4citeb4u/agent-lee-the-sum-of-all-systems/retrieval-generation-fine-tuning

/* LEEWAY HEADER — DO NOT REMOVE

REGION: AI.SKILL.AI_ML TAG: AI.SKILL.AI_ML.RETRIEVAL_GENERATION_FINE_TUNING

COLOR_ONION_HEX: NEON=#39FF14 FLUO=#0DFF94 PASTEL=#E8F5E9

ICON_ASCII: family=lucide glyph=zap

5WH: WHAT = retrieval generation fine tuning skill for Leeway-compliant AI systems WHY = Provides capabilities for ai-ml within the AIskills ecosystem WHO = Leeway Industries (By Leonard Jerome Lee) WHERE = skills/ai-ml/retrieval-generation-fine-tuning/SKILL.md WHEN = 2026 HOW = Leeway-governed skill.md definition with structured capabilities and tags

AGENTS: ASSESS AUDIT

ASSIGNED_SACRED_AGENTS: L8_Delivery: AgentLee.ts (Prime Override)

LICENSE: MIT */

SOVEREIGN ALIGNMENT: This skill is strictly executed by L8_Delivery: AgentLee.ts (Prime Override). No unassigned Clones may natively execute this without Hive Mind routing.

Model Fine-Tuning and Adaptation

Expert in: Adapting and fine-tuning language models for specific domains and use cases.

Capabilities

• Prepare training data for domain adaptation
• Implement LoRA (Low-Rank Adaptation) fine-tuning
• Create instruction tuning datasets
• Implement few-shot in-context learning
• Design domain-specific vocabularies
• Handle catastrophic forgetting in fine-tuning
• Evaluate fine-tuned model performance
• Implement model merging strategies

Use this skill when:

• Models need domain-specific knowledge
• Building specialized AI systems
• Wanting to preserve base model knowledge
• Reducing hallucinations in specific domains
• Adapting commercial models
• Creating proprietary model variants
• Improving task-specific performance
• Working with limited labeled data

Key techniques

• LoRA and QLoRA adaptation
• Instruction tuning and RLHF
• Few-shot prompting for adaptation
• Embedding fine-tuning
• Continued pretraining
• Model merging (TIES, linear interpolation)
• Evaluation metrics for fine-tuning
• Cost-effective adaptation

Tags

fine-tuning llm adaptation lora domain-specific training