langchain/docs/versioned_docs/version-0.2.x/integrations/providers/ctransformers.mdx

# C Transformers

This page covers how to use the [C Transformers](https://github.com/marella/ctransformers) library within LangChain.
It is broken into two parts: installation and setup, and then references to specific C Transformers wrappers.

## Installation and Setup

- Install the Python package with `pip install ctransformers`
- Download a supported [GGML model](https://huggingface.co/TheBloke) (see [Supported Models](https://github.com/marella/ctransformers#supported-models))

## Wrappers

### LLM

There exists a CTransformers LLM wrapper, which you can access with:

```python
from langchain_community.llms import CTransformers
```

It provides a unified interface for all models:

```python
llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2')

print(llm('AI is going to'))
```

If you are getting `illegal instruction` error, try using `lib='avx'` or `lib='basic'`:

```py
llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2', lib='avx')
```

It can be used with models hosted on the Hugging Face Hub:

```py
llm = CTransformers(model='marella/gpt-2-ggml')
```

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

```py
llm = CTransformers(model='marella/gpt-2-ggml', model_file='ggml-model.bin')
```

Additional parameters can be passed using the `config` parameter:

```py
config = {'max_new_tokens': 256, 'repetition_penalty': 1.1}

llm = CTransformers(model='marella/gpt-2-ggml', config=config)
```

See [Documentation](https://github.com/marella/ctransformers#config) for a list of available parameters.

For a more detailed walkthrough of this, see [this notebook](/docs/integrations/llms/ctransformers).