Instructions to use datalab-to/surya-ocr-2-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use datalab-to/surya-ocr-2-gguf with Transformers:

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

pipe = pipeline("image-text-to-text", model="datalab-to/surya-ocr-2-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)

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

processor = AutoProcessor.from_pretrained("datalab-to/surya-ocr-2-gguf")
model = AutoModelForMultimodalLM.from_pretrained("datalab-to/surya-ocr-2-gguf", 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 datalab-to/surya-ocr-2-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 datalab-to/surya-ocr-2-gguf
# Run inference directly in the terminal:
llama cli -hf datalab-to/surya-ocr-2-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf datalab-to/surya-ocr-2-gguf
# Run inference directly in the terminal:
llama cli -hf datalab-to/surya-ocr-2-gguf

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 datalab-to/surya-ocr-2-gguf
# Run inference directly in the terminal:
./llama-cli -hf datalab-to/surya-ocr-2-gguf

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 datalab-to/surya-ocr-2-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf datalab-to/surya-ocr-2-gguf

Use Docker

docker model run hf.co/datalab-to/surya-ocr-2-gguf

LM Studio
Jan

vLLM

How to use datalab-to/surya-ocr-2-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "datalab-to/surya-ocr-2-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": "datalab-to/surya-ocr-2-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/datalab-to/surya-ocr-2-gguf

SGLang

How to use datalab-to/surya-ocr-2-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 "datalab-to/surya-ocr-2-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": "datalab-to/surya-ocr-2-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 "datalab-to/surya-ocr-2-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": "datalab-to/surya-ocr-2-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 datalab-to/surya-ocr-2-gguf with Ollama:
```
ollama run hf.co/datalab-to/surya-ocr-2-gguf
```
Unsloth Desktop
Docker Model Runner
How to use datalab-to/surya-ocr-2-gguf with Docker Model Runner:
```
docker model run hf.co/datalab-to/surya-ocr-2-gguf
```

Lemonade

How to use datalab-to/surya-ocr-2-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull datalab-to/surya-ocr-2-gguf

Run and chat with the model

lemonade run user.surya-ocr-2-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

