This is a North-Micro-Vision-Instruct fine-tune, produced through P-E-W's Heretic (v1.4.0) abliteration engine with Self-Organizing Maps & Magnitude-Preserving Orthogonal Ablation enabled.

Note: The model won't GGUF quant, so it remains untested. (I blame Cohere Labs).

Heretication Results

Score Metric	Value	Parameter	Value
Refusals	4/104	direction_index	13.20
KL Divergence	0.0304	attn.o_proj.max_weights.0	0: 0.58
Initial Refusals	104/104	attn.o_proj.max_weights.1	1: 1.31
		attn.o_proj.max_weights.2	2: 0.39
		attn.o_proj.max_weights.3	3: 1.03
		attn.o_proj.max_weights.4	4: 0.58
		attn.o_proj.max_weight_position	14.22
		attn.o_proj.min_weights.0	0: 0.02
		attn.o_proj.min_weights.1	1: 1.27
		attn.o_proj.min_weights.2	2: 0.16
		attn.o_proj.min_weights.3	3: 0.99
		attn.o_proj.min_weights.4	4: 0.55
		attn.o_proj.min_weight_distance	9.95
		mlp.down_proj.max_weights.0	0: 0.70
		mlp.down_proj.max_weights.1	1: 1.00
		mlp.down_proj.max_weights.2	2: 0.13
		mlp.down_proj.max_weights.3	3: 0.26
		mlp.down_proj.max_weights.4	4: 0.14
		mlp.down_proj.max_weight_position	23.42
		mlp.down_proj.min_weights.0	0: 0.34
		mlp.down_proj.min_weights.1	1: 0.98
		mlp.down_proj.min_weights.2	2: 0.04
		mlp.down_proj.min_weights.3	3: 0.26
		mlp.down_proj.min_weights.4	4: 0.12
		mlp.down_proj.min_weight_distance	3.36

Appendix

Empty system prompt.

Heretication Rituals

   [Trial 220] Refusals:  0/104, KL divergence: 0.0520
   [Trial 385] Refusals:  1/104, KL divergence: 0.0516
   [Trial 151] Refusals:  2/104, KL divergence: 0.0499
   [Trial 346] Refusals:  3/104, KL divergence: 0.0404
 » [Trial 314] Refusals:  4/104, KL divergence: 0.0304
   [Trial 318] Refusals:  5/104, KL divergence: 0.0302
   [Trial 212] Refusals:  9/104, KL divergence: 0.0276
   [Trial 123] Refusals: 15/104, KL divergence: 0.0254
   [Trial 265] Refusals: 16/104, KL divergence: 0.0242
   [Trial 375] Refusals: 18/104, KL divergence: 0.0238
   [Trial 317] Refusals: 20/104, KL divergence: 0.0223
   [Trial 155] Refusals: 21/104, KL divergence: 0.0205
   [Trial 197] Refusals: 22/104, KL divergence: 0.0204
   [Trial 199] Refusals: 23/104, KL divergence: 0.0200
   [Trial 263] Refusals: 25/104, KL divergence: 0.0189
   [Trial 373] Refusals: 28/104, KL divergence: 0.0183

