Instructions to use mlx-community/CodeLlama-13b-Python-4bit-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/CodeLlama-13b-Python-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-13b-Python-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-13b-Python-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-13b-Python-4bit-MLX" --prompt "Once upon a time"
- Xet hash:
- ea30fd334f6c37dc8faaf94c81aa0a1572164cc13272d44a5b8b0de0dfd1e178
- Size of remote file:
- 7.56 GB
- SHA256:
- 2b45430a22714022353516a6e0ed54a272e24b35f3770ee52c2375c7c45c77bb
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