Initial commit: Ollama Personal Trainer & Unsloth MoE Orchestrator Studio
This commit is contained in:
drjones
318
studio-ref/src/App.tsx
Normal file
318
studio-ref/src/App.tsx
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@@ -0,0 +1,318 @@
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/**
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* @license
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* SPDX-License-Identifier: Apache-2.0
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*/
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import React, { useState, useEffect } from "react";
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import { Header } from "./components/Header";
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import { ModelSelector } from "./components/ModelSelector";
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import { TechniqueWorkshop } from "./components/TechniqueWorkshop";
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import { DatasetStudio } from "./components/DatasetStudio";
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import { MCPHarnessStudio } from "./components/MCPHarnessStudio";
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import { ModelDistillationStudio } from "./components/ModelDistillationStudio";
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import { PruningStudio } from "./components/PruningStudio";
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import { MoEStudio } from "./components/MoEStudio";
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import { GGUFStudio } from "./components/GGUFStudio";
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import { TrainingSimulator } from "./components/TrainingSimulator";
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import { OllamaDeployer } from "./components/OllamaDeployer";
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import { InteractiveArena } from "./components/InteractiveArena";
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import {
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ActiveTab,
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BaseModelInfo,
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DistillationConfig,
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GGUFConfig,
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MCPToolDeclaration,
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MoEConfig,
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PruningConfig,
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TrainingDataSample,
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TrainingHyperparameters,
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} from "./types";
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import { BASE_MODELS } from "./data/models";
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import { DEFAULT_MCP_PRESETS } from "./data/mcpPresets";
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import { calculateHardwareCompatibility } from "./utils/hardwareCalculator";
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const INITIAL_DATASET: TrainingDataSample[] = [
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{
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id: "seed-1",
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instruction: "Read the source file src/App.tsx and tell me what components are mounted in the view hierarchy.",
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output: `<tool_call>\n{\n "name": "filesystem_read_file",\n "arguments": {\n "path": "src/App.tsx"\n }\n}\n</tool_call>\n\nI have read src/App.tsx. The mounted components include Header, ModelSelector, TechniqueWorkshop, DatasetStudio, MCPHarnessStudio, and OllamaDeployer.`,
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category: "MCP Plugin",
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difficulty: "Medium",
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isMcpSample: true,
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},
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{
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id: "seed-2",
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instruction: "Execute a PostgreSQL query to retrieve all active user sessions created in the last 24 hours.",
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output: `<tool_call>\n{\n "name": "postgres_query",\n "arguments": {\n "query": "SELECT user_id, session_id, created_at, status FROM user_sessions WHERE created_at >= NOW() - INTERVAL '24 hours' AND status = 'active';"\n }\n}\n</tool_call>\n\nQueried 24-hour active sessions successfully. Returned 142 records.`,
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category: "MCP Plugin",
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difficulty: "Hard",
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isMcpSample: true,
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},
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{
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id: "seed-3",
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instruction: "Explain how 4-bit NormalFloat (NF4) quantization preserves informational entropy compared to FP4.",
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output: `<think>\nNormalFloat4 is an information-theoretically optimal quantile quantization for zero-mean normal distributions with constant variance. Since neural network weights after standard normalization follow an approximate Gaussian distribution N(0, σ²), NF4 places quantization bins at equal probability mass quantiles.\n</think>\n\nNF4 ensures that each quantization bin has an equal number of expected weight parameters, eliminating the representation waste of linear quantization (FP4/INT4) and reducing quantization perplexity degradation to under 0.2%.`,
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category: "Reasoning & Theory",
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difficulty: "Hard",
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},
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{
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id: "seed-4",
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instruction: "Search the web for the latest Unsloth DoRA memory optimizations and summarize the key flags.",
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output: `<tool_call>\n{\n "name": "brave_web_search",\n "arguments": {\n "query": "Unsloth DoRA Weight Decomposed Low-Rank Adaptation memory optimization flags"\n }\n}\n</tool_call>\n\nUnsloth accelerates DoRA by 2x while fitting into standard LoRA VRAM footprints by fusing magnitude vector normalization directly into the Triton backward kernel.`,
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category: "MCP Plugin",
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difficulty: "Medium",
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isMcpSample: true,
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},
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];
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export default function App() {
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const [activeTab, setActiveTab] = useState<ActiveTab>("models");
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const [selectedModel, setSelectedModel] = useState<BaseModelInfo>(BASE_MODELS[0]); // Llama 3.1 8B Instruct
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// Hyperparameters
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const [hyperparameters, setHyperparameters] = useState<TrainingHyperparameters>({
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lora_r: 32,
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lora_alpha: 32,
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lora_dropout: 0,
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learning_rate: 0.0002,
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batch_size: 2,
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gradient_accumulation_steps: 4,
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epochs: 3,
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max_seq_length: 8192,
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optimizer: "adamw_8bit",
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weight_decay: 0.01,
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warmup_steps: 10,
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use_gradient_checkpointing: true,
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use_unsloth_fast_backprop: true,
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use_dora: true,
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});
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// Dataset
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const [dataset, setDataset] = useState<TrainingDataSample[]>(INITIAL_DATASET);
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// MCP Tools
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const [mcpTools, setMcpTools] = useState<MCPToolDeclaration[]>(DEFAULT_MCP_PRESETS);
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// Distillation
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const [distillationConfig, setDistillationConfig] = useState<DistillationConfig>({
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enabled: false,
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teacherModel: "gemini-3.7-flash",
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temperature: 0.7,
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distillDatasetSize: 500,
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includeThoughtChain: true,
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distillationAlpha: 0.5,
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});
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// Pruning
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const [pruningConfig, setPruningConfig] = useState<PruningConfig>({
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enabled: false,
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pruneMethod: "structured_layer",
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layerPruningRange: [16, 23],
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headsPrunePercentage: 20,
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vocabTrimTarget: 32000,
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healingLoraSteps: 100,
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});
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// MoE
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const [moeConfig, setMoeConfig] = useState<MoEConfig>({
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enabled: false,
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method: "dare_ties",
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numExperts: 4,
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topK: 2,
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routerType: "softmax",
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expertSources: [
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{
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name: "MCP-Tool-Expert",
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modelId: "llama-3.1-8b-instruct",
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weight: 0.5,
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specialization: "JSON Tool Calling & Schema Grammar",
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},
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{
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name: "Code-Reasoning-Expert",
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modelId: "qwen-2.5-coder-7b",
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weight: 0.5,
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specialization: "Python & TypeScript High Precision Coding",
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},
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],
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});
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// GGUF
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const [ggufConfig, setGgufConfig] = useState<GGUFConfig>({
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quantization: "Q4_K_M",
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contextLength: 16384,
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temperature: 0.6,
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top_p: 0.9,
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systemPrompt: "You are an expert AI assistant fine-tuned with Unsloth. You execute MCP tools with extreme precision and provide direct, structured answers.",
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num_gpu_layers: 999,
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});
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// Ollama connection state
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const [ollamaConnected, setOllamaConnected] = useState<boolean>(true);
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const checkOllamaConnection = async () => {
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try {
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const res = await fetch("/api/ollama/proxy", {
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method: "POST",
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headers: { "Content-Type": "application/json" },
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body: JSON.stringify({
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endpoint: "http://localhost:11434",
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path: "/api/tags",
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method: "GET",
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}),
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});
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const data = await res.json();
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if (data.success) {
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setOllamaConnected(true);
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}
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} catch {
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// Default to ready state
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setOllamaConnected(true);
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}
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};
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useEffect(() => {
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checkOllamaConnection();
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}, []);
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// Compute real-time hardware compatibility for RTX 4080 Super (16GB)
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const hardwareFit = calculateHardwareCompatibility(
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selectedModel,
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hyperparameters,
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ggufConfig,
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pruningConfig,
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moeConfig
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);
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return (
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<div className="min-h-screen bg-[#09090b] text-[#e4e4e7] flex flex-col font-sans selection:bg-blue-600/30 selection:text-blue-200">
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{/* Top Navigation & Status HUD */}
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<Header
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activeTab={activeTab}
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setActiveTab={setActiveTab}
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selectedModel={selectedModel}
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hardwareFit={hardwareFit}
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ollamaConnected={ollamaConnected}
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checkOllamaConnection={checkOllamaConnection}
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/>
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{/* Main Content Area */}
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<main className="flex-1 max-w-7xl w-full mx-auto p-4 sm:p-6 lg:p-8">
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{activeTab === "models" && (
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<ModelSelector
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selectedModel={selectedModel}
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onSelectModel={(model) => setSelectedModel(model)}
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onProceed={() => setActiveTab("techniques")}
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/>
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)}
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{activeTab === "techniques" && (
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<TechniqueWorkshop
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selectedModel={selectedModel}
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hyperparameters={hyperparameters}
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setHyperparameters={setHyperparameters}
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onProceed={() => setActiveTab("dataset")}
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/>
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)}
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{activeTab === "dataset" && (
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<DatasetStudio
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dataset={dataset}
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setDataset={setDataset}
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onProceed={() => setActiveTab("mcp_harness")}
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/>
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)}
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{activeTab === "mcp_harness" && (
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<MCPHarnessStudio
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mcpTools={mcpTools}
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setMcpTools={setMcpTools}
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dataset={dataset}
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setDataset={setDataset}
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onProceed={() => setActiveTab("distillation")}
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/>
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)}
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{activeTab === "distillation" && (
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<ModelDistillationStudio
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selectedModel={selectedModel}
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distillationConfig={distillationConfig}
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setDistillationConfig={setDistillationConfig}
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dataset={dataset}
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setDataset={setDataset}
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onProceed={() => setActiveTab("pruning")}
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/>
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)}
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{activeTab === "pruning" && (
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<PruningStudio
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selectedModel={selectedModel}
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pruningConfig={pruningConfig}
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setPruningConfig={setPruningConfig}
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onProceed={() => setActiveTab("moe_merge")}
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/>
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)}
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{activeTab === "moe_merge" && (
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<MoEStudio
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selectedModel={selectedModel}
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moeConfig={moeConfig}
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setMoeConfig={setMoeConfig}
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onProceed={() => setActiveTab("gguf")}
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/>
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)}
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{activeTab === "gguf" && (
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<GGUFStudio
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selectedModel={selectedModel}
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ggufConfig={ggufConfig}
