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https://github.com/hwchase17/langchain
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58 lines
1.7 KiB
Plaintext
58 lines
1.7 KiB
Plaintext
# C Transformers
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This page covers how to use the [C Transformers](https://github.com/marella/ctransformers) library within LangChain.
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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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- Install the Python package with `pip install ctransformers`
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- Download a supported [GGML model](https://huggingface.co/TheBloke) (see [Supported Models](https://github.com/marella/ctransformers#supported-models))
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## Wrappers
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### LLM
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There exists a CTransformers LLM wrapper, which you can access with:
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```python
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from langchain.llms import CTransformers
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```
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It provides a unified interface for all models:
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```python
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llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2')
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print(llm('AI is going to'))
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```
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If you are getting `illegal instruction` error, try using `lib='avx'` or `lib='basic'`:
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```py
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llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2', lib='avx')
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```
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It can be used with models hosted on the Hugging Face Hub:
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```py
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llm = CTransformers(model='marella/gpt-2-ggml')
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```
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If a model repo has multiple model files (`.bin` files), specify a model file using:
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```py
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llm = CTransformers(model='marella/gpt-2-ggml', model_file='ggml-model.bin')
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```
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Additional parameters can be passed using the `config` parameter:
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```py
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config = {'max_new_tokens': 256, 'repetition_penalty': 1.1}
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llm = CTransformers(model='marella/gpt-2-ggml', config=config)
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```
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See [Documentation](https://github.com/marella/ctransformers#config) for a list of available parameters.
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For a more detailed walkthrough of this, see [this notebook](/docs/integrations/llms/ctransformers.html).
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