skills.rest
Estom
EstomCOMMUNITY
Explore all skills by this author →
WebsiteGitHub

fine-tuning-expert

Fine-tune large language models with LoRA, QLoRA, and PEFT workflows.

Updated Mar 17, 2026
Search Tags:#evaluation#fine-tuning#lora#peft#qlora#model-deployment#dataset-preparation
One-click install
npx skills add https://github.com/Estom/aiflex --skill fine-tuning-expert-estom
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-expert
Source: https://github.com/Estom/aiflex/tree/main/skills-repo/Jeffallan-skills/fine-tuning-expert
Command: npx skills add https://github.com/Estom/aiflex --skill fine-tuning-expert-estom

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Adapting large language models to specific tasks is complex, error-prone, and resource-constrained; this Skill consolidates best practices for dataset preparation, parameter-efficient fine-tuning, evaluation, and production deployment so teams can achieve reliable task-specific performance without wasting compute or data quality.

Core Features & Use Cases

  • Parameter-efficient methods: Guidance and recommendations for LoRA, QLoRA, PEFT, and rank selection to reduce memory and training cost.
  • Dataset preparation & validation: Formatting examples (Alpaca, ShareGPT), validation checks, deduplication, augmentation, and train/validation splitting.
  • Training & hyperparameters: Training configuration templates, LR scheduling, batch/accumulation planning, and hyperparameter search advice.
  • Evaluation & benchmarking: Perplexity, generation metrics, task-specific evaluations, LLM-as-judge patterns, and model comparison workflows.
  • Deployment & optimization: Adapter merging, GPTQ/AWQ quantization, GGUF export, vLLM/TGI deployment patterns, and inference benchmarking.
  • Use Case: Prepare and QLoRA-fine-tune a base Llama model on a cleaned domain-specific instruction dataset, evaluate against held-out benchmarks, merge adapters, quantize for inference, and deploy with a fast inference server.

Quick Start

Fine-tune a Llama model using QLoRA with an Alpaca-format cleaned dataset, apply a LoRA adapter configuration, run training with the provided hyperparameter recommendations, and produce evaluation metrics and a merged quantized model for deployment.

Frequently Asked Questions about fine-tuning-expert

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

FAQPage Schema
How do I fine-tune a Llama model with QLoRA to reduce GPU memory usage?▼

Fine-tune a Llama model with QLoRA by applying parameter-efficient methods that quantize the base model and attach low-rank adapters, significantly reducing GPU memory usage. This Skill provides memory-aware training configurations and rank selection guidance to optimize compute resources.

What is the best way to prepare an instruction dataset for PEFT training?▼

The best way to prepare an instruction dataset for PEFT training is to format examples using Alpaca or ShareGPT templates, then apply validation checks and deduplication. This Skill outputs dataset validation and train/validation splitting to ensure data quality before training.

Can I use LoRA and PEFT for domain adaptation on a specific task?▼

Yes, you can use LoRA and PEFT for domain adaptation on a specific task. This Skill generates training configurations and hyperparameter tuning recommendations tailored to instruction tuning, allowing reliable task-specific performance without modifying the full model weights.

How do I evaluate and benchmark fine-tuned large language models?▼

Evaluate and benchmark fine-tuned large language models by running perplexity checks, generation metrics, and task-specific evaluations. This Skill provides evaluation scripts and LLM-as-judge patterns to compare model performance against held-out benchmarks.

Does this workflow support merging adapters and exporting quantized models for deployment?▼

Yes, this workflow supports merging adapters and exporting quantized models for deployment. It provides guidance on adapter merging, GPTQ/AWQ quantization, and GGUF export, alongside vLLM and TGI deployment patterns to benchmark inference speed.

Why do I need hyperparameter tuning and LR scheduling during model fine-tuning?▼

You need hyperparameter tuning and LR scheduling during model fine-tuning to stabilize training and prevent loss divergence. This Skill delivers training configuration templates that address batch accumulation, learning rate scheduling, and checkpointing to navigate GPU constraints.

Related Skills

Explore More Data & Analytics Skills→
RachasumanthRachasumanth

data-annotation

Create, format, and validate instruction-tuning and preference datasets for AI model fine-tuning.

Community
Advanced
peteromalletpeteromallet
97

serving-llms-vllm

Deploy LLM inference with vLLM using OpenAI-compatible endpoints, quantization, and tensor parallelism.

Community
Intermediate
NVIDIA-NeMoNVIDIA-NeMo
58

nemo-evaluator

Standardize LLM agent and RAG pipeline evaluation with composable metrics and reproducible benchmarks.

Official
Advanced
liumeixinliumeixin
4

peft-fine-tuning

Fine-tune large language models with LoRA and QLoRA methods.

Community
Advanced