Instructions to use Vishva007/dots.mocr-W4A16-AutoRound-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Local Apps Settings

How to use Vishva007/dots.mocr-W4A16-AutoRound-GPTQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vishva007/dots.mocr-W4A16-AutoRound-GPTQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vishva007/dots.mocr-W4A16-AutoRound-GPTQ",
		"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/Vishva007/dots.mocr-W4A16-AutoRound-GPTQ

SGLang

How to use Vishva007/dots.mocr-W4A16-AutoRound-GPTQ 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 "Vishva007/dots.mocr-W4A16-AutoRound-GPTQ" \
    --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": "Vishva007/dots.mocr-W4A16-AutoRound-GPTQ",
		"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 "Vishva007/dots.mocr-W4A16-AutoRound-GPTQ" \
        --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": "Vishva007/dots.mocr-W4A16-AutoRound-GPTQ",
		"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 Vishva007/dots.mocr-W4A16-AutoRound-GPTQ with Docker Model Runner:
```
docker model run hf.co/Vishva007/dots.mocr-W4A16-AutoRound-GPTQ
```

dots.mocr W4A16 AutoRound Quantized

This repository provides production-grade W4A16 quantized weights for dots-studio/dots.mocr using Intel AutoRound.

Available Variants

Vishva007/dots.mocr-W4A16-AutoRound: Native AutoRound format.
Vishva007/dots.mocr-W4A16-AutoRound-GPTQ: Exported GPTQ format optimized for direct serving in vLLM.

Quantization Details

The quantization recipe was tuned for high accuracy and long-context multimodal parsing while preventing degradation of visual features:

Bits / Weight Type: 4-bit weights, 16-bit activations (W4A16)
Group Size: 32 (Symmetric: True)
Vision Tower Preservation: quant_nontext_module=False (vision encoder preserved in full precision to retain document grounding accuracy)
Tuning Recipe: 1,500 iterations, 1,024 samples, sequence length 8,192 with torch.compile acceleration.

Deployment with vLLM

For high-throughput document parsing, serve the GPTQ variant directly with vLLM:

vllm serve Vishva007/dots.mocr-W4A16-AutoRound-GPTQ \
  --host 0.0.0.0 \
  --port 8000 \
  --trust-remote-code \
  --chat-template-content-format string \
  --dtype bfloat16 \
  --kv-cache-dtype auto \
  --max-model-len 32768 \
  --max-num-seqs 128 \
  --gpu-memory-utilization 0.90 \
  --enable-prefix-caching \
  --enable-chunked-prefill

Benchmark Highlights (Original Base Model)

olmOCR-Bench: 83.9 Overall
OmniDocBench (v1.5): 0.031 TextEdit / 0.029 Read OrderEdit
Native Structured Output: Direct image-to-Markdown, HTML tables, LaTeX formulas, and SVG generation.

🚀 Deploy on RunPod

One-click launch environments pre-configured with PyTorch, CUDA, and dependencies for fine-tuning or quantization.

🎁 Need GPU compute? Sign up via RunPod and get $5–$500 in free credits when you add your first $10.

PyTorch 2.14

Template	CUDA Version	Docker Image	Template ID
PyTorch 2.14 (CUDA 12.6)	12.6	`vishva123/cuda-12.6-pytorch-2.14-runpod`	`d7lxsa4w9m`
PyTorch 2.14 (CUDA 13.0)	13.0	`vishva123/cuda-13.0-pytorch-2.14-runpod`	`yk0y6j6rpg`
PyTorch 2.14 (CUDA 13.2)	13.2	`vishva123/cuda-13.2-pytorch-2.14-runpod`	`gsp4gwx0nw`

PyTorch 2.13

Template	CUDA Version	Docker Image	Template ID
PyTorch 2.13 (CUDA 12.6)	12.6	`vishva123/cuda-12.6-pytorch-2.13-runpod`	`gmlupxnxfk`
PyTorch 2.13 (CUDA 13.0)	13.0	`vishva123/cuda-13.0-pytorch-2.13-runpod`	`y3j8xvk4f4`
PyTorch 2.13 (CUDA 13.2)	13.2	`vishva123/cuda-13.2-pytorch-2.13-runpod`	`vigpissn5w`

PyTorch 2.12

Template	CUDA Version	Docker Image	Template ID
PyTorch 2.12 (CUDA 12.6)	12.6	`vishva123/cuda-12.6-pytorch-2.12-runpod`	`ctmz86zmf0`
PyTorch 2.12 (CUDA 13.0)	13.0	`vishva123/cuda-13.0-pytorch-2.12-runpod`	`qjko5yiwzi`
PyTorch 2.12 (CUDA 13.2)	13.2	`vishva123/cuda-13.2-pytorch-2.12-runpod`	`ifg6xmye0f`

Acknowledgements

Base model developed by dots-studio.
Quantized using Intel AutoRound.

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Safetensors

Model size

3B params

Tensor type

I32

BF16

F16

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