# Retraining Guide for Better Model Performance

## Current Issues

Your model only trained for **40 global steps** across 10 epochs, which means:
- Very little training data (~4 batches per epoch)
- Model hasn't learned language patterns
- Model just repeats input and stops

## Retraining Recommendations

### 1. **Increase Training Data**

The model needs much more data. Check your current data:

```bash
# Check how much data you have
wc -l data/*.txt
```

**Recommendations:**
- **Minimum**: 10,000+ text samples
- **Good**: 100,000+ text samples  
- **Better**: 1,000,000+ text samples

### 2. **Update Training Configuration**

Edit `config.json` for better training:

```json
{
  "training": {
    "batch_size": 32,
    "max_epochs": 50,        // Increase from 10 to 50+
    "learning_rate": 1e-4,
    "weight_decay": 0.01,
    "warmup_steps": 1000,
    "max_grad_norm": 1.0,
    "gradient_accumulation_steps": 4,  // Increase to simulate larger batches
    "use_amp": true,
    "save_dir": "./checkpoints",
    "log_interval": 10,      // More frequent logging
    "eval_interval": 500     // More frequent evaluation
  }
}
```

### 3. **Add Validation Set**

Split your data for validation:

```python
# In train.py, add validation split
from sklearn.model_selection import train_test_split

train_texts, val_texts = train_test_split(texts, test_size=0.1, random_state=42)
```

### 4. **Improve Training Data Quality**

Ensure your training data:
- ✅ Contains complete sentences/paragraphs
- ✅ Has diverse topics and styles
- ✅ Doesn't have excessive padding
- ✅ Uses proper text formatting

### 5. **Monitor Training**

Watch for:
- **Loss decreasing**: Should trend downward
- **Perplexity**: Should decrease (lower is better)
- **Generation quality**: Test periodically during training

### 6. **Training Command**

```bash
# Train with more data
python3 train.py \
    --data data/your_training_data.txt \
    --config config.json \
    --output ./checkpoints \
    --device cpu  # or cuda/mps
```

### 7. **Check Training Progress**

During training, you should see:
```
Epoch 1: Train Loss = 8.5 → Epoch 10: Train Loss = 6.0 → Epoch 50: Train Loss = 3.5
```

If loss stops decreasing, the model has converged.

### 8. **Early Stopping**

Consider adding early stopping if validation loss plateaus:
- Stop if validation loss doesn't improve for 5 epochs
- Save the best model based on validation loss

### 9. **Test During Training**

After each epoch, test generation:

```bash
python3 inference.py \
    --checkpoint checkpoints/checkpoint_epoch_X.pt \
    --prompt "The future of" \
    --optimized
```

Good training should show:
- ✅ Model generates coherent text
- ✅ Model continues beyond input prompt
- ✅ Model doesn't immediately generate padding tokens

## Quick Start Retraining

1. **Get more training data** (most important!)
2. **Update config.json** with more epochs
3. **Start training**:
   ```bash
   python3 train.py --data data/your_data.txt --config config.json
   ```
4. **Monitor loss** - should decrease over time
5. **Test periodically** - check if generation improves

## Expected Results

After proper training:
- Loss should decrease from ~8-10 to ~2-4
- Perplexity should decrease from ~3000 to ~10-50
- Model should generate 50+ tokens before stopping
- Generated text should be coherent and diverse

## Next Steps

1. ✅ Early stopping is now fixed (prevents padding tokens)
2. ⏳ **Retrain with more data and epochs**
3. ⏳ Monitor training metrics
4. ⏳ Test generation quality during training

Good luck with retraining! 🚀