Model CoverageLarge Language Models

EXAONE

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

TaskText Generation
ArchitectureExaoneForCausalLM
Parameters7.8B
HF OrgLGAI-EXAONE

Available Models

  • EXAONE-3.0-7.8B-Instruct
  • EXAONE-3.5-7.8B-Instruct

Architecture

  • ExaoneForCausalLM

Example HF Models

ModelHF ID
EXAONE 3.0 7.8B InstructLGAI-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. Clone and install from source (full instructions):

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

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

$uv run 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:

$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:

$cd /opt/Automodel

3. Fine-tune:

$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 and LLM Fine-Tuning Guide.

Fine-Tuning

See the LLM Fine-Tuning Guide.

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