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--- |
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license: apache-2.0 |
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language: |
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- en |
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- zh |
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base_model: |
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- prithivMLmods/Qwen2-VL-OCR-2B-Instruct |
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pipeline_tag: image-text-to-text |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- Qwen |
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- Hoags |
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--- |
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> [!WARNING] |
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> **Note:** This model contains artifacts and may perform poorly in some cases. |
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# **Hoags-2B-Exp** |
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The **Hoags-2B-Exp** model is a fine-tuned version of Qwen2-VL-2B-Instruct, specifically designed for reasoning tasks, context reasoning, and multi-modal understanding. If you ask for an image description, it will automatically describe the image and answer the question in a conversational manner. |
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# **Key Enhancements** |
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* **Advanced Contextual Reasoning**: Hoags-2B-Exp achieves state-of-the-art performance in reasoning tasks by enhancing logical inference and decision-making. |
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* **Understanding images of various resolution & ratio**: The model excels at visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc. |
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* **Long-Context Video Understanding**: Capable of processing and reasoning over videos of 20 minutes or more for high-quality video-based question answering, content creation, and dialogue. |
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* **Device Integration**: With strong reasoning and decision-making abilities, the model can be integrated into mobile devices, robots, and automation systems for real-time operation based on both visual and textual input. |
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* **Multilingual Support**: Supports text understanding in various languages within images, including English, Chinese, Japanese, Korean, Arabic, most European languages, and Vietnamese. |
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# **Demo Inference** |
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# **How to Use** |
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```python |
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instruction = "Analyze the image and generate a clear, concise description of the scene, objects, and actions. Respond to user queries with accurate, relevant details derived from the visual content. Maintain a natural conversational flow and ensure logical consistency. Summarize or clarify as needed for understanding." |
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``` |
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```python |
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from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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# Load the model with automatic device placement |
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model = Qwen2VLForConditionalGeneration.from_pretrained( |
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"prithivMLmods/Hoags-2B-Exp", torch_dtype="auto", device_map="auto" |
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) |
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# Recommended: Enable flash_attention_2 for better performance in multi-image and video tasks |
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# model = Qwen2VLForConditionalGeneration.from_pretrained( |
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# "prithivMLmods/Hoags-2B-Exp", |
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# torch_dtype=torch.bfloat16, |
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# attn_implementation="flash_attention_2", |
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# device_map="auto", |
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# ) |
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# Load processor |
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processor = AutoProcessor.from_pretrained("prithivMLmods/Hoags-2B-Exp") |
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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": "Analyze the context of this image."}, |
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], |
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} |
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] |
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# Prepare input |
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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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# Inference |
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generated_ids = model.generate(**inputs, max_new_tokens=128) |
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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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# **Buffer Handling** |
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```python |
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buffer = "" |
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for new_text in streamer: |
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buffer += new_text |
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buffer = buffer.replace("<|im_end|>", "") |
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yield buffer |
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``` |
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# **Key Features** |
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1. **Advanced Contextual Reasoning:** |
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- Optimized for **context-aware problem-solving** and **logical inference**. |
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2. **Optical Character Recognition (OCR):** |
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- Extracts and processes text from images with exceptional accuracy. |
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3. **Mathematical and Logical Problem Solving:** |
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- Supports complex reasoning and outputs equations in **LaTeX format**. |
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4. **Conversational and Multi-Turn Interaction:** |
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- Handles **multi-turn dialogue** with enhanced memory retention and response coherence. |
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5. **Multi-Modal Inputs & Outputs:** |
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- Processes images, text, and combined inputs to generate insightful analyses. |
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6. **Secure and Efficient Model Loading:** |
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- Uses **Safetensors** for faster and more secure model weight handling. |
