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> For a complete documentation index, see https://docs.nvidia.com/nemo/automodel/llms.txt.
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# Qwen3 MoE

[Qwen3 MoE](https://qwenlm.github.io/blog/qwen3/) is the Mixture-of-Experts variant of the Qwen3 series from Alibaba Cloud, activating a small fraction of parameters per token for efficient large-scale training.

|                  |                                     |
| ---------------- | ----------------------------------- |
| **Task**         | Text Generation (MoE)               |
| **Architecture** | `Qwen3MoeForCausalLM`               |
| **Parameters**   | 30B – 235B total                    |
| **HF Org**       | [Qwen](https://huggingface.co/Qwen) |

## Available Models

* **Qwen3-30B-A3B**: 30B total parameters, 3B activated per token
* **Qwen3-235B-A22B**: 235B total parameters, 22B activated per token

## Architecture

* `Qwen3MoeForCausalLM`

## Example HF Models

| Model           | HF ID                                                                 |
| --------------- | --------------------------------------------------------------------- |
| Qwen3 30B A3B   | [`Qwen/Qwen3-30B-A3B`](https://huggingface.co/Qwen/Qwen3-30B-A3B)     |
| Qwen3 235B A22B | [`Qwen/Qwen3-235B-A22B`](https://huggingface.co/Qwen/Qwen3-235B-A22B) |

## Example Recipes

| Recipe                                                                                                                                         | Description                          |
| ---------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------ |
| [qwen3\_moe\_30b\_te\_deepep.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/qwen/qwen3_moe_30b_te_deepep.yaml) | SFT — Qwen3 MoE 30B with TE + DeepEP |
| [qwen3\_moe\_30b\_lora.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/qwen/qwen3_moe_30b_lora.yaml)            | LoRA — Qwen3 MoE 30B                 |

## Try with NeMo AutoModel

**1. Install** ([full instructions](/get-started/installation)):

```bash
pip install nemo-automodel
```

**2. Clone the repo** to get the example recipes:

```bash
git clone https://github.com/NVIDIA-NeMo/Automodel.git
cd Automodel
```

**3. Run the recipe** from inside the repo:

```bash
automodel --nproc-per-node=8 examples/llm_finetune/qwen/qwen3_moe_30b_te_deepep.yaml
```

**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:26.04.00
```

**2.** Navigate to the AutoModel directory (where the recipes are):

```bash
cd /opt/Automodel
```

**3. Run the recipe**:

```bash
automodel --nproc-per-node=8 examples/llm_finetune/qwen/qwen3_moe_30b_te_deepep.yaml
```

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) and the [Large MoE Fine-Tuning Guide](/recipes-e2e-examples/large-moe-fine-tuning).

## Hugging Face Model Cards

* [Qwen/Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B)
* [Qwen/Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B)