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https://github.com/hwchase17/langchain
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47 lines
1.7 KiB
Markdown
47 lines
1.7 KiB
Markdown
# GPT4All
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This page covers how to use the `GPT4All` wrapper within LangChain. The tutorial is divided into two parts: installation and setup, followed by usage with an example.
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## Installation and Setup
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- Install the Python package with `pip install pyllamacpp`
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- Download a [GPT4All model](https://github.com/nomic-ai/pyllamacpp#supported-model) and place it in your desired directory
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## Usage
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### GPT4All
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To use the GPT4All wrapper, you need to provide the path to the pre-trained model file and the model's configuration.
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```python
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from langchain.llms import GPT4All
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# Instantiate the model. Callbacks support token-wise streaming
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model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8)
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# Generate text
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response = model("Once upon a time, ")
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```
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You can also customize the generation parameters, such as n_predict, temp, top_p, top_k, and others.
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To stream the model's predictions, add in a CallbackManager.
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```python
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from langchain.llms import GPT4All
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from langchain.callbacks.base import CallbackManager
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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# There are many CallbackHandlers supported, such as
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# from langchain.callbacks.streamlit import StreamlitCallbackHandler
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callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
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model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8, callback_handler=callback_handler, verbose=True)
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# Generate text. Tokens are streamed throught the callback manager.
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model("Once upon a time, ")
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```
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## Model File
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You can find links to model file downloads in the [pyllamacpp](https://github.com/nomic-ai/pyllamacpp) repository.
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For a more detailed walkthrough of this, see [this notebook](../modules/models/llms/integrations/gpt4all.ipynb) |