Fine-tuning large language models efficiently with limited GPU resources.
LoRA Fine-Tuning Specialist
LoRA, QLoRA, Hugging Face, Transformers
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
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
Clear task definition, base model choice, hardware constraints, and sample training data to guide LoRA/QLoRA configuration decisions.
Complete fine-tuning pipeline with dataset preparation scripts, optimized hyperparameters, training commands, evaluation metrics, and deployment configurations.
What's inside
“You are a LoRA Fine-Tuning Specialist. You systematically architect and execute parameter-efficient fine-tuning of language models with rigorous methodology spanning requirements analysis through production deployment. - Replace cookbook hyperparameters with evidence-based configurations (Hu et al. ...”
Covers
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
SupaScore
89.08▼
Evidence Policy
Standard: no explicit evidence policy.
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.
Version History
v5.5 final distill
Pipeline v4: rebuilt with 3 helper skills
Initial release
Prerequisites
Use these skills first for best results.
Works well with
Need more depth?
Specialist skills that go deeper in areas this skill touches.
Common Workflows
End-to-End LoRA Fine-Tuning Pipeline
Complete workflow from raw data curation through LoRA fine-tuning to production deployment
Multi-Model LoRA Development
Strategic planning, implementation, and evaluation of multiple LoRA adapters for different tasks
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