PIQA Benchmarks

┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ Benchmark ┃ Metric               ┃    T314    ┃ Original model ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ PIQA      │ name                 │       piqa │           piqa │
│           │ sample_len           │       1838 │           1838 │
│           │ acc,none             │     0.6975 │         0.6991 │
│           │ acc_stderr,none      │     0.0107 │         0.0107 │
│           │ acc_norm,none        │     0.7035 │         0.7051 │
│           │ acc_norm_stderr,none │     0.0107 │         0.0106 │
└───────────┴──────────────────────┴────────────┴────────────────┘
┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ Benchmark ┃ Metric               ┃    T151    ┃ Original model ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ PIQA      │ name                 │       piqa │           piqa │
│           │ sample_len           │       1838 │           1838 │
│           │ acc,none             │     0.6953 │         0.6991 │
│           │ acc_stderr,none      │     0.0107 │         0.0107 │
│           │ acc_norm,none        │     0.7035 │         0.7051 │
│           │ acc_norm_stderr,none │     0.0107 │         0.0106 │
└───────────┴──────────────────────┴────────────┴────────────────┘
┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ Benchmark ┃ Metric               ┃    T346    ┃ Original model ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ PIQA      │ name                 │       piqa │           piqa │
│           │ sample_len           │       1838 │           1838 │
│           │ acc,none             │     0.6959 │         0.6991 │
│           │ acc_stderr,none      │     0.0107 │         0.0107 │
│           │ acc_norm,none        │     0.7029 │         0.7051 │
│           │ acc_norm_stderr,none │     0.0107 │         0.0106 │
└───────────┴──────────────────────┴────────────┴────────────────┘
┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ Benchmark ┃ Metric               ┃    T220    ┃ Original model ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ PIQA      │ name                 │       piqa │           piqa │
│           │ sample_len           │       1838 │           1838 │
│           │ acc,none             │     0.6975 │         0.6991 │
│           │ acc_stderr,none      │     0.0107 │         0.0107 │
│           │ acc_norm,none        │     0.7024 │         0.7051 │
│           │ acc_norm_stderr,none │     0.0107 │         0.0106 │
└───────────┴──────────────────────┴────────────┴────────────────┘
┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ Benchmark ┃ Metric               ┃    T385    ┃ Original model ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ PIQA      │ name                 │       piqa │           piqa │
│           │ sample_len           │       1838 │           1838 │
│           │ acc,none             │     0.6948 │         0.6991 │
│           │ acc_stderr,none      │     0.0107 │         0.0107 │
│           │ acc_norm,none        │     0.6975 │         0.7051 │
│           │ acc_norm_stderr,none │     0.0107 │         0.0106 │
└───────────┴──────────────────────┴────────────┴────────────────┘

PaCMAP Projection

North Micro Vision Instruct

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.

Developed by Cohere.

Technical deep dive: Read the North Micro Vision technical blog post for architecture, training, and evaluation details.

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.

Quickstart

Installation

Install PyTorch for your platform first. North Micro Vision requires Transformers 5.16.0, together with accelerate for automatic device placement and Pillow for image loading. Until Transformers 5.16.0 is released, install the runtime dependencies and Transformers from source:

bash

uv pip install accelerate pillow
uv pip install "git+https://github.com/huggingface/transformers.git"

Once Transformers 5.16.0 is available on PyPI, install the released package with:

uv pip install accelerate pillow "transformers==5.16.0"

Flash Attention 2 is optional. On supported CUDA systems, install it with:

uv pip install flash-attn --no-build-isolation

If you do not use uv, replace uv pip with pip in the commands above.

Transformers

The following example loads an image from a URL and asks the model to describe it. Prompts can interleave text with one or more images; for text-only prompts, omit the image entries.

python

import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "CohereLabs/North-Micro-Vision-Instruct"

processor = AutoProcessor.from_pretrained(
    model_id,
)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    dtype="auto",
    device_map="auto",
)

# To enable Flash Attention 2, load the model with the following settings:
# model = AutoModelForImageTextToText.from_pretrained(
#     model_id,
#     dtype=torch.bfloat16,
#     attn_implementation="flash_attention_2",
#     device_map="auto",
# )

image_url = "https://cdn-uploads.huggingface.co/production/uploads/66d732effe6684fc16b12c28/Io_5OCmftsmH-n158ZtPs.png"
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": image_url},
            {"type": "text", "text": "What do you see?"},
        ],
    }
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=128,
    do_sample=True,
    temperature=0.7,
    top_p=0.8,
    top_k=20,
)

generated_ids = [
    output_ids[len(input_ids) :]
    for input_ids, output_ids in zip(inputs.input_ids, outputs)
]
response = processor.batch_decode(
    generated_ids,
    skip_special_tokens=True,
    clean_up_tokenization_spaces=False,
)[0]
print(response)

The example uses the recommended Transformers sampling settings. For deterministic output, set do_sample=False and omit temperature, top_p, and top_k.

Grounding Coordinates

Bounding boxes are returned as [x1, y1, x2, y2] on a normalized 0–1000 scale. Map them back to the original image by scaling each axis:

python

x1_px = x1 / 1000 * image_width
y1_px = y1 / 1000 * image_height
x2_px = x2 / 1000 * image_width
y2_px = y2 / 1000 * image_height

vLLM

Public vLLM support is coming soon. Until it is available, use Transformers as shown above. The recommended vLLM settings will be:

python

temperature = 0.7
top_p = 0.8
top_k = 20
min_p = 0.0
presence_penalty = 1.5
repetition_penalty = 1.0

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.

