LoRA Fine-Tuning Specialist

Guides practitioners through end-to-end LoRA and QLoRA fine-tuning workflows, from dataset curation and hyperparameter selection to training optimization, evaluation, model merging, and deployment of parameter-efficient fine-tuned language models.

Gold

v1.0.00 activationsAI & Machine LearningTechnologyadvanced

SupaScore

84.6

Research Quality (15%)

8.5

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.5

Completeness (10%)

8.5

User Satisfaction (20%)

8.3

Decision Usefulness (15%)

8.5

Best for

▸Fine-tuning Llama 2/3, Mistral, or CodeLlama for domain-specific tasks with limited GPU memory
▸Implementing QLoRA fine-tuning for 13B+ parameter models on consumer RTX 4090 or A100 GPUs
▸Creating instruction-following adapters for customer support, coding assistance, or content generation
▸Multi-adapter deployment strategies for serving different specialized models from one base
▸Optimizing LoRA rank, alpha, and target modules for maximum performance-efficiency tradeoffs

What you'll get

●Detailed training configuration with rank=16, alpha=32, target_modules=['q_proj', 'v_proj'], dropout=0.1, and gradient checkpointing enabled for 7B model on 24GB GPU
●Complete data preprocessing pipeline with instruction formatting, deduplication scripts, and train/validation splits optimized for the target task
●Step-by-step evaluation framework with perplexity, BLEU scores, and human evaluation rubrics for measuring fine-tuning success

Not designed for ↓

×Full fine-tuning workflows or pre-training language models from scratch
×Computer vision model fine-tuning or non-transformer architectures
×Real-time inference optimization or production model serving infrastructure
×Creating training datasets from scratch without domain expertise

Expects

Clear task definition, base model choice, hardware constraints, and sample training data to guide LoRA/QLoRA configuration decisions.

Returns

Complete fine-tuning pipeline with dataset preparation scripts, optimized hyperparameters, training commands, evaluation metrics, and deployment configurations.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

loraqlorapeftfine-tuningparameter-efficienthugging-facetransformersadaptermodel-merginggradient-checkpointingmixed-precisiondataset-curationhyperparameter-tuning

Research Foundation: 9 sources (3 academic, 4 official docs, 2 web)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.