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
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Download README.md from behnamebrahimi/mlx-quantized: direct link, hf CLI and curl.
- Browser
- Download file 622 Bytes
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https://huggingface.co/behnamebrahimi/mlx-quantized/resolve/main/README.md
- Command line
-
hf download hf://behnamebrahimi/mlx-quantized/README.md
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curl -L -o README.md https://huggingface.co/behnamebrahimi/mlx-quantized/resolve/main/README.md
622 Bytes
| language: | |
| - en | |
| license: apache-2.0 | |
| tags: | |
| - pretrained | |
| - mlx | |
| pipeline_tag: text-generation | |
| inference: | |
| parameters: | |
| temperature: 0.7 | |
| # behnamebrahimi/mlx-quantized | |
| This model was converted to MLX format from [`mistralai/Mistral-7B-v0.1`]() using mlx-lm version **0.4.0**. | |
| Refer to the [original model card](https://huggingface.co/mistralai/Mistral-7B-v0.1) for more details on the model. | |
| ## Use with mlx | |
| ```bash | |
| pip install mlx-lm | |
| ``` | |
| ```python | |
| from mlx_lm import load, generate | |
| model, tokenizer = load("behnamebrahimi/mlx-quantized") | |
| response = generate(model, tokenizer, prompt="hello", verbose=True) | |
| ``` | |