Benchmark Results

The complete comparison is provided below. We ran vision-language and text-only evaluations with VLMEvalKit, capping generation at 1,024 tokens; see the technical blog post for the full methodology.

	North-Micro-Vision-Instruct	Ministral-3-3B-Instruct	LFM2.5-VL-1.6B	Phi-3.5-vision-instruct	Gemma-4-E2B-it	Qwen3-VL-2B-Instruct	Qwen3.5-2B-Instruct	SmolVLM2.2B
Size	2.4B	3.8B	1.6B	4.2B	5.1B	2.2B	2.1B	2.2B
License	Apache 2.0	Apache 2.0	LFM v1.0	MIT	Apache 2.0	Apache 2.0	Apache 2.0	Apache 2.0
General VQA
MMBench_{DEV_EN_V11}	0.687	0.692	0.696	0.731	0.693	0.744	0.760	0.674
MMStar	0.518	0.531	0.508	0.495	0.529	0.506	0.614	0.460
RealWorldQA	0.622	0.583	0.642	0.580	0.507	0.646	0.693	0.567
GQA_{TestDev_Balanced}	0.574	0.544	0.395	0.650	0.387	0.572	0.539	0.000^‡
Multilingual
MTL_{MMBench_DEV}	0.636	0.674	0.623	0.619	0.648	0.664	0.669	0.454
MMMB	0.728	0.734	0.717	0.686	0.743	0.723	0.745	0.577
Multi-image
BLINK	0.527	0.471	0.484	0.561	0.468	0.514	0.563	0.420
Chart / Document / OCR
ChartQA_Test	0.808	0.791	0.739	0.821	0.422	0.693	0.775	0.682
DocVQA_VAL	0.921	0.896	0.877	0.860	0.732	0.825	0.926	0.799
InfoVQA_VAL	0.652	0.589	0.627	0.561	0.380	0.622	0.731	0.383
OCRBench_{v2_en}	0.367	0.414	0.415	0.339	0.435	0.417	0.481	0.304
OCRBench	0.792	0.735	0.802	0.642	0.719	0.751	0.861	0.727
AI2D_TEST	0.775	0.741	0.728	0.790	0.712	0.713	0.752	0.697
CharXiv_DQ	0.600	0.766	0.516	0.637	0.751	0.595	0.761	0.482
STEM
MMMU_{DEV_VAL}	0.329	0.508	0.380	0.432	0.477	0.379	0.474	0.399
Grounding / Counting
RefCOCO_avg^†	0.732	0.317	0.581	0.451	0.084	0.304	0.785	0.018
CountBench	0.725	0.737	0.910	0.645	0.534	0.848	0.805	0.764
Robustness / Hallucination
HallusionBench	0.615	0.652	0.601	0.585	0.598	0.673	0.655	0.600
Text
MMLU_test	0.504	0.660	0.464	0.355	0.692	0.630	0.543	0.084
MMLU-Pro_test	0.307	0.475	0.199	0.286	0.441	0.428	0.298	0.099
Multi-If	0.373	0.470	0.443	0.304	0.687	0.523	0.464	0.236
IFEval	0.749	0.725	0.776	0.543	0.869	0.734	0.679	0.501

^† Averaged over RefCOCO_val, RefCOCO_testA, RefCOCO_testB, RefCOCO+_val, RefCOCO+_testA, RefCOCO+_testB, RefCOCOg_val, RefCOCOg_test.

^‡ SmolVLM2.2B's GQA output was scored as 0.000 under VLMEvalKit's answer-extraction rules.

Citation

bibtex

@misc{cohere_north_micro_vision_instruct,
    title = {{North Micro Vision}: A 2.4B Native-Resolution Vision-Language Model},
    url = {https://huggingface.co/blog/CohereLabs/meet-north-micro-vision-instruct},
    author = {{Team Cohere}},
    month = {August},
    year = {2026}
}

Contact

For errors or questions about this model card, contact Cohere Labs.

MuXodious/North-Micro-Vision-Instruct-SOMPOA-heresy

North Micro Vision Instruct

Highlights

Model Details

Quickstart

Installation

Transformers

Grounding Coordinates

vLLM

Intended Use

Limitations

Benchmark Results

Citation

Contact

MuXodious/
North-Micro-Vision-Instruct-SOMPOA-heresy