Example Projects for Fine-Tuning Models#
These example projects demonstrate how to fine-tune various large language models (LLMs) and image generation models using NVIDIA AI Workbench. The following projects highlight:
Fine-tuning of popular open-source models including Llama 2, Llama 3, Mistral, Mixtral, and Phi-3.
Parameter-efficient fine-tuning techniques with NeMo Framework.
Customization of image generation models like SDXL.
Best practices for model adaptation and optimization.
These examples are ideal for AI developers and researchers looking to:
Adapt pre-trained models to specific use cases.
Implement efficient fine-tuning strategies.
Optimize model performance for specific tasks.
Leverage NVIDIA’s tools and frameworks for model customization.
Example Project on GitHub |
Description |
Clone and Open |
Support Forum |
|---|---|---|---|
An example project to fine-tune Llama 2 |
|||
An example project to fine-tune a Llama 3 8B Model |
|||
An example project to fine-tune a Mistral 7B model |
|||
An example project to fine-tune a Mixtral 8x7B Model |
|||
An example project to p-tune LLMs with NeMo Framework |
|||
An example project to annotate text with NeMo Framework |
|||
An example project to fine-tune a Nemotron-3 8B model |
|||
An example project to fine-tune a Phi-3 Mini Model |
|||
An example project to customize an SDXL model |