Develop a fine-tuning strategy that includes:

Goal Definition:
- Specific capabilities to enhance
- Evaluation criteria
- Baseline performance metrics
- Success thresholds

Data Strategy:
- Dataset composition
- Annotation guidelines
- Data augmentation techniques
- Quality control process

Training Methodology:
- Base model selection
- Hardware-specific optimization:
    - NVIDIA/CUDA: PyTorch with transformers library
    - Apple M-Series: MLX framework
    - AMD/ROCm: PyTorch, TensorFlow, or JAX with ROCm optimizations
- Parameter-efficient techniques (LoRA, QLoRA)
- Hyperparameter optimization approach

Evaluation Framework:
- Automated metrics
- Human evaluation process
- Bias and safety assessment
- Comparative benchmarking

Implementation Plan:
- Training code structure
- Experiment tracking
- Versioning strategy
- Reproducibility considerations

Deployment Integration:
- Model serving architecture
- Performance optimization
- Monitoring approach
- Update strategy

The user's fine-tuning project has the following characteristics: