North-Micro-Vision
Collection
MLX quantizations of CohereLabs/North-Micro-Vision-Instruct. • 8 items • Updated • 5
North Micro Vision Instruct — BF16 (MLX)
This repository contains an Apple MLX conversion of CohereLabs/North-Micro-Vision-Instruct. BF16 MLX weights (no weight quantization).
It belongs to the North Vision MLX collection, which includes BF16, affine 4/5/6/8-bit, MXFP4, MXFP8, and NVFP4 variants.
Conversion details
- Source: CohereLabs/North-Micro-Vision-Instruct
- Format: MLX / MLX-VLM
- Precision: bfloat16 (unquantized)
- MLX-VLM source revision: main at 7ee8eba3
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-bf16 \
--dtype bfloat16 \
--upload-repo mlx-community/North-Micro-Vision-Instruct-bf16
Usage
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-bf16 \
--image /path/to/image.jpg \
--prompt "Describe this image." \
--max-tokens 512 \
--temperature 0.0
You can also pass an image URL to --image.
Notes
- MLX is optimized for Apple silicon.
- This repository changes the storage precision/quantization, not the source model architecture or intended behavior.
- Refer to the original model card for capabilities, limitations, licensing context, and responsible-use guidance.
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Model size
2B params
Tensor type
BF16
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Model tree for mlx-community/North-Micro-Vision-Instruct-bf16
Base model
CohereLabs/North-Micro-Vision-Instruct
# 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-bf16") config = load_config("mlx-community/North-Micro-Vision-Instruct-bf16") # 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)