---
language:
- en
tags:
- text-generation-inference
datasets:
- 5CD-AI/LLaVA-CoT-o1-Instruct
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
pipeline_tag: image-text-to-text
library_name: transformers
---

![Add a heading.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/t0z6C_PSP37WIVZBc_Y8-.png)

# **Behemoth-3B-070225-post0.1**

> The **Behemoth-3B-070225-post0.1** model is a fine-tuned version of **Qwen2.5-VL-3B-Instruct**, optimized for **Detailed Image Captioning**, **OCR Tasks**, and **Chain-of-Thought Reasoning**. Built on top of the Qwen2.5-VL architecture, this model enhances visual understanding capabilities with focused training on the 50k LLaVA-CoT-o1-Instruct dataset for superior image analysis and detailed reasoning tasks.

# Key Enhancements

* **Detailed Image Captioning**: Advanced capability for generating comprehensive, contextually rich descriptions of visual content with fine-grained detail recognition.

* **Enhanced OCR Performance**: Designed to efficiently extract and recognize text from images with high accuracy across various fonts, layouts, and image qualities.

* **Chain-of-Thought Reasoning**: Specialized in providing step-by-step logical reasoning processes for complex visual analysis tasks, breaking down problems into manageable components.

* **Superior Visual Understanding**: Optimized for precise interpretation of visual elements, spatial relationships, and contextual information within images.

* **Instruction Following**: Enhanced ability to follow detailed instructions for specific image analysis tasks while maintaining reasoning transparency.

* **State-of-the-Art Performance on Vision Tasks**: Achieves competitive results on visual question answering, image captioning, and OCR benchmarks.

* **Efficient 3B Parameter Model**: Provides strong performance while maintaining computational efficiency for broader accessibility.

* **Multi-Modal Reasoning**: Enables comprehensive analysis combining visual perception with logical reasoning chains.

# 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/Behemoth-3B-070225-post0.1", torch_dtype="auto", device_map="auto"
)

processor = AutoProcessor.from_pretrained("prithivMLmods/Behemoth-3B-070225-post0.1")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
            },
            {"type": "text", "text": "Provide a detailed caption for this image and explain your reasoning step by step."},
        ],
    }
]

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=256)
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:

* **Detailed Image Captioning**: Generating comprehensive, nuanced descriptions of visual content for accessibility, content creation, and analysis purposes.
* **OCR Applications**: High-accuracy text extraction from images, documents, signs, and handwritten content.
* **Chain-of-Thought Visual Analysis**: Providing step-by-step reasoning for complex visual interpretation tasks.
* **Educational Content Creation**: Generating detailed explanations of visual materials with logical reasoning chains.
* **Content Accessibility**: Creating detailed alt-text and descriptions for visually impaired users.
* **Visual Question Answering**: Answering complex questions about images with detailed reasoning processes.
* **Document Analysis**: Processing and understanding visual documents with both text extraction and content comprehension.
* **Research and Analysis**: Supporting academic and professional research requiring detailed visual analysis with transparent reasoning.

# Base Training Details

* **Base Model**: Qwen2.5-VL-3B-Instruct
* **Training Dataset**: 50k LLaVA-CoT-o1-Instruct dataset
* **Specialized Training Focus**: Chain-of-thought reasoning, detailed captioning, and OCR tasks
* **Model Size**: 3 billion parameters for efficient deployment

# Limitations

* **Computational Requirements**: While more efficient than larger models, still requires adequate GPU memory for optimal performance.
* **Image Quality Sensitivity**: Performance may degrade on extremely low-quality, heavily occluded, or severely distorted images.
* **Processing Speed**: Chain-of-thought reasoning may result in longer response times compared to direct answer models.
* **Language Coverage**: Primarily optimized for English language tasks, with variable performance on other languages.
* **Context Length**: Limited by the base model's context window for very long reasoning chains.
* **Hallucination Risk**: May occasionally generate plausible but incorrect details, especially in ambiguous visual scenarios.
* **Resource Constraints**: Not optimized for real-time applications on edge devices or low-resource environments.