Instructions to use prithivMLmods/LightOnOCR-3-1B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/LightOnOCR-3-1B-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/LightOnOCR-3-1B-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/LightOnOCR-3-1B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/LightOnOCR-3-1B-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/LightOnOCR-3-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/LightOnOCR-3-1B-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/LightOnOCR-3-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/LightOnOCR-3-1B-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/LightOnOCR-3-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/LightOnOCR-3-1B-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/LightOnOCR-3-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/LightOnOCR-3-1B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/LightOnOCR-3-1B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/LightOnOCR-3-1B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/LightOnOCR-3-1B-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/LightOnOCR-3-1B-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/LightOnOCR-3-1B-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/LightOnOCR-3-1B-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/LightOnOCR-3-1B-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/LightOnOCR-3-1B-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/LightOnOCR-3-1B-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/LightOnOCR-3-1B-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/LightOnOCR-3-1B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/LightOnOCR-3-1B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use prithivMLmods/LightOnOCR-3-1B-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/LightOnOCR-3-1B-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/LightOnOCR-3-1B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/LightOnOCR-3-1B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LightOnOCR-3-1B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

LightOnOCR-3-1B-GGUF

LightOnOCR-3-1B, developed by lightonai, is a highly performant, lightweight OCR and end-to-end document understanding model designed as a seamless, drop-in upgrade for existing LightOnOCR-2 deployments. Released under the Apache 2.0 license, it retains the proven LightOnOCR-2-1B architecture while introducing powerful new visual understanding capabilities, including a grounding mode (triggered by the grounding prompt) that outputs labeled bounding boxes for all document elements in normalized page coordinates, short descriptions for images, and structured HTML data extraction from charts. Alongside its default transcription mode that outputs full-page markdown text from an empty prompt, the model is highly versatile—effortlessly handling complex multi-column layouts, tables, receipts, forms, handwriting, and math notation—offering a ready-to-use, single-model alternative to traditional, multi-stage document processing pipelines when images are optimally preprocessed at 200 DPI.

Model Files

File Name	Quant Type	File Size	File Link	Description
LightOnOCR-3-1B.BF16.gguf	BF16	1.2 GB	Link	Full BF16 weights. Highest quality, largest file size.
LightOnOCR-3-1B.Q3_K_L.gguf	Q3_K_L	368 MB	Link	Lower quality but usable, good for low RAM availability.
LightOnOCR-3-1B.Q3_K_M.gguf	Q3_K_M	347 MB	Link	Low quality.
LightOnOCR-3-1B.Q4_K_M.gguf	Q4_K_M	397 MB	Link	Good quality, default size for most use cases, recommended.
LightOnOCR-3-1B.Q4_K_S.gguf	Q4_K_S	383 MB	Link	Slightly lower quality with more space savings, recommended.
LightOnOCR-3-1B.Q5_K_M.gguf	Q5_K_M	444 MB	Link	High quality, recommended.
LightOnOCR-3-1B.Q5_K_S.gguf	Q5_K_S	437 MB	Link	High quality, recommended.
LightOnOCR-3-1B.Q6_K.gguf	Q6_K	495 MB	Link	Very high quality, near perfect, recommended.
LightOnOCR-3-1B.Q8_0.gguf	Q8_0	639 MB	Link	Extremely high quality, generally unneeded but max available quant.
LightOnOCR-3-1B.mmproj-bf16.gguf	mmproj-bf16	829 MB	Link	Multimodal projection file in BF16 format. Used for vision/language models.
LightOnOCR-3-1B.mmproj-q8_0.gguf	mmproj-q8_0	449 MB	Link	Multimodal projection file in Q8_0 quantization. Smaller size for vision capabilities.