skills.rest
rabbicse
rabbicseCOMMUNITY
Explore all skills by this author →
GitHub

fine-tuning-expert

Optimizes LLM fine-tuning workflows with LoRA, QLora, and PEFT.

2|Updated Apr 18, 2021
Search Tags:#fine-tuning#model evaluation#model deployment#lora#qlora#dataset preparation
One-click install
npx skills add https://github.com/rabbicse/go-projects --skill fine-tuning-expert-rabbicse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-expert
Source: https://github.com/rabbicse/go-projects/tree/main/projects/movie-ticket-booking/.claude/skills/fine-tuning-expert
Command: npx skills add https://github.com/rabbicse/go-projects --skill fine-tuning-expert-rabbicse

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you fine-tune large language models without wasting time on unstable training runs, weak datasets, or deployment mistakes. It turns model adaptation into a structured workflow with validation, tuning, evaluation, and release steps.

Core Features & Use Cases

  • Dataset preparation: Clean, validate, deduplicate, and format training data for instruction tuning or multi-turn chat.
  • Training setup: Choose LoRA, QLoRA, or full fine-tuning with the right rank, batch size, scheduler, and warmup.
  • Evaluation and deployment: Measure perplexity, task metrics, latency, merge adapters, quantize models, and prepare them for serving.
  • Use cases: Adapting a base model to a company support assistant, tuning a code model for internal APIs, or optimizing a domain model for a constrained GPU.

Quick Start

Ask the Skill to design a complete fine-tuning plan for your model, dataset, training settings, evaluation method, and deployment path.

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 an LLM with LoRA or QLoRA for a custom dataset?▼

To fine-tune an LLM with LoRA or QLoRA, you must clean, deduplicate, and format your custom dataset for instruction tuning. You then select the appropriate rank, batch size, scheduler, and learning-rate warmup to establish stable training runs.

What is the best way to prepare a dataset for instruction tuning?▼

Dataset preparation for instruction tuning involves cleaning, validating, deduplicating, and formatting training data. This structured process ensures data quality for multi-turn chat or direct instruction tasks before model adaptation.

Can I use QLoRA to fine-tune a foundation model on a constrained GPU?▼

Yes, QLoRA is specifically applicable for fine-tuning foundation models on constrained GPUs. It applies quantization techniques to reduce memory requirements, allowing you to optimize large models within limited hardware environments.

How do I evaluate and merge adapters after PEFT training?▼

After PEFT training, you evaluate adapters using held-out datasets to measure perplexity and task metrics. Once validated, you merge the adapters with the base model and apply quantization to prepare the model for production serving.

Why does my fine-tuning run fail without learning-rate warmup?▼

Fine-tuning runs often fail without learning-rate warmup because unstable training dynamics cause gradient spikes. Applying a warmup scheduler, checkpointed training, and proper hyperparameter selection ensures reliable model adaptation and prevents wasted computation.

Related Skills

Explore More Software Engineering Skills→
ScientiaCapitalScientiaCapital
1

unsloth-finetuning

Fine-tune Llama, Mistral, Phi, or Gemma models with LoRA and 4-bit quantization.

Community
Intermediate
superfhpsuperfhp

serving-llms-vllm

Serve LLMs with OpenAI-compatible endpoints using vLLM.

Community
Advanced
guccangguccang
3

evaluating-llms-harness

Evaluate large language models against standardized benchmarks using lm-evaluation-harness.

Community
Advanced
eddieluengeddielueng
1

peft-fine-tuning

Train low-rank LoRA/QLoRA adapters for parameter-efficient fine-tuning of large language models.

Community
Advanced