About Fine-Tuning#
Learn how to fine-tune a model by making requests to the NVIDIA NeMo Customizer microservice through the API. Fine-tuned models you have created can be deployed using NVIDIA NIMs.
Fine-Tuning Workflow#
At a high level, the fine-tuning workflow consists of the following steps:
Format a dataset.
Optionally create a customization target for the model if it hasn’t already been created.
Monitor the job until it completes.
Move on to Evaluate the output model.
Model Catalog#
Explore the model families and sizes supported by the NVIDIA NeMo Customizer microservice.
View the available Llama models in the model catalog.
View the available Llama Nemotron models from NVIDIA, including Nano and Super variants for efficient and advanced instruction tuning.
View the available Phi models from Microsoft, designed for strong reasoning capabilities with efficient deployment.
Task Guides#
Perform common fine-tuning tasks.
Create, list, view, and delete customization targets.
View available customization configurations to use when creating a customization job.
Create, list, view, and cancel customization jobs.
Tutorials#
Follow these tutorials to learn how to accomplish common fine-tuning tasks.
Learn how to format datasets for different model types.
Learn how to start a LoRA customization job using a custom dataset.
Learn how to start a SFT customization job using a custom dataset.
Learn how to start a Knowledge Distillation (KD) job using a teacher and student model.
Learn how to check job metrics using MLFlow or Weights & Biases.
Learn how to optimize the token-per-GPU throughput for a LoRA optimization job.
References#
View the available hyperparameters and their valid options that you can set when creating a customization job.
View the OpenAPI specification for Customizer.
View troubleshooting tips for failed jobs.