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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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- Qwen/Qwen2-VL-7B-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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- caption |
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- text-generation-inference |
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- flux |
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--- |
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# **JSONify-Flux-Large** |
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The **JSONify-Flux-Large** model is a fine-tuned version of **Qwen2VL**, specifically trained on **Flux-generated images** and their **corresponding captions**. This model has been trained using a **30M trainable parameter** dataset and is designed to output responses in structured **JSON format** while maintaining state-of-the-art performance in **Optical Character Recognition (OCR)**, **image-to-text conversion**, and **math problem-solving with LaTeX formatting**. |
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### Key Enhancements: |
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* **Optimized for Flux-Generated Image Captioning**: JSONify-Flux-Large has been trained to understand and describe images created using Flux-based generation techniques. |
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* **State-of-the-Art Image Understanding**: Built on Qwen2VL's architecture, JSONify-Flux-Large excels in visual reasoning tasks like DocVQA, RealWorldQA, MTVQA, and more. |
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* **Formatted JSON Output**: Responses are structured in a JSON format, making it ideal for automation, database storage, and further processing. |
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* **Multilingual Support**: Recognizes and extracts text from images in multiple languages, including English, Chinese, Japanese, Arabic, and various European languages. |
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* **Supports Multi-Turn Interactions**: Maintains context in conversations and can provide extended reasoning over multiple inputs. |
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### How to Use |
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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 on the available device(s) |
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model = Qwen2VLForConditionalGeneration.from_pretrained( |
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"prithivMLmods/JSONify-Flux-Large", torch_dtype="auto", device_map="auto" |
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) |
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# Enable flash_attention_2 for better acceleration and memory efficiency |
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# model = Qwen2VLForConditionalGeneration.from_pretrained( |
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# "prithivMLmods/JSONify-Flux-Large", |
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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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# Default processor |
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processor = AutoProcessor.from_pretrained("prithivMLmods/JSONify-Flux-Large") |
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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": "Describe this image in JSON format."}, |
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], |
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} |
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] |
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# Prepare inputs for inference |
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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: Generate JSON-formatted output |
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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) # JSON-formatted response |
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``` |
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### JSON 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. **Flux-Based Vision-Language Model**: |
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- Specifically trained on **Flux-generated images and captions** for precise image-to-text conversion. |
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2. **Optical Character Recognition (OCR)**: |
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- Extracts and processes text from images with high accuracy. |
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3. **Math and LaTeX Support**: |
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- Solves math problems and outputs equations in **LaTeX format**. |
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4. **Structured JSON Output**: |
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- Ensures outputs are formatted in JSON, making it suitable for API responses and automation tasks. |
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5. **Multi-Image and Video Understanding**: |
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- Supports analyzing multiple images and video content up to **20 minutes long**. |
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6. **Secure Weight Format**: |
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- Uses **Safetensors** for enhanced security and faster model loading. |
