初始化项目，由ModelHub XC社区提供模型
Model: SupraLabs/Supra-Router-51M Source: Original Platform
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README.md
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README.md
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library_name: transformers
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tags:
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- router
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- orchestrator
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- slm
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- edge-computing
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- mixture-of-experts
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- text-generation
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pipeline_tag: text-generation
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model_type: llama
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datasets:
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- SupraLabs/Prompt-Routing-Dataset
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language:
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- en
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base_model:
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- SupraLabs/Supra-1.5-50M-Base-exp
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---
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<h1 align="center">Supra-Router-51M · Multi-Task Infrastructure Routing Model</h1>
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<h2 align="center">About the Model</h2>
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GGUF model [here](https://huggingface.co/SupraLabs/Supra-Router-51M-gguf)
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**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.
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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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---
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## Multi-Task Decision Sequence
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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:
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### Expected Output Target Schema:
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```text
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Domain: [Semantic Field] | Complexity: [1-5] | Math: [True/False] | Code: [True/False] | Route: [small model/big model] | Justification: [Rule-driven infrastructure reasoning]
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```
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## Why this works:
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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.
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## Training Telemetry & Optimization
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- Dataset Source: SupraLabs/Prompt-Routing-Dataset (992 samples)
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- Training Duration: 5 Epochs
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- 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.
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- Precision: bfloat16
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- Hardware Footprint: Optimized sequence processing length of 3840 tokens, ensuring rapid inference execution with negligible CPU/GPU overhead (sub-millisecond generation speeds).
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## Inference & Gateway Implementation
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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.
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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MODEL_ID = "SupraLabs/Supra-Router-51M"
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print("[*] Initializing local infrastructure router...")
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tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_ID,
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dtype=torch.bfloat16,
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device_map="auto"
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)
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model.eval()
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# Example prompt showcasing keyword-trap evasion
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user_prompt = "Write a movie script about a chef who gets lost at sea."
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# Format to match internal SFT attention alignment
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formatted_input = f"Task: {user_prompt}\nAnalysis: "
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inputs = tokenizer(formatted_input, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=128,
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do_sample=False,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=tokenizer.eos_token_id
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)
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generated_ids = outputs[0][inputs["input_ids"].shape[1]:]
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print(tokenizer.decode(generated_ids, skip_special_tokens=True).strip())
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```
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## Proven Benchmarks & Defensive Boundaries
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During edge validation testing, Supra-Router-51M demonstrated robust resilience against adversarial prompt strings:
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- 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.
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- 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.
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- Deterministic Offloading: Safely captures multi-step logic paths, calculus concepts, and code generation scripts, instantly assigning them to cloud-scale frontier endpoints.
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