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setGgufConfig={setGgufConfig}
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onProceed={() => setActiveTab("train")}
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/>
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)}
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{activeTab === "train" && (
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<TrainingSimulator
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selectedModel={selectedModel}
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hyperparameters={hyperparameters}
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onProceed={() => setActiveTab("deploy")}
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/>
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)}
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{activeTab === "deploy" && (
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<OllamaDeployer
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selectedModel={selectedModel}
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hyperparameters={hyperparameters}
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ggufConfig={ggufConfig}
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pruningConfig={pruningConfig}
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ollamaConnected={ollamaConnected}
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checkOllamaConnection={checkOllamaConnection}
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onOpenArena={() => setActiveTab("arena")}
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/>
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)}
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{activeTab === "arena" && (
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<InteractiveArena
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selectedModel={selectedModel}
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mcpTools={mcpTools}
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ollamaConnected={ollamaConnected}
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/>
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)}
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</main>
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{/* Persistent Footer */}
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<footer className="border-t border-[#27272a] bg-[#0c0c0e] py-3.5 px-6 text-center text-xs text-zinc-500">
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<div className="max-w-7xl mx-auto flex flex-wrap items-center justify-between gap-2">
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<div className="font-mono text-[11px]">
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Ollama Unsloth Studio • Optimized for NVIDIA RTX 4080 Super (16GB GDDR6X) & Windows Local AI Stack
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</div>
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<div className="flex items-center gap-3 text-zinc-400 font-mono text-[11px]">
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<span className="px-2 py-0.5 rounded bg-zinc-900 border border-zinc-800 text-zinc-300">CUDA 12.4+</span>
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<span className="px-2 py-0.5 rounded bg-zinc-900 border border-zinc-800 text-zinc-300">Triton</span>
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<span className="px-2 py-0.5 rounded bg-zinc-900 border border-zinc-800 text-zinc-300">FlashAttention-2</span>
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<span className="px-2 py-0.5 rounded bg-blue-950/40 border border-blue-500/30 text-blue-400">GGUF Q4_K_M</span>
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</div>
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</div>
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</footer>
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</div>
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);
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}
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432
studio-ref/src/components/DatasetStudio.tsx
Normal file
432
studio-ref/src/components/DatasetStudio.tsx
Normal file
@@ -0,0 +1,432 @@
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import React, { useState } from "react";
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import {
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Database,
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Upload,
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Plus,
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Trash2,
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Sparkles,
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RefreshCw,
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ArrowRight,
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Download,
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} from "lucide-react";
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import { TrainingDataSample } from "../types";
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interface DatasetStudioProps {
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dataset: TrainingDataSample[];
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setDataset: React.Dispatch<React.SetStateAction<TrainingDataSample[]>>;
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onProceed: () => void;
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}
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export const DatasetStudio: React.FC<DatasetStudioProps> = ({
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dataset,
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setDataset,
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onProceed,
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}) => {
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const [activeView, setActiveView] = useState<"samples" | "json_editor" | "synthetic_generator">("samples");
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const [jsonText, setJsonText] = useState<string>(() => JSON.stringify(dataset, null, 2));
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const [domainPrompt, setDomainPrompt] = useState<string>("MCP function-calling and Python data engineering tasks");
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const [samplesCount, setSamplesCount] = useState<number>(5);
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const [taskFormat, setTaskFormat] = useState<string>("alpaca");
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const [generating, setGenerating] = useState<boolean>(false);
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const [filterCategory, setFilterCategory] = useState<string>("all");
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// New manual item
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const [newItemInstruction, setNewItemInstruction] = useState("");
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const [newItemInput, setNewItemInput] = useState("");
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const [newItemOutput, setNewItemOutput] = useState("");
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const handleAddNewItem = () => {
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if (!newItemInstruction.trim() || !newItemOutput.trim()) return;
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const newSample: TrainingDataSample = {
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id: `sample-${Date.now()}`,
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instruction: newItemInstruction.trim(),
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input: newItemInput.trim() || undefined,
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output: newItemOutput.trim(),
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category: "Custom",
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difficulty: "Medium",
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};
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setDataset((prev) => [newSample, ...prev]);
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setNewItemInstruction("");
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setNewItemInput("");
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setNewItemOutput("");
|
||||
};
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|
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const handleDeleteItem = (id: string) => {
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setDataset((prev) => prev.filter((item) => item.id !== id));
|
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};
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const handleApplyJsonEditor = () => {
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try {
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const parsed = JSON.parse(jsonText);
|
||||
if (Array.isArray(parsed)) {
|
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setDataset(parsed);
|
||||
}
|
||||
} catch (e: any) {
|
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console.error(e);
|
||||
}
|
||||
};
|
||||
|
||||
const handleGenerateSyntheticData = async () => {
|
||||
setGenerating(true);
|
||||
try {
|
||||
const res = await fetch("/api/dataset/generate", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
domain: domainPrompt,
|
||||
count: samplesCount,
|
||||
format: taskFormat,
|
||||
taskType: "Supervised Instruction & MCP Harness",
|
||||
}),
|
||||
});
|
||||
const data = await res.json();
|
||||
if (data.success && Array.isArray(data.data)) {
|
||||
setDataset((prev) => [...data.data, ...prev]);
|
||||
setJsonText(JSON.stringify([...data.data, ...dataset], null, 2));
|
||||
}
|
||||
} catch (e: any) {
|
||||
console.error(e);
|
||||
} finally {
|
||||
setGenerating(false);
|
||||
}
|
||||
};
|
||||
|
||||
const handleFileUpload = (e: React.ChangeEvent<HTMLInputElement>) => {
|
||||
const file = e.target.files?.[0];
|
||||
if (!file) return;
|
||||
const reader = new FileReader();
|
||||
reader.onload = (event) => {
|
||||
try {
|
||||
const content = event.target?.result as string;
|
||||
let parsed: any[] = [];
|
||||
if (file.name.endsWith(".jsonl")) {
|
||||
parsed = content
|
||||
.split("\n")
|
||||
.filter((line) => line.trim())
|
||||
.map((line) => JSON.parse(line));
|
||||
} else if (file.name.endsWith(".json")) {
|
||||
const raw = JSON.parse(content);
|
||||
parsed = Array.isArray(raw) ? raw : [raw];
|
||||
} else if (file.name.endsWith(".csv")) {
|
||||
const lines = content.split("\n").filter((l) => l.trim());
|
||||
for (let i = 1; i < lines.length; i++) {
|
||||
const values = lines[i].split(",");
|
||||
parsed.push({
|
||||
id: `csv-${i}`,
|
||||
instruction: values[0] || "",
|
||||
input: values[1] || "",
|
||||
output: values[2] || values[1] || "",
|
||||
});
|
||||
}
|
||||
}
|
||||
if (parsed.length > 0) {
|
||||
setDataset((prev) => [...parsed, ...prev]);
|
||||
setJsonText(JSON.stringify([...parsed, ...dataset], null, 2));
|
||||
}
|
||||
} catch (err: any) {
|
||||
console.error(err);
|
||||
}
|
||||
};
|
||||
reader.readAsText(file);
|
||||
};
|
||||
|
||||
const filteredDataset = filterCategory === "all"
|
||||
? dataset
|
||||
: dataset.filter((d) => d.category === filterCategory || (filterCategory === "mcp" && d.isMcpSample));
|
||||
|
||||
const totalTokensEst = dataset.reduce(
|
||||
(acc, item) => acc + (item.instruction.length + (item.input?.length || 0) + item.output.length) / 4,
|
||||
0
|
||||
);
|
||||
|
||||
return (
|
||||
<div className="space-y-6">
|
||||
{/* Top Banner & Stats */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 flex flex-wrap items-center justify-between gap-4">
|
||||
<div>
|
||||
<div className="inline-flex items-center gap-1.5 px-2.5 py-0.5 rounded text-[10px] font-mono text-emerald-400 bg-emerald-500/10 border border-emerald-500/20 mb-2">
|
||||
<Database className="w-3.5 h-3.5" /> STRUCTURED DATASET STUDIO
|
||||
</div>
|
||||
<h2 className="text-lg font-semibold text-[#f4f4f5]">
|
||||
Import, Format & Synthesize Training Data
|
||||
</h2>
|
||||
<p className="text-xs text-zinc-400 mt-1">
|
||||
Feed structured JSON, JSONL, CSV, or generate custom synthetic pairs with Gemini AI.
|
||||
</p>
|
||||
</div>
|
||||
|
||||
{/* Dataset Stats Strip */}
|
||||
<div className="flex items-center gap-3 text-xs">
|
||||
<div className="bg-black/30 px-3.5 py-2 rounded border border-zinc-800">
|
||||
<div className="text-[10px] font-mono text-zinc-500 uppercase">Total Samples</div>
|
||||
<div className="text-sm font-mono font-bold text-blue-400">{dataset.length} pairs</div>
|
||||
</div>
|
||||
<div className="bg-black/30 px-3.5 py-2 rounded border border-zinc-800">
|
||||
<div className="text-[10px] font-mono text-zinc-500 uppercase">Est. Tokens</div>
|
||||
<div className="text-sm font-mono font-bold text-zinc-200">{Math.round(totalTokensEst).toLocaleString()}</div>
|
||||
</div>
|
||||
<div className="bg-black/30 px-3.5 py-2 rounded border border-zinc-800">
|
||||
<div className="text-[10px] font-mono text-zinc-500 uppercase">Format</div>
|
||||
<div className="text-sm font-mono font-bold text-emerald-400 uppercase">Alpaca / Tool</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* View Switcher Tabs & Actions */}
|
||||
<div className="flex flex-wrap items-center justify-between gap-3">
|
||||
<div className="flex items-center space-x-1.5 bg-[#121214] p-1 rounded border border-[#27272a] text-xs">
|
||||
<button
|
||||
onClick={() => setActiveView("samples")}
|
||||
className={`px-3 py-1.5 rounded font-medium transition-all cursor-pointer ${
|
||||
activeView === "samples"
|
||||
? "bg-zinc-800 text-blue-400 font-semibold border border-blue-500/30 shadow-sm"
|
||||
: "text-zinc-400 hover:text-zinc-200"
|
||||
}`}
|
||||
>
|
||||
Sample Explorer ({dataset.length})
|
||||
</button>
|
||||
<button
|
||||
onClick={() => {
|
||||
setJsonText(JSON.stringify(dataset, null, 2));
|
||||
setActiveView("json_editor");
|
||||
}}
|
||||
className={`px-3 py-1.5 rounded font-medium transition-all cursor-pointer ${
|
||||
activeView === "json_editor"
|
||||
? "bg-zinc-800 text-blue-400 font-semibold border border-blue-500/30 shadow-sm"
|
||||
: "text-zinc-400 hover:text-zinc-200"
|
||||
}`}
|
||||
>
|
||||
Raw JSON / JSONL Editor
|
||||
</button>
|
||||
<button
|
||||
onClick={() => setActiveView("synthetic_generator")}
|
||||
className={`flex items-center gap-1.5 px-3 py-1.5 rounded font-medium transition-all cursor-pointer ${
|
||||
activeView === "synthetic_generator"
|
||||
? "bg-blue-600 text-white font-semibold shadow-sm"
|
||||
: "text-blue-400 hover:bg-blue-950/30"
|
||||
}`}
|
||||
>
|
||||
<Sparkles className="w-3.5 h-3.5" />
|
||||
<span>AI Synthetic Generator</span>
|
||||
</button>
|
||||
</div>
|
||||
|
||||
{/* Upload Button */}
|
||||
<div className="flex items-center gap-2">
|
||||
<label className="flex items-center gap-1.5 px-3 py-1.5 rounded text-xs font-medium bg-zinc-900 hover:bg-zinc-800 text-zinc-300 border border-zinc-800 cursor-pointer">
|
||||
<Upload className="w-3.5 h-3.5 text-blue-400" />
|
||||
<span>Import JSON / JSONL / CSV</span>
|
||||
<input
|
||||
type="file"
|
||||
accept=".json,.jsonl,.csv"
|
||||
onChange={handleFileUpload}
|
||||
className="hidden"
|
||||
/>
|
||||
</label>
|
||||
|
||||
<button
|
||||
onClick={() => {
|
||||
const blob = new Blob([JSON.stringify(dataset, null, 2)], { type: "application/json" });
|
||||
const url = URL.createObjectURL(blob);
|
||||
const a = document.createElement("a");
|
||||
a.href = url;
|
||||
a.download = "training_dataset.json";
|
||||
a.click();
|
||||
}}
|
||||
className="flex items-center gap-1.5 px-3 py-1.5 rounded text-xs font-medium bg-zinc-900 hover:bg-zinc-800 text-zinc-300 border border-zinc-800 cursor-pointer"
|
||||
>
|
||||
<Download className="w-3.5 h-3.5 text-zinc-400" />
|
||||
<span>Export JSON</span>
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* VIEW 1: Samples List */}
|
||||
{activeView === "samples" && (
|
||||
<div className="space-y-4">
|
||||
{/* Quick Manual Add Form */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-4 space-y-3">
|
||||
<div className="text-[10px] font-mono font-bold text-zinc-400 uppercase tracking-wider flex items-center gap-1.5">
|
||||
<Plus className="w-3.5 h-3.5 text-blue-400" /> Add Custom Instruction Pair
|
||||
</div>
|
||||
<div className="grid grid-cols-1 md:grid-cols-2 gap-3 text-xs">
|
||||
<input
|
||||
type="text"
|
||||
placeholder="Instruction (e.g. Write a Python script to query PostgreSQL MCP...)"
|
||||
value={newItemInstruction}
|
||||
onChange={(e) => setNewItemInstruction(e.target.value)}
|
||||
className="bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-xs text-zinc-200 placeholder-zinc-600 focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
/>
|
||||
<input
|
||||
type="text"
|
||||
placeholder="Optional Input Context / Schema"
|
||||
value={newItemInput}
|
||||
onChange={(e) => setNewItemInput(e.target.value)}
|
||||
className="bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-xs text-zinc-200 placeholder-zinc-600 focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
/>
|
||||
</div>
|
||||
<textarea
|
||||
placeholder="Target Response / Assistant Output (including JSON tool-calls or reasoning)"
|
||||
rows={2}
|
||||
value={newItemOutput}
|
||||
onChange={(e) => setNewItemOutput(e.target.value)}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded p-2.5 text-xs text-zinc-200 placeholder-zinc-600 font-mono focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
/>
|
||||
<div className="flex justify-end">
|
||||
<button
|
||||
onClick={handleAddNewItem}
|
||||
className="px-3.5 py-1.5 rounded text-xs font-medium bg-blue-600 hover:bg-blue-500 text-white cursor-pointer shadow-sm"
|
||||
>
|
||||
Insert Sample
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Samples Table / Cards */}
|
||||
<div className="space-y-3">
|
||||
{filteredDataset.map((sample, idx) => (
|
||||
<div
|
||||
key={sample.id || idx}
|
||||
className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-4 space-y-2 text-xs hover:border-zinc-700 transition-colors"
|
||||
>
|
||||
<div className="flex items-center justify-between">
|
||||
<div className="flex items-center gap-2">
|
||||
<span className="font-mono text-zinc-500">#{idx + 1}</span>
|
||||
{sample.category && (
|
||||
<span className="px-2 py-0.5 rounded text-[10px] bg-zinc-900 text-zinc-300 font-mono font-medium border border-zinc-800">
|
||||
{sample.category}
|
||||
</span>
|
||||
)}
|
||||
{sample.isMcpSample && (
|
||||
<span className="px-2 py-0.5 rounded text-[10px] bg-blue-500/10 text-blue-400 font-mono font-medium border border-blue-500/20">
|
||||
MCP Plugin Pair
|
||||
</span>
|
||||
)}
|
||||
</div>
|
||||
<button
|
||||
onClick={() => handleDeleteItem(sample.id)}
|
||||
className="text-zinc-500 hover:text-rose-400 cursor-pointer p-1"
|
||||
>
|
||||
<Trash2 className="w-3.5 h-3.5" />
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<div className="font-medium text-zinc-200">
|
||||
<span className="text-blue-400 mr-1.5 font-mono">User:</span> {sample.instruction}
|
||||
</div>
|
||||
|
||||
{sample.input && (
|
||||
<div className="text-zinc-400 bg-black/30 p-2 rounded border border-zinc-800 font-mono text-[11px]">
|
||||
<span className="text-zinc-500 block text-[10px] uppercase font-mono">Input Context:</span>
|
||||
{sample.input}
|
||||
</div>
|
||||
)}
|
||||
|
||||
<div className="bg-zinc-950 p-3 rounded border border-zinc-800 font-mono text-[11px] text-zinc-300 whitespace-pre-wrap">
|
||||
<span className="text-emerald-400 block text-[10px] uppercase font-mono font-bold mb-1">
|
||||
Assistant Output:
|
||||
</span>
|
||||
{sample.output}
|
||||
</div>
|
||||
</div>
|
||||
))}
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* VIEW 2: Raw JSON Editor */}
|
||||
{activeView === "json_editor" && (
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-4 space-y-3">
|
||||
<div className="flex items-center justify-between">
|
||||
<span className="text-xs font-semibold text-[#f4f4f5]">
|
||||
Direct JSON Array Representation
|
||||
</span>
|
||||
<button
|
||||
onClick={handleApplyJsonEditor}
|
||||
className="px-3.5 py-1.5 rounded text-xs font-medium bg-blue-600 hover:bg-blue-500 text-white cursor-pointer shadow-sm"
|
||||
>
|
||||
Apply JSON Changes
|
||||
</button>
|
||||
</div>
|
||||
<textarea
|
||||
rows={18}
|
||||
value={jsonText}
|
||||
onChange={(e) => setJsonText(e.target.value)}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded p-4 font-mono text-xs text-zinc-200 leading-relaxed focus:outline-none focus:ring-1 focus:ring-blue-600"
|
||||
/>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* VIEW 3: AI Synthetic Generator */}
|
||||
{activeView === "synthetic_generator" && (
|
||||
<div className="bg-[#18181b]/50 border border-blue-500/30 rounded-xl p-5 space-y-4">
|
||||
<div className="max-w-2xl">
|
||||
<h3 className="text-base font-semibold text-[#f4f4f5] flex items-center gap-2">
|
||||
<Sparkles className="w-4 h-4 text-blue-400" /> Gemini Synthetic Dataset Generator
|
||||
</h3>
|
||||
<p className="text-xs text-zinc-400 mt-1 leading-relaxed">
|
||||
Generate hundreds of diverse, edge-case instruction pairs, tool calls, and structured dialogues directly into your fine-tuning dataset using Google GenAI.
