Does it assist with model evaluation?

Absolutely. It generates scripts to measure essential metrics like perplexity, BLEU, and ROUGE, and provides frameworks for testing on held-out evaluation sets.

What fine-tuning methods does this skill support?

The skill supports a wide range of techniques including LoRA, QLoRA, adapter tuning, instruction tuning, and Reinforcement Learning from Human Feedback (RLHF).

How does it handle GPU memory constraints?

It prioritizes parameter-efficient fine-tuning (PEFT) methods and quantization for large models, ensuring you can train effectively even with limited hardware resources.

Can this skill help with dataset preparation?

Yes, it provides specific guidance and scripts for collecting, formatting (such as Alpaca or ShareGPT styles), and validating training data quality to ensure high-performing results.

LLM Fine-Tuning Expert

Name: LLM Fine-Tuning Expert
Author: Jeffallan

byJeffallan

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数据科学与机器学习

Optimizes large language models through parameter-efficient fine-tuning, automated dataset preparation, and production-grade performance benchmarking.

关于

The Fine-Tuning Expert skill transforms Claude into a senior ML engineer capable of guiding you through the entire lifecycle of custom model development. It provides specialized support for parameter-efficient methods like LoRA and QLoRA, ensuring efficient training on constrained hardware while maintaining high model performance. Whether you are preparing instruction-tuning datasets, configuring complex hyperparameters for DeepSpeed, or quantizing models for production deployment, this skill offers production-grade implementation patterns and validation strategies to prevent common pitfalls like overfitting, data leakage, and GPU memory exhaustion.

主要功能

49 GitHub stars
Implements PEFT methods including LoRA and QLoRA for efficient model adaptation.
Guides model merging and quantization workflows for production-ready deployment.
Configures optimal hyperparameters for learning rates, batch sizes, and schedulers.
Provides automated scripts for dataset validation, formatting, and quality checks.
Generates comprehensive evaluation scripts to benchmark model performance against baselines.

使用场景

Adapting a foundation model to follow specific domain instructions and specialized terminology.
Optimizing model size and inference latency through quantization techniques like GPTQ or GGUF.
Preparing high-quality instruction-tuning datasets from raw unstructured conversational data.

关于

主要功能

49 GitHub stars
Implements PEFT methods including LoRA and QLoRA for efficient model adaptation.
Guides model merging and quantization workflows for production-ready deployment.
Configures optimal hyperparameters for learning rates, batch sizes, and schedulers.
Provides automated scripts for dataset validation, formatting, and quality checks.
Generates comprehensive evaluation scripts to benchmark model performance against baselines.

使用场景

Adapting a foundation model to follow specific domain instructions and specialized terminology.
Optimizing model size and inference latency through quantization techniques like GPTQ or GGUF.
Preparing high-quality instruction-tuning datasets from raw unstructured conversational data.