Initial commit: Ollama Personal Trainer & Unsloth MoE Orchestrator Studio
This commit is contained in:
drjones
324
server.ts
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324
server.ts
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import express from "express";
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import path from "path";
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import { createServer as createViteServer } from "vite";
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import { GoogleGenAI, Type } from "@google/genai";
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import dotenv from "dotenv";
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dotenv.config();
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const app = express();
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const PORT = 3000;
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app.use(express.json({ limit: "50mb" }));
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app.use(express.urlencoded({ extended: true, limit: "50mb" }));
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// Lazy initialize Gemini AI client
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function getGeminiAI() {
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const apiKey = process.env.GEMINI_API_KEY;
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if (!apiKey) {
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throw new Error("GEMINI_API_KEY is not configured. Please check your environment variables.");
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}
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return new GoogleGenAI({
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apiKey,
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httpOptions: {
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headers: {
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"User-Agent": "aistudio-build",
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},
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},
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});
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}
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// Health check endpoint
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app.get("/api/health", (_req, res) => {
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res.json({
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status: "ok",
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hasApiKey: Boolean(process.env.GEMINI_API_KEY),
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timestamp: new Date().toISOString(),
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});
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});
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// 1. Generate Synthetic Training Data with Gemini
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app.post("/api/dataset/generate", async (req, res) => {
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try {
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const { domain, count = 5, format = "alpaca", taskType = "instruct", customPrompt = "" } = req.body;
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const ai = getGeminiAI();
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const systemPrompt = `You are an elite LLM fine-tuning data engineer specializing in high-quality Unsloth and SOTA dataset synthesis.
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Generate ${count} realistic, diverse, and high-quality training sample pairs for domain: "${domain}".
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Task Type: "${taskType}".
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Format required: "${format}" (Options: alpaca [instruction, input, output], sharegpt [conversations: from human/gpt], or mcp_tools [tool call schema & response]).
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Ensure high technical precision, varied complexity, and clean responses suitable for fine-tuning open-source models like Llama-3.1, Qwen-2.5, and DeepSeek.
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${customPrompt ? `Additional Instructions: ${customPrompt}` : ""}`;
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const response = await ai.models.generateContent({
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model: "gemini-3.7-flash",
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contents: `Generate ${count} training examples in strict JSON format.`,
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config: {
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systemInstruction: systemPrompt,
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responseMimeType: "application/json",
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responseSchema: {
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type: Type.ARRAY,
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description: "List of training data items",
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items: {
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type: Type.OBJECT,
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properties: {
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id: { type: Type.STRING },
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instruction: { type: Type.STRING },
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input: { type: Type.STRING },
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output: { type: Type.STRING },
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system: { type: Type.STRING },
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category: { type: Type.STRING },
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difficulty: { type: Type.STRING },
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},
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required: ["instruction", "output"],
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},
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},
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},
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});
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const items = JSON.parse(response.text || "[]");
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res.json({ success: true, count: items.length, data: items });
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} catch (error: any) {
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console.error("Error generating dataset:", error);
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res.status(500).json({ success: false, error: error.message || "Failed to generate dataset" });
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}
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});
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// 2. Generate MCP (Model Context Protocol) Function-Calling Training Pairs
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app.post("/api/dataset/generate-mcp", async (req, res) => {
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try {
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const { mcpServers, count = 5, complexity = "advanced" } = req.body;
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const ai = getGeminiAI();
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const systemInstruction = `You are a specialist in MCP (Model Context Protocol) and Function-Calling fine-tuning for local models running on Ollama/vLLM.
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You are given the following MCP tool declarations:
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${JSON.stringify(mcpServers, null, 2)}
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Generate ${count} realistic user queries that require one or multiple tool invocations using the declared tools, followed by synthetic tool execution results and final assistant reasoning.
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Complexity level: ${complexity}.