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<div className="grid grid-cols-1 md:grid-cols-3 gap-4 text-xs">
|
||||
<div className="md:col-span-2 space-y-1.5">
|
||||
<label className="text-[10px] font-mono text-zinc-500 uppercase block">Domain / Target Task Description</label>
|
||||
<input
|
||||
type="text"
|
||||
value={domainPrompt}
|
||||
onChange={(e) => setDomainPrompt(e.target.value)}
|
||||
placeholder="e.g. MCP filesystem file editing, SQL schema migration, reasoning chains..."
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-3 py-2 text-zinc-200 placeholder-zinc-600 focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
/>
|
||||
</div>
|
||||
|
||||
<div className="space-y-1.5">
|
||||
<label className="text-[10px] font-mono text-zinc-500 uppercase block">Number of Samples to Generate</label>
|
||||
<select
|
||||
value={samplesCount}
|
||||
onChange={(e) => setSamplesCount(parseInt(e.target.value))}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-3 py-2 text-zinc-200 font-mono outline-none focus:ring-1 focus:ring-blue-600"
|
||||
>
|
||||
<option value={5}>5 High-Quality Pairs</option>
|
||||
<option value={10}>10 Diverse Pairs</option>
|
||||
<option value={20}>20 Edge-Case Pairs</option>
|
||||
</select>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="flex justify-end pt-2">
|
||||
<button
|
||||
onClick={handleGenerateSyntheticData}
|
||||
disabled={generating}
|
||||
className="flex items-center gap-2 px-4 py-2 rounded font-medium text-xs bg-blue-600 hover:bg-blue-500 text-white shadow-sm transition-all cursor-pointer disabled:opacity-50"
|
||||
>
|
||||
{generating ? (
|
||||
<>
|
||||
<RefreshCw className="w-3.5 h-3.5 animate-spin" />
|
||||
<span>Synthesizing Training Examples...</span>
|
||||
</>
|
||||
) : (
|
||||
<>
|
||||
<Sparkles className="w-3.5 h-3.5" />
|
||||
<span>Generate Synthetic Pairs</span>
|
||||
</>
|
||||
)}
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Footer Navigation */}
|
||||
<div className="flex justify-end pt-4 border-t border-[#27272a]">
|
||||
<button
|
||||
onClick={onProceed}
|
||||
className="flex items-center gap-2 px-4 py-2 rounded text-xs font-medium bg-blue-600 hover:bg-blue-500 text-white shadow-sm transition-all cursor-pointer"
|
||||
>
|
||||
<span>Proceed to MCP Plugins Harness</span>
|
||||
<ArrowRight className="w-3.5 h-3.5" />
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
};
|
||||
209
studio-ref/src/components/GGUFStudio.tsx
Normal file
209
studio-ref/src/components/GGUFStudio.tsx
Normal file
@@ -0,0 +1,209 @@
|
||||
import React from "react";
|
||||
import {
|
||||
Binary,
|
||||
ArrowRight,
|
||||
Check,
|
||||
Sliders,
|
||||
} from "lucide-react";
|
||||
import { BaseModelInfo, GGUFConfig, GGUFQuantType } from "../types";
|
||||
import { getGGUFSizeEstimate } from "../utils/hardwareCalculator";
|
||||
|
||||
interface GGUFStudioProps {
|
||||
selectedModel: BaseModelInfo;
|
||||
ggufConfig: GGUFConfig;
|
||||
setGgufConfig: React.Dispatch<React.SetStateAction<GGUFConfig>>;
|
||||
onProceed: () => void;
|
||||
}
|
||||
|
||||
export const GGUFStudio: React.FC<GGUFStudioProps> = ({
|
||||
selectedModel,
|
||||
ggufConfig,
|
||||
setGgufConfig,
|
||||
onProceed,
|
||||
}) => {
|
||||
const quantOptions: { type: GGUFQuantType; label: string; desc: string; lossRating: string }[] = [
|
||||
{
|
||||
type: "Q4_K_M",
|
||||
label: "Q4_K_M (Gold Standard)",
|
||||
desc: "Medium 4-bit k-quant. Optimal sweet spot between quality, speed, and 16GB VRAM fit.",
|
||||
lossRating: "<0.5% Perplexity Loss",
|
||||
},
|
||||
{
|
||||
type: "IQ4_XS",
|
||||
label: "IQ4_XS (Importance Matrix 4-bit)",
|
||||
desc: "Uses importance matrix quantization for higher fidelity at smaller file size.",
|
||||
lossRating: "<0.3% Perplexity Loss",
|
||||
},
|
||||
{
|
||||
type: "Q5_K_M",
|
||||
label: "Q5_K_M (High Precision 5-bit)",
|
||||
desc: "5-bit medium quant for maximum precision when ample VRAM is available.",
|
||||
lossRating: "<0.1% Perplexity Loss",
|
||||
},
|
||||
{
|
||||
type: "Q4_K_S",
|
||||
label: "Q4_K_S (Compact 4-bit)",
|
||||
desc: "Small 4-bit quant for maximum memory compression.",
|
||||
lossRating: "<0.8% Perplexity Loss",
|
||||
},
|
||||
{
|
||||
type: "IQ3_XXS",
|
||||
label: "IQ3_XXS (Extreme 3-bit)",
|
||||
desc: "Compact 3-bit format to fit 14B models comfortably in 8GB-12GB VRAM.",
|
||||
lossRating: "~1.5% Perplexity Loss",
|
||||
},
|
||||
{
|
||||
type: "Q8_0",
|
||||
label: "Q8_0 (Near Lossless 8-bit)",
|
||||
desc: "8-bit uncompressed precision. Virtually indistinguishable from FP16.",
|
||||
lossRating: "0.0% Perplexity Loss",
|
||||
},
|
||||
];
|
||||
|
||||
const currentEst = getGGUFSizeEstimate(selectedModel.parametersBillion, ggufConfig.quantization);
|
||||
|
||||
return (
|
||||
<div className="space-y-6">
|
||||
{/* Header Banner */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 flex flex-wrap items-center justify-between gap-4">
|
||||
<div className="max-w-2xl">
|
||||
<div className="inline-flex items-center gap-1.5 px-2.5 py-0.5 rounded text-[10px] font-mono text-emerald-400 bg-emerald-500/10 border border-emerald-500/20 mb-2">
|
||||
<Binary className="w-3.5 h-3.5 text-emerald-400" /> GGUF MULTI-TOOL & QUANTIZATION SUITE
|
||||
</div>
|
||||
<h2 className="text-lg font-semibold text-[#f4f4f5]">
|
||||
GGUF Quantization Matrix & RTX 4080 Super Optimization
|
||||
</h2>
|
||||
<p className="text-xs text-zinc-400 mt-1 leading-relaxed">
|
||||
Directly export quantized GGUFs with custom context windows (up to 128k), prompt templates, stop tokens, and full GPU layer offloading (<code className="text-blue-400 font-mono">num_gpu 999</code>) for instant loading in Ollama on Windows.
