SupraLabs/Supra-Router-51M-gguf overview

---

library_name: transformers

tags:

• router
• orchestrator
• slm
• edge-computing
• mixture-of-experts
• text-generation

pipeline_tag: text-generation

model_type: llama

datasets:

• SupraLabs/Prompt-Routing-Dataset

language:

• en

base_model:

• SupraLabs/Supra-Router-51M

license: apache-2.0

---

<h1 align="center">Supra-Router-51M · Multi-Task Infrastructure Routing Model</h1>

!logo

<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.

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Multi-Task Decision Sequence

To run inference, wrap your user query inside the structural framing tokens used during training (Task: [Prompt]\nAnalysis: ). The model will output a deterministic, pipe-separated string containing the full telemetry of the prompt's cognitive requirements:

Expected Output Target Schema:

Domain: [Semantic Field] | Complexity: [1-5] | Math: [True/False] | Code: [True/False] | Route: [small model/big model] | Justification: [Rule-driven infrastructure reasoning]

Why this works:

By forcing a sub-100M parameter model to calculate the semantic domain, structural complexity, and technical flags before it emits the final Route token, the network effectively runs an internal feature-activation map. This multi-task sequence prevents localized weight collapse and guarantees stable routing boundaries.

Training Telemetry & Optimization

• Dataset Source: SupraLabs/Prompt-Routing-Dataset (992 samples)
• Training Duration: 5 Epochs
• Checkpoint Selection: Peak generalization was reached during Epoch 3 (eval_loss: 0.1342). To eliminate late-stage micro-model memorization and validation drift, the training state was automatically rewound and saved at this numerical peak.
• Precision: bfloat16
• Hardware Footprint: Optimized sequence processing length of 3840 tokens, ensuring rapid inference execution with negligible CPU/GPU overhead (sub-millisecond generation speeds).

Inference & Gateway Implementation

Use this direct script to test or wrap the model inside a live production orchestrator or FastAPI gateway. It enforces greedy decoding (do_sample=False) for maximum decision stability.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_ID = "SupraLabs/Supra-Router-51M"

print("[*] Initializing local infrastructure router...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    dtype=torch.bfloat16,
    device_map="auto"
)
model.eval()

# Example prompt showcasing keyword-trap evasion
user_prompt = "Write a movie script about a chef who gets lost at sea."

# Format to match internal SFT attention alignment
formatted_input = f"Task: {user_prompt}\nAnalysis: "
inputs = tokenizer(formatted_input, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=128,
        do_sample=False, 
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id
    )

generated_ids = outputs[0][inputs["input_ids"].shape[1]:]
print(tokenizer.decode(generated_ids, skip_special_tokens=True).strip())

Proven Benchmarks & Defensive Boundaries

During edge validation testing, Supra-Router-51M demonstrated robust resilience against adversarial prompt strings:

• Keyword Trap Evasion: Successfully identifies semantic context rather than matching tokens. Prompts containing words like "script" or "calculus" are correctly parsed as creative writing (not programming/math code) and routed locally to the small model when complexity is low.
• Complexity-Driven Safety Net: In instances where programming syntax or technical boundaries are ambiguous (e.g., complex regex or architectural database frames), the model naturally scales its evaluation metrics to Complexity: 3, automatically triggering a big model route override.
• Deterministic Offloading: Safely captures multi-step logic paths, calculus concepts, and code generation scripts, instantly assigning them to cloud-scale frontier endpoints.