model-trainer

What problem does it solve?

This skill streamlines the complex process of training and fine-tuning language models, making advanced AI development more accessible.

Core Features & Use Cases

• Supervised Fine-Tuning: Adapt pre-trained models to specific tasks with custom datasets.
• RLHF & PEFT: Implement cutting-edge techniques like Reinforcement Learning from Human Feedback and Parameter-Efficient Fine-Tuning for optimized model performance and resource usage.
• Use Case: A researcher wants to fine-tune a large language model on a proprietary dataset to improve its performance on a niche scientific domain.

Quick Start

Use the model-trainer skill to fine-tune a base model using supervised learning on a custom dataset.