fine-tuning

Fine-tune pretrained language models with LoRA, QLoRA, or full methods.

Updated Feb 17, 2026

Search Tags:#fine-tuning #nlp #lora #transformers #qlora #trl #model adaptation

Ready to run

npx skills add https://github.com/Rachasumanth/text2llm001 --skill fine-tuning

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: fine-tuning
Source: https://github.com/Rachasumanth/text2llm001/tree/main/workspace/skills/fine-tuning
Command: npx skills add https://github.com/Rachasumanth/text2llm001 --skill fine-tuning

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires transformers, peft, trl, bitsandbytes, datasets, accelerate, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables users to adapt existing pretrained language models for specific tasks or datasets, improving their performance and relevance without the need for training from scratch.

Core Features & Use Cases

Flexible Fine-tuning: Supports LoRA, QLoRA, and full fine-tuning methods.
Diverse Dataset Handling: Accepts and converts various common dataset formats (Alpaca, ShareGPT, OpenAI chat, preference pairs).
Use Case: Fine-tune a general-purpose LLM on your company's internal documentation to create a specialized chatbot that can answer employee questions accurately about company policies.

Quick Start

Use the fine-tuning skill to adapt the base model using the provided dataset in Alpaca format.

Frequently Asked Questions about fine-tuning

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

FAQPage Schema

How do I fine-tune a pretrained language model using LoRA and QLoRA?▼

Fine-tune pretrained language models using LoRA, QLoRA, or full fine-tuning techniques for instruction, chat, and preference alignment. This process adapts models to specific tasks without training from scratch.

Can I use Alpaca and ShareGPT formats for model adaptation?▼

Model adaptation supports multiple dataset formats including Alpaca, ShareGPT, OpenAI chat, and preference pairs. These formats are accepted and converted automatically for fine-tuning pretrained language models.

What libraries do I need for LoRA fine-tuning with transformers?▼

LoRA fine-tuning requires transformers, peft, trl, bitsandbytes, datasets, and accelerate libraries. These dependencies provide the environment necessary for adapting pretrained models and managing adapters.

Does this approach support chat and preference alignment for custom datasets?▼

Chat and preference alignment are supported for custom datasets during fine-tuning. You can adapt base models using instruction tuning and preference pairs to create specialized chatbots from your internal documentation.

What is the best way to adapt an LLM for company-specific documentation?▼

Adapt an LLM for company documentation by fine-tuning a general-purpose model on internal datasets. Using LoRA or QLoRA techniques improves performance and relevance for specific tasks without training from scratch.