---
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-1.5-50M-Base-exp
---
Supra-Router-51M ยท Multi-Task Infrastructure Routing Model
About the Model
**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.
---
## 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:
```text
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.
```python
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.