Large Language Models (LLMs)

Introduction

Large Language Models (LLMs) power a variety of tasks such as dialogue systems, text classification, summarization, and more. NeMo AutoModel provides a simple interface for loading and fine-tuning LLMs hosted on the Hugging Face Hub.

Run LLMs with NeMo AutoModel

To run LLMs with NeMo AutoModel, make sure you use NeMo container version 26.06.00 or later. If the model you intend to fine-tune requires a newer source than the container provides, use a current checkout so the package and its example recipes stay on one revision:

$
git clone https://github.com/NVIDIA-NeMo/Automodel.git
$
cd Automodel
$
uv sync --locked --all-groups --all-extras

For other installation options (such as uv), refer to the NeMo AutoModel Installation Guide.

Supported Models

NeMo AutoModel supports the AutoModelForCausalLM in the Text Generation category. During preprocessing, it uses transformers.AutoTokenizer, which is sufficient for most LLM cases. If your model requires custom text handling, override the tokenizer in your recipe YAML or provide a custom dataset _target_. Refer to LLM datasets and dataset overview.

This table combines recipe-backed checkpoints with documented model families. Dates show when the current checkpoint first appeared in a recipe, or when a documentation-only family page was added. See the combined model support log for recipe-backed checkpoints of every model type.

Fine-Tuning LLMs with NeMo AutoModel

The models listed above can be fine-tuned using NeMo AutoModel. NeMo AutoModel supports two primary fine-tuning approaches:

1. Parameter-Efficient Fine-Tuning (PEFT): Updates only a small subset of parameters (typically less than 1%) using techniques such as Low-Rank Adaptation (LoRA).
2. Supervised Fine-Tuning (SFT): Updates all or most model parameters for deeper adaptation.

See the Fine-Tuning Guide to learn how to apply both methods to your data.