Granite MoE

IBM Granite MoE models extend the Granite architecture with Mixture-of-Experts layers for more efficient scaling. PowerMoE (IBM Research) also uses this architecture.


Task	Text Generation (MoE)
Architecture	`GraniteMoeForCausalLM`
Parameters	1B – 3B
HF Org	ibm-granite

Available Models

Granite 3.0 1B A400M Base — 1B total, 400M activated
Granite 3.0 3B A800M Instruct — 3B total, 800M activated
PowerMoE-3B (IBM Research) — 3B total
MoE-7B-1B-Active-Shared-Experts (IBM Research, test model)

Architectures

GraniteMoeForCausalLM
GraniteMoeSharedForCausalLM — variant with shared experts

Example HF Models

Model	HF ID
Granite 3.0 1B A400M Base	`ibm-granite/granite-3.0-1b-a400m-base`
Granite 3.0 3B A800M Instruct	`ibm-granite/granite-3.0-3b-a800m-instruct`
PowerMoE 3B	`ibm/PowerMoE-3b`

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:26.06.00

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.