Instructions to use SupraLabs/Supra-Router-51M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SupraLabs/Supra-Router-51M with Transformers:

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

pipe = pipeline("text-generation", model="SupraLabs/Supra-Router-51M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SupraLabs/Supra-Router-51M")
model = AutoModelForCausalLM.from_pretrained("SupraLabs/Supra-Router-51M", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SupraLabs/Supra-Router-51M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SupraLabs/Supra-Router-51M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SupraLabs/Supra-Router-51M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SupraLabs/Supra-Router-51M

SGLang

How to use SupraLabs/Supra-Router-51M 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 "SupraLabs/Supra-Router-51M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SupraLabs/Supra-Router-51M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "SupraLabs/Supra-Router-51M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SupraLabs/Supra-Router-51M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SupraLabs/Supra-Router-51M with Docker Model Runner:
```
docker model run hf.co/SupraLabs/Supra-Router-51M
```

QyrouNnet-AI commited on Jul 9

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@@ -23,6 +23,8 @@ base_model:
 <h2 align="center">About the Model</h2>
 **Supra-Router-51M** is an ultra-lightweight, high-speed infrastructure traffic controller optimized for localized edge orchestration. With only **51.7 million parameters**, this micro-LLM acts as a defensive gateway for multi-model ecosystems, accurately determining when user requests can be processed locally by an Edge SLM or when they must be triaged to a cloud-hosted frontier intelligence layer.
 The model was built by fine-tuning a pre-trained 51M base on the `SupraLabs/Prompt-Routing-Dataset` (992 rows). Rather than acting as a naive binary classifier, the model uses **Multi-Task Sequence Generation** to map out the underlying properties of a prompt before predicting the final routing token, anchoring its attention heads to robust language and structural logic features.

 <h2 align="center">About the Model</h2>
+GGUF model [here](https://huggingface.co/SupraLabs/Supra-Router-51M-gguf)
 **Supra-Router-51M** is an ultra-lightweight, high-speed infrastructure traffic controller optimized for localized edge orchestration. With only **51.7 million parameters**, this micro-LLM acts as a defensive gateway for multi-model ecosystems, accurately determining when user requests can be processed locally by an Edge SLM or when they must be triaged to a cloud-hosted frontier intelligence layer.
 The model was built by fine-tuning a pre-trained 51M base on the `SupraLabs/Prompt-Routing-Dataset` (992 rows). Rather than acting as a naive binary classifier, the model uses **Multi-Task Sequence Generation** to map out the underlying properties of a prompt before predicting the final routing token, anchoring its attention heads to robust language and structural logic features.