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

Libraries

How to use datalab-to/chandra-ocr-2 with Transformers:

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

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

processor = AutoProcessor.from_pretrained("datalab-to/chandra-ocr-2")
model = AutoModelForMultimodalLM.from_pretrained("datalab-to/chandra-ocr-2", 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 datalab-to/chandra-ocr-2 with vLLM:

Install from pip and serve model

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

SGLang

How to use datalab-to/chandra-ocr-2 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/chandra-ocr-2" \
    --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/chandra-ocr-2",
		"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/chandra-ocr-2" \
        --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/chandra-ocr-2",
		"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 datalab-to/chandra-ocr-2 with Docker Model Runner:
```
docker model run hf.co/datalab-to/chandra-ocr-2
```

vikp commited on Mar 16

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verified ·

1 Parent(s): a2128a5

Update README.md

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README.md +31 -24

README.md CHANGED Viewed

@@ -10,6 +10,11 @@ tags:
   - vision-language
 ---
 # Chandra 2
 Chandra 2 is an OCR model that outputs markdown, HTML, and JSON. It is highly accurate at extracting text from images and PDFs, while preserving layout information.
@@ -46,7 +51,7 @@ chandra input.pdf ./output --method hf
 ## Usage
-### With vLLM
 ```python
 from chandra.model import InferenceManager
@@ -118,8 +123,33 @@ print(markdown)
 | Qwen 3 VL 8B              |   70.2   |   75.1   |   45.6   |   37.5   |        89.1         |     62.1     |      43.0      | 94.3 |   64.6 ± 1.1   | Own benchmarks |
 | Gemini Flash 2 (Anchored) |   54.5   |   56.1   |   72.1   |   34.2   |        64.7         |     61.5     |      71.5      | 95.6 |   63.8 ± 1.2   | olmocr repo |
 ### Multilingual Benchmark (43 Languages)
 <img src="multilingual.png" width="600px"/>
 | Language | Datalab API | Chandra 2 | Chandra 1 | Gemini 2.5 Flash | GPT-5 Mini |
@@ -131,7 +161,6 @@ print(markdown)
 | da | 90.1% | 91.1% | 88.4% | 86.0% | 87.7% |
 | de | 93.8% | 94.8% | 83.0% | 88.3% | 93.8% |
 | el | 89.9% | 85.6% | 85.5% | 83.5% | 82.4% |
-| en | 95.2% | 96.6% | - | 90.3% | 88.3% |
 | es | 91.8% | 89.3% | 88.7% | 86.8% | 97.1% |
 | fa | 82.2% | 75.1% | 69.6% | 61.8% | 56.4% |
 | fi | 85.7% | 83.4% | 78.4% | 86.0% | 84.7% |
@@ -169,28 +198,6 @@ print(markdown)
 | zh | 87.8% | 88.7% | 88.3% | 70.0% | 70.4% |
 | **Average** | **80.4%** | **77.8%** | **69.4%** | **67.6%** | **60.5%** |
-## Examples
-<img src="handwritten_form.png" width="600px"/>
-| Type | Name | Link |
-|------|------|------|
-| Tables | Statistical Distribution | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/tables/complex_tables.png) |
-| Tables | Financial Table | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/tables/financial_table.png) |
-| Forms | Registration Form | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/forms/handwritten_form.png) |
-| Forms | Lease Form | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/forms/lease_filled.png) |
-| Math | CS229 Textbook | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/math/cs229.png) |
-| Math | Handwritten Math | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/math/handwritten_math.png) |
-| Math | Chinese Math | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/math/chinese_math.png) |
-| Handwriting | Cursive Writing | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/handwriting/cursive_writing.png) |
-| Handwriting | Handwritten Notes | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/handwriting/handwritten_notes.png) |
-| Languages | Arabic | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/arabic.png) |
-| Languages | Japanese | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/japanese.png) |
-| Languages | Hindi | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/hindi.png) |
-| Languages | Russian | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/russian.png) |
-| Other | Charts | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/other/charts.png) |
-| Other | Chemistry | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/other/chemistry.png) |
 ## Commercial Usage
 Code is Apache 2.0. Model weights use a modified OpenRAIL-M license: free for research, personal use, and startups under $2M funding/revenue. Cannot be used competitively with our API. For broader commercial licensing, see [pricing](https://www.datalab.to/pricing?utm_source=gh-chandra).

   - vision-language
 ---
+<p align="center">
+  <img src="datalab-logo.png" alt="Datalab Logo" width="150"/>
+</p>
+<h1 align="center">Datalab</h1>
 # Chandra 2
 Chandra 2 is an OCR model that outputs markdown, HTML, and JSON. It is highly accurate at extracting text from images and PDFs, while preserving layout information.
 ## Usage
+### With vLLM (recommended)
 ```python
 from chandra.model import InferenceManager
 | Qwen 3 VL 8B              |   70.2   |   75.1   |   45.6   |   37.5   |        89.1         |     62.1     |      43.0      | 94.3 |   64.6 ± 1.1   | Own benchmarks |
 | Gemini Flash 2 (Anchored) |   54.5   |   56.1   |   72.1   |   34.2   |        64.7         |     61.5     |      71.5      | 95.6 |   63.8 ± 1.2   | olmocr repo |
+## Examples
+<img src="handwritten_form.png" width="600px"/>
+| Type | Name | Link |
+|------|------|------|
+| Tables | Statistical Distribution | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/tables/complex_tables.png) |
+| Tables | Financial Table | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/tables/financial_table.png) |
+| Forms | Registration Form | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/forms/handwritten_form.png) |
+| Forms | Lease Form | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/forms/lease_filled.png) |
+| Math | CS229 Textbook | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/math/cs229.png) |
+| Math | Handwritten Math | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/math/handwritten_math.png) |
+| Math | Chinese Math | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/math/chinese_math.png) |
+| Handwriting | Cursive Writing | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/handwriting/cursive_writing.png) |
+| Handwriting | Handwritten Notes | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/handwriting/handwritten_notes.png) |
+| Languages | Arabic | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/arabic.png) |
+| Languages | Japanese | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/japanese.png) |
+| Languages | Hindi | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/hindi.png) |
+| Languages | Russian | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/russian.png) |
+| Other | Charts | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/other/charts.png) |
+| Other | Chemistry | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/other/chemistry.png) |
 ### Multilingual Benchmark (43 Languages)
+This is from our internal multilingual evaluations.
 <img src="multilingual.png" width="600px"/>
 | Language | Datalab API | Chandra 2 | Chandra 1 | Gemini 2.5 Flash | GPT-5 Mini |
 | da | 90.1% | 91.1% | 88.4% | 86.0% | 87.7% |
 | de | 93.8% | 94.8% | 83.0% | 88.3% | 93.8% |
 | el | 89.9% | 85.6% | 85.5% | 83.5% | 82.4% |
 | es | 91.8% | 89.3% | 88.7% | 86.8% | 97.1% |
 | fa | 82.2% | 75.1% | 69.6% | 61.8% | 56.4% |
 | fi | 85.7% | 83.4% | 78.4% | 86.0% | 84.7% |
 | zh | 87.8% | 88.7% | 88.3% | 70.0% | 70.4% |
 | **Average** | **80.4%** | **77.8%** | **69.4%** | **67.6%** | **60.5%** |
 ## Commercial Usage
 Code is Apache 2.0. Model weights use a modified OpenRAIL-M license: free for research, personal use, and startups under $2M funding/revenue. Cannot be used competitively with our API. For broader commercial licensing, see [pricing](https://www.datalab.to/pricing?utm_source=gh-chandra).