HF_SPACE_INSTRUCTIONS.md

OpenFinancial Chatbot - HF Space Trainer

This is a self-contained training script designed to run in a Hugging Face Space.

🚀 Quick Setup Instructions

1. Create a New HF Space

1. Go to https://huggingface.co/new-space
2. Choose Gradio as the SDK
3. Set hardware to CPU Basic (free) or T4 GPU (paid)
4. Name it something like openfinancial-trainer

2. Upload Files to Your Space

Upload these files to your HF Space:

• hf_space_trainer.py → rename to app.py
• requirements_hf_space.txt → rename to requirements.txt
• Your training CSV files (from the trainingData folder)

3. Training Data Format

Your CSV should have columns like:

• Question and Answer, OR
• Input and Output, OR
• Prompt and Response

The script will automatically detect the column names.

4. Start Training

1. Wait for the space to build (2-3 minutes)
2. Click "🚀 Start Training"
3. Monitor progress in real-time
4. Training takes 15-30 minutes on CPU, 5-10 minutes on GPU

5. Download Your Model

After training completes:

1. Go to your space's Files tab
2. Download the entire trained_model folder
3. Copy it to your local project

🎯 What This Does

• Loads your training data automatically
• Trains TinyLlama model for financial Q&A
• Saves model locally in the space
• Provides simple web interface
• Works on both CPU and GPU

💡 Pro Tips

• Free Option: Use CPU Basic (slower but free)
• Fast Option: Use T4 GPU (~$0.60/hour, much faster)
• Multiple Files: Script tries common CSV names automatically
• Resume Training: Refresh status to see if training completed

📁 Expected Output

After training, you'll have a trained_model folder containing:

• config.json - Model configuration
• pytorch_model.bin - Trained weights
• tokenizer.json - Tokenizer files
• Other supporting files

Copy this folder to your local backend directory and use it with your chatbot!