Instructions to use behnamebrahimi/mlx-quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use behnamebrahimi/mlx-quantized 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("behnamebrahimi/mlx-quantized") 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 behnamebrahimi/mlx-quantized with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "behnamebrahimi/mlx-quantized" --prompt "Once upon a time"
- Atomic Chat
- Xet hash:
- 8960fd6b218c207e0cfb6a09730ef9373c921ca02c1864998d022a9a85e4fe65
- Size of remote file:
- 4.26 GB
- SHA256:
- 59dc4953c9e20f04fcffbc1758a95783d7a80f6bca2c2f277d0dcabae499354c
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