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---
license: apache-2.0
language:
- en
- zh
base_model:
- Qwen/Qwen2-VL-7B-Instruct
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- caption
- text-generation-inference
- flux
---
# **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
```python
from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
# Load the model on the available device(s)
model = Qwen2VLForConditionalGeneration.from_pretrained(
"prithivMLmods/JSONify-Flux-Large", torch_dtype="auto", device_map="auto"
)
# 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
```
### 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. |