LLM Development

Core Principles

• Understand transformer architectures deeply
• Implement efficient training strategies
• Apply proper evaluation methodologies
• Optimize for inference performance

Model Architecture

Attention Mechanisms

• Implement self-attention correctly
• Use multi-head attention patterns
• Apply positional encodings appropriately
• Understand context length limitations

Tokenization

• Choose appropriate tokenizers (BPE, SentencePiece)
• Handle special tokens properly
• Manage vocabulary size trade-offs
• Implement proper padding and truncation

Fine-Tuning Techniques

Parameter-Efficient Methods

• Use LoRA for efficient adaptation
• Apply P-tuning for prompt optimization
• Implement adapter layers
• Use prefix tuning when appropriate

Full Fine-Tuning

• Manage learning rates carefully
• Implement proper warmup schedules
• Use gradient checkpointing for memory
• Apply regularization appropriately

Training Infrastructure

Distributed Training

• Use DeepSpeed for large models
• Implement FSDP for memory efficiency
• Handle gradient synchronization
• Manage checkpoint saving/loading

Memory Optimization

• Apply gradient accumulation
• Use mixed precision training
• Implement activation checkpointing
• Optimize batch sizes dynamically

Evaluation

• Use appropriate metrics (perplexity, BLEU, etc.)
• Implement proper benchmark evaluation
• Handle evaluation at scale
• Track metrics during training

Deployment

• Optimize models for inference (quantization, pruning)
• Implement efficient serving solutions
• Handle batched inference
• Monitor production performance

Project Structure

• Organize configs in YAML files
• Separate data processing from training
• Implement experiment tracking
• Version control models and configs