Instructions to use arianraje/mimo-7b-gdn-hybrid-1.8B-OPD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arianraje/mimo-7b-gdn-hybrid-1.8B-OPD with Transformers:

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

pipe = pipeline("text-generation", model="arianraje/mimo-7b-gdn-hybrid-1.8B-OPD")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("arianraje/mimo-7b-gdn-hybrid-1.8B-OPD", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use arianraje/mimo-7b-gdn-hybrid-1.8B-OPD with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arianraje/mimo-7b-gdn-hybrid-1.8B-OPD"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arianraje/mimo-7b-gdn-hybrid-1.8B-OPD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/arianraje/mimo-7b-gdn-hybrid-1.8B-OPD

SGLang

How to use arianraje/mimo-7b-gdn-hybrid-1.8B-OPD 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 "arianraje/mimo-7b-gdn-hybrid-1.8B-OPD" \
    --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": "arianraje/mimo-7b-gdn-hybrid-1.8B-OPD",
		"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 "arianraje/mimo-7b-gdn-hybrid-1.8B-OPD" \
        --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": "arianraje/mimo-7b-gdn-hybrid-1.8B-OPD",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use arianraje/mimo-7b-gdn-hybrid-1.8B-OPD with Docker Model Runner:
```
docker model run hf.co/arianraje/mimo-7b-gdn-hybrid-1.8B-OPD
```

MiMo-7B GDN Hybrid — stage3-1.8B-OPD

Decayed final of the 1.8B-token rung of the MiMo on-policy-distillation (OPD) WSD ladder (1:4 uniform hybrid of Qwen2 attention and Gated DeltaNet layers, teacher XiaomiMiMo/MiMo-7B-RL-0530). Run mimo-mathmix-h32k-runpod-1p8b-v1-20260903; snapshots/final, step 11632, 1,800,027,023 consumed generation tokens; model window 65,536.

Recipe: extends arianraje/mimo-7b-gdn-hybrid-1.6B-OPD from its pre-decay state (1,521.4M tokens) with TWO changes vs that row. Deliberate: rollout horizon horizon_max 16,384 → 32,768 (reasoning rollouts trained at H=32K for the whole rung). Forced by trainer memory: sampler max_model_len 65,536 → 49,152, whose only effect is that 32K-prompt retrieval (RUG) items have their generation clamped to ~16K — the budget they had on every H=16K rung. Everything else (stage-3 math mixture, decay 600 steps, queue 6.0) is inherited. The pre-decay trunk of this rung is public at arianraje/mimo-7b-gdn-opd-predecay-1721m-step11144.

Evaluation (TABLES.md protocol, raw JSONs under `full_eval/`)

Metric	1.6B-OPD	1.8B-OPD
AIME24 think pass@1 / pass@8 @32K (n=30×8)	62.1 / 83.3	63.3 / 76.7
AIME25 think pass@1 / pass@8 @32K (n=30×8)	47.5 / 73.3	49.2 / 73.3
MATH-500 think pass@1 @32K	93.8	93.4
MATH-500 no-think pass@1 @4K	68.2	70.2
GSM8K no-think strict / flexible @1K	57.6 / 63.8	58.5 / 65.2
MMLU 5-shot	54.2	53.8
PIQA / HellaSwag / ARC-E / ARC-C / Winogrande	72.6 / 60.3 / 63.6 / 39.1 / 59.3	72.0 / 60.3 / 62.8 / 39.6 / 59.7
NIAH multikey 4K / 8K / 16K / 32K (n=500)	98.6 / 98.0 / 96.6 / 89.4	96.8 / 97.2 / 95.0 / 84.6
NIAH single, multiquery (all lengths)	≥99.8	≥99.8
trunc / mean gen tokens: MATH-500, AIME24, AIME25	3.4%/8169, 30.4%/22273, 35.8%/23806	3.6%/8168, 26.7%/21925, 32.5%/23552

AIME cells are n=30 problems (±~14 pts on pass@1) — trend data; MATH-500 (n=500) is the powered cell. full_eval/*.generations.json carry every sample.

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Custom architecture; needs the mimo_gdn model registration from the training repo (transformers 4.57.x + flash-linear-attention 0.5.x), same as the other arianraje/mimo-7b-gdn-hybrid-*-OPD checkpoints.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for arianraje/mimo-7b-gdn-hybrid-1.8B-OPD

Base model

XiaomiMiMo/MiMo-7B-RL-0530

Finetuned

(15)

this model

MiMo-7B GDN Hybrid — stage3-1.8B-OPD

Evaluation (TABLES.md protocol, raw JSONs under full_eval/)

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Model tree for arianraje/mimo-7b-gdn-hybrid-1.8B-OPD

Evaluation (TABLES.md protocol, raw JSONs under `full_eval/`)