131 lines
4.7 KiB
Markdown
131 lines
4.7 KiB
Markdown
---
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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. |