Distributed LLM Orchestration

NAL acts as the real-time data fabric converting raw blockchain data into neural tensors. Using TorchScript's JIT compilation, it achieves 15μs/tensor conversion latency through:

1. Memory-mapped I/O buffers for EVM calldata

2. CUDA-accelerated Solana account encoding (Base58 → FP32 SIMD ops)

3. Sparse attention masks with 92% sparsity ratio via probabilistic sampling The layer implements ε=0.03 differential privacy via PySyft's Opacus, adding Laplacian noise (λ=1.2) to cross-chain references. For state synchronization, it uses a CRDT-inspired conflict resolution protocol where tensor deltas are merged using element-wise L2-norm prioritization.

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import asyncio
from aiortc import RTCDataChannel
from transformers import AutoModelForCausalLM

class LLMOrchestrator:
    def __init__(self, model_repo="auctor/llm-ensemble-v4"):
        self.models = {
            "decision": AutoModelForCausalLM.from_pretrained(f"{model_repo}-decision"),
            "risk": AutoModelForCausalLM.from_pretrained(f"{model_repo}-risk"),
            "arbitrage": AutoModelForCausalLM.from_pretrained(f"{model_repo}-arbitrage")
        }
        self.rtc_channels = {}  # WebRTC data channels for low-latency comms
        
    async def handle_request(self, request: dict) -> dict:
        """Process requests through ensemble model pipeline"""
        with torch.inference_mode():
            decision_logits = self.models['decision'](request['input_ids'])
            risk_assessment = self.models['risk'](decision_logits)
            arbitrage_ops = self.models['arbitrage'](risk_assessment)
            
        return self._package_response(arbitrage_ops)

    def _package_response(self, tensor: torch.Tensor) -> dict:
        """Convert tensor output to optimized binary format"""
        return {
            'payload': tensor.numpy().tobytes(),
            'compression': 'float16',
            'metadata': {'version': '0xfea21c', 'quantized': True}
        }

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Key Implementation Details:

• Hybrid WebRTC+gRPC communication layer

• Quantized model weights (8-bit precision)

• CUDA-optimized kernels for transformer layers

• Merkle-rooted model versioning