Large language models (LLMs)

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This page provides notebook examples for fine-tuning large language models (LLMs) using Serverless GPU compute. These examples demonstrate various approaches to fine-tuning including parameter-efficient methods like Low-Rank Adaptation (LoRA) and full supervised fine-tuning.

Fine-tune Qwen2-0.5B model

The following notebook provides an example of how to efficiently fine-tune the Qwen2-0.5B model using:

• Transformer reinforcement learning (TRL) for supervised fine-tuning
• Liger Kernels for memory-efficient training with optimized Triton kernels.
• LoRA for parameter-efficient fine-tuning.

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Fine-tune Llama-3.2-3B with Unsloth

This notebook demonstrates how to fine-tune Llama-3.2-3B using the Unsloth library.

Unsloth Llama

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Video demo

This video walks through the notebook in detail (12 minutes).

Supervised fine-tuning using DeepSpeed and TRL

This notebook demonstrates how to use the Serverless GPU Python API to run supervised fine-tuning (SFT) using the Transformer Reinforcement Learning (TRL) library with DeepSpeed ZeRO Stage 3 optimization.

TRL DeepSpeed

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LORA fine-tuning using Axolotl

This notebook demostrates how to use the Serverless GPU Python API to LORA fine-tune an Olmo3 7B model using the Axolotl library.

Axolotl

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