MiMo-V2-Flash#

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

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

Example Recipes#

Recipe

Description

mimo_v2_flash_hellaswag.yaml

SFT — MiMo-V2-Flash on HellaSwag

Try with NeMo AutoModel#

1. Install (full instructions):

pip install nemo-automodel

2. Clone the repo to get the example recipes:

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

3. Run the recipe from inside the repo:

automodel --nproc-per-node=8 examples/llm_finetune/mimo_v2_flash/mimo_v2_flash_hellaswag.yaml
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:26.02.00

2. Navigate to the AutoModel directory:

cd /opt/Automodel

3. Run the recipe:

automodel --nproc-per-node=8 examples/llm_finetune/mimo_v2_flash/mimo_v2_flash_hellaswag.yaml

See the Installation Guide and LLM Fine-Tuning Guide.

Fine-Tuning#

See the LLM Fine-Tuning Guide.

Hugging Face Model Cards#