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
-
https://huggingface.co/behnamebrahimi/mlx-quantized/resolve/main/README.md
- Command line
-
hf download hf://behnamebrahimi/mlx-quantized/README.md
-
curl -L -o README.md https://huggingface.co/behnamebrahimi/mlx-quantized/resolve/main/README.md
622 Bytes
metadata
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 for more details on the model.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("behnamebrahimi/mlx-quantized")
response = generate(model, tokenizer, prompt="hello", verbose=True)