Instructions to use mlx-community/zephyr-7b-beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/zephyr-7b-beta 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/zephyr-7b-beta") 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/zephyr-7b-beta 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/zephyr-7b-beta" --prompt "Once upon a time"
- Atomic Chat
Download tokenizer.model from mlx-community/zephyr-7b-beta: direct link, hf CLI and curl.
- Browser
- Download file 493 kB
-
https://huggingface.co/mlx-community/zephyr-7b-beta/resolve/main/tokenizer.model
- Command line
-
hf download hf://mlx-community/zephyr-7b-beta/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/mlx-community/zephyr-7b-beta/resolve/main/tokenizer.model
493 kB
- Xet hash:
- 1e090c2d2774ea7875da72d682c12600bd69085e9c28674b917a49fe82ccffe2
- Size of remote file:
- 493 kB
- SHA256:
- dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
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