Instructions to use dipta007/VCInspector-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dipta007/VCInspector-3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="dipta007/VCInspector-3B")
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("dipta007/VCInspector-3B")
model = AutoModelForImageTextToText.from_pretrained("dipta007/VCInspector-3B")
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 dipta007/VCInspector-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dipta007/VCInspector-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dipta007/VCInspector-3B",
		"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/dipta007/VCInspector-3B

SGLang

How to use dipta007/VCInspector-3B 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 "dipta007/VCInspector-3B" \
    --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": "dipta007/VCInspector-3B",
		"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 "dipta007/VCInspector-3B" \
        --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": "dipta007/VCInspector-3B",
		"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 dipta007/VCInspector-3B with Docker Model Runner:
```
docker model run hf.co/dipta007/VCInspector-3B
```

dipta007 commited on Apr 11

Commit

e3db426

verified ·

1 Parent(s): 0d91fd4

Update README.md

Browse files

Files changed (1) hide show

README.md +21 -17

README.md CHANGED Viewed

@@ -18,15 +18,18 @@ arxiv: 2509.16538
 # VC-Inspector-3B
-<a href="https://arxiv.org/abs/2509.16538" target="_blank">
-    <img alt="arXiv" src="https://img.shields.io/badge/arXiv-2509.16538-b31b1b.svg" style="display: inline-block; vertical-align: middle;"/>
-</a>
-<a href="https://huggingface.co/collections/dipta007/vc-inspector" target="_blank">
-    <img alt="Models" src="https://img.shields.io/badge/HuggingFace-Models-orange" style="display: inline-block; vertical-align: middle;"/>
-</a>
-<a href="https://huggingface.co/datasets/dipta007/ActivityNet-FG-It" target="_blank">
-    <img alt="Dataset" src="https://img.shields.io/badge/HuggingFace-Dataset-blue" style="display: inline-block; vertical-align: middle;"/>
-</a>
 ## Introduction
@@ -252,14 +255,15 @@ VC-Inspector outputs two components:
 If you find this work useful, please cite our paper:
 ```bibtex
-@misc{dipta2025advancingreferencefreeevaluationvideo,
-      title={Advancing Reference-free Evaluation of Video Captions with Factual Analysis},
-      author={Shubhashis Roy Dipta and Tz-Ying Wu and Subarna Tripathi},
-      year={2025},
-      eprint={2509.16538},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV},
-      url={https://arxiv.org/abs/2509.16538},
 }
 ```

 # VC-Inspector-3B
+<p align="center">
+  <a href="https://arxiv.org/abs/2509.16538">
+    <img src="https://img.shields.io/badge/%F0%9F%94%A5_Accepted_at-ACL_2026_(Main)_%F0%9F%94%A5-b12a00?style=for-the-badge&labelColor=ffb300" alt="Accepted at ACL 2026 (Main)">
+  </a>
+</p>
+[![ACL 2026 (Main)](https://img.shields.io/badge/ACL%202026-Main-blue)](https://arxiv.org/abs/2509.16538)
+[![Paper](https://img.shields.io/badge/arXiv-2509.16538-red)](https://arxiv.org/abs/2509.16538)
+[![Models](https://img.shields.io/badge/HuggingFace-Models-orange)](https://huggingface.co/collections/dipta007/vc-inspector)
+[![Dataset](https://img.shields.io/badge/HuggingFace-Dataset-yellow)](https://huggingface.co/datasets/dipta007/ActivityNet-FG-It)
+[![Python 3.12](https://img.shields.io/badge/python-3.12-blue.svg)](https://www.python.org/downloads/)
 ## Introduction
 If you find this work useful, please cite our paper:
 ```bibtex
+@inproceedings{dipta2026vcinspector,
+  title={VC-Inspector: Advancing Reference-free Evaluation of Video Captions with Factual Analysis},
+  author={Shubhashis Roy Dipta and Tz-Ying Wu and Subarna Tripathi},
+  booktitle={Proceedings of the Association for Computational Linguistics: ACL 2026},
+  year={2026},
+  eprint={2509.16538},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  url={https://arxiv.org/abs/2509.16538},
 }
 ```