Model CoverageLarge Language Models

OLMoE

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

TaskText Generation (MoE)
ArchitectureOLMoEForCausalLM
Parameters7B total / 1B active
HF Orgallenai

Available Models

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

Architecture

  • OLMoEForCausalLM

Example HF Models

ModelHF ID
OLMoE 1B 7Ballenai/OLMoE-1B-7B-0924
OLMoE 1B 7B Instructallenai/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.

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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