Instructions to use MWirelabs/kren-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MWirelabs/kren-vision with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="MWirelabs/kren-vision")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("MWirelabs/kren-vision")
model = AutoModelForImageTextToText.from_pretrained("MWirelabs/kren-vision")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MWirelabs/kren-vision with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MWirelabs/kren-vision"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MWirelabs/kren-vision",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/MWirelabs/kren-vision

SGLang

How to use MWirelabs/kren-vision with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MWirelabs/kren-vision" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MWirelabs/kren-vision",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MWirelabs/kren-vision" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MWirelabs/kren-vision",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use MWirelabs/kren-vision with Docker Model Runner:
```
docker model run hf.co/MWirelabs/kren-vision
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Kren Vision

Kren Vision is a fine-tuned vision-language model for optical character recognition (OCR) of Northeast Indian languages. It is part of the Kren AI Stack by MWire Labs, focused on building foundational language technology for Northeast India's indigenous languages.

Built on an open-source vision-language model with LoRA fine-tuning on 618k deduplicated synthetic OCR samples across 6 Latin-script NE languages.

Supported Languages

Language	Script
Mizo	Latin
Garo	Latin
Khasi	Latin
Kokborok	Latin
Nagamese	Latin
Nyishi	Latin

Performance

Evaluated on 500 held-out test samples:

Metric	Score
Exact Match	92.60%
CER	0.85%

Usage

from transformers import AutoProcessor, AutoModelForImageTextToText
from qwen_vl_utils import process_vision_info
import torch

processor = AutoProcessor.from_pretrained("MWirelabs/kren-vision")
model = AutoModelForImageTextToText.from_pretrained(
    "MWirelabs/kren-vision",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {"role": "user", "content": [
        {"type": "image", "image": "your_image.jpg"},
        {"type": "text", "text": "OCR the text in this image."}
    ]}
]

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

generated_ids = model.generate(**inputs, max_new_tokens=128)
trimmed = [out[len(inp):] for inp, out in zip(inputs.input_ids, generated_ids)]
output = processor.batch_decode(trimmed, skip_special_tokens=True)
print(output[0])

Training

Data: 618k deduplicated synthetic OCR samples across 6 languages
Fine-tuning: LoRA (r=16, alpha=32) on vision and language projection layers
Hardware: NVIDIA RTX 6000 Ada (48GB)
Epochs: 2

Citation

@misc{kren-vision-2026,
  title={Kren Vision: OCR for Northeast Indian Languages},
  author={MWire Labs},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/MWirelabs/kren-vision}
}

License

CC-BY-4.0 — MWire Labs, 2026

Downloads last month: -

Safetensors

Model size

2B params

Tensor type

BF16