Instructions to use mlx-community/CodeLlama-70b-Python-hf-4bit-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/CodeLlama-70b-Python-hf-4bit-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-70b-Python-hf-4bit-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-70b-Python-hf-4bit-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-70b-Python-hf-4bit-MLX" --prompt "Once upon a time"
metadata
language:
- code
license: llama2
tags:
- llama-2
- mlx
pipeline_tag: text-generation
mlx-community/CodeLlama-70b-Python-hf-4bit-MLX
This model was converted to MLX format from codellama/CodeLlama-70b-Python-hf.
Refer to the original model card for more details on the model.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/CodeLlama-70b-Python-hf-4bit-MLX")
response = generate(model, tokenizer, prompt="Write python code for Fibonacci serie.", verbose=True)
