Instructions to use prithivMLmods/Caption-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Caption-Pro with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Caption-Pro")
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("prithivMLmods/Caption-Pro")
model = AutoModelForImageTextToText.from_pretrained("prithivMLmods/Caption-Pro")
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 prithivMLmods/Caption-Pro with vLLM:

Install from pip and serve model

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

SGLang

How to use prithivMLmods/Caption-Pro 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 "prithivMLmods/Caption-Pro" \
    --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": "prithivMLmods/Caption-Pro",
		"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 "prithivMLmods/Caption-Pro" \
        --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": "prithivMLmods/Caption-Pro",
		"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 prithivMLmods/Caption-Pro with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Caption-Pro
```

prithivMLmods commited on Feb 17, 2025

Commit

748ca0d

verified ·

1 Parent(s): 6ee72d6

Update README.md

Browse files

Files changed (1) hide show

README.md +11 -16

README.md CHANGED Viewed

@@ -11,43 +11,42 @@ tags:
 - text-generation-inference
 - label
 ---
-![10.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/tCeb4MVz79GCiOmMxkLcN.png)
-# **LabelPRO**
-**LabelPRO** is an advanced image caption and annotation generator optimized for generating detailed, structured JSON outputs. Built upon a powerful vision-language architecture with enhanced OCR and multilingual support, LabelPRO extracts high-quality captions and annotations from images for seamless integration into your applications.
 #### Key Enhancements:
-* **Advanced Image Understanding**: Fine-tuned on millions of annotated images, LabelPRO delivers precise comprehension and interpretation of visual content.
 * **Optimized for JSON Output**: Produces structured JSON data containing captions and detailed annotations—perfect for integration with databases, APIs, and automation pipelines.
 * **Enhanced OCR Capabilities**: Accurately extracts textual content from images in multiple languages, including English, Chinese, Japanese, Korean, Arabic, and more.
 * **Multimodal Processing**: Seamlessly handles both image and text inputs, generating comprehensive annotations based on the provided image.
 * **Multilingual Support**: Recognizes and processes text within images across various languages.
 * **Secure and Optimized Model Weights**: Employs safetensors for efficient and secure model loading.
----
 ### How to Use
 ```python
 from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
 from qwen_vl_utils import process_vision_info
-# Load the LabelPRO model with optimized parameters
 model = Qwen2VLForConditionalGeneration.from_pretrained(
-    "prithivMLmods/LabelPRO", torch_dtype="auto", device_map="auto"
 )
 # Recommended acceleration for performance optimization:
 # model = Qwen2VLForConditionalGeneration.from_pretrained(
-#     "prithivMLmods/LabelPRO",
 #     torch_dtype=torch.bfloat16,
 #     attn_implementation="flash_attention_2",
 #     device_map="auto",
 # )
-# Load the default processor for LabelPRO
-processor = AutoProcessor.from_pretrained("prithivMLmods/LabelPRO")
 # Define the input messages with both an image and a text prompt
 messages = [
@@ -88,8 +87,6 @@ output_text = processor.batch_decode(
 print(output_text)
 ```
----
 ### **Key Features**
 1. **Annotation-Ready Training Data**
@@ -110,6 +107,4 @@ print(output_text)
 6. **Secure and Efficient Model Weights**
    - Uses safetensors for enhanced security and optimized model performance.
----
-**LabelPRO** streamlines the process of generating image captions and annotations, making it an ideal solution for applications that require detailed visual content analysis and structured data integration.

 - text-generation-inference
 - label
 ---
+![VSXzdfgvsdxf.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/nNF_6UCnmgHKjNmLaA2QA.png)
+# **Caption-Pro**
+**Caption-Pro** is an advanced image caption and annotation generator optimized for generating detailed, structured JSON outputs. Built upon a powerful vision-language architecture with enhanced OCR and multilingual support, Caption-Pro extracts high-quality captions and annotations from images for seamless integration into your applications.
 #### Key Enhancements:
+* **Advanced Image Understanding**: Fine-tuned on millions of annotated images, Caption-Pro delivers precise comprehension and interpretation of visual content.
 * **Optimized for JSON Output**: Produces structured JSON data containing captions and detailed annotations—perfect for integration with databases, APIs, and automation pipelines.
 * **Enhanced OCR Capabilities**: Accurately extracts textual content from images in multiple languages, including English, Chinese, Japanese, Korean, Arabic, and more.
 * **Multimodal Processing**: Seamlessly handles both image and text inputs, generating comprehensive annotations based on the provided image.
 * **Multilingual Support**: Recognizes and processes text within images across various languages.
 * **Secure and Optimized Model Weights**: Employs safetensors for efficient and secure model loading.
 ### How to Use
 ```python
 from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
 from qwen_vl_utils import process_vision_info
+# Load the Caption-Pro model with optimized parameters
 model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "prithivMLmods/Caption-Pro", torch_dtype="auto", device_map="auto"
 )
 # Recommended acceleration for performance optimization:
 # model = Qwen2VLForConditionalGeneration.from_pretrained(
+#     "prithivMLmods/Caption-Pro",
 #     torch_dtype=torch.bfloat16,
 #     attn_implementation="flash_attention_2",
 #     device_map="auto",
 # )
+# Load the default processor for Caption-Pro
+processor = AutoProcessor.from_pretrained("prithivMLmods/Caption-Pro")
 # Define the input messages with both an image and a text prompt
 messages = [
 print(output_text)
 ```
 ### **Key Features**
 1. **Annotation-Ready Training Data**
 6. **Secure and Efficient Model Weights**
    - Uses safetensors for enhanced security and optimized model performance.
+**Caption-Pro** streamlines the process of generating image captions and annotations, making it an ideal solution for applications that require detailed visual content analysis and structured data integration.