Instructions to use Motif-Technologies/Motif-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Motif-Technologies/Motif-3 with Transformers:

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

pipe = pipeline("text-generation", model="Motif-Technologies/Motif-3", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Motif-Technologies/Motif-3", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Motif-Technologies/Motif-3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Motif-Technologies/Motif-3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Motif-Technologies/Motif-3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Motif-Technologies/Motif-3

SGLang

How to use Motif-Technologies/Motif-3 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 "Motif-Technologies/Motif-3" \
    --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": "Motif-Technologies/Motif-3",
		"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 "Motif-Technologies/Motif-3" \
        --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": "Motif-Technologies/Motif-3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Motif-Technologies/Motif-3 with Docker Model Runner:
```
docker model run hf.co/Motif-Technologies/Motif-3
```

leejunhyeok commited on Aug 9

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@@ -78,28 +78,28 @@ For contextual comparison, Motif 3 is compared with strong open-weight models us
 (*: public dataset only)
 <div align="center">
-| Benchmark | **Motif 3**<br><sup>314B-A13B</sup> | MiniMax-3<br><sup>428B-A23B</sup> | GLM-5.1<br><sup>744B-A40B</sup> | Kimi-K2.6<br><sup>1T-A32B</sup> | Qwen-3.5<br><sup>397B-A17B</sup> | DS-v4-Pro<br><sup>1.6T-A49B</sup> |
 |:---|:---:|:---:|:---:|:---:|:---:|:---:|
 | **Agentic** | | | | | | |
-| GDPVal v2 | 38.7 | 44.4 | 37.8 | 34.4 | 23.0 | 40.2 |
-| τ²-Bench Telecom | 94.7 | 88.9 | 97.7 | 95.9 | 95.6 | 96.2 |
-| τ³-Banking | 35.3 | 15.3 | 13.6 | 23.3 | 13.4 | 30.1 |
-| ITBench* | 51.5 | — | 40.3 | 31.2 | 34.1 | 38.3 |
 | **Coding** | | | | | | |
-| SWE-Bench Verified | 76.2 | 75.0 | 76.4 | 76.2 | 80.0 | 77.4 |
-| Terminal-Bench 2.1 | 74.9 | 65.2 | 61.8 | 65.9 | 51.3 | 64.0 |
-| SciCode | 40.6 | 45.4 | 43.8 | 53.5 | 42.0 | 50.0 |
 | **Reasoning & Knowledge** | | | | | | |
-| IMOAnswerBench | 83.2 | — | 83.8 | 81.8 | 80.9 | 89.8 |
-| Apex-Shortlist | 75.5 | — | 71.1 | 77.4 | 61.4 | 85.8 |
-| GPQA Diamond | 83.4 | 92.9 | 86.8 | 91.1 | 89.3 | 88.8 |
-| HLE | 37.0 | 39.0 | 30.1 | 37.5 | 29.0 | 37.5 |
-| CritPt | 6.6 | 3.7 | 4.6 | 8.0 | 1.7 | 12.9 |
-| OmniScience — Accuracy | 30.1 | 16.7 | 23.7 | 32.6 | 30.8 | 42.9 |
-| OmniScience — Non-Hallucination | 71.6 | 81.6 | 70.1 | 59.5 | 11.1 | 5.9 |
 | **Long Context & Instruction Following** | | | | | | |
-| AA-LCR | 72.3 | 80.3 | 68.0 | 76.7 | 72.7 | 70.0 |
-| IFBench | 78.2 | 82.9 | 76.3 | 76.0 | 78.8 | 76.5 |
 </div>

 (*: public dataset only)
 <div align="center">
+| Benchmark | **Motif 3**<br><sup>314B-A13B</sup> | MiniMax-3<br><sup>428B-A23B</sup> | GLM-5.1<br><sup>744B-A40B</sup> | Kimi-K2.6<br><sup>1T-A32B</sup> | Qwen-3.7<br><sup>max<sup> | DS-v4-Pro<br><sup>1.6T-A49B</sup> |
 |:---|:---:|:---:|:---:|:---:|:---:|:---:|
 | **Agentic** | | | | | | |
+| GDPVal v2 | 38.7 | 44.4 | 37.8 | 34.4 | 39.0 | 40.2 |
+| τ²-Bench Telecom | 94.7 | 88.9 | 97.7 | 95.9 | 94.7 | 96.2 |
+| τ³-Banking | 35.3 | 15.3 | 13.6 | 23.3 | 12.0 | 30.1 |
+| ITBench* | 51.5 | — | 40.3 | 31.2 | 42.5 | 38.3 |
 | **Coding** | | | | | | |
+| SWE-Bench Verified | 76.2 | 75.0 | 76.4 | 76.2 | 80.4 | 77.4 |
+| Terminal-Bench 2.1 | 74.9 | 65.2 | 61.8 | 65.9 | 75.0 | 64.0 |
+| SciCode | 40.6 | 45.4 | 43.8 | 53.5 | 53.5 | 50.0 |
 | **Reasoning & Knowledge** | | | | | | |
+| IMOAnswerBench | 83.2 | — | 83.8 | 81.8 | 90.0 | 89.8 |
+| Apex-Shortlist | 75.5 | — | 71.1 | 77.4 | 44.5 | 85.8 |
+| GPQA Diamond | 83.4 | 92.9 | 86.8 | 91.1 | 92.4 | 88.8 |
+| HLE | 37.0 | 39.0 | 30.1 | 37.5 | 41.4 | 37.5 |
+| CritPt | 6.6 | 3.7 | 4.6 | 8.0 | 11.4 | 12.9 |
+| OmniScience — Accuracy | 30.1 | 16.7 | 23.7 | 32.6 | 31.0 | 42.9 |
+| OmniScience — Non-Hallucination | 71.6 | 81.6 | 70.1 | 59.5 | 74 | 5.9 |
 | **Long Context & Instruction Following** | | | | | | |
+| AA-LCR | 72.3 | 80.3 | 68.0 | 76.7 | 75.0 | 70.0 |
+| IFBench | 78.2 | 82.9 | 76.3 | 76.0 | 79.1 | 76.5 |
 </div>