Instructions to use prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked 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-3-0.8B-MTP-Repacked:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked: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-3-0.8B-MTP-Repacked:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked"
# 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-3-0.8B-MTP-Repacked",
		"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-3-0.8B-MTP-Repacked:Q4_K_M

SGLang

How to use prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked 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-3-0.8B-MTP-Repacked" \
    --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-3-0.8B-MTP-Repacked",
		"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-3-0.8B-MTP-Repacked" \
        --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-3-0.8B-MTP-Repacked",
		"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-3-0.8B-MTP-Repacked with Ollama:
```
ollama run hf.co/prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked:Q4_K_M
```
Unsloth Desktop

How to use prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked:Q4_K_M
```

Lemonade

How to use prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked:Q4_K_M

Run and chat with the model

lemonade run user.LightOnOCR-3-0.8B-MTP-Repacked-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

LightOnOCR-3-0.8B-MTP-Repacked

LightOnOCR-3-0.8B, developed by lightonai, is the lightest and most efficient model in the LightOnOCR-3 family, designed for end-to-end Optical Character Recognition (OCR) and comprehensive document understanding. Built on the Qwen3.5 vision-language architecture, it streamlines complex document pipelines into a single model featuring two primary modes: a default transcription mode that outputs full-page Markdown text, and a "grounding" mode that returns bounding box coordinates and semantic labels for every visual element. Beyond standard text extraction, the model provides advanced visual understanding by automatically describing images and converting charts into structured HTML data tables. Capable of processing diverse formats—including tables, receipts, forms, multi-column layouts, and math notation—LightOnOCR-3-0.8B offers a fast, versatile, and Apache 2.0-licensed solution for both commercial and research document processing tasks.

MTP — Speculative Decoding: Yes | GGUF — LightOnOCR-3-0.8B-MTP(GGUF)

Multi-Token Prediction (MTP) GGUF is a specialized GGUF model file format extension that integrates speculative decoding directly into the model weights to significantly accelerate local inference. Unlike traditional speculative decoding which requires a separate, smaller "draft" model, MTP GGUF files include additional output heads within the main model architecture that predict multiple future tokens in a single forward pass.

Model Files [GGUF]

File Name	Quant Type	File Size	File Link	Description
LightOnOCR-3-0.8B-MTP.BF16.gguf	BF16	1.56 GB	Link	Full BF16 weights. Highest quality, largest file size.
LightOnOCR-3-0.8B-MTP.Q3_K_L.gguf	Q3_K_L	502 MB	Link	Lower quality but usable, good for low RAM availability.
LightOnOCR-3-0.8B-MTP.Q3_K_M.gguf	Q3_K_M	476 MB	Link	Low quality.
LightOnOCR-3-0.8B-MTP.Q4_K_M.gguf	Q4_K_M	542 MB	Link	Good quality, default size for most use cases, recommended.
LightOnOCR-3-0.8B-MTP.Q4_K_S.gguf	Q4_K_S	517 MB	Link	Slightly lower quality with more space savings, recommended.
LightOnOCR-3-0.8B-MTP.Q5_K_M.gguf	Q5_K_M	593 MB	Link	High quality, recommended.
LightOnOCR-3-0.8B-MTP.Q5_K_S.gguf	Q5_K_S	578 MB	Link	High quality, recommended.
LightOnOCR-3-0.8B-MTP.Q6_K.gguf	Q6_K	647 MB	Link	Very high quality, near perfect, recommended.
LightOnOCR-3-0.8B-MTP.Q8_0.gguf	Q8_0	834 MB	Link	Extremely high quality, generally unneeded but max available quant.
LightOnOCR-3-0.8B-MTP.mmproj-bf16.gguf	mmproj-bf16	207 MB	Link	Multimodal projection file in BF16 format. Used for vision/OCR capabilities.

MTP-Repacked [Transformers, BF16]

File Name	File Type	File Size	File Link	Description
chat_template.jinja	Jinja Template	7.76 kB	Link	Chat template for formatting conversations and prompts.
config.json	JSON	2.69 kB	Link	Core model architecture and configuration parameters.
generation_config.json	JSON	205 Bytes	Link	Default text generation parameters (e.g., temperature, top_p).
merges.txt	Text	3.35 MB	Link	Byte-Pair Encoding (BPE) merge rules for the tokenizer.
model-00001-of-00003.safetensors	Safetensors	696 MB	Link	Model weight shard 1/3. Required to load the model.
model-00002-of-00003.safetensors	Safetensors	694 MB	Link	Model weight shard 2/3. Required to load the model.
model-00003-of-00003.safetensors	Safetensors	358 MB	Link	Model weight shard 3/3. Required to load the model.
model.safetensors.index.json	JSON	45 kB	Link	Index file mapping model weights to their respective shards.
preprocessor_config.json	JSON	390 Bytes	Link	Configuration for preprocessing vision/image inputs.
processor_config.json	JSON	1.19 kB	Link	Configuration for the multimodal processor (text + vision).
tokenizer.json	JSON	20 MB	Link	Complete tokenizer vocabulary and encoding rules.
tokenizer_config.json	JSON	1.17 kB	Link	Tokenizer-specific configuration parameters.
vocab.json	JSON	6.72 MB	Link	Tokenizer vocabulary mapping tokens to IDs.

llama.cpp

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

MTP-repackaged and quantized using llama.cpp by — prithivMLmods.

Downloads last month: -

Safetensors

Model size

0.9B params

Tensor type

BF16

Model tree for prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked

Base model

lightonai/LightOnOCR-3-0.8B

Quantized

(16)

this model

Collections including prithivMLmods/LightOnOCR-3-0.8B-MTP-Repacked