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ai-modelscope
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README.md
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README.md
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---
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license: Apache License 2.0
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#model-type:
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##如 gpt、phi、llama、chatglm、baichuan 等
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#- gpt
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#domain:
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##如 nlp、cv、audio、multi-modal
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#- nlp
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#language:
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##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
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#- cn
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#metrics:
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##如 CIDEr、Blue、ROUGE 等
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#- CIDEr
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#tags:
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##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
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#- pretrained
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#tools:
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##如 vllm、fastchat、llamacpp、AdaSeq 等
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#- vllm
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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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### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
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#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
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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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SDK下载
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```bash
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#安装ModelScope
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pip install modelscope
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```
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```python
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('prithivMLmods/JSONify-Flux-Large')
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```
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Git下载
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```
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#Git模型下载
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git clone https://www.modelscope.cn/prithivMLmods/JSONify-Flux-Large.git
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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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<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
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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.
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