mlops/training/trl-fine-tuning

Fine-tune language models on prompt-response datasets with LoRA.

2|1|Updated May 10, 2026

Search Tags:#LoRA #dataset #transformers #finetuning #language model #custom training

Ready to run

npx skills add https://github.com/zli5460/hermes-agent-X-Phoenix-Architecture --skill mlops-training-trl-fine-tuning

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: mlops/training/trl-fine-tuning
Source: https://github.com/zli5460/hermes-agent-X-Phoenix-Architecture/tree/main/skills/mlops/training/trl-fine-tuning
Command: npx skills add https://github.com/zli5460/hermes-agent-X-Phoenix-Architecture --skill mlops-training-trl-fine-tuning

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies the process of customizing large language models through supervised fine-tuning, enabling users to tailor models for specific applications efficiently.

Core Features & Use Cases

Instruction Tuning: Adjusts pre-trained models for better adherence to user instructions, ideal for chatbot and virtual assistant development.
Task-Specific Fine-Tuning: Refines models on domain-specific datasets such as legal, medical, or technical texts for improved accuracy.
Use Case: A developer can fine-tune a language model on customer service dialogues to enhance chatbot responsiveness in their support system.

Quick Start

Use the finetuning skill to train a language model on your custom dataset by specifying training parameters and dataset format.

Frequently Asked Questions about mlops/training/trl-fine-tuning

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

FAQPage Schema

How do I fine-tune a language model on a custom dataset?▼

Fine-tuning a language model requires a dataset in prompt-response format to execute supervised training. This process adjusts the model for domain adaptation or instruction following.

Does this fine-tuning skill support parameter-efficient methods like LoRA?▼

Yes, the skill supports parameter-efficient fine-tuning methods like LoRA. This allows you to streamline language model training while optimizing performance for specific tasks without updating all parameters.

What format does the dataset need to be in for instruction tuning?▼

The dataset must be in a prompt-response format for instruction tuning. This structure allows the model to learn how to adhere to user instructions, which is ideal for chatbot and virtual assistant development.

Can I use transformers and torch for task-specific domain adaptation?▼

Yes, you can use transformers and torch for task-specific fine-tuning on domain-specific datasets like legal, medical, or technical texts. This refines the model to improve accuracy for your specific use case.

What dependencies are required to run the language model training scripts?▼

You need transformers, datasets, peft, accelerate, and torch installed to run the training scripts. These dependencies provide the foundational framework for model loading, dataset processing, and execution acceleration.