How does alpha scaling impact the training process?

Alpha acts as a learning rate multiplier for adapter weights. The standard formula is alpha = r (1.0x scaling); increasing alpha to 2r results in more aggressive training, while alpha = r/2 prioritizes stability.

What is the recommended LoRA rank for general fine-tuning?

A rank (r) of 16 is generally considered the optimal balance between performance and parameter count, while a rank of 8 is recommended for memory-constrained environments.

Should I target Attention layers or MLP layers during tuning?

Target attention layers (q_proj, v_proj) for changing reasoning patterns or style, and target MLP layers (gate, up, down_proj) for factual knowledge injection and domain adaptation.

Can I prevent catastrophic forgetting during sequential training?

Yes, by using the sequential training patterns in this skill, you can freeze base weights and use adapters to accumulate knowledge across domains like medical or legal without losing prior information.

QLoRA Experimentation & Optimization

Name: QLoRA Experimentation & Optimization
Author: atrawog

byatrawog

0•

Data Science & ML

Optimizes LLM fine-tuning via advanced QLoRA patterns, hyperparameter tuning, and memory-efficient implementation strategies.

The qlora skill provides a comprehensive framework for conducting high-level QLoRA experiments, specifically optimized for developers using Unsloth and Jupyter workflows. It offers prescriptive guidance on critical fine-tuning decisions, such as selecting the optimal LoRA rank (r) and alpha scaling factors to balance performance against VRAM constraints. Beyond basic tuning, the skill includes implementation patterns for sophisticated techniques like continual learning to prevent catastrophic forgetting and multi-adapter hot-swapping for dynamic inference. Whether you are performing domain adaptation or style injection, this skill provides the benchmarks and code snippets necessary to maximize the efficiency of quantization-aware parameter-efficient fine-tuning.

Key Features

010 GitHub stars

02Target module strategy selection for knowledge injection versus style adaptation

03Multi-adapter hot-swapping workflows for modular inference systems

04Automated LoRA rank and alpha scaling recommendations based on GPU VRAM

05Sequential training patterns for continual learning and knowledge retention

06Optimized Unsloth integration for 4-bit and BF16 quantization comparisons

Use Cases

01Fine-tuning large language models on consumer-grade hardware with limited memory

02Injecting domain-specific knowledge (medical, legal, technical) into base models without full retraining

03Creating modular AI systems that switch between specialized task adapters at runtime

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add atrawog/overthink-plugins qlora

For use in Claude.ai and ChatGPT

Download Skill