fine-tuning-expert

Name: fine-tuning-expert
Availability: InStock
Author: marwan78888

Configure LoRA/QLoRA fine-tuning workflows with JSONL datasets and hyperparameters.

16|Updated Apr 19, 2026

Search Tags:#llm #fine-tuning #adapter #lora #peft #qlora #dataset-preparation

Ready to run

npx skills add https://github.com/Marwan78888/Neuron-Cli --skill fine-tuning-expert-marwan78888

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: fine-tuning-expert
Source: https://github.com/Marwan78888/Neuron-Cli/tree/main/scratch/claude-skills-main/skills/fine-tuning-expert
Command: npx skills add https://github.com/Marwan78888/Neuron-Cli --skill fine-tuning-expert-marwan78888

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Configure and optimize fine-tuning of large language models using PEFT adapters.

Core Features & Use Cases

End-to-end LoRA/QLoRA workflow for efficient fine-tuning of large models
Dataset preparation guidance for JSONL formats and transfer learning
Hyperparameter recommendations and deployment guidance for fine-tuned models

Quick Start

Set up a LoRA-based fine-tuning workflow with a prepared dataset and your chosen base model.

Frequently Asked Questions about fine-tuning-expert

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

FAQPage Schema

How do I configure LoRA adapters for fine-tuning a large language model?▼

Configure LoRA adapters for LLM fine-tuning by setting up an end-to-end PEFT workflow that includes preparing your dataset, applying adapter configurations, and setting hyperparameters for efficient training.

What's the best way to prepare a JSONL dataset for PEFT fine-tuning?▼

Prepare a JSONL dataset for PEFT fine-tuning by formatting your training data into structured JSON lines, ensuring the format aligns with transfer learning requirements and validation hooks provided in the workflow.

Can I use QLoRA for efficient fine-tuning on large models?▼

Yes, QLoRA is fully supported for efficient fine-tuning of large models. The workflow configures QLoRA adapters alongside standard LoRA, enabling memory-efficient training while maintaining model performance.

Does this LLM fine-tuning workflow integrate with Hugging Face PEFT?▼

The workflow integrates directly with Hugging Face PEFT and OpenAI fine-tuning APIs, providing an end-to-end pipeline that connects dataset preparation, adapter configuration, and model deployment.

How do I deploy a fine-tuned model after applying PEFT adapters?▼

Deploy a fine-tuned model after applying PEFT adapters by following the workflow's deployment guidance, which connects the trained adapters to integration pipelines and includes validation hooks to ensure deployment readiness.

When should I use PEFT adapters instead of full LLM fine-tuning?▼

Use PEFT adapters instead of full LLM fine-tuning when you need efficient parameter optimization, want to reduce computational costs with LoRA or QLoRA, and require integration with RLHF pipelines without training the entire model.