Can I train multiple adapters without retraining the base model?

Yes, this skill includes implementation patterns for multi-adapter hot-swapping, allowing you to load lightweight adapters onto a single frozen base model at inference time.

What is the recommended alpha scaling for QLoRA?

Generally, setting alpha equal to the rank (1.0x scaling) is the standard for a balanced training. You can use alpha=2r for more aggressive convergence if the learning rate is adjusted accordingly.

How much memory does 4-bit NF4 quantization save?

4-bit NF4 quantization typically provides 11-15% memory savings compared to BF16 while maintaining nearly identical model quality, making it ideal for memory-constrained environments.

Which target modules should I use for knowledge injection?

Use 'mlp_only' (gate, up, and down projections) to focus on factual knowledge injection, whereas 'attention_only' is better for changing reasoning styles or formatting.

QLoRA Experimentation & Optimization

Name: QLoRA Experimentation & Optimization
Author: atrawog

byatrawog

Data Science & ML

Optimizes Large Language Model fine-tuning through advanced QLoRA experimentation patterns, rank selection, and adapter management.

About

This skill provides a comprehensive framework for conducting advanced QLoRA experiments, enabling developers to fine-tune AI models efficiently. It covers critical implementation patterns including alpha scaling, optimal rank selection, and strategic target module identification. Whether you are performing knowledge injection via MLP layers or implementing multi-adapter hot-swapping for dynamic inference, this skill offers the specialized code snippets and guidelines needed to maximize performance while minimizing VRAM usage using the Unsloth framework within Jupyter environments.

Key Features

Multi-adapter hot-swapping for dynamic task-specific inference
Optimized 4-bit NF4 vs. BF16 quantization memory analysis
0 GitHub stars
Continual learning patterns to prevent catastrophic forgetting
Automated rank and alpha scaling comparison benchmarking
Strategic target module selection for knowledge vs. reasoning tasks

Use Cases

Fine-tuning Qwen or Llama models with specialized domain knowledge
Conducting hyperparameter sweeps to find optimal LoRA configurations
Building modular AI systems with swappable, lightweight task adapters

About

Key Features

Multi-adapter hot-swapping for dynamic task-specific inference
Optimized 4-bit NF4 vs. BF16 quantization memory analysis
0 GitHub stars
Continual learning patterns to prevent catastrophic forgetting
Automated rank and alpha scaling comparison benchmarking
Strategic target module selection for knowledge vs. reasoning tasks

Use Cases

Fine-tuning Qwen or Llama models with specialized domain knowledge
Conducting hyperparameter sweeps to find optimal LoRA configurations
Building modular AI systems with swappable, lightweight task adapters