Fine-tune Foundation Models on Amazon SageMaker using @remote decorator

In this example we will go through the steps required for fine-tuning foundation models on Amazon SageMaker by using @remote decorator for executing SageMaker Training jobs.

You can run this repository from Amazon SageMaker Studio or from your local IDE.

For additional information, take a look at the AWS Blog Fine-tune Falcon 7B and other LLMs on Amazon SageMaker with @remote decorator

This notebook is inspired by Philipp Schmid Blogs

Prerequistes

The notebooks are currently using the latest PyTorch Training Container available for the region us-east-1. If you are running the notebooks in a different region, make sure to update the ImageUri in the file config.yaml.

If you want to operate in a different AWS region

1. Navigate [Available Deep Learning Containers Images](Available Deep Learning Containers Images)
2. Select the right Hugging Face TGI container for model training based on your selected region
3. Update ImageUri in the file config.yaml

Notebooks

1. [Supervised - QLoRA] Falcon-7B
2. [Supervised - QLoRA, FSDP] Llama-13B
3. [Self-supervised - QLoRA, FSDP] Llama-13B
4. [Self-supervised - QLoRA] Mistral-7B
5. [Supervised - QLoRA, FSDP] Mixtral-8x7B
6. [Supervised - QLoRA, DDP] Code-Llama 13B
7. [Supervised - QLORA, DDP] Llama-3 8B
8. [Supervised - QLoRA, DDP] Llama-3.1 8B
9. [Supervised - QLoRA, DDP] Arcee AI Llama-3.1 Supernova Lite
10. [Supervised - QLoRA] Llama-3.2 1B
11. [Supervised - QLoRA] Llama-3.2 3B
12. [Supervised - QLoRA, FSDP] Codestral-22B
13. [Supervised - LoRA] TinyLlama 1.1B
14. [Supervised - LoRA] Arcee Lite 1.5B
15. [Supervised - LoRA] SmolLM2-1.7B-Instruct
16. [Supervised - QLORA, FSDP] Qwen 2.5 7B
17. [Supervised - QLORA] Falcon3 3B
18. [Supervised - QLORA, FSDP] Falcon3 7B
19. [Supervised - QLORA, FSDP] Llama-3.1 70B
20. [Self-supervised - DoRA, FSDP] Mistral-7B v0.3
21. [Supervised - QLORA, FSDP] Llama-3.3 70B