Text Generation with Fine-tuned GPT

Fine-tune a GPT model for custom text generation.

Generation task:
- Output type: [CREATIVE/TECHNICAL/CONVERSATIONAL]
- Domain: [DESCRIBE YOUR DOMAIN]
- Style: [FORMAL/CASUAL/SPECIFIC STYLE]
- Length: [SHORT/MEDIUM/LONG]

GPT fine-tuning requirements:
1. Data preparation:
   - Training data format
   - Prompt engineering
   - Quality filtering
2. Model selection:
   - GPT-2/GPT-Neo/GPT-J
   - Model size tradeoffs
   - Licensing considerations
3. Fine-tuning:
   - Full fine-tuning vs. adapters
   - Learning rate scheduling
   - Overfitting prevention
4. Generation control:
   - Temperature tuning
   - Top-k/Top-p sampling
   - Repetition penalty
   - Length control
5. Evaluation:
   - Perplexity
   - Human evaluation
   - Task-specific metrics
6. Safety:
   - Output filtering
   - Toxicity detection
   - Prompt injection prevention
7. Deployment:
   - Efficient inference
   - Streaming generation
   - Rate limiting

Balance creativity with coherence.