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fine-tuning-expert

Automates LLM fine-tuning with LoRA/QLoRA, dataset prep, hyperparameters, and deployment optimization.

Updated May 31, 2026
Search Tags:#deployment#fine-tuning#LLM#PEFT#parameter-efficient
One-click install
npx skills add https://github.com/fanguyun/SkillManager --skill fine-tuning-expert-fanguyun
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-expert
Source: https://github.com/fanguyun/SkillManager/tree/main/fine-tuning-expert
Command: npx skills add https://github.com/fanguyun/SkillManager --skill fine-tuning-expert-fanguyun

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the complexity and time-consuming nature of fine-tuning large language models (LLMs), offering streamlined processes for parameter-efficient fine-tuning, dataset preparation, and deployment optimization.

Core Features & Use Cases

  • Fine-Tuning Configuration: Automate the setup of fine-tuning runs with LoRA/QLoRA adapters, JSONL dataset preparation, hyperparameter configuration, and training evaluation.
  • Data Preparation: Validate and format datasets, split them into train/validation sets, and augment examples for better model performance.
  • Deployment Optimization: Merge adapter weights into base models, quantize models for inference, and optimize inference performance with tools like vLLM and llama.cpp.
  • Use Case: A machine learning engineer can use this Skill to train and deploy a fine-tuned LLM for a specific task like code generation or text summarization, saving time and reducing the risk of errors in the process.

Quick Start

Run the skill with the following command: fine-tuning-expert train --dataset path/to/dataset.jsonl --model_name meta-llama/Llama-3.1-8B

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 large language model with LoRA or QLoRA?▼

You can fine-tune a large language model using LoRA or QLoRA adapters by automating dataset preparation, hyperparameter configuration, and training evaluation to reduce memory usage and training time.

What is the best way to prepare a dataset for LLM fine-tuning?▼

The best way to prepare a dataset for LLM fine-tuning is to validate and format records into JSONL files, split them into train and validation sets, and augment examples to improve model performance.

Can I use vLLM and llama.cpp to optimize LLM inference after fine-tuning?▼

Yes, you can optimize LLM inference after fine-tuning by merging adapter weights into base models, quantizing them, and deploying with vLLM or llama.cpp to achieve optimized inference performance.

Does this parameter-efficient fine-tuning workflow support the Hugging Face transformers and peft libraries?▼

Yes, this parameter-efficient fine-tuning workflow requires the transformers, datasets, peft, and trl libraries to automate training runs, dataset preparation, and adapter configuration.

How do I merge LoRA adapter weights into a base model for deployment?▼

You merge LoRA adapter weights into a base model for deployment by consolidating trained parameters with original weights and applying quantization to prepare for optimized inference.

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