Instructions to use XiaomiMiMo/MiMo-V2.6-Flash-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XiaomiMiMo/MiMo-V2.6-Flash-RL with Transformers:

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

pipe = pipeline("text-generation", model="XiaomiMiMo/MiMo-V2.6-Flash-RL", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("XiaomiMiMo/MiMo-V2.6-Flash-RL", trust_remote_code=True, device_map="auto")

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use XiaomiMiMo/MiMo-V2.6-Flash-RL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XiaomiMiMo/MiMo-V2.6-Flash-RL"
# 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-V2.6-Flash-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XiaomiMiMo/MiMo-V2.6-Flash-RL

SGLang

How to use XiaomiMiMo/MiMo-V2.6-Flash-RL 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-V2.6-Flash-RL" \
    --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-V2.6-Flash-RL",
		"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-V2.6-Flash-RL" \
        --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-V2.6-Flash-RL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XiaomiMiMo/MiMo-V2.6-Flash-RL with Docker Model Runner:
```
docker model run hf.co/XiaomiMiMo/MiMo-V2.6-Flash-RL
```

SaylorTwift HF Staff commited on 23 days ago

Commit

647aa17

verified ·

1 Parent(s): 24aa22d

Add Agents' Last Exam result (27.6, overall-pass-rate, from model card)

Browse files

Files changed (1) hide show

.eval_results/MiMo-V2.6-Flash-RL.yaml +10 -0

.eval_results/MiMo-V2.6-Flash-RL.yaml CHANGED Viewed

@@ -30,3 +30,13 @@
   source:
     url: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL
     name: "MiMo-V2.6-Flash-RL model card"

   source:
     url: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL
     name: "MiMo-V2.6-Flash-RL model card"
+- dataset:
+    id: agents-last-exam/agents-last-exam
+    task_id: overall-pass-rate
+  value: 27.6
+  date: "2026-09-22"
+  source:
+    url: https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL
+    name: "MiMo-V2.6-Flash-RL model card"
+  notes: "Harness not specified."