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Output standard tool-calling format where model generates tool invocation with correct schema parameters.`;
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const response = await ai.models.generateContent({
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model: "gemini-3.7-flash",
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contents: "Generate MCP tool calling training examples.",
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config: {
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systemInstruction,
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responseMimeType: "application/json",
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responseSchema: {
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type: Type.ARRAY,
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items: {
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type: Type.OBJECT,
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properties: {
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id: { type: Type.STRING },
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userQuery: { type: Type.STRING },
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toolCalls: {
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type: Type.ARRAY,
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items: {
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type: Type.OBJECT,
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properties: {
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name: { type: Type.STRING },
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arguments: { type: Type.OBJECT },
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},
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required: ["name"],
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},
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},
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simulatedToolResult: { type: Type.STRING },
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assistantResponse: { type: Type.STRING },
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thoughtChain: { type: Type.STRING },
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},
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required: ["userQuery", "toolCalls", "assistantResponse"],
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},
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},
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},
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});
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const data = JSON.parse(response.text || "[]");
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res.json({ success: true, count: data.length, data });
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} catch (error: any) {
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console.error("Error generating MCP dataset:", error);
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res.status(500).json({ success: false, error: error.message || "Failed to generate MCP dataset" });
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}
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});
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// 3. Teacher-to-Student Model Distillation Pair Generator
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app.post("/api/distillation/distill-sample", async (req, res) => {
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try {
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const { teacherPrompt, studentArchitecture, includeReasoning = true } = req.body;
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const ai = getGeminiAI();
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const systemInstruction = `You are acting as an elite Teacher model distilling reasoning, knowledge, and structured responses to a compact student model (${studentArchitecture || "8B Student"}).
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Provide a comprehensive, high-quality reference response. ${includeReasoning ? "Include a <think>...</think> chain-of-thought section before the final answer." : ""}`;
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const response = await ai.models.generateContent({
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model: "gemini-3.7-flash",
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contents: teacherPrompt,
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config: {
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systemInstruction,
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temperature: 0.7,
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},
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});
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res.json({
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success: true,
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teacherResponse: response.text || "",
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model: "gemini-3.7-flash (Teacher Mode)",
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});
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} catch (error: any) {
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console.error("Error in distillation:", error);
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res.status(500).json({ success: false, error: error.message || "Failed to generate teacher response" });
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}
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});
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// 4. AI Training Optimization Advisor
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app.post("/api/advisor/optimize-config", async (req, res) => {
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try {
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const { modelName, targetHardware, vramGb, datasetSize, targetTask, selectedTechniques } = req.body;
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const ai = getGeminiAI();
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const prompt = `Analyze this LLM fine-tuning setup and recommend the optimal hyperparameters and Unsloth/GGUF quantization strategy:
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- Base Model: ${modelName}
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- Target GPU: ${targetHardware} (${vramGb} GB VRAM)
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- Dataset Size: ${datasetSize} samples
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- Goal/Task: ${targetTask}
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- Selected Techniques: ${JSON.stringify(selectedTechniques)}
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Provide:
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1. Exact LoRA rank (r), alpha, target_modules for Unsloth
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2. Micro batch size, gradient accumulation steps, learning rate, lr_scheduler
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3. Exact VRAM estimation during training & inference
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4. GGUF Quantization recommendation (e.g. Q4_K_M, IQ4_XS) with explanation
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5. Key warnings or recommendations for Windows RTX 4080 Super`;
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const response = await ai.models.generateContent({
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model: "gemini-3.7-flash",
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contents: prompt,
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config: {
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responseMimeType: "application/json",
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responseSchema: {
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type: Type.OBJECT,
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properties: {
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recommendedLoRA_r: { type: Type.INTEGER },
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recommendedLoRA_alpha: { type: Type.INTEGER },
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batchSize: { type: Type.INTEGER },
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gradAccumSteps: { type: Type.INTEGER },
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learningRate: { type: Type.STRING },
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trainingVramEstimateGb: { type: Type.NUMBER },
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inferenceVramEstimateGb: { type: Type.NUMBER },
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recommendedQuantization: { type: Type.STRING },
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fitProbabilityPercent: { type: Type.INTEGER },
