https://github.com/agiflow/llama31
Test Llama 3.1 tool calling
https://github.com/agiflow/llama31
Last synced: about 1 year ago
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Test Llama 3.1 tool calling
- Host: GitHub
- URL: https://github.com/agiflow/llama31
- Owner: AgiFlow
- Created: 2024-07-24T11:21:20.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-25T05:21:54.000Z (almost 2 years ago)
- Last Synced: 2025-03-29T17:11:08.477Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 825 KB
- Stars: 21
- Watchers: 2
- Forks: 6
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
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README
# Llama 3.1 Tools Review
Testing Llama 3.1 tools with llama.cpp and 4-bit Quantized 8B Instruct. This includes:
- Running Meta's example.
- Check if multiple-tools can be selected from single prompt.
- Check if LLM is able to use previous tool call response.
- Check complex JSON extract with tool call.
## Video walkthrough
[
](https://youtu.be/xuzRIWVxb_w)
## Prerequisites
Before you begin, ensure you have met the following requirements:
- You have installed Python 3.9 or later.
- You have installed [Poetry](https://python-poetry.org/docs/#installation).
## Installation
1. Clone the repository:
```sh
git clone https://github.com/AgiFlow/llama31
cd llama31
```
2. Install the dependencies:
```sh
poetry install
```
3. Activate the virtual environment:
```sh
poetry shell
```
4. Download models
This repo uses [lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF](https://huggingface.co/lmstudio-community/Meta-Llama-3.1-8B-Instruct-GGUF/tree/main) from lmstudio-community for testing. You can also use other models of your choice.
## Running the Jupyter Notebook
1. Launch Jupyter Notebook:
```sh
poetry run jupyter notebook
```
## Contributing
To contribute to this project, please fork the repository and create a pull request. For major changes, please open an issue first to discuss what you would like to change.
## License
This project uses the following license: [MIT License](LICENSE).