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

Libraries

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/dots.ocr-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.ocr-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/dots.ocr-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.ocr-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/dots.ocr-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.ocr-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/dots.ocr-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.ocr-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/dots.ocr-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/dots.ocr-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.ocr-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.ocr-GGUF:Q4_K_M

SGLang

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

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.dots.ocr-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

dots.ocr-GGUF

dots.ocr is a multilingual document layout parsing model developed by rednote-hilab that unifies layout detection and content recognition within a single vision-language model (VLM), built upon a compact 1.7B-parameter LLM foundation (based on Qwen2.5-VL). It achieves state-of-the-art performance on OmniDocBench across text recognition, table parsing, and reading order tasks, while delivering formula recognition results comparable to much larger models like Gemini 2.5 Pro and Doubao-1.5. The model supports over 100 languages and handles diverse document types including academic papers, books, slides, financial reports, exam papers, magazines, and newspapers, outputting structured JSON with bounding boxes, layout categories (Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title), and extracted text formatted as LaTeX for formulas, HTML for tables, and Markdown for all other content. It can be switched between full layout parsing, detection-only, OCR-only, and grounding OCR modes simply by changing the input prompt, and supports inference via both HuggingFace Transformers and vLLM, with vLLM version 0.9.1 recommended for production deployment.

Model Files

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

Downloads last month: 431

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for prithivMLmods/dots.ocr-GGUF

Base model

dots-studio/dots.ocr

Quantized

(12)

this model

Collection including prithivMLmods/dots.ocr-GGUF

AUG 2026

Collection

Collection of Collections and Models — August 2026 • 18 items • Updated about 3 hours ago • 1