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fine-tuning-expert

Configure LoRA/QLoRA adapters and hyperparameters for fine-tuning large language models.

2|Updated Jun 29, 2026
Search Tags:#fine-tuning#LLM#model optimization#PEFT#custom model
One-click install
npx skills add https://github.com/Axel-DaMage/opencode-config --skill fine-tuning-expert-axel-damage
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-expert
Source: https://github.com/Axel-DaMage/opencode-config/tree/main/skills/fine-tuning-expert
Command: npx skills add https://github.com/Axel-DaMage/opencode-config --skill fine-tuning-expert-axel-damage

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides expert-level fine-tuning capabilities for Large Language Models (LLMs) and custom models, enabling users to configure LoRA/QLoRA adapters, prepare training datasets, set hyperparameters, and optimize model performance.

Core Features & Use Cases

  • Fine-Tuning Configuration: Configure LoRA/QLoRA adapters, prepare JSONL training datasets, set hyperparameters for fine-tuning runs, and train custom models.
  • Model Optimization: Implement parameter-efficient methods, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, and quantizing models.
  • Use Case: For a developer looking to fine-tune a LLM for a specific task, such as code generation or summarization, this Skill provides the necessary tools and guidance.

Quick Start

Use the fine-tuning-expert skill to fine-tune a LLM model for code generation with the following command: fine-tuning-expert --task code-generation --model gpt-3.5 --dataset code-training-data.jsonl --epochs 3 --lr 2e-5.

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 for a specific task like code generation?▼

Fine-tuning an LLM for code generation involves configuring LoRA/QLoRA adapters, preparing a JSONL training dataset, setting hyperparameters like learning rate and epochs, and executing the training run using Python libraries.

What is the difference between LoRA and QLoRA for model optimization?▼

LoRA and QLoRA are parameter-efficient fine-tuning methods that optimize model performance by adapting adapters rather than the full model, with QLoRA adding quantization to reduce memory requirements during training.

Can I use Hugging Face PEFT and TRL for instruction tuning?▼

Yes, instruction tuning is fully supported by configuring Hugging Face PEFT and TRL libraries to implement parameter-efficient transfer learning and optimize large language models for specific tasks.

Do I need Python and PyTorch to train custom models with RLHF?▼

Yes, training custom models with RLHF or DPO requires Python and machine learning libraries like PyTorch and Transformers to configure the environment, load datasets, and execute the optimization process.

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

The best way to prepare a training dataset for LLM fine-tuning is formatting your data into JSONL files, ensuring the structure matches your instruction tuning or task-specific requirements before setting hyperparameters.

Does this approach support OpenAI models and quantizing models?▼

Yes, the fine-tuning approach supports OpenAI models and includes capabilities for quantizing models, allowing you to apply parameter-efficient methods and optimize performance across various platforms.

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