Instructions to use mlx-community/CodeLlama-7b-Python-hf-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/CodeLlama-7b-Python-hf-4bit 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-hf-4bit") 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-Python-hf-4bit 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-hf-4bit" --prompt "Once upon a time"
- Xet hash:
- aef268cef40c133a0355e55a1516f5f2a916a2430e1f4554e4fc4ef035163888
- Size of remote file:
- 3.98 GB
- SHA256:
- 68d70f3c705992ef6c957a45a91ddbfb9ffa9d0c7d17bf919ba7f5d769acf381
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.