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tips: {
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type: Type.ARRAY,
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items: { type: Type.STRING },
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},
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modelfileParameters: {
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type: Type.OBJECT,
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properties: {
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temperature: { type: Type.NUMBER },
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top_p: { type: Type.NUMBER },
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num_ctx: { type: Type.INTEGER },
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num_gpu: { type: Type.INTEGER },
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},
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},
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},
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required: [
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"recommendedLoRA_r",
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"batchSize",
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"gradAccumSteps",
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"learningRate",
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"trainingVramEstimateGb",
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"recommendedQuantization",
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"fitProbabilityPercent",
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"tips",
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],
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},
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},
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});
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const parsed = JSON.parse(response.text || "{}");
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res.json({ success: true, config: parsed });
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} catch (error: any) {
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console.error("Error optimizing config:", error);
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res.status(500).json({ success: false, error: error.message || "Failed to generate optimization config" });
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}
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});
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// 5. Proxy to Local Ollama instance (handles CORS and local connectivity check)
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app.post("/api/ollama/proxy", async (req, res) => {
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try {
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const { endpoint = "http://localhost:11434", path: apiPath = "/api/tags", method = "GET", body } = req.body;
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const url = `${endpoint.replace(/\/$/, "")}${apiPath.startsWith("/") ? "" : "/"}${apiPath}`;
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const controller = new AbortController();
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const timeoutId = setTimeout(() => controller.abort(), 8000);
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const fetchOptions: RequestInit = {
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method,
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headers: { "Content-Type": "application/json" },
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signal: controller.signal,
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};
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if (body && (method === "POST" || method === "PUT")) {
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fetchOptions.body = JSON.stringify(body);
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}
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const response = await fetch(url, fetchOptions);
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clearTimeout(timeoutId);
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const contentType = response.headers.get("content-type");
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if (contentType && contentType.includes("application/json")) {
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const data = await response.json();
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res.json({ success: true, status: response.status, data });
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} else {
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const text = await response.text();
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res.json({ success: true, status: response.status, text });
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}
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} catch (error: any) {
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res.status(502).json({
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success: false,
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error: `Could not reach Ollama at the specified endpoint. Ensure Ollama is running (e.g. 'ollama serve' or default service).`,
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details: error.message,
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});
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}
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});
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// 6. Gitea Repository Sync Proxy Endpoint
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app.post("/api/gitea/push", async (req, res) => {
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try {
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const { exec } = await import("child_process");
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const { promisify } = await import("util");
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const execAsync = promisify(exec);
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const remoteUrl = "https://drjones:czapiewski@gitea.thetempleofdoom.com/drjones/ollama-personal-trainer.git";
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await execAsync("git add .", { cwd: process.cwd() });
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await execAsync('git commit -m "Auto-sync from Ollama Personal Trainer Studio UI"', { cwd: process.cwd() }).catch(() => {});
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await execAsync(`git push ${remoteUrl} main --force`, { cwd: process.cwd() });
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res.json({ success: true, message: "Pushed latest code & configs to Gitea successfully!" });
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} catch (error: any) {
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console.error("Gitea push error:", error);
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res.status(500).json({ success: false, error: error.message || "Failed to push to Gitea" });
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}
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});
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// Setup Vite middleware for full-stack SPA development
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async function startServer() {
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if (process.env.NODE_ENV !== "production") {
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const vite = await createViteServer({
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server: { middlewareMode: true },
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appType: "spa",
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});
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app.use(vite.middlewares);
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} else {
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const distPath = path.join(process.cwd(), "dist");
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app.use(express.static(distPath));
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app.get("*", (_req, res) => {
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res.sendFile(path.join(distPath, "index.html"));
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});
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}
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app.listen(PORT, "0.0.0.0", () => {
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console.log(`Ollama Unsloth Studio server running on http://0.0.0.0:${PORT}`);
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});
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}
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startServer();
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