TRAINING_GUIDE.md

🤖 Textilindo AI Training Guide for Hugging Face Spaces

🚀 Training Options on Hugging Face Spaces

Option 1: Quick Training (Recommended for HF Spaces)

Use the lightweight training script designed for HF Spaces constraints.

Access Training Interface:

• Visit: https://harismlnaslm-Textilindo-AI.hf.space/train
• Click "Start Lightweight Training"
• Monitor progress in the training log

Manual Training:

python quick_train.py

Option 2: Use Existing Scripts

Run the full training scripts (may be resource-intensive):

# Check if training is ready
python scripts/check_training_ready.py

# Run lightweight training
python scripts/train_textilindo_ai_optimized.py

# Test the trained model
python scripts/test_textilindo_ai.py

Option 3: External Training + Upload

Train on external resources and upload the model:

1. Train locally or on cloud:

   python scripts/train_textilindo_ai.py
   

2. Upload trained model to HF Hub:

   huggingface-cli upload your-username/textilindo-trained-model ./models/trained-model
   

3. Use the uploaded model in your space

🔧 Training Configuration

For HF Spaces (Limited Resources):

• Model: distilgpt2 (small, fast)
• Batch Size: 1
• Epochs: 1
• Max Length: 128 tokens
• Training Time: ~5 minutes

For External Training (Full Resources):

• Model: meta-llama/Llama-3.1-8B-Instruct
• Batch Size: 4-8
• Epochs: 3
• Max Length: 2048 tokens
• Training Time: Hours

📊 Training Data

Your space includes these training datasets:

• data/lora_dataset_20250829_113330.jsonl (33 samples)
• data/lora_dataset_20250910_145055.jsonl
• data/textilindo_training_data.jsonl
• data/training_data.jsonl

🎯 Training Endpoints

Web Interface:

• Training UI: /train
• Start Training: POST /train/start
• Check Status: GET /train/status
• View Data: GET /train/data

API Usage:

# Start training
curl -X POST "https://harismlnaslm-Textilindo-AI.hf.space/train/start"

# Check resources
curl "https://harismlnaslm-Textilindo-AI.hf.space/train/status"

# View training data
curl "https://harismlnaslm-Textilindo-AI.hf.space/train/data"

⚠️ Limitations of HF Spaces Training

Resource Constraints:

• CPU Only: No GPU acceleration
• Memory: Limited to ~4GB RAM
• Time: 5-minute timeout for training
• Storage: Limited disk space

Recommended Approach:

1. Quick Demo Training: Use quick_train.py for testing
2. Full Training: Use external resources (Google Colab, AWS, etc.)
3. Model Upload: Upload pre-trained models to HF Hub

🚀 External Training Options

Google Colab (Free GPU):

# Upload your training data
# Run: python scripts/train_textilindo_ai.py
# Download trained model
# Upload to HF Hub

Local Training:

# Setup environment
python scripts/setup_textilindo_training.py

# Download model
python scripts/download_model.py

# Run training
python scripts/train_textilindo_ai.py

# Test model
python scripts/test_textilindo_ai.py

Cloud Training (AWS/GCP):

# Use the monitoring script
python scripts/train_with_monitoring.py

📈 Training Progress Monitoring

On HF Spaces:

• Check the training log in the web interface
• Use /train/status endpoint for resource monitoring

External Training:

# Use monitoring script
python scripts/train_with_monitoring.py

# Check logs
tail -f logs/training.log

🧪 Testing Trained Models

Quick Test:

python quick_train.py  # Includes testing

Full Testing:

python scripts/test_textilindo_ai.py
python scripts/test_model.py

API Testing:

# Test chat endpoint
curl -X POST "https://harismlnaslm-Textilindo-AI.hf.space/chat" \
  -H "Content-Type: application/json" \
  -d '{"message": "dimana lokasi textilindo?"}'

🔧 Troubleshooting

Common Issues:

1. "Out of Memory"

   • Use smaller models (distilgpt2)
   • Reduce batch size
   • Use external training

2. "Training Timeout"

   • HF Spaces has 5-minute limit
   • Use external resources for full training

3. "Model Not Found"

   • Check if model is downloaded
   • Use python scripts/download_model.py

4. "Data Not Found"

   • Verify data files exist in data/ directory
   • Check file permissions

📚 Next Steps

1. Start with Quick Training: Test the setup with quick_train.py
2. Monitor Resources: Use /train/status to check available resources
3. External Training: For full training, use external resources
4. Model Upload: Upload trained models to Hugging Face Hub
5. Integration: Use uploaded models in your space

🎉 Success Indicators

• ✅ Training completes without errors
• ✅ Model saves to ./models/ directory
• ✅ Test responses are generated
• ✅ Chat interface works with trained model
• ✅ API endpoints respond correctly

---

Happy Training! 🚀