#
🚀 Tutorials: How To Fine-tune & Run LLMs

Learn how to run and fine-tune models for optimal performance with AWS Trainium.

### Llama 3.1

Instruction Fine-tuning of Llama 3.1 8B with LoRA on the Dolly dataset

### Qwen3

Fine-tune Qwen3 8B with LoRA on the Simple Recipes dataset

### Llama 3.2 on SageMaker

Continuous Pretraining of Llama 3.2 1B on SageMaker Hyperpod

##
What you’ll learn

These tutorials will guide you through the complete process of fine-tuning large language models on AWS Trainium:

- 📊 Data Preparation: Load and preprocess datasets for supervised fine-tuning
- 🔧 Model Configuration: Set up LoRA adapters and distributed training parameters
- ⚡ Training Optimization: Leverage tensor parallelism, gradient checkpointing, and mixed precision
- 💾 Checkpoint Management: Consolidate and merge model checkpoints for deployment
- 🚀 Model Deployment: Export and test your fine-tuned models for inference

Choose the tutorial that best fits your use case and start fine-tuning your LLMs on AWS Trainium today!