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LLM Tuning Patterns

Fine-tune LLMs with QLoRA, LoRA, and DPO patterns.

Updated Feb 25, 2026
Search Tags:#llm#fine-tuning#lora#transformers#peft#qlora#dpo
One-click install
npx skills add https://github.com/HermeticOrmus/LibreMLOps-Claude-Code --skill llm-tuning-patterns
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: LLM Tuning Patterns
Source: https://github.com/HermeticOrmus/LibreMLOps-Claude-Code/tree/main/plugins/llm-fine-tuning/skills/llm-tuning-patterns
Command: npx skills add https://github.com/HermeticOrmus/LibreMLOps-Claude-Code --skill llm-tuning-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers, peft, trl, datasets, torch, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides expert patterns and code examples for fine-tuning Large Language Models (LLMs), addressing the complexities of efficient training, dataset preparation, and model evaluation.

Core Features & Use Cases

  • Efficient Fine-Tuning: Implement QLoRA and LoRA for memory-efficient training on consumer hardware.
  • Dataset Preparation: Format instruction datasets and understand label masking for optimal training.
  • Preference Alignment: Utilize Direct Preference Optimization (DPO) to align models with human preferences.
  • Model Evaluation: Integrate with lm-evaluation-harness for standardized benchmarking.
  • Use Case: Fine-tune a Llama-2 7B model for a specific task like customer support summarization using QLoRA, ensuring efficient use of GPU VRAM and achieving high performance.

Quick Start

Use the LLM Tuning Patterns skill to perform QLoRA fine-tuning on a Llama-2 7B model with the provided instruction dataset.

Frequently Asked Questions about LLM Tuning Patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune a Llama-2 7B model with QLoRA on consumer hardware?▼

Fine-tuning a Llama-2 7B model with QLoRA involves using peft and transformers to train memory-efficient adapters, enabling high-performance instruction tuning on consumer GPU hardware.

What is the best way to prepare instruction datasets for LLM fine-tuning?▼

Preparing instruction datasets for LLM fine-tuning requires formatting examples correctly and applying label masking to ensure the model only learns from the target outputs during the training process.

How does Direct Preference Optimization (DPO) align LLMs with human preferences?▼

Direct Preference Optimization (DPO) aligns LLMs by fine-tuning the model directly on human preference datasets to increase the likelihood of preferred responses over rejected outputs.

Can I use lm-evaluation-harness to benchmark my fine-tuned LoRA adapters?▼

Yes, lm-evaluation-harness supports benchmarking fine-tuned LoRA adapters by providing standardized evaluation metrics to measure model performance after merging adapters with the base transformers model.

Do I need to merge LoRA adapters with the base transformers model before inference?▼

Merging LoRA adapters with the base transformers model integrates the trained weights permanently for standalone deployment, though peft also supports loading separate adapters dynamically for inference.

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