Instructions to use mlx-community/CodeLlama-7b-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-7b-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-7b-Python-4bit-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-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-7b-Python-4bit-MLX" --prompt "Once upon a time"
- Xet hash:
- 50e22a5fb9a0e3f8c07dd546e4a96489f0b4a2c72ff55c408ea2a5ee2fe88122
- Size of remote file:
- 3.98 GB
- SHA256:
- dede4f2e2c6551b1d49b073279daa971ae895f21fd1b33ec2a5919e06b30274e
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