> This page is for version 0.5.0 · 26.06.
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# 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.

> **Info**
>
> |                  |                                                   |
> | ---------------- | ------------------------------------------------- |
> | **Task**         | Text Generation (MoE)                             |
> | **Architecture** | `GraniteMoeForCausalLM`                           |
> | **Parameters**   | 1B – 3B                                           |
> | **HF Org**       | [ibm-granite](https://huggingface.co/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`](https://huggingface.co/ibm-granite/granite-3.0-1b-a400m-base)         |
| Granite 3.0 3B A800M Instruct | [`ibm-granite/granite-3.0-3b-a800m-instruct`](https://huggingface.co/ibm-granite/granite-3.0-3b-a800m-instruct) |
| PowerMoE 3B                   | [`ibm/PowerMoE-3b`](https://huggingface.co/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](/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.

#### Run with Docker

**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

* [ibm-granite/granite-3.0-1b-a400m-base](https://huggingface.co/ibm-granite/granite-3.0-1b-a400m-base)
* [ibm/PowerMoE-3b](https://huggingface.co/ibm/PowerMoE-3b)