computer Vision | LLM Frameworks, Model Training, Inference & Serving, Evaluation & Observability |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [transformers](/tools/huggingface-transformers.md) | [FastChat](/tools/lm-sys-fastchat.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Steady (60%) |
| Days since push | 0d | 71d |
| Open issues (now) | 2.5k | 1.0k |
| Full report | [trust report](/tools/huggingface-transformers/trust.md) | [trust report](/tools/lm-sys-fastchat/trust.md) |

## Decision facts: transformers

- **Requirements:** Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+
- **Adopt for:** Transformers is a versatile library for training and deploying state-of-the-art models across various domains such as NLP, computer vision, speech recognition, and multi-modal tasks. It supports PyTorch 2.4+ and Python 3
- **License detail:** Transformers is distributed under the Apache-2.0 license, ensuring wide permissions for use in both open-source and proprietary systems.

## Decision facts: FastChat

- **Adopt for:** FastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB

## Choose when

### Choose transformers if…

- Requirements: Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+.
- Tags unique to transformers: pretrained models, deep-learning, machine-learning, python.
- Also covers Speech & Audio, Computer Vision.
- The library excels in scenarios where you need highly optimized and pre-trained models available for a wide range of data types including text, vision, audio, and multimodal inputs.

### Choose FastChat if…

- Tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving.
- Also covers Evaluation & Observability.
- - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench.

## When NOT to use transformers

- If the specific task or dataset size does not benefit from state-of-the-art models due to computational inefficiency or overfitting, alternatives may be more suitable.
- It might not be the best choice for projects that strictly require compatibility with frameworks other than PyTorch and Python versions older than 3.10.

## When NOT to use FastChat

- - You require a proprietary or closed-source framework; FastChat is open-source under Apache-2.0 license and its use might be unsuitable for environments requiring proprietary solutions.
- - Your chatbot evaluation needs do not align with the types of data used in FastChat's datasets (e.g., human votes, MT-Bench evaluations).
- - You prefer a more user-friendly setup without the need to clone a repository and manually install dependencies; FastChat requires installation from source with additional steps for Rust and CMake on
- + Mac.

## Common questions

### What is the difference between transformers and FastChat?

transformers: Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models. FastChat: An open platform for training, serving, and evaluating large language models. See the comparison table for live GitHub stats and shared categories.

### When should I choose transformers over FastChat?

Choose transformers over FastChat when Requirements: Min 4 GB RAM; Works with Python 3.10+ and PyTorch 2.4+; Tags unique to transformers: pretrained models, deep-learning, machine-learning, python; Also covers Speech & Audio, Computer Vision; The library excels in scenarios where you need highly optimized and pre-trained models available for a wide range of data types including text, vision, audio, and multimodal inputs.

### When should I choose FastChat over transformers?

Choose FastChat over transformers when Tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving; Also covers Evaluation & Observability; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench.

### When should I avoid transformers?

If the specific task or dataset size does not benefit from state-of-the-art models due to computational inefficiency or overfitting, alternatives may be more suitable. It might not be the best choice for projects that strictly require compatibility with frameworks other than PyTorch and Python versions older than 3.10.

### When should I avoid FastChat?

- You require a proprietary or closed-source framework; FastChat is open-source under Apache-2.0 license and its use might be unsuitable for environments requiring proprietary solutions. - Your chatbot evaluation needs do not align with the types of data used in FastChat's datasets (e.g., human votes, MT-Bench evaluations). - You prefer a more user-friendly setup without the need to clone a repository and manually install dependencies; FastChat requires installation from source with additional steps for Rust and CMake on + Mac.

### Is transformers or FastChat more popular on GitHub?

transformers has more GitHub stars (162,482 vs 39,490). Stars measure visibility, not whether either tool fits your constraints.

### Are transformers and FastChat open source?

Yes - both are open-source projects on GitHub (transformers: Apache-2.0, FastChat: Apache-2.0).

### Where can I find alternatives to transformers or FastChat?

GraphCanon lists graph-backed alternatives at [transformers alternatives](/tools/huggingface-transformers/alternatives) and [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) ([transformers markdown twin](/tools/huggingface-transformers/alternatives.md), [FastChat markdown twin](/tools/lm-sys-fastchat/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/huggingface-transformers-vs-lm-sys-fastchat.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, transformers or FastChat?

transformers: Very active. FastChat: Steady. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for transformers and FastChat?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [transformers trust report](/tools/huggingface-transformers/trust); [FastChat trust report](/tools/lm-sys-fastchat/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=huggingface-transformers`](/api/graphcanon/graph?tool=huggingface-transformers)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_