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llm

llm

github.com/mindrallyDevelopment287728 installsUpdated 3w ago
Repository

About llm

A skill for LLM development, covering model architecture, training, fine-tuning, evaluation, and deployment. It is intended for developers working across the model lifecycle, from tokenization choices to inference optimization.

Key features

  1. 01Transformer architecture and attention
  2. 02Tokenizer selection and handling
  3. 03LoRA and other fine-tuning methods
  4. 04Distributed training and memory optimization
  5. 05Evaluation, inference, and deployment

Use cases

  1. 01Planning a fine-tuning approach
  2. 02Scaling training across distributed infrastructure
  3. 03Evaluating models with benchmarks
  4. 04Optimizing models for production inference

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/mindrally/skills --skill llm

After installing, type/llmin Claude Code to confirm it loaded.

Details

From the project README

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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