Upload README.md

README.md

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+---
+license: mit
+language:
+- en
+pipeline_tag: text-classification
+tags:
+- dristi
+- router
+- compound-ai
+- system-one
+- system-two
+- classification
+- fast-inference
+---
+
+# ⚡ DRISTI: Compound AI Routing Engine
+
+**Dristi** is an ultra-fast, strictly-typed, non-autoregressive probabilistic decision engine. Built as a "System One" router, it handles routine AI routing tasks in milliseconds for fractions of a cent, intelligently routing difficult or uncertain queries to heavy "System 2" models.
+
+Unlike standard generative models that hallucinate and take seconds to return text, Dristi returns strict JSON probabilities and logic routing decisions.
+
+## 🏗️ Architecture: The Dual-Engine Cascade
+
+This repository represents one half of the **Dristi Dual-Engine Cascade**. Dristi uses two specialized mathematical models to balance extreme speed and deep intelligence:
+
+1. **V1 (The Speed Demon):** Built on `DistilBERT`. Hits ~40ms latency and runs on GPU. The ultimate frontline router.
+2. **V2 (The Heavy Thinker):** Built on `DeBERTa-v3`. Uses Disentangled Attention to understand complex sarcasm and highly technical logic. Loaded into CPU RAM as a local safety net.
+
+When integrated using the Dristi framework, queries flow to **V1** first. If V1 lacks confidence or mathematically detects an anomaly, it instantly cascades the query to **V2** entirely locally.
+
+## ✨ Enterprise Features
+* **Mathematical Self-Awareness (OOD Detection):** Rejects alien or unsafe queries using Cosine Similarity against a pre-computed mathematical centroid.
+* **Compound AI Routing:** Natively cascades from Fast models to Heavy models (or external APIs) based on calibrated confidence thresholds.
+* **Calibrated Confidence:** Uses L-BFGS Temperature Scaling.
+* **Self-Healing API:** Designed to actively learn from human/API feedback in production.
+
+## 🚀 How to Use
+
+To use these weights, you should use the official open-source Dristi inference engine, which handles the complex routing, OOD detection, and FastAPI deployment automatically.
+
+1. Clone the core architecture:
+   ```bash
+   git clone https://github.com/SWARAJCHATTARAJ/DRISTI.git
+   cd DRISTI
+   ```
+2. Download these model weights (`pytorch_model.bin`) and place them in the `checkpoints/` directory of your cloned repository.
+3. Start the dual-engine router or the FastAPI server!
+   ```bash
+   python -m inference.dual_engine
+   # or
+   uvicorn inference.api:app --host 0.0.0.0 --port 8000
+   ```
+
+*Note: The custom architecture python file (`dristi_model.py`) is included in this Hugging Face repository for reference and compatibility with advanced HF loaders, but the official Dristi pipeline is recommended for production deployment.*