[ 02 // NEURAL MODEL ARCHITECTURE & OPEN-WEIGHT PIPELINE ]

ZERO-SHOT PRIOR FLOW

How TabPFN tabular prior estimation and Groq-accelerated Gemma 2 reasoning automate midnight demand clearance with zero gradient updates and sub-second latency.

STEP 01 // TELEMETRY INGESTION

8:00 PM Raw Stock Ingestion

Line cook inputs marinated chicken (kg), paneer cubes (kg), and roll breads alongside atmospheric signals (monsoon rain, exam schedule).

INPUT SCHEMA
7 VECTORS
INGEST LATENCY
< 14 MS
PRICE BASIS
MANDI INDEX
STEP 02 // PRIOR INFERENCE

TabPFN Zero-Shot Spoilage Estimation

TabPFN evaluates tabular patterns in one single forward pass across synthetic campus historical records without fine-tuning gradient passes.

TRAIN LOSS
ZERO-SHOT
INFERENCE TIME
0.38 SEC
DEVICE
CPU-COMPACT
STEP 03 // VIRAL REASONING

Gemma 2 (Groq LPU) Deal Synthesis

Google Gemma 2 9B-IT running on Groq LPUs ingests the predicted discount and creates two high-margin clearance combos and localized campus WhatsApp copy.

ENGINE
GROQ LPU
TOKEN SPEED
420 T/S
TEMPERATURE
0.70 JSON
STEP 04 // VOICE GENERATION

Low-Latency Kitchen Voice Dispatch

ElevenLabs Turbo v2.5 streams an actionable 2-line Hinglish preparation priority command to the kitchen line cook with zero delay.

VOICE ID
GEORGE / TURBO
BITRATE
128 KBPS
FALLBACK
WEB SPEECH API
END-TO-END DATA STREAM

Autonomous Arbitrage Flow

01. KITCHEN STOCK JSON
Surplus weights + environmental flags
IN
↓
02. TABPFN FORWARD PASS
Prior space evaluation & risk classification
0.3s
↓
03. GROQ GEMMA 2 COMBO REASONING
Structured viral deals + WhatsApp markdown
420 t/s
↓
04. DUAL DISPATCH LAYER
WhatsApp hostel groups & line cook speaker
OUT

Enterprise food delivery aggregators charge thirty percent commissions and demand twenty-four-hour promotion lead times. When midnight rolls spoil, small stalls absorb hundred percent of the loss. By pairing TabPFN with open-weight Gemma 2 on local hardware, neighborhood food businesses capture institutional arbitrage margins completely toll-free.