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--- ---
license: Apache License 2.0 license: apache-2.0
language:
#model-type: - en
##如 gpt、phi、llama、chatglm、baichuan 等 - zh
#- gpt base_model:
- Qwen/Qwen2-VL-7B-Instruct
#domain: pipeline_tag: image-text-to-text
##如 nlp、cv、audio、multi-modal library_name: transformers
#- nlp tags:
- caption
#language: - text-generation-inference
##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa - flux
#- cn
#metrics:
##如 CIDEr、Blue、ROUGE 等
#- CIDEr
#tags:
##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
#- pretrained
#tools:
##如 vllm、fastchat、llamacpp、AdaSeq 等
#- vllm
--- ---
### 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。 ![9.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/esgwb8sdL5LbyDuQFLWnT.png)
#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型 # **JSONify-Flux-Large**
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**.
### Key Enhancements:
* **Optimized for Flux-Generated Image Captioning**: JSONify-Flux-Large has been trained to understand and describe images created using Flux-based generation techniques.
* **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.
* **Formatted JSON Output**: Responses are structured in a JSON format, making it ideal for automation, database storage, and further processing.
* **Multilingual Support**: Recognizes and extracts text from images in multiple languages, including English, Chinese, Japanese, Arabic, and various European languages.
* **Supports Multi-Turn Interactions**: Maintains context in conversations and can provide extended reasoning over multiple inputs.
### How to Use
SDK下载
```bash
#安装ModelScope
pip install modelscope
```
```python ```python
#SDK模型下载 from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from modelscope import snapshot_download from qwen_vl_utils import process_vision_info
model_dir = snapshot_download('prithivMLmods/JSONify-Flux-Large')
``` # Load the model on the available device(s)
Git下载 model = Qwen2VLForConditionalGeneration.from_pretrained(
``` "prithivMLmods/JSONify-Flux-Large", torch_dtype="auto", device_map="auto"
#Git模型下载 )
git clone https://www.modelscope.cn/prithivMLmods/JSONify-Flux-Large.git
# Enable flash_attention_2 for better acceleration and memory efficiency
# model = Qwen2VLForConditionalGeneration.from_pretrained(
# "prithivMLmods/JSONify-Flux-Large",
# torch_dtype=torch.bfloat16,
# attn_implementation="flash_attention_2",
# device_map="auto",
# )
# Default processor
processor = AutoProcessor.from_pretrained("prithivMLmods/JSONify-Flux-Large")
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
},
{"type": "text", "text": "Describe this image in JSON format."},
],
}
]
# Prepare inputs for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference: Generate JSON-formatted output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text) # JSON-formatted response
``` ```
<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> ### JSON Buffer Handling
```python
buffer = ""
for new_text in streamer:
buffer += new_text
buffer = buffer.replace("<|im_end|>", "")
yield buffer
```
### **Key Features**
1. **Flux-Based Vision-Language Model**:
- Specifically trained on **Flux-generated images and captions** for precise image-to-text conversion.
2. **Optical Character Recognition (OCR)**:
- Extracts and processes text from images with high accuracy.
3. **Math and LaTeX Support**:
- Solves math problems and outputs equations in **LaTeX format**.
4. **Structured JSON Output**:
- Ensures outputs are formatted in JSON, making it suitable for API responses and automation tasks.
5. **Multi-Image and Video Understanding**:
- Supports analyzing multiple images and video content up to **20 minutes long**.
6. **Secure Weight Format**:
- Uses **Safetensors** for enhanced security and faster model loading.

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{
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
}

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{
"_name_or_path": "..local/models/resolve/prithivMLmods/JSONify-Flux-Large",
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"vocab_size": 152064
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preprocessor_config.json Normal file
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special_tokens_map.json Normal file
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