Instructions to use TengfeiLiuCoder/Reviso with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TengfeiLiuCoder/Reviso with Transformers:

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

pipe = pipeline("image-text-to-text", model="TengfeiLiuCoder/Reviso")
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("TengfeiLiuCoder/Reviso")
model = AutoModelForMultimodalLM.from_pretrained("TengfeiLiuCoder/Reviso", 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

vLLM

How to use TengfeiLiuCoder/Reviso with vLLM:

Install from pip and serve model

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

SGLang

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

Reviso / README.md

TengfeiLiuCoder

Add files using upload-large-folder tool

7423a36 verified 5 days ago

preview code

Raw History Blame Contribute Delete

2.04 kB

---
library_name: transformers
base_model: Qwen/Qwen3.5-9B
tags:
  - video-captioning
  - agentic
  - arxiv:2611.03602
---
# Reviso: Not All Visual Evidence Deserves Equal Attention in Video Captioning

[Paper](https://arxiv.org/abs/2611.03602) | [Code](https://github.com/pkucs-Ltf/Reviso)

![Overview of Reviso: CFP observation planning, tool-assisted captioning, supervised fine-tuning, and GRPO.](assets/framework.png)

Reviso is an agentic video captioning model that learns what matters in each video and actively gathers evidence to describe it. From a global preview, it builds a hierarchical **Context–Focus–Points (CFP)** observation plan, then uses temporal revisiting, image cropping, and OCR to inspect important details. Across multiple rounds, it updates its understanding and plan before generating the final caption.


This release contains the **Checkpoint**: full model weights in FP32, stored in nine `safetensors` shards (approximately 37.64 GB), together with the tokenizer and processor.(Base model :Qwen 3.5 9B)

## Installation

Use Python 3.12. The PyTorch command below uses CUDA 12.6; select another compatible build from the [PyTorch installation instructions](https://pytorch.org/get-started/previous-versions/) if needed.

```bash
pip install torch==2.10.0 torchvision==0.25.0 --index-url https://download.pytorch.org/whl/cu126
pip install "transformers==5.10.4" "accelerate>=1.9,<2" safetensors pillow huggingface_hub
```

## Instructions 



Load the complete checkpoint directory with Transformers:

```python
import torch
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration
model_path = "./Reviso"  # Use "." when running inside this checkpoint directory.
processor = AutoProcessor.from_pretrained(model_path)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_path,
    dtype=torch.bfloat16,
    device_map="auto",
).eval()
```

The example loads the FP32 weights in BF16 on compatible hardware. 
[project repository](https://github.com/pkucs-Ltf/Reviso).

	---
	library_name: transformers
	base_model: Qwen/Qwen3.5-9B
	tags:
	- video-captioning
	- agentic
	- arxiv:2611.03602
	---

	# Reviso: Not All Visual Evidence Deserves Equal Attention in Video Captioning

	[Paper](https://arxiv.org/abs/2611.03602) \| [Code](https://github.com/pkucs-Ltf/Reviso)

	![Overview of Reviso: CFP observation planning, tool-assisted captioning, supervised fine-tuning, and GRPO.](assets/framework.png)

	Reviso is an agentic video captioning model that learns what matters in each video and actively gathers evidence to describe it. From a global preview, it builds a hierarchical Context–Focus–Points (CFP) observation plan, then uses temporal revisiting, image cropping, and OCR to inspect important details. Across multiple rounds, it updates its understanding and plan before generating the final caption.


	This release contains the Checkpoint: full model weights in FP32, stored in nine `safetensors` shards (approximately 37.64 GB), together with the tokenizer and processor.(Base model :Qwen 3.5 9B)

	## Installation

	Use Python 3.12. The PyTorch command below uses CUDA 12.6; select another compatible build from the [PyTorch installation instructions](https://pytorch.org/get-started/previous-versions/) if needed.

	```bash
	pip install torch==2.10.0 torchvision==0.25.0 --index-url https://download.pytorch.org/whl/cu126
	pip install "transformers==5.10.4" "accelerate>=1.9,<2" safetensors pillow huggingface_hub
	```

	## Instructions



	Load the complete checkpoint directory with Transformers:

	```python
	import torch
	from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

	model_path = "./Reviso" # Use "." when running inside this checkpoint directory.

	processor = AutoProcessor.from_pretrained(model_path)
	model = Qwen3_5ForConditionalGeneration.from_pretrained(
	model_path,
	dtype=torch.bfloat16,
	device_map="auto",
	).eval()
	```

	The example loads the FP32 weights in BF16 on compatible hardware.
	[project repository](https://github.com/pkucs-Ltf/Reviso).