Instructions to use Dharma-AI/Dharma-OCR-LITE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dharma-AI/Dharma-OCR-LITE with Transformers:

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

pipe = pipeline("image-text-to-text", model="Dharma-AI/Dharma-OCR-LITE")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Dharma-AI/Dharma-OCR-LITE")
model = AutoModelForImageTextToText.from_pretrained("Dharma-AI/Dharma-OCR-LITE")
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

vLLM

How to use Dharma-AI/Dharma-OCR-LITE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Dharma-AI/Dharma-OCR-LITE"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Dharma-AI/Dharma-OCR-LITE",
		"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/Dharma-AI/Dharma-OCR-LITE

SGLang

How to use Dharma-AI/Dharma-OCR-LITE 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 "Dharma-AI/Dharma-OCR-LITE" \
    --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": "Dharma-AI/Dharma-OCR-LITE",
		"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 "Dharma-AI/Dharma-OCR-LITE" \
        --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": "Dharma-AI/Dharma-OCR-LITE",
		"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 Dharma-AI/Dharma-OCR-LITE with Docker Model Runner:
```
docker model run hf.co/Dharma-AI/Dharma-OCR-LITE
```

GabrielPimenta99 commited on Apr 17

Commit

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Update README.md

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@@ -35,7 +35,7 @@ datasets:
 DharmaOCR Lite achieves **state-of-the-art performance** on [DharmaOCR-Benchmark](https://huggingface.co/datasets/Dharma-AI/DharmaOCR-Benchmark), outperforming all evaluated open-source and commercial baselines — including GPT-4o, GPT-5.4, Claude Opus 4.6, Gemini 3.1 Pro, Google Document AI, Amazon Textract, and olmOCR-2-7B — while being significantly cheaper and faster to run.
-For the full methodology, training details, and ablation studies, see our paper: **[DharmaOCR: Specialized Small Language Models for Structured OCR that Outperform Open-Source and Commercial Baselines](link_to_paper)**.
 <p align="center">
   <img src="images/cost_x_score.png" width="1300"/>
@@ -559,11 +559,14 @@ vllm serve dharma-ai/DharmaOCR-Lite \
 ## Citation
 ```bibtex
-@article{dharmaocr2026,
-    title={DharmaOCR: Specialized Small Language Models for Structured OCR that Outperform Open-Source and Commercial Baselines},
-    author={Pimenta de Freitas Cardoso, Gabriel and Chacon, Caio Lucas da Silva and Oliveira, Jonas Felipe da Fonseca and Araujo, Paulo Henrique de Medeiros},
-    year={2026},
-    journal={arXiv preprint}
 }
 ```

 DharmaOCR Lite achieves **state-of-the-art performance** on [DharmaOCR-Benchmark](https://huggingface.co/datasets/Dharma-AI/DharmaOCR-Benchmark), outperforming all evaluated open-source and commercial baselines — including GPT-4o, GPT-5.4, Claude Opus 4.6, Gemini 3.1 Pro, Google Document AI, Amazon Textract, and olmOCR-2-7B — while being significantly cheaper and faster to run.
+For the full methodology, training details, and ablation studies, see our paper: **[DharmaOCR: Specialized Small Language Models for Structured OCR that Outperform Open-Source and Commercial Baselines](https://arxiv.org/abs/2604.14314)**.
 <p align="center">
   <img src="images/cost_x_score.png" width="1300"/>
 ## Citation
 ```bibtex
+@misc{cardoso2026dharmaocrspecializedsmalllanguage,
+      title={DharmaOCR: Specialized Small Language Models for Structured OCR that outperform Open-Source and Commercial Baselines},
+      author={Gabriel Pimenta de Freitas Cardoso and Caio Lucas da Silva Chacon and Jonas Felipe da Fonseca Oliveira and Paulo Henrique de Medeiros Araujo},
+      year={2026},
+      eprint={2604.14314},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2604.14314},
 }
 ```