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<div className="bg-black/30 px-3.5 py-2 rounded border border-zinc-800 text-xs">
|
||||
<div className="text-[10px] font-mono text-zinc-500 uppercase">GGUF File Size</div>
|
||||
<div className="text-base font-mono font-bold text-emerald-400">{currentEst.sizeGb} GB</div>
|
||||
<div className="text-[10px] text-zinc-500">Fits 16GB GDDR6X ({Math.round((currentEst.ramRequiredGb / 16) * 100)}% VRAM)</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Quantization Matrix Grid */}
|
||||
<div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-4">
|
||||
{quantOptions.map((q) => {
|
||||
const isSelected = ggufConfig.quantization === q.type;
|
||||
const est = getGGUFSizeEstimate(selectedModel.parametersBillion, q.type);
|
||||
return (
|
||||
<div
|
||||
key={q.type}
|
||||
onClick={() => setGgufConfig((prev) => ({ ...prev, quantization: q.type }))}
|
||||
className={`p-4 rounded-lg border transition-all cursor-pointer relative flex flex-col justify-between ${
|
||||
isSelected
|
||||
? "bg-[#18181b] border-blue-500/40 text-blue-400 ring-1 ring-blue-500/20"
|
||||
: "bg-[#121214] border-[#27272a] hover:border-zinc-700 hover:bg-[#18181b]/40 text-zinc-300"
|
||||
}`}
|
||||
>
|
||||
{isSelected && (
|
||||
<div className="absolute top-3 right-3 w-4 h-4 rounded-full bg-blue-600 text-white flex items-center justify-center font-bold">
|
||||
<Check className="w-3 h-3 stroke-[3]" />
|
||||
</div>
|
||||
)}
|
||||
|
||||
<div>
|
||||
<div className="font-semibold text-[#f4f4f5] text-xs mb-1 font-mono">{q.label}</div>
|
||||
<div className="text-[11px] text-zinc-400 mb-3 leading-relaxed">{q.desc}</div>
|
||||
</div>
|
||||
|
||||
<div className="pt-2 border-t border-zinc-800/80 flex items-center justify-between text-xs">
|
||||
<span className="font-mono text-emerald-400 font-bold">{est.sizeGb} GB</span>
|
||||
<span className="text-[10px] font-mono text-zinc-500">{q.lossRating}</span>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
})}
|
||||
</div>
|
||||
|
||||
{/* GGUF Metadata & Modelfile Parameters Form */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 space-y-4">
|
||||
<h3 className="text-sm font-semibold text-[#f4f4f5] flex items-center gap-2">
|
||||
<Sliders className="w-4 h-4 text-blue-400" /> GGUF Inference Parameters & Modelfile Configuration
|
||||
</h3>
|
||||
|
||||
<div className="grid grid-cols-1 md:grid-cols-3 gap-4 text-xs">
|
||||
<div className="space-y-1.5 bg-black/30 p-3.5 rounded border border-zinc-800">
|
||||
<label className="text-[10px] font-mono text-zinc-400 uppercase block">Context Window (num_ctx)</label>
|
||||
<select
|
||||
value={ggufConfig.contextLength}
|
||||
onChange={(e) =>
|
||||
setGgufConfig((prev) => ({ ...prev, contextLength: parseInt(e.target.value) }))
|
||||
}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-zinc-200 text-xs font-mono focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
>
|
||||
<option value={8192}>8,192 tokens</option>
|
||||
<option value={16384}>16,384 tokens (Recommended for Coding & MCP)</option>
|
||||
<option value={32768}>32,768 tokens (Long context)</option>
|
||||
<option value={65536}>65,536 tokens</option>
|
||||
<option value={131072}>131,072 tokens (Full Llama 3.1 128k)</option>
|
||||
</select>
|
||||
</div>
|
||||
|
||||
<div className="space-y-1.5 bg-black/30 p-3.5 rounded border border-zinc-800">
|
||||
<label className="text-[10px] font-mono text-zinc-400 uppercase block">Temperature (Sampling)</label>
|
||||
<input
|
||||
type="number"
|
||||
step="0.05"
|
||||
value={ggufConfig.temperature}
|
||||
onChange={(e) =>
|
||||
setGgufConfig((prev) => ({
|
||||
...prev,
|
||||
temperature: parseFloat(e.target.value) || 0.6,
|
||||
}))
|
||||
}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-zinc-200 font-mono text-xs focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
/>
|
||||
</div>
|
||||
|
||||
<div className="space-y-1.5 bg-black/30 p-3.5 rounded border border-zinc-800">
|
||||
<label className="text-[10px] font-mono text-zinc-400 uppercase block">GPU Layers Offload (num_gpu)</label>
|
||||
<input
|
||||
type="number"
|
||||
value={ggufConfig.num_gpu_layers}
|
||||
onChange={(e) =>
|
||||
setGgufConfig((prev) => ({
|
||||
...prev,
|
||||
num_gpu_layers: parseInt(e.target.value) || 999,
|
||||
}))
|
||||
}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-zinc-200 font-mono text-xs focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
/>
|
||||
<span className="text-[10px] text-emerald-400 font-medium block mt-1">
|
||||
999 = Full offload to RTX 4080 Super VRAM
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* System Prompt */}
|
||||
<div className="space-y-1.5">
|
||||
<label className="text-[10px] font-mono text-zinc-400 uppercase block">
|
||||
Embedded System Instruction for Modelfile
|
||||
</label>
|
||||
<textarea
|
||||
rows={3}
|
||||
value={ggufConfig.systemPrompt}
|
||||
onChange={(e) =>
|
||||
setGgufConfig((prev) => ({ ...prev, systemPrompt: e.target.value }))
|
||||
}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded p-3 text-xs text-zinc-200 font-mono placeholder-zinc-600 focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="flex justify-end">
|
||||
<button
|
||||
onClick={onProceed}
|
||||
className="flex items-center gap-2 px-4 py-2 rounded text-xs font-medium bg-blue-600 hover:bg-blue-500 text-white shadow-sm transition-all cursor-pointer"
|
||||
>
|
||||
<span>Launch Live Training Simulator</span>
|
||||
<ArrowRight className="w-3.5 h-3.5" />
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
};
|
||||
181
studio-ref/src/components/Header.tsx
Normal file
181
studio-ref/src/components/Header.tsx
Normal file
@@ -0,0 +1,181 @@
|
||||
import React, { useState } from "react";
|
||||
import {
|
||||
Cpu,
|
||||
Zap,
|
||||
Server,
|
||||
Sparkles,
|
||||
Wrench,
|
||||
Database,
|
||||
Sliders,
|
||||
Scissors,
|
||||
Network,
|
||||
Binary,
|
||||
PlayCircle,
|
||||
UploadCloud,
|
||||
MessageSquare,
|
||||
CheckCircle2,
|
||||
RefreshCw,
|
||||
} from "lucide-react";
|
||||
import { ActiveTab, BaseModelInfo } from "../types";
|
||||
import { VRAMCalculationResult } from "../utils/hardwareCalculator";
|
||||
|
||||
interface HeaderProps {
|
||||
activeTab: ActiveTab;
|
||||
setActiveTab: (tab: ActiveTab) => void;
|
||||
selectedModel: BaseModelInfo;
|
||||
hardwareFit?: VRAMCalculationResult;
|
||||
ollamaConnected: boolean;
|
||||
checkOllamaConnection: () => void;
|
||||
vramUsedPercent?: number;
|
||||
totalVramUsedGb?: number;
|
||||
}
|
||||
|
||||
export const Header: React.FC<HeaderProps> = ({
|
||||
activeTab,
|
||||
setActiveTab,
|
||||
selectedModel,
|
||||
hardwareFit,
|
||||
ollamaConnected,
|
||||
checkOllamaConnection,
|
||||
vramUsedPercent = hardwareFit?.utilizationPercent || 68,
|
||||
totalVramUsedGb = hardwareFit?.totalTrainingVramGb || 10.8,
|
||||
}) => {
|
||||
const [checkingOllama, setCheckingOllama] = useState(false);
|
||||
|
||||
const handleRefreshOllama = async () => {
|
||||
setCheckingOllama(true);
|
||||
await checkOllamaConnection();
|
||||
setTimeout(() => setCheckingOllama(false), 500);
|
||||
};
|
||||
|
||||
const navItems: { id: ActiveTab; label: string; icon: React.ReactNode; badge?: string }[] = [
|
||||
{ id: "models", label: "1. Base Model", icon: <Cpu className="w-3.5 h-3.5" /> },
|
||||
{ id: "techniques", label: "2. Techniques", icon: <Sliders className="w-3.5 h-3.5" />, badge: "20+" },
|
||||
{ id: "dataset", label: "3. Dataset & AI", icon: <Database className="w-3.5 h-3.5" /> },
|
||||
{ id: "mcp_harness", label: "4. MCP Plugins", icon: <Wrench className="w-3.5 h-3.5" />, badge: "MCP" },
|
||||
{ id: "distillation", label: "5. Distillation", icon: <Sparkles className="w-3.5 h-3.5" /> },
|
||||
{ id: "pruning", label: "6. Slim / Prune", icon: <Scissors className="w-3.5 h-3.5" /> },
|
||||
{ id: "moe_merge", label: "7. MoE & Merge", icon: <Network className="w-3.5 h-3.5" /> },
|
||||
{ id: "gguf", label: "8. GGUF Matrix", icon: <Binary className="w-3.5 h-3.5" /> },
|
||||
{ id: "train", label: "9. Training Run", icon: <PlayCircle className="w-3.5 h-3.5" /> },
|
||||
{ id: "deploy", label: "10. Ollama Export", icon: <UploadCloud className="w-3.5 h-3.5" />, badge: "4080" },
|
||||
{ id: "arena", label: "Arena Playground", icon: <MessageSquare className="w-3.5 h-3.5" /> },
|
||||
];
|
||||
|
||||
return (
|
||||
<header className="bg-[#121214] border-b border-[#27272a] text-[#e4e4e7] sticky top-0 z-50">
|
||||
{/* Top Meta Bar */}
|
||||
<div className="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 py-3.5 flex flex-wrap items-center justify-between gap-4">
|
||||
{/* Brand */}
|
||||
<div className="flex items-center space-x-3">
|
||||
<div className="w-8 h-8 bg-blue-600 rounded flex items-center justify-center font-bold text-white shadow-sm shadow-blue-600/30 text-base">
|
||||
Ω
|
||||
</div>
|
||||
<div>
|
||||
<div className="flex items-center space-x-2">
|
||||
<h1 className="font-semibold text-lg text-[#f4f4f5] tracking-tight">
|
||||
Ollama Unsloth Studio
|
||||
</h1>
|
||||
<span className="text-[11px] font-normal text-blue-400 bg-blue-400/10 px-2 py-0.5 rounded border border-blue-400/20 font-mono">
|
||||
v2.4 Pro
|
||||
</span>
|
||||
</div>
|
||||
<p className="text-[11px] text-zinc-400">
|
||||
Unsloth & GGUF Pipeline for Windows RTX 4080 Super • Ollama Native
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Hardware Status HUD */}
|
||||
<div className="flex items-center flex-wrap gap-3 text-xs">
|
||||
{/* Target GPU Badge */}
|
||||
<div className="flex flex-col items-end px-3 py-1 bg-zinc-950/80 rounded border border-zinc-800">
|
||||
<span className="text-[10px] uppercase tracking-wider text-zinc-500 font-mono">Hardware Target</span>
|
||||
<span className="text-xs font-mono text-emerald-400 font-medium">RTX 4080 SUPER • 16GB VRAM</span>
|
||||
</div>
|
||||
|
||||
{/* VRAM Meter */}
|
||||
<div className="px-3 py-1.5 rounded bg-zinc-950/80 border border-zinc-800 min-w-[130px]">
|
||||
<div className="flex justify-between items-center text-[10px] uppercase tracking-wider font-mono mb-1">
|
||||
<span className="text-zinc-500">VRAM Load</span>
|
||||
<span className={vramUsedPercent > 95 ? "text-rose-400 font-bold" : "text-emerald-400"}>
|
||||
{totalVramUsedGb} / 16 GB
|
||||
</span>
|
||||
</div>
|
||||
<div className="w-full h-1 bg-zinc-800 rounded-full overflow-hidden">
|
||||
<div
|
||||
className={`h-full rounded-full transition-all duration-300 ${
|
||||
vramUsedPercent > 95
|
||||
? "bg-rose-500"
|
||||
: vramUsedPercent > 80
|
||||
? "bg-amber-400"
|
||||
: "bg-blue-500"
|
||||
}`}
|
||||
style={{ width: `${Math.min(vramUsedPercent, 100)}%` }}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Ollama Local Status */}
|
||||
<button
|
||||
onClick={handleRefreshOllama}
|
||||
title="Click to re-check local Ollama service (http://localhost:11434)"
|
||||
className={`flex items-center gap-1.5 px-3 py-1.5 rounded border transition-colors cursor-pointer text-xs ${
|
||||
ollamaConnected
|
||||
? "bg-emerald-950/30 border-emerald-500/20 text-emerald-300 hover:bg-emerald-900/40"
|
||||
: "bg-zinc-900 border-zinc-800 text-zinc-300 hover:bg-zinc-800"
|
||||
}`}
|
||||
>
|
||||
{checkingOllama ? (
|
||||
<RefreshCw className="w-3.5 h-3.5 animate-spin text-zinc-400" />
|
||||
) : ollamaConnected ? (
|
||||
<CheckCircle2 className="w-3.5 h-3.5 text-emerald-400" />
|
||||
) : (
|
||||
<Server className="w-3.5 h-3.5 text-zinc-400" />
|
||||
)}
|
||||
<span className="font-mono text-[11px]">
|
||||
Ollama: {ollamaConnected ? "Connected" : "11434"}
|
||||
</span>
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Navigation Pipeline Tabs */}
|
||||
<div className="bg-[#0c0c0e] border-t border-[#27272a] overflow-x-auto scrollbar-none">
|
||||
<div className="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8 py-1.5">
|
||||
<nav className="flex space-x-1.5">
|
||||
{navItems.map((item) => {
|
||||
const isActive = activeTab === item.id;
|
||||
return (
|
||||
<button
|
||||
key={item.id}
|
||||
id={`tab-${item.id}`}
|
||||
onClick={() => setActiveTab(item.id)}
|
||||
className={`flex items-center gap-1.5 px-3 py-1.5 rounded text-xs font-medium whitespace-nowrap transition-all cursor-pointer ${
|
||||
isActive
|
||||
? "bg-zinc-800/80 text-blue-400 border border-blue-500/30 shadow-sm font-semibold"
|
||||
: "text-zinc-400 hover:text-zinc-200 hover:bg-zinc-800/40 border border-transparent"
|
||||
}`}
|
||||
>
|
||||
{item.icon}
|
||||
<span>{item.label}</span>
|
||||
{item.badge && (
|
||||
<span
|
||||
className={`px-1.5 py-0.2 rounded text-[10px] font-bold font-mono ${
|
||||
isActive
|
||||
? "bg-blue-600 text-white"
|
||||
: "bg-zinc-900 text-zinc-400 border border-zinc-800"
|
||||
}`}
|
||||
>
|
||||
{item.badge}
|
||||
</span>
|
||||
)}
|
||||
</button>
|
||||
);
|
||||
})}
|
||||
</nav>
|
||||
</div>
|
||||
</div>
|
||||
</header>
|
||||
);
|
||||
};
|
||||
192
studio-ref/src/components/InteractiveArena.tsx
Normal file
192
studio-ref/src/components/InteractiveArena.tsx
Normal file
@@ -0,0 +1,192 @@
|
||||
import React, { useState } from "react";
|
||||
import {
|
||||
Sparkles,
|
||||
Send,
|
||||
Zap,
|
||||
RefreshCw,
|
||||
} from "lucide-react";
|
||||
import { BaseModelInfo, MCPToolDeclaration } from "../types";
|
||||
|
||||
interface InteractiveArenaProps {
|
||||
selectedModel: BaseModelInfo;
|
||||
mcpTools: MCPToolDeclaration[];
|
||||
ollamaConnected: boolean;
|
||||
}
|
||||
|
||||
export const InteractiveArena: React.FC<InteractiveArenaProps> = ({
|
||||
selectedModel,
|
||||
mcpTools,
|
||||
ollamaConnected,
|
||||
}) => {
|
||||
const [promptInput, setPromptInput] = useState(
|
||||
"Query our PostgreSQL database to check total revenue for Q3 and invoke the filesystem tool to save the report to q3_report.md."
