README.md

metadata

title: Qwen2-VL Amazon Listing Generator
emoji: 🛒
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: apache-2.0

🛒 Qwen2-VL Amazon Listing Generator (LoRA)

This Hugging Face Space showcases a fine-tuned Qwen2-VL-7B model with LoRA adapter trained to generate Amazon-style product listings from product images.

🚀 Features

• Vision-Language Model: Qwen2-VL-7B-Instruct with custom LoRA adapter
• Amazon Listing Generation: Creates structured product listings with:
  • Product title
  • Bullet points (key features)
  • Product description
  • Keywords
  • Product category
• CPU Optimized: Runs on free CPU hardware (may take 1-2 minutes per generation)

🔧 Model Details

• Base Model: Qwen/Qwen2-VL-7B-Instruct
• LoRA Adapter: soupstick/qwen2vl-amazon-ft-lora
• Fine-tuning: Specialized for e-commerce product listing generation

🎯 How to Use

1. Upload Image: Click on the image upload area and select a product photo
2. Optional Prompt: Modify the instruction if needed (default works well)
3. Generate: Click "Generate Listing" and wait for results
4. Review Output: Get structured Amazon-style listing in JSON format

📋 Expected Output Format

{
  "title": "Product Title Here",
  "bullet_points": [
    "• Key feature 1",
    "• Key feature 2",
    "• Key feature 3"
  ],
  "description": "Detailed product description...",
  "keywords": "relevant, product, keywords",
  "category": "Product > Category > Subcategory"
}

⚡ Performance Notes

• CPU Mode: This demo runs on CPU hardware for free access
• Processing Time: 1-2 minutes per generation due to CPU limitations
• Image Size: Automatically resized to 512px for optimal performance
• Memory Optimized: Uses float32 and low memory settings

🔗 Links

• Model Repository
• Base Model
• Qwen2-VL Paper

⚠️ Limitations

• Demo Purpose: This is a prototype for concept demonstration
• Accuracy: Results depend on training data quality and model size
• Speed: CPU inference is slower than GPU (upgrade hardware for faster results)
• Languages: Primarily trained on English product descriptions

🛠️ Technical Stack

• Framework: Transformers, PEFT (LoRA), Gradio
• Model: Qwen2-VL-7B with custom LoRA adapter on Unsloth-AI
• Hardware: CPU-optimized for Hugging Face Spaces free tier

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Built with ❤️ using Hugging Face Spaces