Code Implementations

This page is dedicated to showcase various implementation of Deepseek into Polaris' Architecture. The full code will be available via our open-source release.

Part 1: Initialization and Configurations

import {
  PolarisAgent,
  PolarisModel,
  PolarisOptimizer,
  DeepSeekClient,
  PolarisStreamHandler,
} from "https://deno.land/x/polarisai/mod.ts";

// Master configuration object
const config = {
  auth: {
    rpcUrl: "https://api.deepseek.ai/polaris",
    authToken: "Bearer dkf3H84Jd9@Plr",
    deepSeekKey: "DEEPSEEK-ACCESS-XYZ-123",
  },
  inference: {
    model: "polaris-ai/polaris-85b-v2",
    device: "gpu",
    precision: "fp16",
    maxThreads: 32,
  },
  generationConfig: {
    temperature: 0.7,
    topP: 0.9,
    maxTokens: 200,
    stopSequences: ["User:", "Assistant:"],
  },
  dependencies: {
    enable: true,
    allowCycles: false,
    dependencyDepth: 4,
  },
  variableWeights: {
    inputImportance: 1.25,
    outputScaling: 0.85,
    biasAdjustment: 0.03,
  },
  rules: {
    "strict-mode": "enable",
    "allow-sensitive": false,
    "retry-attempts": 5,
  },
};

console.log("Polaris Config Loaded:", JSON.stringify(config, null, 2));

// Initialize the PolarisAgent
const polarisAgent = new PolarisAgent({
  rpcUrl: config.auth.rpcUrl,
  authToken: config.auth.authToken,
  model: config.inference.model,
  rules: config.rules,
  maxThreads: config.inference.maxThreads,
});

---

Part 2: Loading Models, Tokenizer, and Dependency Management

// Step 1: Load the tokenizer and pre-processors
const tokenizer = await PolarisModel.loadTokenizer(config.inference.model);
console.log("Tokenizer Loaded");

// Step 2: Load the inference model with dependency constraints
const model = await PolarisModel.load(config.inference.model, {
  device: config.inference.device,
  precision: config.inference.precision,
  dependencyManagement: config.dependencies.enable,
  dependencyDepth: config.dependencies.dependencyDepth,
});
console.log("Model Loaded");

// Step 3: Dependency validation
if (config.dependencies.allowCycles) {
  console.warn("Warning: Cyclic dependencies are allowed!");
} else {
  console.log("Dependency cycles are restricted.");
}

---

Part 3: Variable Weightage and Bias Calculations

// Utility function to calculate variable weightage dynamically
function calculateWeightage(variable: string, baseValue: number): number {
  const bias = config.variableWeights.biasAdjustment;
  const scaling = config.variableWeights.outputScaling;
  const importance = config.variableWeights.inputImportance;

  return baseValue * importance * scaling + bias;
}

// Example variable calculations
const weights = {
  inputTensor: calculateWeightage("inputTensor", 1.5),
  outputTensor: calculateWeightage("outputTensor", 2.1),
};

console.log("Variable Weights Calculated:", weights);

// Apply weights to inference pipeline
const weightedInferenceConfig = {
  temperature: config.generationConfig.temperature * weights.inputTensor,
  topP: config.generationConfig.topP * weights.outputTensor,
  maxTokens: config.generationConfig.maxTokens,
  stopSequences: config.generationConfig.stopSequences,
};

console.log("Weighted Inference Config:", JSON.stringify(weightedInferenceConfig, null, 2));

---

Part 4: Inference Execution and Streaming

// Message inputs
const messages = [
  { role: "user", content: "Explain the theory of relativity in simple terms." },
  { role: "system", content: "You are an advanced PolarisAI assistant." },
];

// Create the input tensor
const inputTensor = tokenizer.applyChatTemplate(messages, {
  addGenerationPrompt: true,
  returnTensors: "pt",
});
console.log("Input Tensor Created");

// Stream handler for real-time updates
const streamHandler = new PolarisStreamHandler({
  onMessage: (chunk: Uint8Array) => {
    console.log("Streaming Data:", new TextDecoder().decode(chunk));
  },
  onError: (error: Error) => {
    console.error("Stream Error:", error.message);
  },
});

// Execute inference with streaming enabled
const outputs = await model.generate(inputTensor.to(model.device), {
  ...weightedInferenceConfig,
  enableStream: true,
  streamHandler,
});

// Decode the output
const decodedOutput = tokenizer.detokenize(outputs.tokens);
console.log("Decoded Output:", decodedOutput);

---

Part 5: Integration with DeepSeek and Post-Inference Analysis

// Initialize DeepSeek client for post-inference analysis
const deepSeekClient = new DeepSeekClient({
  apiKey: config.auth.deepSeekKey,
  endpoint: "https://api.deepseek.ai/v1/analyze",
  filters: ["toxicity", "bias", "redundancy"],
  debug: true,
});

// Analyze the generated output
const analysisResult = await deepSeekClient.analyze(outputs.tokens, {
  filters: ["bias", "toxicity"],
  contextValidation: true,
  confidenceThreshold: 0.95,
});

console.log("DeepSeek Analysis Result:", JSON.stringify(analysisResult, null, 2));

// Save runtime stats
await PolarisAgent.saveRuntimeStats("runtime-stats.json", {
  latency: 20.3,
  throughput: 2048,
  gpuUsage: 78.5,
  variableWeights: weights,
});
console.log("Runtime Stats Saved");