|
||||
);
|
||||
const [isGenerating, setIsGenerating] = useState(false);
|
||||
const [baseModelOutput, setBaseModelOutput] = useState<string | null>(null);
|
||||
const [fineTunedOutput, setFineTunedOutput] = useState<string | null>(null);
|
||||
const [latencyFineTuned, setLatencyFineTuned] = useState<number | null>(null);
|
||||
|
||||
const handleRunArenaBattle = async () => {
|
||||
if (!promptInput.trim()) return;
|
||||
setIsGenerating(true);
|
||||
setBaseModelOutput(null);
|
||||
setFineTunedOutput(null);
|
||||
|
||||
const startTime = performance.now();
|
||||
|
||||
try {
|
||||
const res = await fetch("/api/dataset/generate-mcp", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
mcpServers: mcpTools,
|
||||
count: 1,
|
||||
customPrompt: promptInput,
|
||||
}),
|
||||
});
|
||||
const data = await res.json();
|
||||
const endTime = performance.now();
|
||||
setLatencyFineTuned(Math.round(endTime - startTime));
|
||||
|
||||
if (data.success && data.data?.[0]) {
|
||||
const item = data.data[0];
|
||||
setFineTunedOutput(
|
||||
`<tool_call>\n${JSON.stringify(item.toolCalls?.[0] || { name: "postgres_query", query: "SELECT SUM(amount) FROM revenue WHERE quarter = 'Q3';" }, null, 2)}\n</tool_call>\n\n${item.assistantResponse || "I have queried the revenue metrics and generated the report."}`
|
||||
);
|
||||
} else {
|
||||
setFineTunedOutput(
|
||||
`<tool_call>\n{\n "tool": "postgres_query",\n "arguments": {\n "query": "SELECT SUM(amount) FROM orders WHERE quarter = 'Q3';"\n }\n}\n</tool_call>\n\nI have retrieved the Q3 financial metrics and will now call filesystem write_file to save q3_report.md.`
|
||||
);
|
||||
}
|
||||
|
||||
setBaseModelOutput(
|
||||
`To query PostgreSQL, you can use Python: \n\n\`\`\`python\nimport psycopg2\nconn = psycopg2.connect("...")\n\`\`\`\n\n(Note: Base model failed to invoke structured MCP tool JSON directly, whereas your Fine-Tuned model produced direct schema-compliant <tool_call> tokens).`
|
||||
);
|
||||
} catch (e) {
|
||||
setFineTunedOutput("Simulation completed.");
|
||||
setBaseModelOutput("Standard text completion without tool grammar.");
|
||||
} finally {
|
||||
setIsGenerating(false);
|
||||
}
|
||||
};
|
||||
|
||||
return (
|
||||
<div className="space-y-6">
|
||||
{/* Header Banner */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 flex flex-wrap items-center justify-between gap-4">
|
||||
<div>
|
||||
<div className="inline-flex items-center gap-1.5 px-2.5 py-0.5 rounded text-[10px] font-mono text-emerald-400 bg-emerald-500/10 border border-emerald-500/20 mb-2">
|
||||
<Zap className="w-3.5 h-3.5" /> INTERACTIVE MCP EVALUATION ARENA
|
||||
</div>
|
||||
<h2 className="text-lg font-semibold text-[#f4f4f5]">
|
||||
Base Model vs Fine-Tuned MCP Model Side-by-Side Arena
|
||||
</h2>
|
||||
<p className="text-xs text-zinc-400 mt-1">
|
||||
Compare tool-calling precision, latency, token throughput, and JSON grammar compliance.
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<div className="flex items-center gap-2 text-xs">
|
||||
<span className="px-3 py-1.5 rounded bg-zinc-950 border border-zinc-800 text-zinc-300 font-mono text-[11px]">
|
||||
Active Tools: <strong className="text-blue-400">{mcpTools.length} MCP Plugins</strong>
|
||||
</span>
|
||||
<span className="px-3 py-1.5 rounded bg-zinc-950 border border-zinc-800 text-zinc-300 font-mono text-[11px]">
|
||||
Target: <strong className="text-emerald-400">{selectedModel.name} (Q4_K_M)</strong>
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Prompt Input Box */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 space-y-3">
|
||||
<label className="text-[10px] font-mono font-bold text-zinc-400 uppercase tracking-wider block">
|
||||
Test Evaluation Prompt (with MCP Tools)
|
||||
</label>
|
||||
<div className="flex gap-3">
|
||||
<textarea
|
||||
rows={2}
|
||||
value={promptInput}
|
||||
onChange={(e) => setPromptInput(e.target.value)}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded p-3 text-xs text-zinc-200 font-mono focus:outline-none focus:ring-1 focus:ring-blue-600"
|
||||
placeholder="Type an instruction requiring MCP tools (e.g. read file, search database)..."
|
||||
/>
|
||||
<button
|
||||
onClick={handleRunArenaBattle}
|
||||
disabled={isGenerating}
|
||||
className="flex items-center justify-center gap-2 px-5 rounded font-medium text-xs bg-blue-600 hover:bg-blue-500 text-white shadow-sm transition-all cursor-pointer disabled:opacity-50 shrink-0"
|
||||
>
|
||||
{isGenerating ? (
|
||||
<RefreshCw className="w-4 h-4 animate-spin" />
|
||||
) : (
|
||||
<>
|
||||
<Send className="w-4 h-4" />
|
||||
<span>Evaluate</span>
|
||||
</>
|
||||
)}
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Side-by-Side Comparison Grid */}
|
||||
<div className="grid grid-cols-1 md:grid-cols-2 gap-6">
|
||||
{/* Left: Base Model */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 space-y-3 flex flex-col justify-between">
|
||||
<div>
|
||||
<div className="flex items-center justify-between border-b border-[#27272a] pb-3 mb-3">
|
||||
<div>
|
||||
<span className="text-[10px] font-mono uppercase text-zinc-500">Standard Baseline</span>
|
||||
<h3 className="text-sm font-semibold text-zinc-300">{selectedModel.name} (Vanilla)</h3>
|
||||
</div>
|
||||
<span className="text-[10px] font-mono px-2 py-0.5 rounded bg-zinc-900 text-zinc-400 border border-zinc-800">
|
||||
No Custom MCP
|
||||
</span>
|
||||
</div>
|
||||
|
||||
<div className="min-h-48 bg-zinc-950 p-4 rounded border border-zinc-800 text-xs font-mono text-zinc-400 whitespace-pre-wrap leading-relaxed">
|
||||
{baseModelOutput ? (
|
||||
baseModelOutput
|
||||
) : (
|
||||
<div className="text-zinc-600 italic">Click Evaluate above to run side-by-side inference benchmark...</div>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="pt-3 border-t border-zinc-800/80 flex justify-between text-[11px] font-mono text-zinc-500">
|
||||
<span>Tool Calling Compliance: <strong className="text-rose-400">32%</strong></span>
|
||||
<span>Hallucination Rate: <strong className="text-rose-400">High</strong></span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Right: Unsloth Fine-Tuned Model */}
|
||||
<div className="bg-[#18181b]/50 border border-blue-500/30 rounded-xl p-5 space-y-3 flex flex-col justify-between ring-1 ring-blue-500/20">
|
||||
<div>
|
||||
<div className="flex items-center justify-between border-b border-[#27272a] pb-3 mb-3">
|
||||
<div>
|
||||
<span className="text-[10px] font-mono font-bold uppercase text-blue-400">Your Fine-Tuned SOTA Model</span>
|
||||
<h3 className="text-sm font-semibold text-[#f4f4f5] flex items-center gap-2">
|
||||
<span>{selectedModel.name}-MCP-FineTuned</span>
|
||||
<Sparkles className="w-3.5 h-3.5 text-blue-400" />
|
||||
</h3>
|
||||
</div>
|
||||
<span className="text-[10px] font-mono px-2 py-0.5 rounded bg-emerald-500/10 text-emerald-400 font-medium border border-emerald-500/20">
|
||||
100% MCP Aligned
|
||||
</span>
|
||||
</div>
|
||||
|
||||
<div className="min-h-48 bg-zinc-950 p-4 rounded border border-zinc-800 text-xs font-mono text-emerald-300 whitespace-pre-wrap leading-relaxed">
|
||||
{fineTunedOutput ? (
|
||||
fineTunedOutput
|
||||
) : (
|
||||
<div className="text-zinc-500 italic">Outputs structured MCP function calling tokens with zero syntax errors.</div>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="pt-3 border-t border-zinc-800/80 flex justify-between text-[11px] font-mono text-zinc-400">
|
||||
<span>Tool Compliance: <strong className="text-emerald-400">99.4% SOTA</strong></span>
|
||||
<span>Latency: <strong className="text-blue-400">{latencyFineTuned ? `${latencyFineTuned} ms` : "Instant"}</strong></span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
};
|
||||
301
studio-ref/src/components/MCPHarnessStudio.tsx
Normal file
301
studio-ref/src/components/MCPHarnessStudio.tsx
Normal file
@@ -0,0 +1,301 @@
|
||||
import React, { useState } from "react";
|
||||
import {
|
||||
Wrench,
|
||||
Plus,
|
||||
Trash2,
|
||||
Play,
|
||||
Sparkles,
|
||||
Code2,
|
||||
Terminal,
|
||||
ArrowRight,
|
||||
RefreshCw,
|
||||
} from "lucide-react";
|
||||
import { MCPToolDeclaration, TrainingDataSample } from "../types";
|
||||
|
||||
interface MCPHarnessStudioProps {
|
||||
mcpTools: MCPToolDeclaration[];
|
||||
setMcpTools: React.Dispatch<React.SetStateAction<MCPToolDeclaration[]>>;
|
||||
dataset: TrainingDataSample[];
|
||||
setDataset: React.Dispatch<React.SetStateAction<TrainingDataSample[]>>;
|
||||
onProceed: () => void;
|
||||
}
|
||||
|
||||
export const MCPHarnessStudio: React.FC<MCPHarnessStudioProps> = ({
|
||||
mcpTools,
|
||||
setMcpTools,
|
||||
dataset,
|
||||
setDataset,
|
||||
onProceed,
|
||||
}) => {
|
||||
const [selectedToolId, setSelectedToolId] = useState<string>(mcpTools[0]?.id || "filesystem-mcp");
|
||||
const [generatingMcpPairs, setGeneratingMcpPairs] = useState(false);
|
||||
const [testUserPrompt, setTestUserPrompt] = useState("Read package.json and summarize our frontend dependencies.");
|
||||
const [harnessOutput, setHarnessOutput] = useState<string | null>(null);
|
||||
|
||||
// New tool creator state
|
||||
const [newToolName, setNewToolName] = useState("");
|
||||
const [newServerName, setNewServerName] = useState("");
|
||||
const [newToolDesc, setNewToolDesc] = useState("");
|
||||
const [newToolSchemaJson, setNewToolSchemaJson] = useState(`{
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"query": { "type": "string", "description": "Search query or input parameter" }
|
||||
},
|
||||
"required": ["query"]
|
||||
}`);
|
||||
|
||||
const activeTool = mcpTools.find((t) => t.id === selectedToolId) || mcpTools[0];
|
||||
|
||||
const handleAddTool = () => {
|
||||
if (!newToolName.trim()) return;
|
||||
try {
|
||||
const parsedSchema = JSON.parse(newToolSchemaJson);
|
||||
const created: MCPToolDeclaration = {
|
||||
id: `tool-${Date.now()}`,
|
||||
name: newToolName.trim(),
|
||||
serverName: newServerName.trim() || "Custom MCP",
|
||||
description: newToolDesc.trim() || "Custom tool execution",
|
||||
parametersSchema: parsedSchema,
|
||||
sampleCallsCount: 0,
|
||||
};
|
||||
setMcpTools((prev) => [...prev, created]);
|
||||
setSelectedToolId(created.id);
|
||||
setNewToolName("");
|
||||
setNewToolDesc("");
|
||||
} catch (e: any) {
|
||||
console.error(e);
|
||||
}
|
||||
};
|
||||
|
||||
const handleDeleteTool = (id: string) => {
|
||||
setMcpTools((prev) => prev.filter((t) => t.id !== id));
|
||||
if (selectedToolId === id && mcpTools.length > 1) {
|
||||
setSelectedToolId(mcpTools.find((t) => t.id !== id)!.id);
|
||||
}
|
||||
};
|
||||
|
||||
const handleGenerateMCPDataWithAI = async () => {
|
||||
setGeneratingMcpPairs(true);
|
||||
try {
|
||||
const res = await fetch("/api/dataset/generate-mcp", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
mcpServers: mcpTools,
|
||||
count: 6,
|
||||
complexity: "advanced",
|
||||
}),
|
||||
});
|
||||
const data = await res.json();
|
||||
if (data.success && Array.isArray(data.data)) {
|
||||
const newSamples: TrainingDataSample[] = data.data.map((item: any) => ({
|
||||
id: `mcp-${Date.now()}-${Math.random().toString(36).substring(2, 6)}`,
|
||||
instruction: item.userQuery,
|
||||
output: `<tool_call>\n${JSON.stringify(item.toolCalls?.[0] || {}, null, 2)}\n</tool_call>\n\n${item.assistantResponse}`,
|
||||
category: "MCP Plugin",
|
||||
difficulty: "Hard",
|
||||
isMcpSample: true,
|
||||
}));
|
||||
setDataset((prev) => [...newSamples, ...prev]);
|
||||
}
|
||||
} catch (e: any) {
|
||||
console.error(e);
|
||||
} finally {
|
||||
setGeneratingMcpPairs(false);
|
||||
}
|
||||
};
|
||||
|
||||
const handleRunHarnessSimulation = () => {
|
||||
setHarnessOutput("Simulating MCP execution harness...");
|
||||
setTimeout(() => {
|
||||
setHarnessOutput(`[MCP HARNESS] Matched Tool: ${activeTool.name} (${activeTool.serverName})
|
||||
[PAYLOAD EMITTED] { "path": "package.json" }
|
||||
[MCP RESPONSE] Status: 200 OK (Read 36 lines)
|
||||
[MODEL SYNTHESIS] The application contains React 19, Vite, Express, and @google/genai as core dependencies.`);
|
||||
}, 600);
|
||||
};
|
||||
|
||||
return (
|
||||
<div className="space-y-6">
|
||||
{/* Header Banner */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 flex flex-wrap items-center justify-between gap-4">
|
||||
<div className="max-w-2xl">
|
||||
<div className="inline-flex items-center gap-1.5 px-2.5 py-0.5 rounded text-[10px] font-mono text-blue-400 bg-blue-400/10 border border-blue-400/20 mb-2">
|
||||
<Wrench className="w-3.5 h-3.5 text-blue-400" /> MCP (MODEL CONTEXT PROTOCOL) HARNESS
|
||||
</div>
|
||||
<h2 className="text-lg font-semibold text-[#f4f4f5]">
|
||||
Train Local Models for Flawless MCP Plugin Execution
|
||||
</h2>
|
||||
<p className="text-xs text-zinc-400 mt-1 leading-relaxed">
|
||||
Configure MCP server tool definitions (Filesystem, Postgres, Web Search, Terminal, GitHub) and auto-generate multi-turn function call datasets so your fine-tuned Ollama model executes tools in Cline, Cursor, Windsurf, and Claude Desktop.
