How can I make my training faster?

This skill recommends using Unsloth for 2x faster training and reduced memory consumption compared to standard HuggingFace trainers.

Does it cover dataset preparation?

Yes, it provides code for loading datasets from HuggingFace Hub, creating prompt templates, and tokenizing data correctly for LLMs.

Can I resume training from a checkpoint?

Absolutely. The skill includes standardized methods for saving checkpoints and resuming training using the HuggingFace Trainer API.

What frameworks are supported for fine-tuning?

The skill supports PyTorch, HuggingFace Transformers, Datasets, and the TRL library, with a strong recommendation for Unsloth for performance.

Does this skill handle instruction tuning?

Yes, it includes specific patterns for Supervised Fine-Tuning (SFT) using the SFTTrainer for chat and instruction-following tasks.

Model Fine-Tuning & Training

Name: Model Fine-Tuning & Training
Author: atrawog

byatrawog

Data Science & ML

Provides expert guidance and implementation patterns for fine-tuning Large Language Models using PyTorch and HuggingFace.

About

This skill equips Claude with specialized knowledge for adapting pre-trained LLMs to specific tasks or domains using industry-standard frameworks like PyTorch and HuggingFace. It covers the entire training lifecycle, from dataset preparation and tokenization to advanced techniques like Supervised Fine-Tuning (SFT) and checkpoint management. By including recommendations for high-performance libraries like Unsloth, it helps developers optimize training speed and memory efficiency, making it an essential resource for building custom AI models and instruction-following agents.

Key Features

Optimized training workflows using Unsloth for 2x faster performance
End-to-end dataset preparation, tokenization, and formatting guides
Manual PyTorch training loops for full customization and control
Comprehensive HuggingFace Trainer and SFTTrainer implementation patterns
Detailed hyperparameter guidance and checkpoint management strategies
0 GitHub stars

Use Cases

Adapting a base LLM to follow specific domain-specific instructions or brand voices
Improving model accuracy on niche datasets like legal, medical, or technical documentation
Implementing efficient parameter-tuning workflows to reduce hardware requirements

About

Key Features

Optimized training workflows using Unsloth for 2x faster performance
End-to-end dataset preparation, tokenization, and formatting guides
Manual PyTorch training loops for full customization and control
Comprehensive HuggingFace Trainer and SFTTrainer implementation patterns
Detailed hyperparameter guidance and checkpoint management strategies
0 GitHub stars

Use Cases

Adapting a base LLM to follow specific domain-specific instructions or brand voices
Improving model accuracy on niche datasets like legal, medical, or technical documentation
Implementing efficient parameter-tuning workflows to reduce hardware requirements