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https://github.com/guslovesmath/llama3_macsilicon

Repository for running LLMs efficiently on Mac silicon (M1, M2, M3). Features Jupyter notebook for Meta-Llama-3 setup using MLX framework, with install guide & perf tips. Aims to optimize LLM performance on Mac silicon for devs & researchers.
https://github.com/guslovesmath/llama3_macsilicon

apple llama3 llms mac machine-learning

Last synced: 13 days ago
JSON representation

Host: GitHub
URL: https://github.com/guslovesmath/llama3_macsilicon
Owner: GusLovesMath
Created: 2024-05-03T22:00:07.000Z (7 months ago)
Default Branch: main
Last Pushed: 2024-05-04T13:12:52.000Z (7 months ago)
Last Synced: 2024-11-02T13:06:04.076Z (13 days ago)
Topics: apple, llama3, llms, mac, machine-learning
Language: Jupyter Notebook
Homepage: https://medium.com/@guslovesmath/efficiently-running-meta-llama-3-on-mac-silicon-m1-m2-m3-61585c9bc741
Size: 93.8 KB
Stars: 8
Watchers: 2
Forks: 1
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

# Meta-Llama-3 on Mac Silicon

## Overview
This Jupyter notebook demonstrates how to run the Meta-Llama-3 model on Apple's Mac silicon devices from [**My Medium Post**](https://medium.com/@guslovesmath/efficiently-running-meta-llama-3-on-mac-silicon-m1-m2-m3-61585c9bc741). It includes examples of generating responses from simple prompts and delves into more complex scenarios like solving mathematical problems.

## Requirements
- Apple Mac with M1, M2, or M3 chip
- macOS Monterey or later
- Python 3.x
- Required Python packages: `ipywidgets`, `torch`, `mlx-lm`

## Setup
Clone this repository and install the necessary packages:

```bash
pip install ipywidgets torch mlx-lm
```

[MLX Cummunity on Hugging Face](https://huggingface.co/mlx-community)