model-trainer

slug	model_trainer
name	Model Trainer
summary	Train and fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face infrastructure
description	This skill enables training and fine-tuning of language models using TRL (Transformer Reinforcement Learning). It supports various training methods including supervised fine-tuning, reinforcement learning from human feedback (RLHF), and parameter-efficient fine-tuning (PEFT). The skill integrates with Hugging Face Jobs infrastructure for scalable training.
version	0.1.0
tags	["machine learning","training","fine-tuning","TRL","Hugging Face","LLM"]
triggers	["train model","fine-tune","TRL","reinforcement learning","RLHF","PEFT","LoRA","QLoRA"]
priority	default
assets	[]
recommended_tools	["run_command","write_file","read_file","search_code"]

Model Trainer Skill

Overview

This skill helps you train and fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face infrastructure.

Core Capabilities

1. Supervised Fine-Tuning

• Load pre-trained models from Hugging Face Hub
• Prepare datasets for fine-tuning
• Configure training arguments
• Handle model checkpointing and saving

2. Reinforcement Learning from Human Feedback (RLHF)

• Set up reward models
• Configure PPO (Proximal Policy Optimization) training
• Manage reward datasets
• Monitor training metrics

3. Parameter-Efficient Fine-Tuning (PEFT)

• LoRA (Low-Rank Adaptation) configuration
• QLoRA for quantized training
• Memory-efficient fine-tuning strategies

4. Training Infrastructure

• Hugging Face Jobs integration
• Multi-GPU training setup
• Distributed training configuration
• Resource optimization

Usage Instructions

Basic Fine-Tuning

1. Choose a base model from Hugging Face Hub
2. Prepare your training dataset
3. Configure training arguments
4. Set up the trainer with appropriate parameters
5. Monitor training progress and metrics

RLHF Training

1. Set up a reward model
2. Prepare preference datasets
3. Configure PPO training parameters
4. Run training with appropriate safety constraints
5. Evaluate model performance

PEFT Training

1. Choose PEFT method (LoRA/QLoRA)
2. Configure adapter parameters
3. Set up memory-efficient training
4. Save and load adapter weights

Dependencies

• transformers
• trl
• datasets
• accelerate
• peft
• bitsandbytes (for quantization)
• wandb (for experiment tracking)

Best Practices

• Start with small learning rates for fine-tuning
• Use appropriate batch sizes based on available memory
• Implement gradient clipping for stable training
• Save checkpoints regularly
• Monitor training metrics closely
• Use appropriate evaluation metrics for your use case

Integration Notes

• Works seamlessly with Hugging Face Hub for model storage
• Supports integration with Weights & Biases for experiment tracking
• Compatible with various model architectures (BERT, GPT, T5, etc.)
• Can be used with custom datasets and evaluation metrics