Model Releases
CohereLabs/North-Micro-Vision-Instruct · Hugging Face
North Micro Vision Instruct is a 2.4B-parameter open-weight vision-language model with native-resolution image support, released under the Apache 2.0 license. It is designed as a compact foundation fo
North Micro Vision Instruct is a 2.4B-parameter open-weight vision-language model with native-resolution image support, released under the Apache 2.0 license. It is designed as a compact foundation for prototyping, task-specific fine-tuning, and specialized multimodal applications. Highlights Native-resolution image processing that preserves aspect ratios and fine visual detail. Broad image-understanding capabilities across VQA, captioning, grounding, OCR, charts, and documents. Multilingual and multi-image support. Compact 2.4B-parameter scale suited to customization and deployment experimentation. Apache 2.0-licensed model weights. Model Details Property Value Model ID CohereLabs/North-Micro-Vision-Instruct Total parameters 2.4B Language model 2B parameters Vision encoder 400M parameters; custom-trained starting from SigLIP 2 SO400M Inputs Interleaved text and images Output Text Languages English, German, French, Spanish, Italian, Portuguese, Hindi, Japanese, Korean, Chinese, Arabic, and more Tokenizer vocabulary size 262,144 LM Backbone context window 128K tokens Multimodal training context 8K tokens Checkpoint precision bfloat16 License Apache 2.0 The language backbone supports a 128K-token context window, but the validated operating range for multimodal prompts is up to 8K tokens. Longer multimodal contexts may rely on extrapolation and have not been benchmarked. Intended Use North Micro Vision Instruct is intended for research and development use cases such as: Prototyping and task-specific fine-tuning. General visual question answering and image captioning. Multilingual and multi-image understanding. Visual grounding and spatial understanding. OCR, chart and document understanding, and structured information extraction. Limitations The model is intended as a compact foundation for customization rather than a replacement for larger general-purpose chat assistants. It is not a reasoning model and has limited math and code-generation capabilities. Tool calling and agentic workflows are not supported. System prompts are not recommended because the model was not trained with them, although the chat template accepts the system role. Multimodal training used an 8K-token context; longer contexts have not been validated. Native-resolution inputs can increase memory use and latency as image dimensions grow. submitted by /u/pmttyji [link] [comments]
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Source: r/LocalLLaMA | 2026-08-12