OLMoE#

OLMoE is Allen AI’s open Mixture-of-Experts language model. It activates 1B parameters per token from a 7B total parameter pool.

Task

Text Generation (MoE)

Architecture

OLMoEForCausalLM

Parameters

7B total / 1B active

HF Org

allenai

Available Models#

  • OLMoE-1B-7B-0924: 7B total, 1B activated

  • OLMoE-1B-7B-0924-Instruct: instruction-tuned variant

Architecture#

  • OLMoEForCausalLM

Example HF Models#

Model

HF ID

OLMoE 1B 7B

allenai/OLMoE-1B-7B-0924

OLMoE 1B 7B Instruct

allenai/OLMoE-1B-7B-0924-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):

pip install nemo-automodel

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

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:

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.

Run with Docker

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:26.02.00

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.

Hugging Face Model Cards#