Instructions to use samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb with Transformers:

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

pipe = pipeline("image-text-to-text", model="samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb")
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 AutoProcessor, AutoModelForSeq2SeqLM

processor = AutoProcessor.from_pretrained("samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb")
model = AutoModelForSeq2SeqLM.from_pretrained("samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb",
		"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/samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb

SGLang

How to use samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb 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 "samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb" \
    --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": "samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb",
		"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 "samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb" \
        --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": "samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb with Docker Model Runner:
```
docker model run hf.co/samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb
```

LightOnOCR-2-1B-sam-44-mss-alb

Finetuned OCR model for Medieval Samaritan Hebrew & Samaritan Aramaic Manuscripts

Overview

LightOnOCR-2-1B-sam-44-mss-alb is a specialized OCR model finetuned from lightonai/LightOnOCR-2-1B-base.
It is trained on 44 medieval Samaritan Hebrew and Samaritan Aramaic manuscripts, using mild FineVision albumentations to improve robustness to real-world degradation such as ink fading, page warping, and historical noise.

This model is designed for high-accuracy OCR on historical Samaritan scripts and performs well on both clean and degraded manuscript images.

Base model

Base: lightonai/LightOnOCR-2-1B-base
Architecture: Vision–Language OCR (encoder → mmproj → decoder)
Context length: 16,384 tokens
Training precision: bf16
Image preprocessing: longest_edge = 1540

Training dataset

This model was trained on:

samaritan-ai/sam_44_mss_albumentations

Dataset characteristics

44 medieval manuscripts
Languages:
- Samaritan Hebrew
- Samaritan Aramaic
FineVision albumentations:
- mild geometric distortions
- blur, noise, sharpening
- contrast/brightness variation
- realistic manuscript degradation
Aligned SAM-based segmentation
275k+ training samples

Training details

Frameworks:
- Unsloth 2026.1.4
- Transformers 5.1.0
- PEFT + LoRA
Precision: bf16
Image preprocessing: longest_edge = 1540
Early stopping:
- patience: 3
- eval_steps: 1000
- patience_in_steps: 3000

Evaluation results

Before vs after finetuning

Metric	Before training	After training
CER	721.54	1.64
WER	524.51	6.17
Perfect matches	0 / 100	78 / 100

JSON summary

{
  "before_training": {
    "cer": 721.5432960893855,
    "wer": 524.512987012987,
    "perfect_matches": 0,
    "total_samples": 100
  },
  "after_training": {
    "cer": 1.6410614525139664,
    "wer": 6.1688311688311686,
    "perfect_matches": 78,
    "total_samples": 100
  },
  "early_stopping": {
    "patience": 3,
    "eval_steps": 1000,
    "patience_in_steps": 3000
  }
}

GGUF weights

This model uses the standard multimodal GGUF layout:
LLM weights + mmproj weights (two files per quantization).

LLM weights

LightOnOCR-2-1B-sam-44-mss-alb-f16.gguf   (1.19 GB)
LightOnOCR-2-1B-sam-44-mss-alb-f32.gguf   (2.39 GB)
LightOnOCR-2-1B-sam-44-mss-alb-q8_0.gguf  (0.63 GB)

mmproj weights

mmproj-LightOnOCR-2-1B-sam-44-mss-alb-f16.gguf   (0.82 GB)
mmproj-LightOnOCR-2-1B-sam-44-mss-alb-f32.gguf   (1.63 GB)
mmproj-LightOnOCR-2-1B-sam-44-mss-alb-q8_0.gguf  (0.45 GB)

Note:
Multimodal GGUF models must remain split into LLM + mmproj.
Merging is not supported by GGUF or llama.cpp.

Ollama Modelfile example

FROM ./LightOnOCR-2-1B-sam-44-mss-alb-q8_0.gguf
FROM ./mmproj-LightOnOCR-2-1B-sam-44-mss-alb-f32.gguf

TEMPLATE {{ .Prompt }}
RENDERER qwen3-vl-instruct
PARSER qwen3-vl-instruct

PARAMETER top_p 0.9
PARAMETER temperature 0.2
PARAMETER num_ctx 16384
PARAMETER num_predict 4096

Usage (Python)

from PIL import Image
from transformers import AutoProcessor, AutoModelForVision2Seq

processor = AutoProcessor.from_pretrained("samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb")
model = AutoModelForVision2Seq.from_pretrained("samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb")

img = Image.open("page.png")
inputs = processor(images=img, return_tensors="pt")

out = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(out[0], skip_special_tokens=True))

License

Base model: same license as lightonai/LightOnOCR-2-1B-base
Dataset: see samaritan-ai/sam_44_mss_albumentations
This finetuned model: same license as base unless otherwise specified

Acknowledgements

LightOnAI for the base model
Unsloth for efficient finetuning
FineVision for augmentation tools
The Samaritan manuscript preservation community

Downloads last month: 6

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for samaritan-ai/LightOnOCR-2-1B-sam-44-mss-alb

Base model

lightonai/LightOnOCR-2-1B-base

Finetuned

(18)

this model