Image-Text-to-Text
MLX
Safetensors
English
cohere_compass
mlx-vlm
vision-language
multimodal
cohere
north
conversational
Instructions to use mlx-community/North-Micro-Vision-Instruct-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/North-Micro-Vision-Instruct-bf16 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-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) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 2,057 Bytes
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library_name: mlx
license: apache-2.0
pipeline_tag: image-text-to-text
language:
- en
tags:
- mlx
- mlx-vlm
- vision-language
- multimodal
- cohere
- north
base_model: CohereLabs/North-Micro-Vision-Instruct
---
# North Micro Vision Instruct — BF16 (MLX)
This repository contains an Apple MLX conversion of
[CohereLabs/North-Micro-Vision-Instruct](https://huggingface.co/CohereLabs/North-Micro-Vision-Instruct). BF16 MLX weights (no weight quantization).
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
- Precision: bfloat16 (unquantized)
- 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-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:
~~~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-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](https://huggingface.co/CohereLabs/North-Micro-Vision-Instruct) for capabilities,
limitations, licensing context, and responsible-use guidance.
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