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llm

Large Language Model development, training, fine-tuning, and deployment best practices.

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LLM Development

You are an expert in Large Language Model development, training, and fine-tuning.

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

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