> This page is for version 0.7.0 · 26.10.
> For other versions, use one of these documentation indexes:
> - Nightly (default): https://docs.nvidia.com/nemo/automodel/nightly/llms.txt
> - Latest: https://docs.nvidia.com/nemo/automodel/latest/llms.txt
> - 0.7.0 · 26.10: https://docs.nvidia.com/nemo/automodel/v0.7/llms.txt
> - 0.5.0 · 26.06: https://docs.nvidia.com/nemo/automodel/v0.5/llms.txt
> - 0.4.0 · 26.04: https://docs.nvidia.com/nemo/automodel/v0.4/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/automodel/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/automodel/_mcp/server.

# 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 [`26.10.00`](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo-automodel?version=26.10.00) 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:

```bash
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](/nemo/automodel/v0.7/get-started/installation).

## Supported Models

NeMo AutoModel supports the [AutoModelForCausalLM](https://huggingface.co/transformers/v3.5.1/model_doc/auto.html#automodelforcausallm) in the [Text Generation](https://huggingface.co/models?pipeline_tag=text-generation\&sort=trending) 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](/nemo/automodel/v0.7/datasets/text-dataset) and [dataset overview](/nemo/automodel/v0.7/datasets/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](/nemo/automodel/v0.7/model-coverage/release-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](/nemo/automodel/v0.7/recipes-e2e-examples/sft-peft) to learn how to apply both methods to your data.

> **Tip**
>
> In these guides, the `SQuAD v1.1` dataset is used for demonstration purposes, but you can use custom data. Update the recipe YAML `dataset` and `validation_dataset` sections accordingly. Refer to [LLM Datasets](/nemo/automodel/v0.7/datasets/text-dataset) and [Dataset Overview](/nemo/automodel/v0.7/datasets/overview).