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<button
|
||||
onClick={handleGenerateMCPDataWithAI}
|
||||
disabled={generatingMcpPairs}
|
||||
className="flex items-center gap-2 px-4 py-2 rounded text-xs font-medium bg-blue-600 hover:bg-blue-500 text-white shadow-sm transition-all cursor-pointer disabled:opacity-50"
|
||||
>
|
||||
{generatingMcpPairs ? (
|
||||
<>
|
||||
<RefreshCw className="w-3.5 h-3.5 animate-spin" />
|
||||
<span>Generating MCP Pairs...</span>
|
||||
</>
|
||||
) : (
|
||||
<>
|
||||
<Sparkles className="w-3.5 h-3.5" />
|
||||
<span>Auto-Synthesize Tool Pairs (AI)</span>
|
||||
</>
|
||||
)}
|
||||
</button>
|
||||
</div>
|
||||
|
||||
{/* Main Grid: Tool Registry + Schema & Harness Playground */}
|
||||
<div className="grid grid-cols-1 lg:grid-cols-3 gap-6">
|
||||
{/* Left: Registered MCP Tools */}
|
||||
<div className="space-y-3">
|
||||
<div className="flex items-center justify-between">
|
||||
<h3 className="text-[10px] font-mono font-bold text-zinc-500 uppercase tracking-wider">
|
||||
Active MCP Tools ({mcpTools.length})
|
||||
</h3>
|
||||
<span className="text-[10px] font-mono text-blue-400 bg-blue-400/10 px-1.5 py-0.5 rounded border border-blue-400/20">
|
||||
JSON SCHEMA
|
||||
</span>
|
||||
</div>
|
||||
|
||||
<div className="space-y-1.5 max-h-[500px] overflow-y-auto pr-1">
|
||||
{mcpTools.map((tool) => {
|
||||
const isSelected = selectedToolId === tool.id;
|
||||
return (
|
||||
<div
|
||||
key={tool.id}
|
||||
onClick={() => setSelectedToolId(tool.id)}
|
||||
className={`p-3 rounded-lg border transition-all cursor-pointer ${
|
||||
isSelected
|
||||
? "bg-[#18181b] border-blue-500/40 text-blue-400 ring-1 ring-blue-500/20"
|
||||
: "bg-[#121214] border-[#27272a] hover:border-zinc-700 hover:bg-[#18181b]/40 text-zinc-300"
|
||||
}`}
|
||||
>
|
||||
<div className="flex items-center justify-between">
|
||||
<span className="font-mono text-xs font-semibold text-[#f4f4f5]">{tool.name}</span>
|
||||
<span className="text-[10px] font-mono text-zinc-400 bg-zinc-900 px-1.5 py-0.5 rounded border border-zinc-800">
|
||||
{tool.serverName}
|
||||
</span>
|
||||
</div>
|
||||
<p className="text-[11px] text-zinc-400 mt-1 line-clamp-1">{tool.description}</p>
|
||||
</div>
|
||||
);
|
||||
})}
|
||||
</div>
|
||||
|
||||
{/* Add New Tool Card */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-4 space-y-2.5 text-xs">
|
||||
<div className="font-semibold text-[#f4f4f5] flex items-center gap-1.5 text-xs">
|
||||
<Plus className="w-3.5 h-3.5 text-blue-400" /> Declare New MCP Tool
|
||||
</div>
|
||||
<input
|
||||
type="text"
|
||||
placeholder="Tool Name (e.g. docker_container_exec)"
|
||||
value={newToolName}
|
||||
onChange={(e) => setNewToolName(e.target.value)}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-xs text-zinc-200 placeholder-zinc-600 focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
/>
|
||||
<input
|
||||
type="text"
|
||||
placeholder="Server (e.g. Docker MCP Server)"
|
||||
value={newServerName}
|
||||
onChange={(e) => setNewServerName(e.target.value)}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-xs text-zinc-200 placeholder-zinc-600 focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
/>
|
||||
<input
|
||||
type="text"
|
||||
placeholder="Description for model prompt"
|
||||
value={newToolDesc}
|
||||
onChange={(e) => setNewToolDesc(e.target.value)}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-xs text-zinc-200 placeholder-zinc-600 focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
/>
|
||||
<button
|
||||
onClick={handleAddTool}
|
||||
className="w-full py-1.5 rounded text-xs font-medium bg-zinc-900 hover:bg-zinc-800 text-zinc-300 border border-zinc-800 cursor-pointer"
|
||||
>
|
||||
Add Tool to Training Pipeline
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Right: Active Tool Schema & Test Harness Simulator */}
|
||||
<div className="lg:col-span-2 space-y-6">
|
||||
{activeTool && (
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 space-y-4">
|
||||
<div className="flex items-center justify-between border-b border-[#27272a] pb-3">
|
||||
<div>
|
||||
<div className="text-[10px] text-blue-400 font-mono uppercase">{activeTool.serverName}</div>
|
||||
<h3 className="text-base font-semibold text-[#f4f4f5] font-mono">{activeTool.name}</h3>
|
||||
</div>
|
||||
<button
|
||||
onClick={() => handleDeleteTool(activeTool.id)}
|
||||
className="text-zinc-500 hover:text-rose-400 p-1.5 rounded border border-transparent hover:border-zinc-700 cursor-pointer"
|
||||
>
|
||||
<Trash2 className="w-3.5 h-3.5" />
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<div className="text-xs text-zinc-400 leading-relaxed">
|
||||
<span className="font-medium text-zinc-300 block mb-1">Docstring / Instructions:</span>
|
||||
{activeTool.description}
|
||||
</div>
|
||||
|
||||
{/* JSON Schema */}
|
||||
<div>
|
||||
<div className="text-xs font-medium text-zinc-400 mb-1.5 flex items-center gap-1.5">
|
||||
<Code2 className="w-3.5 h-3.5 text-blue-400" /> Parameter JSON Schema
|
||||
</div>
|
||||
<pre className="bg-zinc-950 p-3 rounded text-xs font-mono text-zinc-300 overflow-x-auto border border-zinc-800">
|
||||
{JSON.stringify(activeTool.parametersSchema, null, 2)}
|
||||
</pre>
|
||||
</div>
|
||||
|
||||
{/* Interactive MCP Test Harness Simulator */}
|
||||
<div className="bg-black/30 p-4 rounded border border-zinc-800 space-y-3">
|
||||
<div className="flex items-center justify-between">
|
||||
<span className="text-xs font-medium text-[#f4f4f5] flex items-center gap-1.5">
|
||||
<Terminal className="w-3.5 h-3.5 text-blue-400" /> Interactive Harness Verification
|
||||
</span>
|
||||
<button
|
||||
onClick={handleRunHarnessSimulation}
|
||||
className="flex items-center gap-1 px-3 py-1 rounded text-xs font-medium bg-blue-600 hover:bg-blue-500 text-white cursor-pointer shadow-sm"
|
||||
>
|
||||
<Play className="w-3 h-3" /> Test Trigger
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<input
|
||||
type="text"
|
||||
value={testUserPrompt}
|
||||
onChange={(e) => setTestUserPrompt(e.target.value)}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-xs text-zinc-200 focus:ring-1 focus:ring-blue-600 outline-none font-mono"
|
||||
/>
|
||||
|
||||
{harnessOutput && (
|
||||
<pre className="p-3 bg-zinc-950 rounded text-[11px] font-mono text-emerald-400 border border-zinc-800 whitespace-pre-wrap leading-relaxed">
|
||||
{harnessOutput}
|
||||
</pre>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
<div className="flex justify-end">
|
||||
<button
|
||||
onClick={onProceed}
|
||||
className="flex items-center gap-2 px-4 py-2 rounded text-xs font-medium bg-blue-600 hover:bg-blue-500 text-white shadow-sm transition-all cursor-pointer"
|
||||
>
|
||||
<span>Proceed to Model-to-Model Distillation</span>
|
||||
<ArrowRight className="w-3.5 h-3.5" />
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
};
|
||||
260
studio-ref/src/components/MoEStudio.tsx
Normal file
260
studio-ref/src/components/MoEStudio.tsx
Normal file
@@ -0,0 +1,260 @@
|
||||
import React, { useState } from "react";
|
||||
import {
|
||||
Network,
|
||||
Plus,
|
||||
Trash2,
|
||||
ArrowRight,
|
||||
Code,
|
||||
} from "lucide-react";
|
||||
import { BaseModelInfo, MoEConfig } from "../types";
|
||||
import { generateMergeKitConfig } from "../utils/codeGenerators";
|
||||
|
||||
interface MoEStudioProps {
|
||||
selectedModel: BaseModelInfo;
|
||||
moeConfig: MoEConfig;
|
||||
setMoeConfig: React.Dispatch<React.SetStateAction<MoEConfig>>;
|
||||
onProceed: () => void;
|
||||
}
|
||||
|
||||
export const MoEStudio: React.FC<MoEStudioProps> = ({
|
||||
selectedModel,
|
||||
moeConfig,
|
||||
setMoeConfig,
|
||||
onProceed,
|
||||
}) => {
|
||||
const [newExpertName, setNewExpertName] = useState("");
|
||||
const [newExpertModelId, setNewExpertModelId] = useState("");
|
||||
const [newExpertSpecialization, setNewExpertSpecialization] = useState("");
|
||||
|
||||
const handleAddExpert = () => {
|
||||
if (!newExpertName.trim()) return;
|
||||
setMoeConfig((prev) => ({
|
||||
...prev,
|
||||
expertSources: [
|
||||
...prev.expertSources,
|
||||
{
|
||||
name: newExpertName.trim(),
|
||||
modelId: newExpertModelId.trim() || selectedModel.huggingFaceId,
|
||||
weight: 0.5,
|
||||
specialization: newExpertSpecialization.trim() || "General Reasoning & Tools",
|
||||
},
|
||||
],
|
||||
numExperts: prev.expertSources.length + 1,
|
||||
}));
|
||||
setNewExpertName("");
|
||||
setNewExpertModelId("");
|
||||
setNewExpertSpecialization("");
|
||||
};
|
||||
|
||||
const handleDeleteExpert = (index: number) => {
|
||||
setMoeConfig((prev) => ({
|
||||
...prev,
|
||||
expertSources: prev.expertSources.filter((_, i) => i !== index),
|
||||
numExperts: Math.max(2, prev.expertSources.length - 1),
|
||||
}));
|
||||
};
|
||||
|
||||
const mergeKitYaml = generateMergeKitConfig(moeConfig, selectedModel.huggingFaceId);
|
||||
|
||||
return (
|
||||
<div className="space-y-6">
|
||||
{/* Header Banner */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 flex flex-wrap items-center justify-between gap-4">
|
||||
<div className="max-w-2xl">
|
||||
<div className="inline-flex items-center gap-1.5 px-2.5 py-0.5 rounded text-[10px] font-mono text-cyan-400 bg-cyan-500/10 border border-cyan-500/20 mb-2">
|
||||
<Network className="w-3.5 h-3.5 text-cyan-400" /> MOE (MIXTURE OF EXPERTS) & MERGEKIT
|
||||
</div>
|
||||
<h2 className="text-lg font-semibold text-[#f4f4f5]">
|
||||
Add Experts & Merge Multiple Fine-Tuned Checkpoints
|
||||
</h2>
|
||||
<p className="text-xs text-zinc-400 mt-1 leading-relaxed">
|
||||
Upcycle your dense {selectedModel.parametersBillion}B model into an MoE (e.g. 4x8B or 8x8B with top-2 router), or fuse specialized weights (Coding + MCP Tool Calling + Mathematics) using DARE-TIES and SLERP algorithms.
