How does it handle dataset preparation?

It includes logic for validating JSONL training data, performing schema checks, analyzing token lengths, and removing duplicates to ensure high-quality training input.

What fine-tuning methods does this skill support?

The skill specializes in Parameter-Efficient Fine-Tuning (PEFT) methods including LoRA and QLoRA, but also provides guidance for full fine-tuning, OpenAI fine-tuning, and RLHF/DPO.

Does it assist with model deployment after training?

Yes, the skill provides templates for merging LoRA adapters back into base models and optimizing them for serving through quantization and latency benchmarking.

Can this skill help with GPU memory constraints?

Yes, it provides specific configurations for QLoRA and 4-bit quantization to allow training large models (like Llama 3) on hardware with limited VRAM.

Fine-Tuning Expert

Name: Fine-Tuning Expert
Author: JasonLo

byJasonLo

0•

Data Science & ML

Optimizes and adapts foundation models through parameter-efficient fine-tuning (PEFT), dataset preparation, and production-ready ML workflows.

The Fine-Tuning Expert skill transforms Claude into a senior machine learning engineer focused on the end-to-end lifecycle of LLM adaptation. It provides specialized expertise in parameter-efficient fine-tuning (PEFT) techniques like LoRA and QLoRA, ensuring high-performance results even with limited hardware. The skill guides users through the entire process, from rigorous dataset validation and hyperparameter optimization to model evaluation and final deployment through quantization. It is an essential tool for developers looking to specialize foundation models for niche domains, improve task-specific accuracy, or reduce inference latency.

Key Features

01Expert LoRA and QLoRA configuration for parameter-efficient training

02Comprehensive evaluation workflows using perplexity and task-specific metrics

030 GitHub stars

04Hyperparameter guidance for learning rates, schedulers, and batch sizes

05Deployment optimization including adapter merging and 4-bit quantization

06Automated dataset validation, formatting, and quality check scripts

Use Cases

01Adapting foundation models to specialized domain-specific tasks and knowledge

02Optimizing LLM performance on resource-constrained hardware using QLoRA

03Standardizing instruction-tuning workflows for production-grade AI applications

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jasonlo/skill-sommelier ss-fine-tuning-expert

For use in Claude.ai and ChatGPT

Key Features

01Expert LoRA and QLoRA configuration for parameter-efficient training

02Comprehensive evaluation workflows using perplexity and task-specific metrics

030 GitHub stars

04Hyperparameter guidance for learning rates, schedulers, and batch sizes

05Deployment optimization including adapter merging and 4-bit quantization

06Automated dataset validation, formatting, and quality check scripts

Use Cases

01Adapting foundation models to specialized domain-specific tasks and knowledge

02Optimizing LLM performance on resource-constrained hardware using QLoRA

03Standardizing instruction-tuning workflows for production-grade AI applications

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jasonlo/skill-sommelier ss-fine-tuning-expert

For use in Claude.ai and ChatGPT