Instructions to use prithivMLmods/LightOnOCR-2-1B-f32-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/LightOnOCR-2-1B-f32-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/LightOnOCR-2-1B-f32-GGUF")
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/LightOnOCR-2-1B-f32-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/LightOnOCR-2-1B-f32-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/LightOnOCR-2-1B-f32-GGUF",
	filename="LightOnOCR-2-1B-BF16.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/LightOnOCR-2-1B-f32-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/LightOnOCR-2-1B-f32-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/LightOnOCR-2-1B-f32-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/LightOnOCR-2-1B-f32-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/LightOnOCR-2-1B-f32-GGUF:BF16

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-2-1B-f32-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/LightOnOCR-2-1B-f32-GGUF:BF16

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-2-1B-f32-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/LightOnOCR-2-1B-f32-GGUF:BF16

Use Docker

docker model run hf.co/prithivMLmods/LightOnOCR-2-1B-f32-GGUF:BF16

LM Studio
Jan

vLLM

How to use prithivMLmods/LightOnOCR-2-1B-f32-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/LightOnOCR-2-1B-f32-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-2-1B-f32-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-2-1B-f32-GGUF:BF16

SGLang

How to use prithivMLmods/LightOnOCR-2-1B-f32-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-2-1B-f32-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-2-1B-f32-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-2-1B-f32-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-2-1B-f32-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-2-1B-f32-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/LightOnOCR-2-1B-f32-GGUF:BF16
```

Unsloth Studio

How to use prithivMLmods/LightOnOCR-2-1B-f32-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/LightOnOCR-2-1B-f32-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/LightOnOCR-2-1B-f32-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/LightOnOCR-2-1B-f32-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use prithivMLmods/LightOnOCR-2-1B-f32-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/LightOnOCR-2-1B-f32-GGUF:BF16
```

Lemonade

How to use prithivMLmods/LightOnOCR-2-1B-f32-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/LightOnOCR-2-1B-f32-GGUF:BF16

Run and chat with the model

lemonade run user.LightOnOCR-2-1B-f32-GGUF-BF16

List all available models

lemonade list

prithivMLmods commited on Feb 8

Commit

578f64d

verified ·

1 Parent(s): e3496bc

Update README.md

Browse files

Files changed (1) hide show

README.md +27 -1

README.md CHANGED Viewed

@@ -21,6 +21,32 @@ tags:
 - pdf
 - tables
 - forms
 base_model:
 - lightonai/LightOnOCR-2-1B
----

 - pdf
 - tables
 - forms
+- llama.cpp
 base_model:
 - lightonai/LightOnOCR-2-1B
+---
+# **LightOnOCR-2-1B-f32-GGUF**
+> LightOnOCR-2-1B from lightonai is LightOn's flagship 1B-parameter end-to-end vision-language OCR model—the recommended variant for most tasks—refined via RLVR training on a 2.5x scaled 43M-page corpus with enhanced French, arXiv, scan, and LaTeX coverage for converting PDFs, scans, and document images into clean, naturally ordered text at 3.3× Chandra OCR speed, 1.7× OlmOCR, 5× dots.ocr, and 5.71 pages/s on H100 (~<$0.01/1k pages) while achieving state-of-the-art 83.2±0.9 on OlmOCR-Bench (outperforming Chandra-9B by 1.5+ points) across tables, receipts, forms, multi-column layouts, and math without brittle pipelines. Part of the fully differentiable LightOnOCR-2 family (including bbox variants for image localization, base models for fine-tuning, and soup merges), it uses a native-resolution ViT encoder (from Mistral-Small-3.1), MLP projector, and Qwen3 decoder with 1540px longest-edge preprocessing (200 DPI PDFs) for superior accuracy on degraded scans, scientific docs, and European languages under Apache 2.0, supporting LoRA/PEFT fine-tuning via Transformers for domain adaptation.
+## LightOnOCR-2-1B [GGUF]
+| File Name | Quant Type | File Size | File Link |
+| - | - | - | - |
+| LightOnOCR-2-1B-BF16.gguf | BF16 | 1.2 GB | [Download](https://huggingface.co/prithivMLmods/LightOnOCR-2-1B-f32-GGUF/blob/main/LightOnOCR-2-1B-BF16.gguf) |
+| LightOnOCR-2-1B-F16.gguf | F16 | 1.2 GB | [Download](https://huggingface.co/prithivMLmods/LightOnOCR-2-1B-f32-GGUF/blob/main/LightOnOCR-2-1B-F16.gguf) |
+| LightOnOCR-2-1B-F32.gguf | F32 | 2.39 GB | [Download](https://huggingface.co/prithivMLmods/LightOnOCR-2-1B-f32-GGUF/blob/main/LightOnOCR-2-1B-F32.gguf) |
+| LightOnOCR-2-1B-Q8_0.gguf | Q8_0 | 639 MB | [Download](https://huggingface.co/prithivMLmods/LightOnOCR-2-1B-f32-GGUF/blob/main/LightOnOCR-2-1B-Q8_0.gguf) |
+| LightOnOCR-2-1B.mmproj-BF16.gguf | mmproj-BF16 | 829 MB | [Download](https://huggingface.co/prithivMLmods/LightOnOCR-2-1B-f32-GGUF/blob/main/LightOnOCR-2-1B.mmproj-BF16.gguf) |
+| LightOnOCR-2-1B.mmproj-F16.gguf | mmproj-F16 | 819 MB | [Download](https://huggingface.co/prithivMLmods/LightOnOCR-2-1B-f32-GGUF/blob/main/LightOnOCR-2-1B.mmproj-F16.gguf) |
+| LightOnOCR-2-1B.mmproj-F32.gguf | mmproj-F32 | 1.64 GB | [Download](https://huggingface.co/prithivMLmods/LightOnOCR-2-1B-f32-GGUF/blob/main/LightOnOCR-2-1B.mmproj-F32.gguf) |
+## Quants Usage
+(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
+Here is a handy graph by ikawrakow comparing some lower-quality quant
+types (lower is better):
+![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)