Instructions to use mlx-community/CodeLlama-7b-Python-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/CodeLlama-7b-Python-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-Python-mlx") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use mlx-community/CodeLlama-7b-Python-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-Python-mlx" --prompt "Once upon a time"
- Atomic Chat
Download tokenizer.model from mlx-community/CodeLlama-7b-Python-mlx: direct link, hf CLI and curl.
- Browser
- Download file 500 kB
-
https://huggingface.co/mlx-community/CodeLlama-7b-Python-mlx/resolve/0ccd90fdb34529eb39f0bc02de2cb115c1350390/tokenizer.model
- Command line
-
hf download hf://mlx-community/CodeLlama-7b-Python-mlx@0ccd90fdb34529eb39f0bc02de2cb115c1350390/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/mlx-community/CodeLlama-7b-Python-mlx/resolve/0ccd90fdb34529eb39f0bc02de2cb115c1350390/tokenizer.model
500 kB
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