Instructions to use Motif-Technologies/Motif-2-12.7B-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Motif-Technologies/Motif-2-12.7B-Reasoning with Transformers:

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

pipe = pipeline("text-generation", model="Motif-Technologies/Motif-2-12.7B-Reasoning", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Motif-Technologies/Motif-2-12.7B-Reasoning", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Motif-Technologies/Motif-2-12.7B-Reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Motif-Technologies/Motif-2-12.7B-Reasoning"
# 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-2-12.7B-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Motif-Technologies/Motif-2-12.7B-Reasoning

SGLang

How to use Motif-Technologies/Motif-2-12.7B-Reasoning 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-2-12.7B-Reasoning" \
    --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-2-12.7B-Reasoning",
		"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-2-12.7B-Reasoning" \
        --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-2-12.7B-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Motif-Technologies/Motif-2-12.7B-Reasoning with Docker Model Runner:
```
docker model run hf.co/Motif-Technologies/Motif-2-12.7B-Reasoning
```

leejunhyeok commited on Dec 12, 2025

Commit

c2ca405

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@@ -74,10 +74,9 @@ For maximum performance, we highly recommend using the options below.
 ```--compilation_config '{"full_cuda_graph": true}'``` : Activates cuda [full graph capture](https://docs.vllm.ai/en/stable/design/cuda_graphs/#cudagraphmodes)
 ```--rope-scaling '{"rope_type":"yarn","factor":2.0,"original_max_position_embeddings":65536}'```: Apply [yarn](https://arxiv.org/abs/2309.00071) to support 128K context length
 ```--enable-auto-tool-choice --tool-call-parser hermes``` : Enables [tool calling](https://docs.vllm.ai/en/latest/features/tool_calling/)
-```--logits-processors logit_:WrappedPerReqLogitsProcessor``` : Enables VLLM_THINK_BUDGET_RATIO env variable and repetition-based auto-stop for thinking autostop
 ```--reasoning-parser deepseek_r1``` : Parses [reasoning outputs](https://docs.vllm.ai/en/latest/features/reasoning_outputs/)
-### how to use
 ```bash
 pip install -U "huggingface_hub[cli]"
 hf download Motif-Technologies/Motif-2-12.7B-Reasoning \

 ```--compilation_config '{"full_cuda_graph": true}'``` : Activates cuda [full graph capture](https://docs.vllm.ai/en/stable/design/cuda_graphs/#cudagraphmodes)
 ```--rope-scaling '{"rope_type":"yarn","factor":2.0,"original_max_position_embeddings":65536}'```: Apply [yarn](https://arxiv.org/abs/2309.00071) to support 128K context length
 ```--enable-auto-tool-choice --tool-call-parser hermes``` : Enables [tool calling](https://docs.vllm.ai/en/latest/features/tool_calling/)
+```--logits-processors logit_:WrappedPerReqLogitsProcessor```: Enables a ratio-based thinking budget and repetition-based auto-stop. The model is guided to think for ```(model_max_len - input_prompt_len) * VLLM_THINK_BUDGET_RATIO``` tokens, using the rest of the context window to generate the response
 ```--reasoning-parser deepseek_r1``` : Parses [reasoning outputs](https://docs.vllm.ai/en/latest/features/reasoning_outputs/)
 ```bash
 pip install -U "huggingface_hub[cli]"
 hf download Motif-Technologies/Motif-2-12.7B-Reasoning \