> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/automodel/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/automodel/_mcp/server.

# MiMo-V2-Flash

[MiMo-V2-Flash](https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash) is Xiaomi's hybrid attention Mixture-of-Experts language model. It alternates full and sliding-window attention layers, uses a `sigmoid_with_bias` router with group-limited expert routing, and ships as an FP8 HF checkpoint.

|                  |                                                       |
| ---------------- | ----------------------------------------------------- |
| **Task**         | Text Generation (MoE, hybrid attention)               |
| **Architecture** | `MiMoV2FlashForCausalLM`                              |
| **Parameters**   | Approx. several hundred B total / much smaller active |
| **HF Org**       | [XiaomiMiMo](https://huggingface.co/XiaomiMiMo)       |

## Available Models

* **MiMo-V2-Flash**: hybrid full/sliding-window attention with FP8 weights.

## Architecture

* `MiMoV2FlashForCausalLM`
* Sliding-window attention via the `MiMoV2FlashAttention(is_swa=True)` path.
* MoE blocks use the shared `nemo_automodel.components.moe.layers.MoE` with `score_func="sigmoid_with_bias"` and `gate_precision=fp32` so routing decisions stay numerically stable when activations are bf16.
* FP8 round-trip in `MiMoV2FlashStateDictAdapter` covers the bulk of attention/expert weights; layer norms, the gate, `lm_head`, and `embed_tokens` stay in bf16 per `NON_QUANTIZED_KEY_PATTERNS`.

## Example HF Models

| Model         | HF ID                                                                         |
| ------------- | ----------------------------------------------------------------------------- |
| MiMo-V2-Flash | [`XiaomiMiMo/MiMo-V2-Flash`](https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash) |

## Example Recipes

| Recipe                                                                                                                                                 | Description                      |
| ------------------------------------------------------------------------------------------------------------------------------------------------------ | -------------------------------- |
| [mimo\_v2\_flash\_hellaswag.yaml](https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/llm_finetune/mimo_v2_flash/mimo_v2_flash_hellaswag.yaml) | SFT — MiMo-V2-Flash on HellaSwag |

## 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/mimo_v2_flash/mimo_v2_flash_hellaswag.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:26.06.00
```

**2. Navigate to the AutoModel directory**:

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

**3. Run the recipe**:

```bash
automodel --nproc-per-node=8 examples/llm_finetune/mimo_v2_flash/mimo_v2_flash_hellaswag.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).

## Hugging Face Model Cards

* [XiaomiMiMo/MiMo-V2-Flash](https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash)