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

Libraries

How to use prithivMLmods/dots.mocr-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/dots.mocr-GGUF", 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 AutoModel
model = AutoModel.from_pretrained("prithivMLmods/dots.mocr-GGUF", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/dots.mocr-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/dots.mocr-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/dots.mocr-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/dots.mocr-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/dots.mocr-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf prithivMLmods/dots.mocr-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/dots.mocr-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf prithivMLmods/dots.mocr-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/dots.mocr-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/dots.mocr-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/dots.mocr-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/dots.mocr-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/dots.mocr-GGUF",
		"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/prithivMLmods/dots.mocr-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/dots.mocr-GGUF 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 "prithivMLmods/dots.mocr-GGUF" \
    --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": "prithivMLmods/dots.mocr-GGUF",
		"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 "prithivMLmods/dots.mocr-GGUF" \
        --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": "prithivMLmods/dots.mocr-GGUF",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/dots.mocr-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/dots.mocr-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use prithivMLmods/dots.mocr-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/dots.mocr-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/dots.mocr-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/dots.mocr-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.dots.mocr-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

dots.mocr-GGUF / README.md

prithivMLmods

Update README.md

5cd9f01 verified 4 days ago

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metadata

license: mit
language:
  - en
  - zh
  - multilingual
base_model:
  - dots-studio/dots.mocr
library_name: transformers
tags:
  - text-generation-inference
  - llama-cpp
  - image-to-text
  - ocr
  - document-parse
  - layout
  - table
  - formula
  - transformers
  - custom_code
pipeline_tag: image-text-to-text

dots.mocr-GGUF

dots.mocr is an advanced multimodal OCR model developed by rednote-hilab as the successor to dots.ocr, built on a 3B-parameter vision-language model foundation that extends beyond standard document parsing to unify layout detection, content recognition, structured graphics parsing, grounding, semantic understanding, and interactive dialogue within a single framework. It achieves state-of-the-art performance among models of comparable size across multiple benchmarks, including OmniDocBench (v1.5), olmOCR-bench (83.9%), and XDocParse, surpassing competing specialized models like MonkeyOCR-pro-3B, GLM-OCR, PaddleOCR-VL-1.5, and HuanyuanOCR, while approaching the performance of much larger general VLMs like Gemini 3 Pro. A distinctive capability of dots.mocr is its ability to parse structured graphics — including charts, UI layouts, scientific figures, chemical formulas, and logos — directly into SVG code, with a companion model dots.mocr-svg specifically optimized for this image-to-SVG task, achieving scores of 0.902 on UniSVG, 0.905 on ChartMimic, and 0.901 on ChemDraw. The model supports multilingual document parsing across 100+ languages, handles diverse document types, outputs structured JSON with bounding boxes and layout categories, and supports inference via both HuggingFace Transformers and vLLM (officially integrated since vLLM v0.11.0), with additional capabilities including web parsing, scene text spotting, and general visual question answering.

Model Files

File Name	Quant Type	File Size	File Link
dots.mocr.BF16.gguf	BF16	3.56 GB	Download
dots.mocr.F16.gguf	F16	3.56 GB	Download
dots.mocr.Q3_K_L.gguf	Q3_K_L	980 MB	Download
dots.mocr.Q3_K_M.gguf	Q3_K_M	924 MB	Download
dots.mocr.Q3_K_S.gguf	Q3_K_S	861 MB	Download
dots.mocr.Q4_K_M.gguf	Q4_K_M	1.12 GB	Download
dots.mocr.Q4_K_S.gguf	Q4_K_S	1.07 GB	Download
dots.mocr.Q5_K_M.gguf	Q5_K_M	1.29 GB	Download
dots.mocr.Q5_K_S.gguf	Q5_K_S	1.26 GB	Download
dots.mocr.Q6_K.gguf	Q6_K	1.46 GB	Download
dots.mocr.Q8_0.gguf	Q8_0	1.89 GB	Download
dots.mocr.mmproj-bf16.gguf	mmproj-bf16	2.53 GB	Download
dots.mocr.mmproj-f16.gguf	mmproj-f16	2.53 GB	Download
dots.mocr.mmproj-q8_0.gguf	mmproj-q8_0	1.34 GB	Download

llama.cpp

LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp