Can I run these fine-tuning processes on consumer GPUs?

Yes, the skill specifically emphasizes memory-efficient techniques like QLoRA, which can often run on GPUs with as little as 8GB of VRAM.

Does it help with data preparation?

Yes, it includes standardized templates for Alpaca and ChatML formats, along with a complete pipeline for tokenization and dataset mapping.

How do I deploy the fine-tuned model?

The skill provides instructions for merging LoRA adapters back into the base model for standalone deployment or managing multiple adapters for runtime switching.

What fine-tuning methods does this skill support?

The skill provides guidance and implementation patterns for Full Fine-Tuning, LoRA (Low-Rank Adaptation), QLoRA (Quantized LoRA), Prefix Tuning, and Prompt Tuning.

Fine-Tuning AI Models

Name: Fine-Tuning AI Models
Author: pluginagentmarketplace

bypluginagentmarketplace

•

Ciencia de Datos y ML

Adapts and optimizes Large Language Models using LoRA, QLoRA, and instruction tuning for domain-specific applications.

Acerca de

The Fine-Tuning skill provides a comprehensive toolkit for adapting LLMs to specialized tasks and datasets. It streamlines the implementation of Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA and QLoRA, allowing developers to train powerful models on consumer-grade hardware while maintaining high performance. With built-in support for instruction dataset formatting (Alpaca, ChatML), training pipeline configuration, and adapter merging, this skill is essential for AI engineers looking to bridge the gap between base models and production-ready, domain-expert assistants.

Características Principales

Configurable training pipelines with gradient checkpointing and cosine schedules
2 GitHub stars
Implementation of LoRA and QLoRA for memory-efficient training
Automated instruction dataset preparation and formatting templates
Comprehensive troubleshooting for common training issues like NaN loss or OOM
Multi-adapter management and weight merging for deployment

Casos de Uso

Training coding assistants on private enterprise codebases for improved local generation
Adapting base LLMs to understand and generate domain-specific medical or legal documentation
Optimizing model performance for specialized chat interfaces using instruction tuning

Acerca de

Características Principales

Configurable training pipelines with gradient checkpointing and cosine schedules
2 GitHub stars
Implementation of LoRA and QLoRA for memory-efficient training
Automated instruction dataset preparation and formatting templates
Comprehensive troubleshooting for common training issues like NaN loss or OOM
Multi-adapter management and weight merging for deployment

Casos de Uso

Training coding assistants on private enterprise codebases for improved local generation
Adapting base LLMs to understand and generate domain-specific medical or legal documentation
Optimizing model performance for specialized chat interfaces using instruction tuning