> 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.

# EXAONE

EXAONE is a bilingual (Korean-English) language model series from LG AI Research, with strong performance on Korean-language benchmarks.

|                  |                                                   |
| ---------------- | ------------------------------------------------- |
| **Task**         | Text Generation                                   |
| **Architecture** | `ExaoneForCausalLM`                               |
| **Parameters**   | 7.8B                                              |
| **HF Org**       | [LGAI-EXAONE](https://huggingface.co/LGAI-EXAONE) |

## Available Models

* **EXAONE-3.0-7.8B-Instruct**
* **EXAONE-3.5-7.8B-Instruct**

## Architecture

* `ExaoneForCausalLM`

## Example HF Models

| Model                    | HF ID                                                                                                 |
| ------------------------ | ----------------------------------------------------------------------------------------------------- |
| EXAONE 3.0 7.8B Instruct | [`LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct`](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct) |

## Try with NeMo AutoModel

Install NeMo AutoModel and follow the fine-tuning guide to configure a recipe for this model.

**1. Install** ([full instructions](/get-started/installation)):

```bash
pip install nemo-automodel
```

**2. Clone the repo** to get example recipes you can adapt:

```bash
git clone https://github.com/NVIDIA-NeMo/Automodel.git
cd Automodel
```

**3. Fine-tune** by adapting a base LLM recipe — override the model ID on the CLI:

```bash
automodel --nproc-per-node=8 examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml \
  --model.pretrained_model_name_or_path <MODEL_HF_ID>
```

Replace `<MODEL_HF_ID>` with the model ID from **Example HF Models** above.

**1. Pull the container** and mount a checkpoint directory:

```bash
docker run --gpus all -it --rm \
  --shm-size=8g \
  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
  nvcr.io/nvidia/nemo-automodel:26.06.00
```

**2.** The recipes are at `/opt/Automodel/examples/` — navigate there:

```bash
cd /opt/Automodel
```

**3. Fine-tune**:

```bash
automodel --nproc-per-node=8 examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml \
  --model.pretrained_model_name_or_path <MODEL_HF_ID>
```

See the [Installation Guide](/get-started/installation) and [LLM Fine-Tuning Guide](/recipes-e2e-examples/sft-peft).

## Fine-Tuning

See the [LLM Fine-Tuning Guide](/recipes-e2e-examples/sft-peft).

## Hugging Face Model Cards

* [LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)