README_SPACE.md

RazorShield Hugging Face Space Deployment Guide

This document details the ZeroGPU architecture, environment setup, and deployment procedure for deploying RazorShield to Hugging Face Spaces (vedantjadhav701/razorshield-api).

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1. ZeroGPU Deployment Strategy

The Space uses Hugging Face Spaces spaces.GPU decorator for dynamically allocated GPU acceleration:

• CPU Workloads: Request validation (preprocessing.py), feature adaptation (adapter.py), calibrated XGBoost inference (decision_engine.py), merchant rolling temporal state (merchant_state.py), persistent incident engine (incident_engine.py), grounding validation (validator.py), and template fallback generation (fallback.py).
• ZeroGPU Workload: CausalLM token generation using Qwen/Qwen2.5-0.5B-Instruct wrapped with @spaces.GPU.

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2. Environment Variables

Supported environment configuration:

• SLM_MODEL: Qwen/Qwen2.5-0.5B-Instruct (default)
• SLM_MAX_NEW_TOKENS: 160 (default)
• SLM_TEMPERATURE: 0.1 (default)
• POLICY_MODE: BALANCED (default)

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3. Git Deployment Steps to Hugging Face Space

To deploy this backend repository to Hugging Face Space vedantjadhav701/razorshield-api:

# 1. Add Hugging Face Space remote
git remote add hf https://huggingface.co/spaces/vedantjadhav701/razorshield-api

# 2. Push repository to Hugging Face Space
git push hf main