README.md

---
license: apache-2.0
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
language:
- en
- zh
tags:
- text-generation-inference
- science
- universal truth
- chamaeleontis
- reason
- vl
pipeline_tag: image-text-to-text
library_name: transformers
---

![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ykkRj6WMEM2ILHayuPFT4.png)

# **Chamaeleontis-7B-Reason-rVL**

> The **Chamaeleontis-7B-Reason-rVL** model is a general-purpose multimodal reasoning model built on **Qwen2.5-VL-7B-Instruct**, optimized for **image and video frame understanding**, **contextual analysis**, and **natural language reasoning**. It excels at extracting structured insights from both static visuals and video streams through step-by-step logical interpretation and visual common sense grounding.

> Chamaeleontis: Reason-based Qwen2.5VL model for visual understanding and CoT reasoning

---

## Key Capabilities

* **Visual Frame Reasoning**: Understands and interprets key frames from videos or images with attention to spatial and physical relationships.
* **Chain-of-Thought Natural Language Output**: Generates thoughtful, logically connected answers for visual queries.
* **General-Purpose Multimodal Insight**: Applicable across diverse domains for both still image and sequential video analysis.
* **Custom Frame Selection Logic**: Applies smart frame sampling for high-relevance visual inputs during training and inference.
* **Common Sense Visual Comprehension**: Supports physical reasoning, object interaction detection, and real-world logic estimation from visuals.
* **Q\&A Over Visual Inputs**: Ideal for use cases like question answering, summarization, and temporal information extraction from visual sources.

---

## 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/Chamaeleontis-7B-Reason-rVL", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Chamaeleontis-7B-Reason-rVL")

messages = [
    {
        "role": "user",
        "content": [
            {"type": "video", "video": "path/to/your/video.mp4"},
            {"type": "text", "text": "Briefly describe the physical events and interactions in this video using logical steps."},
        ],
    }
]

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)
```

---

## Use Cases

* **Image and Video Q\&A**: Responds to natural language queries with logical, visual-grounded answers.
* **Physical Event Interpretation**: Understands object motion, cause-effect dynamics, and temporal interactions.
* **Visual Insight Summarization**: Extracts core narrative and insights from multimedia content.
* **Educational Content Understanding**: Supports reasoning-based tasks in educational and research video analysis.
* **Multimodal Reasoning**: Merges visual and textual understanding to support complex interpretation tasks.

---

## Limitations

* **Experimental Architecture**: While robust, it may need tuning for domain-specific datasets.
* **Inference Overhead**: High GPU memory requirements for long or high-resolution video processing.
* **Frame Sampling Bias**: Quality depends on relevance and spacing of sampled frames.
* **Context Window Boundaries**: Very long inputs may require segmenting or hierarchical reasoning strategies.