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ctransformers

Python bindings for the Transformer models implemented in C/C++ using GGML library.

Updated Oct 31, 2026
GitHub Docs
Platforms
macos
Pricing
free-open-source
Status
active
License
MIT

What it does

Core capabilities at a glance

  • Ctransformers
  • Transformers

Deep dive

The full breakdown - performance, comparisons, and setup

ctransformers

ctransformers is a local-AI tool - Python bindings for the Transformer models implemented in C/C++ using GGML library.

Overview

Python bindings for the Transformer models implemented in C/C++ using GGML library.

If a model repo has multiple model files ('.bin' or '.gguf' files), specify a model file using:

It can also be used with LangChain. Low-level APIs are not fully supported.

  • 'model_path_or_repo_id' : The path to a model file or directory or the name of a Hugging Face Hub model repo. - 'model_type' : The model type. - 'model_file' : The name of the model file in repo or directory. - 'config' : 'AutoConfig' object. - 'lib' : The path to a shared library or one of 'avx2', 'avx', 'basic'. - 'local_files_only' : Whether or not to only look at local files (i.e., do not try to download the model). - 'revision' : The specific model version to use. It can be a branch name, a tag name, or a commit id. - 'hf' : Whether to create a Hugging Face Transformers model.

  • 'model_path' : The path to a model file. - 'model_type' : The model type. - 'config' : 'Config' object. - 'lib' : The path to a shared library or one of 'avx2', 'avx', 'basic'.

  • 'tokens' : The list of tokens. - 'decode' : Whether to decode the text as UTF-8 string.

ctransformers is open-source, written primarily in C, with 1,886 GitHub stars under the MIT license. The latest release is v0.2.27 (2023-09-10).

Key capabilities

From the project's documentation:

  • model_path_or_repo_id: The path to a model file or directory or the name of a Hugging Face Hub model repo.
  • model_type: The model type.
  • model_file: The name of the model file in repo or directory.
  • lib: The path to a shared library or one of avx2, avx, basic.
  • local_files_only: Whether or not to only look at local files (i.e., do not try to download the model).
  • revision: The specific model version to use. It can be a branch name, a tag name, or a commit id.

Install

A quick way to get started (always check the official docs for the latest):

pip install ctransformers

How it fits a local-AI stack

ctransformers runs on your own hardware, so pair it with a model and a GPU sized to your needs. Use the VRAM calculator to pick a model that fits your card, and see what you can run for hardware guidance. Related local-AI tools in the directory:

Sources

Stats from GitHub, 2026-10-30.

Frequently asked

Quick answers to common questions

What is ctransformers?

ctransformers is a other tool for local AI workloads. Python bindings for the Transformer models implemented in C/C++ using GGML library.

Is ctransformers free and open source?

Yes, ctransformers has 1,886 GitHub stars and is licensed under MIT. You can self-host it for free on macos.

What platforms does ctransformers support?

ctransformers runs on macos.

What hardware do I need for ctransformers?

The hardware requirements depend on which models you run. Check our hardware directory for compatible GPUs and systems. ctransformers has 1,886 GitHub stars and an active community.

Does ctransformers support GPU acceleration?

ctransformers supports GPU acceleration via CUDA, Metal, or Vulkan depending on your platform. For the best performance, pair it with an NVIDIA RTX 4090 or 5090.

What are the best alternatives to ctransformers?

Popular alternatives include other other tools in our directory. Browse our full collection at /tool for comparisons, community reviews, and benchmark data to find the right fit for your workflow.

How much does ctransformers cost?

ctransformers is free-open-source. It is completely free and open source to self-host.

Pairs well with

Complementary tools, models, and hardware