vikp commited on May 27

Commit

ec487b4

verified ·

1 Parent(s): b3e5bd9

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -28

README.md CHANGED Viewed

@@ -9,7 +9,6 @@ tags:
   - layout
 ---
 <h1 align="center">Datalab</h1>
 <p align="center">
   <strong>State of the Art models for Document Intelligence</strong>
@@ -49,19 +48,17 @@ Commercial self-hosting of the model weights requires a license — see [Commerc
 |                            Detection                             |                                   OCR                                   |
 |:----------------------------------------------------------------:|:-----------------------------------------------------------------------:|
-|  <img src="assets/excerpt.png" width="280"/>  |  <img src="assets/excerpt_text.png" width="280"/> |
 |                               Layout                               |                       Table Recognition                       |
 |:------------------------------------------------------------------:|:-------------------------------------------------------------:|
-| <img src="assets/excerpt_layout.png" width="280"/> | <img src="assets/scanned_tablerec.png" width="280"/> |
 Surya is named for the [Hindu sun god](https://en.wikipedia.org/wiki/Surya), who has universal vision.
 ## Examples
-Each row links to five annotated views of the same page: text-line detection, OCR, layout, reading order, and (when present) table recognition.
 | Name              |              Detection              |                                       OCR |                                       Layout |                                          Order |                                       Table Rec |
 |-------------------|:-----------------------------------:|------------------------------------------:|---------------------------------------------:|------------------------------------------------:|------------------------------------------------:|
 | Newspaper         | [Image](assets/newspaper.png) | [Image](assets/newspaper_text.png) | [Image](assets/newspaper_layout.png) | [Image](assets/newspaper_reading.png) |                                                  |
@@ -70,10 +67,6 @@ Each row links to five annotated views of the same page: text-line detection, OC
 | Handwritten Notes | [Image](assets/handwritten.png) | [Image](assets/handwritten_text.png) | [Image](assets/handwritten_layout.png) | [Image](assets/handwritten_reading.png) | [Image](assets/handwritten_tablerec.png) |
 | Corporate Doc     | [Image](assets/corporate.png) | [Image](assets/corporate_text.png) | [Image](assets/corporate_layout.png) | [Image](assets/corporate_reading.png) | [Image](assets/corporate_tablerec.png) |
-# Commercial usage
-The Surya code is licensed under Apache 2.0. The model weights use a modified AI Pubs Open Rail-M license (free for research, personal use, and startups under $5M funding/revenue). For broader commercial licensing of the model weights, visit our pricing page [here](https://www.datalab.to/pricing?utm_source=gh-surya).
 # Installation
 Install with:
@@ -82,24 +75,6 @@ Install with:
 pip install surya-ocr
 ```
-## Upgrading from Surya v1
-If you have v1 code, you can migrate to this:
-```python
-# v2
-from surya.inference import SuryaInferenceManager
-from surya.recognition import RecognitionPredictor
-manager = SuryaInferenceManager()              # auto-spawns vllm or llama-server
-rec = RecognitionPredictor(manager)
-predictions = rec([image])
-```
-What's different:
-- `SuryaInferenceManager` replaces `FoundationPredictor`. Same manager instance is shared across `LayoutPredictor`, `RecognitionPredictor`, `TableRecPredictor`.
-- Output schemas changed: see the per-section JSON tables below. Highlights — `text_lines` → `blocks` (with `html`); layout dropped `top_k`, added `count`; table_rec dropped `is_header` / `colspan` / `rowspan` from cells.
 # Usage
 Surya 2 runs layout, OCR, and table recognition through a single VLM served
@@ -360,7 +335,7 @@ standard quality benchmark for document parsers.
 ## olmOCR-bench
-Pareto-optimal on the size-vs-score frontier, and best in class under 3B params.
 | Model                       | Params    | Score    |
 |-----------------------------|----------:|---------:|

   - layout
 ---
 <h1 align="center">Datalab</h1>
 <p align="center">
   <strong>State of the Art models for Document Intelligence</strong>
 |                            Detection                             |                                   OCR                                   |
 |:----------------------------------------------------------------:|:-----------------------------------------------------------------------:|
+|  <img src="assets/excerpt.png" width="140"/>  |  <img src="assets/excerpt_text.png" width="140"/> |
 |                               Layout                               |                       Table Recognition                       |
 |:------------------------------------------------------------------:|:-------------------------------------------------------------:|
+| <img src="assets/excerpt_layout.png" width="140"/> | <img src="assets/scanned_tablerec.png" width="140"/> |
 Surya is named for the [Hindu sun god](https://en.wikipedia.org/wiki/Surya), who has universal vision.
 ## Examples
 | Name              |              Detection              |                                       OCR |                                       Layout |                                          Order |                                       Table Rec |
 |-------------------|:-----------------------------------:|------------------------------------------:|---------------------------------------------:|------------------------------------------------:|------------------------------------------------:|
 | Newspaper         | [Image](assets/newspaper.png) | [Image](assets/newspaper_text.png) | [Image](assets/newspaper_layout.png) | [Image](assets/newspaper_reading.png) |                                                  |
 | Handwritten Notes | [Image](assets/handwritten.png) | [Image](assets/handwritten_text.png) | [Image](assets/handwritten_layout.png) | [Image](assets/handwritten_reading.png) | [Image](assets/handwritten_tablerec.png) |
 | Corporate Doc     | [Image](assets/corporate.png) | [Image](assets/corporate_text.png) | [Image](assets/corporate_layout.png) | [Image](assets/corporate_reading.png) | [Image](assets/corporate_tablerec.png) |
 # Installation
 Install with:
 pip install surya-ocr
 ```
 # Usage
 Surya 2 runs layout, OCR, and table recognition through a single VLM served
 ## olmOCR-bench
+Pareto-optimal, and best in class under 3B params.
 | Model                       | Params    | Score    |
 |-----------------------------|----------:|---------:|