Instructions to use Durgaram/dots.mocr-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Durgaram/dots.mocr-4bit with Transformers:

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

pipe = pipeline("image-text-to-text", model="Durgaram/dots.mocr-4bit", trust_remote_code=True)
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 AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Durgaram/dots.mocr-4bit", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Durgaram/dots.mocr-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Durgaram/dots.mocr-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Durgaram/dots.mocr-4bit",
		"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/Durgaram/dots.mocr-4bit

SGLang

How to use Durgaram/dots.mocr-4bit 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 "Durgaram/dots.mocr-4bit" \
    --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": "Durgaram/dots.mocr-4bit",
		"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 "Durgaram/dots.mocr-4bit" \
        --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": "Durgaram/dots.mocr-4bit",
		"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 Durgaram/dots.mocr-4bit with Docker Model Runner:
```
docker model run hf.co/Durgaram/dots.mocr-4bit
```

Dots MOCR – 4-bit Quantized (NF4)

🔍 Introduction

This repository provides a 4-bit quantized version of dots.mocr, optimized using BitsAndBytes (NF4 precision) for efficient, low-memory inference.

The original model is a powerful multimodal OCR system capable of:

Document parsing
Layout understanding
Multilingual OCR
Structured outputs (JSON / Markdown / SVG)

This version enables deployment on low-VRAM GPUs while maintaining strong performance.

⚙️ Key Features

4-bit quantization (NF4)
Reduced VRAM usage (~70–80%)
Faster inference
Compatible with Hugging Face Transformers
Supports OCR and document parsing
Suitable for edge and local deployments

🛠️ Installation (Base Setup)

⚠️ This model depends on the original dots.mocr repository.

conda create -n dots_mocr python=3.12
conda activate dots_mocr

git clone https://github.com/rednote-hilab/dots.mocr.git
cd dots.mocr

pip install -e .
pip install flash-attn==2.8.0.post2

🚀 Usage (Quantized Inference)

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_id = "rednote-hilab/dots.mocr"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="float16",
    bnb_4bit_use_double_quant=True
)

tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True
)

# Example usage
inputs = tokenizer("Extract text from image", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📊 Quantization Details

Parameter	Value
Precision	4-bit
Quant Type	NF4
Compute Dtype	float16
Double Quant	Enabled
Library	BitsAndBytes

📌 Use Cases

Document OCR
PDF parsing
Layout detection
Structured data extraction
AI-powered document understanding
Edge deployment of large OCR models

⚠️ Limitations

Slight accuracy drop compared to full precision
GPU recommended for optimal performance
Some layers remain in higher precision
Not fully optimized for CPU inference

🔮 Future Work

GGUF conversion for CPU inference
FlashAttention optimization improvements
Integration with full OCR pipelines
Web UI (Gradio / Streamlit demo)
Benchmark comparisons (VRAM vs accuracy)

🙌 Acknowledgement

Base Model: rednote-hilab/dots.mocr
Quantization: BitsAndBytes
Framework: Hugging Face Transformers

📄 License

MIT License

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Safetensors

Model size

3B params

Tensor type

F32

F16

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