114 lines
5.5 KiB
Markdown
114 lines
5.5 KiB
Markdown
---
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language:
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- en
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tags:
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- text-generation-inference
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datasets:
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- 5CD-AI/LLaVA-CoT-o1-Instruct
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base_model:
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- Qwen/Qwen2.5-VL-3B-Instruct
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pipeline_tag: image-text-to-text
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library_name: transformers
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---
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# **Behemoth-3B-070225-post0.1**
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> 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.
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# Key Enhancements
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* **Detailed Image Captioning**: Advanced capability for generating comprehensive, contextually rich descriptions of visual content with fine-grained detail recognition.
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* **Enhanced OCR Performance**: Designed to efficiently extract and recognize text from images with high accuracy across various fonts, layouts, and image qualities.
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* **Chain-of-Thought Reasoning**: Specialized in providing step-by-step logical reasoning processes for complex visual analysis tasks, breaking down problems into manageable components.
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* **Superior Visual Understanding**: Optimized for precise interpretation of visual elements, spatial relationships, and contextual information within images.
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* **Instruction Following**: Enhanced ability to follow detailed instructions for specific image analysis tasks while maintaining reasoning transparency.
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* **State-of-the-Art Performance on Vision Tasks**: Achieves competitive results on visual question answering, image captioning, and OCR benchmarks.
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* **Efficient 3B Parameter Model**: Provides strong performance while maintaining computational efficiency for broader accessibility.
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* **Multi-Modal Reasoning**: Enables comprehensive analysis combining visual perception with logical reasoning chains.
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# Quick Start with Transformers
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```python
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
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from qwen_vl_utils import process_vision_info
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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"prithivMLmods/Behemoth-3B-070225-post0.1", torch_dtype="auto", device_map="auto"
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)
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processor = AutoProcessor.from_pretrained("prithivMLmods/Behemoth-3B-070225-post0.1")
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
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},
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{"type": "text", "text": "Provide a detailed caption for this image and explain your reasoning step by step."},
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],
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}
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]
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to("cuda")
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generated_ids = model.generate(**inputs, max_new_tokens=256)
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generated_ids_trimmed = [
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out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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output_text = processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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print(output_text)
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```
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# Intended Use
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This model is intended for:
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* **Detailed Image Captioning**: Generating comprehensive, nuanced descriptions of visual content for accessibility, content creation, and analysis purposes.
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* **OCR Applications**: High-accuracy text extraction from images, documents, signs, and handwritten content.
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* **Chain-of-Thought Visual Analysis**: Providing step-by-step reasoning for complex visual interpretation tasks.
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* **Educational Content Creation**: Generating detailed explanations of visual materials with logical reasoning chains.
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* **Content Accessibility**: Creating detailed alt-text and descriptions for visually impaired users.
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* **Visual Question Answering**: Answering complex questions about images with detailed reasoning processes.
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* **Document Analysis**: Processing and understanding visual documents with both text extraction and content comprehension.
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* **Research and Analysis**: Supporting academic and professional research requiring detailed visual analysis with transparent reasoning.
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# Base Training Details
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* **Base Model**: Qwen2.5-VL-3B-Instruct
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* **Training Dataset**: 50k LLaVA-CoT-o1-Instruct dataset
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* **Specialized Training Focus**: Chain-of-thought reasoning, detailed captioning, and OCR tasks
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* **Model Size**: 3 billion parameters for efficient deployment
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# Limitations
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* **Computational Requirements**: While more efficient than larger models, still requires adequate GPU memory for optimal performance.
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* **Image Quality Sensitivity**: Performance may degrade on extremely low-quality, heavily occluded, or severely distorted images.
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* **Processing Speed**: Chain-of-thought reasoning may result in longer response times compared to direct answer models.
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* **Language Coverage**: Primarily optimized for English language tasks, with variable performance on other languages.
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* **Context Length**: Limited by the base model's context window for very long reasoning chains.
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* **Hallucination Risk**: May occasionally generate plausible but incorrect details, especially in ambiguous visual scenarios.
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* **Resource Constraints**: Not optimized for real-time applications on edge devices or low-resource environments. |