Instructions to use XiaomiMiMo/MiMo-V2-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XiaomiMiMo/MiMo-V2-Flash with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="XiaomiMiMo/MiMo-V2-Flash", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XiaomiMiMo/MiMo-V2-Flash", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("XiaomiMiMo/MiMo-V2-Flash", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use XiaomiMiMo/MiMo-V2-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XiaomiMiMo/MiMo-V2-Flash"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XiaomiMiMo/MiMo-V2-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XiaomiMiMo/MiMo-V2-Flash

SGLang

How to use XiaomiMiMo/MiMo-V2-Flash with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "XiaomiMiMo/MiMo-V2-Flash" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XiaomiMiMo/MiMo-V2-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "XiaomiMiMo/MiMo-V2-Flash" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XiaomiMiMo/MiMo-V2-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XiaomiMiMo/MiMo-V2-Flash with Docker Model Runner:
```
docker model run hf.co/XiaomiMiMo/MiMo-V2-Flash
```

Can this be loaded in transformers? FP8Linear: size mismatch for weight_scale_inv

#21

by mratsim - opened Dec 27, 2025

Discussion

mratsim

Dec 27, 2025

On Transformers v4.57.3, I get the following error trying to load the model:

RuntimeError: Error(s) in loading state_dict for FP8Linear:
        size mismatch for weight_scale_inv: copying a param with shape torch.Size([8, 32]) from checkpoint, the shape in current model is torch.Size([6, 32]).

Full stacktrace

``` Traceback (most recent call last): File "[...]/main_mimo_v2_flash-nvfp4.py", line 108, in model = AutoModelForCausalLM.from_pretrained( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "[...]/.venv/lib/python3.12/site-packages/transformers/models/auto/auto_factory.py", line 597, in from_pretrained return model_class.from_pretrained( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "[...]/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 277, in _wrapper return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "[...]/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 5048, in from_pretrained ) = cls._load_pretrained_model( ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "[...]/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 5468, in _load_pretrained_model _error_msgs, disk_offload_index = load_shard_file(args) ^^^^^^^^^^^^^^^^^^^^^ File "[...]/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 843, in load_shard_file disk_offload_index = _load_state_dict_into_meta_model( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "[...]/.venv/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 120, in decorate_context return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "[...]/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 770, in _load_state_dict_into_meta_model _load_parameter_into_model(model, param_name, param.to(param_device)) File "[...]/.venv/lib/python3.12/site-packages/transformers/modeling_utils.py", line 667, in _load_parameter_into_model module.load_state_dict({param_type: tensor}, strict=False, assign=True) File "[...]/.venv/lib/python3.12/site-packages/torch/nn/modules/module.py", line 2629, in load_state_dict raise RuntimeError( RuntimeError: Error(s) in loading state_dict for FP8Linear: size mismatch for weight_scale_inv: copying a param with shape torch.Size([8, 32]) from checkpoint, the shape in current model is torch.Size([6, 32]). ```

Called with

model = AutoModelForCausalLM.from_pretrained(MODEL_ID, offload_folder = "./offload/")

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