Instructions to use mlx-community/CodeLlama-7b-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/CodeLlama-7b-mlx with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("mlx-community/CodeLlama-7b-mlx") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use mlx-community/CodeLlama-7b-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "mlx-community/CodeLlama-7b-mlx" --prompt "Once upon a time"
metadata
pipeline_tag: text-generation
library_name: mlx
inference: false
tags:
- facebook
- meta
- llama
- llama-2
- codellama
- mlx
license: llama2
CodeLlama
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This model is designed for general code synthesis and understanding. This is the repository for the 7B base model, in npz format suitable for use in Apple's MLX framework.
Weights have been converted to float16 from the original bfloat16 type, because numpy is not compatible with bfloat16 out of the box.
How to use with MLX.
# Install mlx, mlx-examples, huggingface-cli
pip install mlx
pip install huggingface_hub hf_transfer
git clone https://github.com/ml-explore/mlx-examples.git
# Download model
export HF_HUB_ENABLE_HF_TRANSFER=1
huggingface-cli download --local-dir CodeLlama-7b-mlx mlx-llama/CodeLlama-7b-mlx
# Run example
python mlx-examples/llama/llama.py --prompt "int main(char argc, char **argv) {" CodeLlama-7b-mlx/ CodeLlama-7b-mlx/tokenizer.model
Please, refer to the original model card for details on CodeLlama.