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
```

Vik Paruchuri commited on about 1 month ago

Commit

af93b47

1 Parent(s): 808e461

Adjust scoring 85.9 -> 85.8

Browse files

Files changed (3) hide show

.eval_results/olmocrbench.yaml +18 -18
README.md +5 -5
bench.png +0 -0

.eval_results/olmocrbench.yaml CHANGED Viewed

@@ -1,80 +1,80 @@
 - dataset:
     id: allenai/olmOCR-bench
     task_id: overall
-  value: 85.9
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
-    user: davanstrien
 - dataset:
     id: allenai/olmOCR-bench
     task_id: arxiv_math
-  value: 90.2
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
-    user: davanstrien
 - dataset:
     id: allenai/olmOCR-bench
     task_id: old_scans_math
-  value: 89.3
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
-    user: davanstrien
 - dataset:
     id: allenai/olmOCR-bench
     task_id: table_tests
-  value: 89.9
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
-    user: davanstrien
 - dataset:
     id: allenai/olmOCR-bench
     task_id: old_scans
-  value: 49.8
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
-    user: davanstrien
 - dataset:
     id: allenai/olmOCR-bench
     task_id: headers_footers
-  value: 92.5
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
-    user: davanstrien
 - dataset:
     id: allenai/olmOCR-bench
     task_id: multi_column
-  value: 83.5
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
-    user: davanstrien
 - dataset:
     id: allenai/olmOCR-bench
     task_id: long_tiny_text
-  value: 92.1
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
-    user: davanstrien
 - dataset:
     id: allenai/olmOCR-bench
     task_id: baseline
-  value: 99.6
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
-    user: davanstrien

 - dataset:
     id: allenai/olmOCR-bench
     task_id: overall
+  value: 85.8
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
+    user: vikp
 - dataset:
     id: allenai/olmOCR-bench
     task_id: arxiv_math
+  value: 86.9
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
+    user: vikp
 - dataset:
     id: allenai/olmOCR-bench
     task_id: old_scans_math
+  value: 89.1
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
+    user: vikp
 - dataset:
     id: allenai/olmOCR-bench
     task_id: table_tests
+  value: 92.1
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
+    user: vikp
 - dataset:
     id: allenai/olmOCR-bench
     task_id: old_scans
+  value: 51.1
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
+    user: vikp
 - dataset:
     id: allenai/olmOCR-bench
     task_id: headers_footers
+  value: 91.4
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
+    user: vikp
 - dataset:
     id: allenai/olmOCR-bench
     task_id: multi_column
+  value: 82.1
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
+    user: vikp
 - dataset:
     id: allenai/olmOCR-bench
     task_id: long_tiny_text
+  value: 93.7
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
+    user: vikp
 - dataset:
     id: allenai/olmOCR-bench
     task_id: baseline
+  value: 99.9
   source:
     url: https://huggingface.co/datalab-to/chandra-ocr-2
     name: Chandra OCR 2 Model Card
+    user: vikp

README.md CHANGED Viewed

@@ -21,7 +21,7 @@ Try Chandra in the [free playground](https://www.datalab.to/playground), or use
 ## What's New in Chandra 2
-- 85.9% olmocr bench score (sota), 77.8% multilingual bench score (12% improvement over Chandra 1)
 - Significant improvements to math, tables, complex layouts
 - Improved layout, especially on wider documents
 - Significantly better image captioning
@@ -112,10 +112,10 @@ print(markdown)
 | **Model** |  ArXiv   | Old Scans Math |  Tables  | Old Scans | Headers and Footers | Multi column | Long tiny text | Base |    Overall     | Source |
 |:----------|:--------:|:--------------:|:--------:|:---------:|:-------------------:|:------------:|:--------------:|:----:|:--------------:|:------:|
-| Datalab API               | **90.4** | **90.2** | **90.7** | **54.6** |        91.6         |     83.7     |    **92.3**    | **99.9** | **86.7 ± 0.8** | Own benchmarks |
-| Chandra 2                 |   90.2   |   89.3   |   89.9   |   49.8   |        92.5         |     83.5     |      92.1      | 99.6 |   85.9 ± 0.8   | Own benchmarks |
-| dots.ocr 1.5              |   85.9   |   85.5   | **90.7** |   48.2   |        94.0         |   **85.3**   |      81.6      | 99.7 |   83.9         | dots.ocr repo |
-| Chandra 1                 |   82.2   |   80.3   |   88.0   |   50.4   |        90.8         |     81.2     |    **92.3**    | **99.9** |   83.1 ± 0.9   | Own benchmarks |
 | olmOCR 2                  |   83.0   |   82.3   |   84.9   |   47.7   |      **96.1**       |     83.7     |      81.9      | 99.6 |   82.4         | olmocr repo |
 | dots.ocr                  |   82.1   |   64.2   |   88.3   |   40.9   |        94.1         |     82.4     |      81.2      | 99.5 |   79.1 ± 1.0   | dots.ocr repo |
 | olmOCR v0.3.0             |   78.6   |   79.9   |   72.9   |   43.9   |        95.1         |     77.3     |      81.2      | 98.9 |   78.5 ± 1.1   | olmocr repo |

 ## What's New in Chandra 2
+- 85.8% olmocr bench score (sota), 77.8% multilingual bench score (12% improvement over Chandra 1)
 - Significant improvements to math, tables, complex layouts
 - Improved layout, especially on wider documents
 - Significantly better image captioning
 | **Model** |  ArXiv   | Old Scans Math |  Tables  | Old Scans | Headers and Footers | Multi column | Long tiny text | Base |    Overall     | Source |
 |:----------|:--------:|:--------------:|:--------:|:---------:|:-------------------:|:------------:|:--------------:|:----:|:--------------:|:------:|
+| Datalab API               | **90.4** | **90.2** | 90.7 | **54.6** |        91.6         |     83.7     |      92.3      | **99.9** | **86.7 ± 0.8** | Own benchmarks |
+| Chandra 2                 |   86.9   |   89.1   | **92.1** |   51.1   |        91.4         |     82.1     |    **93.7**    | **99.9** |   85.8 ± 0.8   | Own benchmarks |
+| dots.ocr 1.5              |   85.9   |   85.5   | 90.7 |   48.2   |        94.0         |   **85.3**   |      81.6      | 99.7 |   83.9         | dots.ocr repo |
+| Chandra 1                 |   82.2   |   80.3   |   88.0   |   50.4   |        90.8         |     81.2     |      92.3      | **99.9** |   83.1 ± 0.9   | Own benchmarks |
 | olmOCR 2                  |   83.0   |   82.3   |   84.9   |   47.7   |      **96.1**       |     83.7     |      81.9      | 99.6 |   82.4         | olmocr repo |
 | dots.ocr                  |   82.1   |   64.2   |   88.3   |   40.9   |        94.1         |     82.4     |      81.2      | 99.5 |   79.1 ± 1.0   | dots.ocr repo |
 | olmOCR v0.3.0             |   78.6   |   79.9   |   72.9   |   43.9   |        95.1         |     77.3     |      81.2      | 98.9 |   78.5 ± 1.1   | olmocr repo |

bench.png CHANGED Viewed