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This page covers how to use the C Transformers library within LangChain. It is broken into two parts: installation and setup, and then references to specific C Transformers wrappers.

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Installation and Setup

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Wrappers

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LLM

There exists a CTransformers LLM wrapper, which you can access with: 
from langchain_community.llms import CTransformers
It provides a unified interface for all models: 
llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2')

print(llm.invoke('AI is going to'))
If you are getting illegal instruction error, try using lib='avx' or lib='basic': 
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: 
llm = CTransformers(model='marella/gpt-2-ggml')
If a model repo has multiple model files (.bin files), specify a model file using: 
llm = CTransformers(model='marella/gpt-2-ggml', model_file='ggml-model.bin')
Additional parameters can be passed using the config parameter: 
config = {'max_new_tokens': 256, 'repetition_penalty': 1.1}

llm = CTransformers(model='marella/gpt-2-ggml', config=config)
See Documentation for a list of available parameters. For a more detailed walkthrough of this, see this notebook.
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