Instructions to use r0b0tlab/MiMo-V2.6-Flash-RL-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use r0b0tlab/MiMo-V2.6-Flash-RL-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="r0b0tlab/MiMo-V2.6-Flash-RL-NVFP4", 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("r0b0tlab/MiMo-V2.6-Flash-RL-NVFP4", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use r0b0tlab/MiMo-V2.6-Flash-RL-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "r0b0tlab/MiMo-V2.6-Flash-RL-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "r0b0tlab/MiMo-V2.6-Flash-RL-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/r0b0tlab/MiMo-V2.6-Flash-RL-NVFP4

SGLang

How to use r0b0tlab/MiMo-V2.6-Flash-RL-NVFP4 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 "r0b0tlab/MiMo-V2.6-Flash-RL-NVFP4" \
    --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": "r0b0tlab/MiMo-V2.6-Flash-RL-NVFP4",
		"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 "r0b0tlab/MiMo-V2.6-Flash-RL-NVFP4" \
        --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": "r0b0tlab/MiMo-V2.6-Flash-RL-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use r0b0tlab/MiMo-V2.6-Flash-RL-NVFP4 with Docker Model Runner:
```
docker model run hf.co/r0b0tlab/MiMo-V2.6-Flash-RL-NVFP4
```

r0b0tlab commited on 8 days ago

Commit

034825b

verified ·

1 Parent(s): e9df65c

card: grade Q200 hard reasoning and record that serve's throughput and memory

Browse files

Files changed (1) hide show

README.md +3 -1

README.md CHANGED Viewed

@@ -88,7 +88,9 @@ Disclosures:
 - Seven multi-turn rows were regenerated after 1200s client timeouts. This invocation scored the repaired 200-row file and did not regenerate the other 193.
 - The package lane summary had pointed all three AST categories at the parallel_multiple file and reported micro 0.195. The numbers above are the official score-file headers from the same run.
-- Q200v2 text-180 was kept from the earlier FINAL3 serve, not remeasured here. Auto-graded 151/160 (GSM8K 78/80, HumanEval 38/40, IFEval 35/40). The 20 hard_reasoning rows were answered and left ungraded. Not a core-subset quality claim.
 ## Systems, MTP-500k-mm, 2026-09-27

 - Seven multi-turn rows were regenerated after 1200s client timeouts. This invocation scored the repaired 200-row file and did not regenerate the other 193.
 - The package lane summary had pointed all three AST categories at the parallel_multiple file and reported micro 0.195. The numbers above are the official score-file headers from the same run.
+- Q200v2 text-180 was kept from the earlier FINAL3 serve, not remeasured on the MTP boot. Think-off, workers 1, max tokens 8192, 180/180 stopped, longest completion 5844. GSM8K 78/80, HumanEval 38/40, IFEval 35/40. Independent review of the 20 hard_reasoning answers is 17/20. Failures: hard-04 answered 19208/715, while the 4x4 Hilbert determinant is 1/6048000; hard-12 claimed every graph of minimum degree 2 has a cycle, which is false for an infinite 2-regular graph; hard-16 did not give the uniform-lift height 1/(2π). Total 168/180. Not a core-subset quality claim.
+- On that same FINAL3 boot, the serial quality run produced 51,977 completion tokens in 2,254.2 seconds, 23.06 client tok/s including prefill. A separate 1024-token harness on that boot, truncated at the token cap, measured concurrency-1 aggregate 27.25 and 26.02 tok/s (per-request decode 27.51 and 26.25) and concurrency-2 aggregate 32.39 and 34.08 tok/s. That harness is not the quality-run rate and not the MTP systems throughput.
+- Memory on the Q200 boot: weights 84.436 GB, KV cache 6.575 GB, startup available 9.166 GB, full-token pool 1,044,581, SWA pool 20,891. Graph reservations were target-verify 1.495 GB and draft-decode 0.134 GB; prefill, decode, and draft-extend graphs were 0. KV is the calibrated FP8 pool. Host available at admission was 4.84 GiB and 8.71 GiB, above a 4 GiB floor.
 ## Systems, MTP-500k-mm, 2026-09-27