---
license: apache-2.0
datasets:
- HuggingFaceFV/finevideo
- APRIL-AIGC/UltraVideo
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
tags:
- text-generation-inference
- Document
- Video
- Reasoning
- VIREX
language:
- en
pipeline_tag: image-text-to-text
library_name: transformers
---

![VIREX-062225-exp .png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/U7tipNhzPydO-wws_Gqs1.png)

# **VIREX-062225-exp**

> The **VIREX-062225-exp** (Video Information Retrieval and Extraction eXpert - experimental) model is a fine-tuned version of **Qwen2.5-VL-7B-Instruct**, specifically optimized for **advanced video understanding**, **image comprehension**, **sense of reasoning**, and **natural language decision-making through long chain-of-thought (CoT) reasoning**. Built on the robust Qwen2.5-VL architecture, this experimental model excels at extracting meaningful insights from visual content through sophisticated video-image frame sampling and multimodal reasoning.

> [!note]
VIREX: Video Information Retrieval and Extraction eXpert [ experimental ]

# Key Enhancements

* **Advanced Video Information Retrieval**: Capable of understanding complex video sequences, extracting key information, and providing detailed analysis of visual narratives across extended durations.

* **Enhanced Image Understanding with Physical Common Sense**: Designed to comprehend real-world physics, spatial relationships, and contextual understanding in both static images and dynamic video content.

* **Long Chain-of-Thought Reasoning**: Implements sophisticated reasoning pathways to provide detailed, logical explanations and decision-making processes in natural language.

* **Custom Video-Image Frame Sampling**: Utilizes redesigned dataset methodology with intelligent frame sampling techniques for optimal training on video understanding tasks.

* **Multimodal Decision Making**: Enables complex decision-making through integration of visual information and natural language processing with contextual understanding.

* **State-of-the-Art Video Comprehension**: Achieves superior performance on video understanding benchmarks through modular combination of FineVideo and UltraVideo datasets.

# Quick Start with Transformers

```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "prithivMLmods/VIREX-062225-exp", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/VIREX-062225-exp")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "video",
                "video": "path/to/your/video.mp4",
            },
            {"type": "text", "text": "Analyze this video and explain the physical interactions you observe using chain-of-thought reasoning."},
        ],
    }
]

text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=512)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```

# Intended Use

This model is intended for:

* **Video Content Analysis**: Deep understanding of video sequences, temporal relationships, and narrative comprehension.
* **Physical Common Sense Reasoning**: Analysis of real-world physics, object interactions, and spatial relationships in visual content.
* **Chain-of-Thought Video Q&A**: Detailed reasoning and explanation for complex video-based questions with step-by-step logical analysis.
* **Temporal Information Extraction**: Retrieval of time-sensitive information and sequential understanding from video content.
* **Multimodal Decision Support**: Integration of visual understanding with natural language reasoning for decision-making applications.
* **Educational and Research Applications**: Analysis of instructional videos, research content, and educational material with detailed explanations.
* **Content Summarization**: Intelligent summarization of video content with contextual understanding and key insight extraction.

# Limitations

* **Experimental Status**: As an experimental model, performance may vary across different use cases and requires further validation.
* **Computational Requirements**: High memory and processing demands for video understanding tasks, not optimized for real-time applications.
* **Video Length Constraints**: Performance may degrade with extremely long videos due to context window limitations.
* **Domain Specificity**: Optimized primarily for general video understanding; specialized domains may require additional fine-tuning.
* **Frame Sampling Dependency**: Performance is dependent on the quality and relevance of frame sampling during inference.
* **Reasoning Complexity**: While capable of chain-of-thought reasoning, extremely complex logical chains may still present challenges.

# Model Capabilities

## Video Understanding
- Temporal sequence analysis
- Object tracking and identification
- Scene transition recognition
- Action and activity recognition

## Physical Common Sense
- Physics-based reasoning
- Spatial relationship understanding
- Cause-and-effect analysis
- Real-world interaction comprehension

## Chain-of-Thought Reasoning
- Step-by-step logical analysis
- Detailed explanation generation
- Multi-step problem solving
- Contextual reasoning pathways

## Training Details

| Parameter               | Value                                               |
|-------------------------|-----------------------------------------------------|
| **Dataset Size**        | 11,750 samples (Modular Combination of FineVideo and UltraVideo) |
| **Model Architecture**  | `Qwen2_5_VLForConditionalGeneration`                |
| **Hardware**            | 3 × NVIDIA A40 (27 vCPUs)                          |
| **Total Disk**          | 250,000 MB                                          |
| **Training Time**       | 4,489 seconds (~1.25 hours)                        |
| **Learning Rate**       | 1e-5                                                |
| **Scheduler**           | Linear Decay                                        |
| **Warmup Steps**        | 500                                                 |
| **Precision**           | bfloat16                                            |
| **Training Method**     | Custom dataset with redesigned video-to-image frame sampling |

## References

- **Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution**  
  [https://arxiv.org/pdf/2409.12191](https://arxiv.org/pdf/2409.12191)

- **Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond**  
  [https://arxiv.org/pdf/2308.12966](https://arxiv.org/pdf/2308.12966)

- **Chain-of-Thought Prompting Elicits Reasoning in Large Language Models**  
  [https://arxiv.org/pdf/2201.11903](https://arxiv.org/pdf/2201.11903)

- **Video Understanding with Large Language Models: A Survey**  
  [https://arxiv.org/pdf/2312.17432](https://arxiv.org/pdf/2312.17432)