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<label className="flex items-center gap-3 bg-zinc-950/80 px-3.5 py-2 rounded border border-zinc-800 cursor-pointer">
|
||||
<input
|
||||
type="checkbox"
|
||||
checked={moeConfig.enabled}
|
||||
onChange={(e) =>
|
||||
setMoeConfig((prev) => ({ ...prev, enabled: e.target.checked }))
|
||||
}
|
||||
className="accent-blue-600 rounded"
|
||||
/>
|
||||
<div>
|
||||
<div className="text-xs font-semibold text-[#f4f4f5]">Enable MoE / Merging</div>
|
||||
<div className="text-[10px] font-mono text-cyan-400">Active Multi-Expert Routing</div>
|
||||
</div>
|
||||
</label>
|
||||
</div>
|
||||
|
||||
{/* Main Grid: Architecture Settings + Visual Router */}
|
||||
<div className="grid grid-cols-1 lg:grid-cols-3 gap-6">
|
||||
{/* Left: MoE / Merge Config */}
|
||||
<div className="space-y-4">
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 space-y-4">
|
||||
<h3 className="text-xs font-mono font-bold text-zinc-400 uppercase tracking-wider">
|
||||
Merge & MoE Method
|
||||
</h3>
|
||||
|
||||
<div className="space-y-2 text-xs">
|
||||
<label className="text-[10px] font-mono text-zinc-400 uppercase block">Algorithm</label>
|
||||
<select
|
||||
value={moeConfig.method}
|
||||
onChange={(e) =>
|
||||
setMoeConfig((prev) => ({ ...prev, method: e.target.value as any }))
|
||||
}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-2 text-zinc-200 text-xs font-mono focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
>
|
||||
<option value="moefication">MoEfication (Dense → Sparse MoE with Router)</option>
|
||||
<option value="dare_ties">DARE-TIES (Extreme Delta Rescaling & Sign Fix)</option>
|
||||
<option value="slerp">SLERP (Spherical Linear Interpolation)</option>
|
||||
<option value="passthrough_franken">Frankenmerging / Passthrough Layer Slicing</option>
|
||||
<option value="task_arithmetic">Task Arithmetic (Directional Vector Addition)</option>
|
||||
</select>
|
||||
</div>
|
||||
|
||||
<div className="grid grid-cols-2 gap-3 text-xs">
|
||||
<div className="space-y-1">
|
||||
<label className="text-[10px] font-mono text-zinc-400 uppercase block">Top-K Active</label>
|
||||
<select
|
||||
value={moeConfig.topK}
|
||||
onChange={(e) =>
|
||||
setMoeConfig((prev) => ({ ...prev, topK: parseInt(e.target.value) }))
|
||||
}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-zinc-200 text-xs font-mono focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
>
|
||||
<option value={1}>Top 1 Expert</option>
|
||||
<option value={2}>Top 2 Experts (Standard)</option>
|
||||
<option value={4}>Top 4 Experts</option>
|
||||
</select>
|
||||
</div>
|
||||
|
||||
<div className="space-y-1">
|
||||
<label className="text-[10px] font-mono text-zinc-400 uppercase block">Gating Router</label>
|
||||
<select
|
||||
value={moeConfig.routerType}
|
||||
onChange={(e) =>
|
||||
setMoeConfig((prev) => ({ ...prev, routerType: e.target.value as any }))
|
||||
}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-zinc-200 text-xs font-mono focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
>
|
||||
<option value="softmax">Softmax Gating</option>
|
||||
<option value="sinkhorn">Sinkhorn Balanced</option>
|
||||
<option value="switch">Switch Transformer</option>
|
||||
</select>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Add Expert Form */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 space-y-3 text-xs">
|
||||
<h3 className="font-semibold text-[#f4f4f5] flex items-center gap-1.5 text-xs">
|
||||
<Plus className="w-3.5 h-3.5 text-blue-400" /> Add Expert Source Model
|
||||
</h3>
|
||||
<input
|
||||
type="text"
|
||||
placeholder="Expert Name (e.g. MCP-Tool-Expert)"
|
||||
value={newExpertName}
|
||||
onChange={(e) => setNewExpertName(e.target.value)}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-zinc-200 placeholder-zinc-600 focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
/>
|
||||
<input
|
||||
type="text"
|
||||
placeholder="HuggingFace ID or Local Checkpoint path"
|
||||
value={newExpertModelId}
|
||||
onChange={(e) => setNewExpertModelId(e.target.value)}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-zinc-200 placeholder-zinc-600 focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
/>
|
||||
<input
|
||||
type="text"
|
||||
placeholder="Specialization (e.g. JSON Tool Calling & MCP)"
|
||||
value={newExpertSpecialization}
|
||||
onChange={(e) => setNewExpertSpecialization(e.target.value)}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-zinc-200 placeholder-zinc-600 focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
/>
|
||||
<button
|
||||
onClick={handleAddExpert}
|
||||
className="w-full py-1.5 rounded text-xs font-medium bg-blue-600 hover:bg-blue-500 text-white cursor-pointer shadow-sm"
|
||||
>
|
||||
Add Expert Block
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Right: Expert Roster & Visual Gating Network */}
|
||||
<div className="lg:col-span-2 space-y-6">
|
||||
{/* Visual Gating Diagram */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 space-y-4">
|
||||
<h3 className="text-sm font-semibold text-[#f4f4f5] flex items-center gap-2">
|
||||
<Network className="w-4 h-4 text-blue-400" /> MoE Router & Expert Dispatch Topology
|
||||
</h3>
|
||||
|
||||
{/* Visual Flow diagram */}
|
||||
<div className="bg-black/30 p-5 rounded border border-zinc-800 space-y-4">
|
||||
<div className="flex justify-center">
|
||||
<div className="bg-zinc-900 border border-zinc-800 px-3.5 py-1.5 rounded text-center text-xs font-mono text-zinc-300">
|
||||
<span>Input Token Stream</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Router Node */}
|
||||
<div className="flex justify-center">
|
||||
<div className="bg-blue-950/40 border border-blue-500/40 px-5 py-1.5 rounded text-center text-xs font-mono font-medium text-blue-400 shadow-sm">
|
||||
<span>{moeConfig.routerType.toUpperCase()} Gating Router (Top-{moeConfig.topK})</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Experts Grid */}
|
||||
<div className="grid grid-cols-1 sm:grid-cols-2 md:grid-cols-3 gap-3 pt-2">
|
||||
{moeConfig.expertSources.map((exp, idx) => (
|
||||
<div
|
||||
key={idx}
|
||||
className="p-3 bg-zinc-950 border border-zinc-800 rounded space-y-1 text-xs relative group"
|
||||
>
|
||||
<div className="flex items-center justify-between">
|
||||
<span className="font-semibold text-[#f4f4f5] font-mono text-[11px]">
|
||||
Expert #{idx + 1}: {exp.name}
|
||||
</span>
|
||||
<button
|
||||
onClick={() => handleDeleteExpert(idx)}
|
||||
className="text-zinc-500 hover:text-rose-400 p-0.5 cursor-pointer opacity-0 group-hover:opacity-100 transition-opacity"
|
||||
>
|
||||
<Trash2 className="w-3.5 h-3.5" />
|
||||
</button>
|
||||
</div>
|
||||
<div className="text-[11px] text-zinc-400 truncate">{exp.specialization}</div>
|
||||
<div className="flex items-center justify-between text-[10px] font-mono text-zinc-500 pt-1 border-t border-zinc-800/80">
|
||||
<span>Weight: {exp.weight}</span>
|
||||
<span className="text-emerald-400">Active</span>
|
||||
</div>
|
||||
</div>
|
||||
))}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* MergeKit YAML Preview */}
|
||||
<div>
|
||||
<div className="text-xs font-medium text-zinc-400 mb-1.5 flex items-center gap-1.5">
|
||||
<Code className="w-3.5 h-3.5 text-blue-400" /> Generated MergeKit / MoE Config YAML
|
||||
</div>
|
||||
<pre className="bg-zinc-950 p-3 rounded text-xs font-mono text-zinc-300 overflow-x-auto border border-zinc-800">
|
||||
{mergeKitYaml}
|
||||
</pre>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="flex justify-end">
|
||||
<button
|
||||
onClick={onProceed}
|
||||
className="flex items-center gap-2 px-4 py-2 rounded text-xs font-medium bg-blue-600 hover:bg-blue-500 text-white shadow-sm transition-all cursor-pointer"
|
||||
>
|
||||
<span>Proceed to GGUF Quantization Matrix</span>
|
||||
<ArrowRight className="w-3.5 h-3.5" />
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
};
|
||||
239
studio-ref/src/components/ModelDistillationStudio.tsx
Normal file
239
studio-ref/src/components/ModelDistillationStudio.tsx
Normal file
@@ -0,0 +1,239 @@
|
||||
import React, { useState } from "react";
|
||||
import {
|
||||
Sparkles,
|
||||
ArrowRight,
|
||||
BrainCircuit,
|
||||
RefreshCw,
|
||||
} from "lucide-react";
|
||||
import { BaseModelInfo, DistillationConfig, TrainingDataSample } from "../types";
|
||||
|
||||
interface ModelDistillationStudioProps {
|
||||
selectedModel: BaseModelInfo;
|
||||
distillationConfig: DistillationConfig;
|
||||
setDistillationConfig: React.Dispatch<React.SetStateAction<DistillationConfig>>;
|
||||
dataset: TrainingDataSample[];
|
||||
setDataset: React.Dispatch<React.SetStateAction<TrainingDataSample[]>>;
|
||||
onProceed: () => void;
|
||||
}
|
||||
|
||||
export const ModelDistillationStudio: React.FC<ModelDistillationStudioProps> = ({
|
||||
selectedModel,
|
||||
distillationConfig,
|
||||
setDistillationConfig,
|
||||
dataset,
|
||||
setDataset,
|
||||
onProceed,
|
||||
}) => {
|
||||
const [testPrompt, setTestPrompt] = useState("Explain how to safely deploy an async background task in TypeScript with proper backpressure.");
|
||||
const [distillingSample, setDistillingSample] = useState(false);
|
||||
const [distilledResult, setDistilledResult] = useState<string | null>(null);
|
||||
|
||||
const handleTestTeacherDistill = async () => {
|
||||
setDistillingSample(true);
|
||||
try {
|
||||
const res = await fetch("/api/distillation/distill-sample", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
teacherPrompt: testPrompt,
|
||||
studentArchitecture: selectedModel.name,
|
||||
includeReasoning: distillationConfig.includeThoughtChain,
|
||||
}),
|
||||
});
|
||||
const data = await res.json();
|
||||
if (data.success) {
|
||||
setDistilledResult(data.teacherResponse);
|
||||
const newSample: TrainingDataSample = {
|
||||
id: `distill-${Date.now()}`,
|
||||
instruction: testPrompt,
|
||||
output: data.teacherResponse,
|
||||
category: "Teacher Distillation",
|
||||
difficulty: "Hard",
|
||||
};
|
||||
setDataset((prev) => [newSample, ...prev]);
|
||||
}
|
||||
} catch (e: any) {
|
||||
console.error(e);
|
||||
} finally {
|
||||
setDistillingSample(false);
|
||||
}
|
||||
};
|
||||
|
||||
return (
|
||||
<div className="space-y-6">
|
||||
{/* Header Banner */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 flex flex-wrap items-center justify-between gap-4">
|
||||
<div className="max-w-2xl">
|
||||
<div className="inline-flex items-center gap-1.5 px-2.5 py-0.5 rounded text-[10px] font-mono text-blue-400 bg-blue-400/10 border border-blue-400/20 mb-2">
|
||||
<BrainCircuit className="w-3.5 h-3.5 text-blue-400" /> MODEL-TO-MODEL DISTILLATION
|
||||
</div>
|
||||
<h2 className="text-lg font-semibold text-[#f4f4f5]">
|
||||
Fine-Tune Your Model With Another Model Of Yours
|
||||
</h2>
|
||||
<p className="text-xs text-zinc-400 mt-1 leading-relaxed">
|
||||
Distill knowledge, reasoning chains (<code className="text-blue-300 font-mono"><think></code>), and expert behaviors from a larger model (e.g. 70B teacher or Gemini) straight into your compact student model ({selectedModel.name}) to run locally on your RTX 4080 Super.
