--- library_name: mlx license: apache-2.0 pipeline_tag: image-text-to-text language: - en tags: - mlx - mlx-vlm - vision-language - multimodal - cohere - north - quantized base_model: CohereLabs/North-Micro-Vision-Instruct base_model_relation: quantized --- # North Micro Vision Instruct — 8-bit affine (MLX) This repository contains an Apple MLX conversion of [CohereLabs/North-Micro-Vision-Instruct](https://huggingface.co/CohereLabs/North-Micro-Vision-Instruct). 8-bit affine MLX quantization with group size 64. It belongs to the [North Vision MLX collection](https://huggingface.co/collections/mlx-community/north-vision-6a7c9be6ccc1cd992a83aecb), which includes BF16, affine 4/5/6/8-bit, MXFP4, MXFP8, and NVFP4 variants. ## Conversion details - Source: [CohereLabs/North-Micro-Vision-Instruct](https://huggingface.co/CohereLabs/North-Micro-Vision-Instruct) - Format: MLX / MLX-VLM - Quantization: bits: 8; group size: 64; mode: affine - MLX-VLM source revision: main at 7ee8eba3 The repository was regenerated and uploaded directly with the MLX-VLM conversion CLI: ~~~bash python -m mlx_vlm convert \ --hf-path CohereLabs/North-Micro-Vision-Instruct \ --mlx-path North-Micro-Vision-Instruct-8bit \ --quantize --q-bits 8 --q-group-size 64 --q-mode affine \ --upload-repo mlx-community/North-Micro-Vision-Instruct-8bit ~~~ ## Usage Cohere Compass support is available on the current MLX-VLM main branch. Install it directly from GitHub: ~~~bash pip install -U "mlx-vlm @ git+https://github.com/Blaizzy/mlx-vlm.git" ~~~ Run vision-language inference: ~~~bash mlx_vlm.generate \ --model mlx-community/North-Micro-Vision-Instruct-8bit \ --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](https://huggingface.co/CohereLabs/North-Micro-Vision-Instruct) for capabilities, limitations, licensing context, and responsible-use guidance.