North-Micro-Vision
Collection
MLX quantizations of CohereLabs/North-Micro-Vision-Instruct. • 8 items • Updated • 2
How to use mlx-community/North-Micro-Vision-Instruct-mxfp8 with MLX:
# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config
# Load the model
model, processor = load("mlx-community/North-Micro-Vision-Instruct-mxfp8")
config = load_config("mlx-community/North-Micro-Vision-Instruct-mxfp8")
# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."
# Apply chat template
formatted_prompt = apply_chat_template(
processor, config, prompt, num_images=1
)
# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)This repository contains an Apple MLX conversion of CohereLabs/North-Micro-Vision-Instruct. MXFP8 MLX quantization with group size 32.
It belongs to the North Vision MLX collection, which includes BF16, affine 4/5/6/8-bit, MXFP4, MXFP8, and NVFP4 variants.
The repository was regenerated and uploaded directly with the MLX-VLM conversion CLI:
python -m mlx_vlm convert \
--hf-path CohereLabs/North-Micro-Vision-Instruct \
--mlx-path North-Micro-Vision-Instruct-mxfp8 \
--quantize --q-bits 8 --q-group-size 32 --q-mode mxfp8 \
--upload-repo mlx-community/North-Micro-Vision-Instruct-mxfp8
Cohere Compass support is available on the current MLX-VLM main branch. Install it directly from GitHub:
pip install -U "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm.git"
Run vision-language inference:
mlx_vlm.generate \
--model mlx-community/North-Micro-Vision-Instruct-mxfp8 \
--image /path/to/image.jpg \
--prompt "Describe this image." \
--max-tokens 512 \
--temperature 0.0
You can also pass an image URL to --image.
8-bit
Base model
CohereLabs/North-Micro-Vision-Instruct