mirror of
https://github.com/nomic-ai/gpt4all
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38 lines
1.5 KiB
Markdown
38 lines
1.5 KiB
Markdown
# GPT4All Python API
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The `GPT4All` package provides Python bindings and an API to our C/C++ model backend libraries.
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The source code, README, and local build instructions can be found [here](https://github.com/nomic-ai/gpt4all/tree/main/gpt4all-bindings/python).
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## Quickstart
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```bash
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pip install gpt4all
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```
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In Python, run the following commands to retrieve a GPT4All model and generate a response
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to a prompt.
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**Download Note:**
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By default, models are stored in `~/.cache/gpt4all/` (you can change this with `model_path`). If the file already exists, model download will be skipped.
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```python
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import gpt4all
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gptj = gpt4all.GPT4All("ggml-gpt4all-j-v1.3-groovy")
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messages = [{"role": "user", "content": "Name 3 colors"}]
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gptj.chat_completion(messages)
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```
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## Give it a try!
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[Google Colab Tutorial](https://colab.research.google.com/drive/1QRFHV5lj1Kb7_tGZZGZ-E6BfX6izpeMI?usp=sharing)
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## Supported Models
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Python bindings support the following ggml architectures: `gptj`, `llama`, `mpt`. See API reference for more details.
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## Best Practices
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There are two methods to interface with the underlying language model, `chat_completion()` and `generate()`. Chat completion formats a user-provided message dictionary into a prompt template (see API documentation for more details and options). This will usually produce much better results and is the approach we recommend. You may also prompt the model with `generate()` which will just pass the raw input string to the model.
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## API Reference
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::: gpt4all.gpt4all.GPT4All
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