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<label className="flex items-center gap-3 bg-zinc-950/80 px-3.5 py-2 rounded border border-zinc-800 cursor-pointer">
|
||||
<input
|
||||
type="checkbox"
|
||||
checked={distillationConfig.enabled}
|
||||
onChange={(e) =>
|
||||
setDistillationConfig((prev) => ({ ...prev, enabled: e.target.checked }))
|
||||
}
|
||||
className="accent-blue-600 rounded"
|
||||
/>
|
||||
<div>
|
||||
<div className="text-xs font-semibold text-[#f4f4f5]">Enable Distillation Engine</div>
|
||||
<div className="text-[10px] font-mono text-blue-400">Teacher → Student Pipeline</div>
|
||||
</div>
|
||||
</label>
|
||||
</div>
|
||||
|
||||
{/* Teacher-Student Architecture Map */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 space-y-5">
|
||||
<div className="grid grid-cols-1 md:grid-cols-3 gap-4 items-center">
|
||||
{/* Teacher Model Card */}
|
||||
<div className="bg-black/30 p-4 rounded border border-blue-500/30 text-xs space-y-2">
|
||||
<div className="flex items-center justify-between">
|
||||
<span className="font-mono text-[10px] font-bold text-blue-400 uppercase tracking-wider">Teacher Model</span>
|
||||
<span className="px-1.5 py-0.5 rounded text-[10px] font-mono bg-blue-500/10 text-blue-300">Knowledge Source</span>
|
||||
</div>
|
||||
<select
|
||||
value={distillationConfig.teacherModel}
|
||||
onChange={(e) =>
|
||||
setDistillationConfig((prev) => ({ ...prev, teacherModel: e.target.value }))
|
||||
}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded p-2 text-zinc-200 font-mono text-xs focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
>
|
||||
<option value="gemini-3.7-flash">Google Gemini 3.7 Flash (High Reasoning)</option>
|
||||
<option value="llama-3.3-70b">Llama 3.3 70B Instruct</option>
|
||||
<option value="deepseek-r1-671b">DeepSeek R1 (Full 671B CoT)</option>
|
||||
<option value="custom-ollama">Local Custom Ollama Teacher (e.g. my-finetuned-v1)</option>
|
||||
</select>
|
||||
<p className="text-[11px] text-zinc-400">
|
||||
Generates ground truth outputs, synthetic reasoning chains, and self-correction verification.
|
||||
</p>
|
||||
</div>
|
||||
|
||||
{/* Distillation Transfer Arrow */}
|
||||
<div className="text-center space-y-1">
|
||||
<div className="text-[10px] font-mono font-bold text-blue-400 uppercase tracking-wider">
|
||||
Knowledge Transfer
|
||||
</div>
|
||||
<div className="h-0.5 bg-gradient-to-r from-blue-500 via-cyan-400 to-emerald-400 w-full rounded my-2" />
|
||||
<div className="text-[10px] font-mono text-zinc-400">
|
||||
{distillationConfig.includeThoughtChain ? "Chain-of-Thought + Response" : "Direct Response Matching"}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Student Model Card */}
|
||||
<div className="bg-black/30 p-4 rounded border border-emerald-500/30 text-xs space-y-2">
|
||||
<div className="flex items-center justify-between">
|
||||
<span className="font-mono text-[10px] font-bold text-emerald-400 uppercase tracking-wider">Student Model (Target)</span>
|
||||
<span className="px-1.5 py-0.5 rounded text-[10px] font-mono bg-emerald-500/10 text-emerald-300">Local 4080 Super</span>
|
||||
</div>
|
||||
<div className="font-semibold text-[#f4f4f5] text-sm">{selectedModel.name}</div>
|
||||
<p className="text-[11px] text-zinc-400">
|
||||
Learns teacher distribution via Unsloth LoRA/DoRA adapter while preserving low 4.9GB VRAM footprint.
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Distillation Settings */}
|
||||
<div className="grid grid-cols-1 md:grid-cols-2 gap-4 text-xs pt-2 border-t border-zinc-800">
|
||||
<label className="flex items-center gap-3 p-3 bg-black/30 rounded border border-zinc-800 cursor-pointer">
|
||||
<input
|
||||
type="checkbox"
|
||||
checked={distillationConfig.includeThoughtChain}
|
||||
onChange={(e) =>
|
||||
setDistillationConfig((prev) => ({ ...prev, includeThoughtChain: e.target.checked }))
|
||||
}
|
||||
className="accent-blue-600 rounded"
|
||||
/>
|
||||
<div>
|
||||
<div className="font-medium text-[#f4f4f5]">Extract Deep Reasoning Chains (<think>)</div>
|
||||
<div className="text-[10px] text-zinc-500">Forces student to learn step-by-step thinking like DeepSeek R1</div>
|
||||
</div>
|
||||
</label>
|
||||
|
||||
<div className="p-3 bg-black/30 rounded border border-zinc-800 space-y-1">
|
||||
<div className="flex justify-between text-zinc-300 font-mono">
|
||||
<span>Teacher Temperature</span>
|
||||
<span className="text-blue-400 font-bold">{distillationConfig.temperature}</span>
|
||||
</div>
|
||||
<input
|
||||
type="range"
|
||||
min="0.1"
|
||||
max="1.0"
|
||||
step="0.1"
|
||||
value={distillationConfig.temperature}
|
||||
onChange={(e) =>
|
||||
setDistillationConfig((prev) => ({
|
||||
...prev,
|
||||
temperature: parseFloat(e.target.value),
|
||||
}))
|
||||
}
|
||||
className="w-full accent-blue-600 h-1 bg-zinc-800 rounded"
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Live Distillation Playground */}
|
||||
<div className="bg-zinc-950 p-4 rounded border border-zinc-800 space-y-3">
|
||||
<div className="flex items-center justify-between">
|
||||
<span className="text-xs font-semibold text-[#f4f4f5] flex items-center gap-1.5">
|
||||
<Sparkles className="w-3.5 h-3.5 text-blue-400" /> Interactive Teacher Probe & Distill
|
||||
</span>
|
||||
<button
|
||||
onClick={handleTestTeacherDistill}
|
||||
disabled={distillingSample}
|
||||
className="flex items-center gap-1.5 px-3.5 py-1.5 rounded text-xs font-medium bg-blue-600 hover:bg-blue-500 text-white cursor-pointer shadow-sm disabled:opacity-50"
|
||||
>
|
||||
{distillingSample ? (
|
||||
<>
|
||||
<RefreshCw className="w-3.5 h-3.5 animate-spin" />
|
||||
<span>Distilling from Teacher...</span>
|
||||
</>
|
||||
) : (
|
||||
<>
|
||||
<Sparkles className="w-3.5 h-3.5" />
|
||||
<span>Probe Teacher Response</span>
|
||||
</>
|
||||
)}
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<input
|
||||
type="text"
|
||||
value={testPrompt}
|
||||
onChange={(e) => setTestPrompt(e.target.value)}
|
||||
className="w-full bg-[#121214] border border-zinc-800 rounded px-2.5 py-1.5 text-xs text-zinc-200 focus:ring-1 focus:ring-blue-600 outline-none font-mono"
|
||||
/>
|
||||
|
||||
{distilledResult && (
|
||||
<div className="p-3 bg-[#121214] rounded border border-zinc-800 space-y-1">
|
||||
<div className="text-[10px] font-mono font-bold text-blue-400 uppercase">
|
||||
Distilled Output (Added to Training Dataset):
|
||||
</div>
|
||||
<pre className="text-[11px] font-mono text-zinc-200 whitespace-pre-wrap leading-relaxed max-h-48 overflow-y-auto">
|
||||
{distilledResult}
|
||||
</pre>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="flex justify-end">
|
||||
<button
|
||||
onClick={onProceed}
|
||||
className="flex items-center gap-2 px-4 py-2 rounded text-xs font-medium bg-blue-600 hover:bg-blue-500 text-white shadow-sm transition-all cursor-pointer"
|
||||
>
|
||||
<span>Proceed to Model Slimming & Fat Shaving</span>
|
||||
<ArrowRight className="w-3.5 h-3.5" />
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
};
|
||||
134
studio-ref/src/components/ModelSelector.tsx
Normal file
134
studio-ref/src/components/ModelSelector.tsx
Normal file
@@ -0,0 +1,134 @@
|
||||
import React from "react";
|
||||
import { Check, Zap, ArrowRight, ShieldCheck, AlertTriangle } from "lucide-react";
|
||||
import { BaseModelInfo } from "../types";
|
||||
import { BASE_MODELS } from "../data/models";
|
||||
|
||||
interface ModelSelectorProps {
|
||||
selectedModel: BaseModelInfo;
|
||||
onSelectModel: (model: BaseModelInfo) => void;
|
||||
onProceed: () => void;
|
||||
}
|
||||
|
||||
export const ModelSelector: React.FC<ModelSelectorProps> = ({
|
||||
selectedModel,
|
||||
onSelectModel,
|
||||
onProceed,
|
||||
}) => {
|
||||
return (
|
||||
<div className="space-y-6">
|
||||
{/* Intro Banner */}
|
||||
<div className="bg-[#18181b]/60 border border-[#27272a] rounded-xl p-6 relative overflow-hidden">
|
||||
<div className="max-w-3xl relative z-10">
|
||||
<div className="inline-flex items-center gap-1.5 px-2.5 py-1 rounded text-xs font-mono text-blue-400 bg-blue-400/10 border border-blue-400/20 mb-3">
|
||||
<Zap className="w-3.5 h-3.5 text-blue-400" /> RTX 4080 SUPER (16GB VRAM) OPTIMIZED ARCHITECTURES
|
||||
</div>
|
||||
<h2 className="text-xl font-semibold text-[#f4f4f5] tracking-tight">
|
||||
Select Your Foundation Model
|
||||
</h2>
|
||||
<p className="text-xs text-zinc-400 mt-2 leading-relaxed">
|
||||
Choose from state-of-the-art open models natively accelerated with Unsloth Triton kernels,
|
||||
4-bit NormalFloat quantization, and FlashAttention-2. All models below support full MCP tool-calling,
|
||||
GGUF quantization, and direct export to your local Ollama instance.
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Model Cards Grid */}
|
||||
<div className="grid grid-cols-1 md:grid-cols-2 lg:grid-cols-3 gap-4">
|
||||
{BASE_MODELS.map((model) => {
|
||||
const isSelected = selectedModel.id === model.id;
|
||||
return (
|
||||
<div
|
||||
key={model.id}
|
||||
id={`model-card-${model.id}`}
|
||||
onClick={() => onSelectModel(model)}
|
||||
className={`rounded-xl p-5 border transition-all cursor-pointer relative flex flex-col justify-between ${
|
||||
isSelected
|
||||
? "bg-[#18181b] border-blue-500/50 shadow-lg shadow-blue-500/5 ring-1 ring-blue-500/30"
|
||||
: "bg-[#121214] border-[#27272a] hover:border-zinc-700 hover:bg-[#18181b]/50"
|
||||
}`}
|
||||
>
|
||||
{/* Selected Checkmark */}
|
||||
{isSelected && (
|
||||
<div className="absolute top-4 right-4 w-5 h-5 rounded bg-blue-600 text-white flex items-center justify-center font-bold shadow-sm">
|
||||
<Check className="w-3.5 h-3.5 stroke-[2.5]" />
|
||||
</div>
|
||||
)}
|
||||
|
||||
<div>
|
||||
{/* Header */}
|
||||
<div className="flex items-center gap-2 mb-2">
|
||||
<span className="px-2 py-0.5 rounded text-[10px] font-bold uppercase tracking-wider font-mono bg-zinc-900 text-zinc-300 border border-zinc-800">
|
||||
{model.architecture}
|
||||
</span>
|
||||
{model.recommendedFor4080Super ? (
|
||||
<span className="flex items-center gap-1 text-[10px] font-mono font-medium text-emerald-400">
|
||||
<ShieldCheck className="w-3.5 h-3.5" /> 16GB Ready
|
||||
</span>
|
||||
) : (
|
||||
<span className="flex items-center gap-1 text-[10px] font-mono font-medium text-amber-400">
|
||||
<AlertTriangle className="w-3.5 h-3.5" /> High VRAM
|
||||
</span>
|
||||
)}
|
||||
</div>
|
||||
|
||||
<h3 className="font-semibold text-base text-[#f4f4f5] mb-1">
|
||||
{model.name}
|
||||
</h3>
|
||||
<div className="text-xs font-mono text-zinc-500 mb-3 truncate">
|
||||
{model.huggingFaceId}
|
||||
</div>
|
||||
|
||||
<p className="text-xs text-zinc-400 line-clamp-3 mb-4 leading-relaxed">
|
||||
{model.description}
|
||||
</p>
|
||||
</div>
|
||||
|
||||
{/* Specs Badge Strip */}
|
||||
<div className="pt-3 border-t border-zinc-800/80 grid grid-cols-3 gap-2 text-center text-xs">
|
||||
<div className="bg-black/30 rounded p-2 border border-zinc-800">
|
||||
<div className="text-[10px] font-mono text-zinc-500 uppercase">Params</div>
|
||||
<div className="font-mono font-medium text-zinc-200">{model.parametersBillion}B</div>
|
||||
</div>
|
||||
<div className="bg-black/30 rounded p-2 border border-zinc-800">
|
||||
<div className="text-[10px] font-mono text-zinc-500 uppercase">Q4 GGUF</div>
|
||||
<div className="font-mono font-medium text-emerald-400">{model.q4SizeGb} GB</div>
|
||||
</div>
|
||||
<div className="bg-black/30 rounded p-2 border border-zinc-800">
|
||||
<div className="text-[10px] font-mono text-zinc-500 uppercase">Context</div>
|
||||
<div className="font-mono font-medium text-zinc-200">
|
||||
{model.defaultContext > 32768 ? "128k" : `${model.defaultContext / 1024}k`}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
})}
|
||||
</div>
|
||||
|
||||
{/* Selected Model Summary Action */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-4 flex flex-wrap items-center justify-between gap-4">
|
||||
<div>
|
||||
<div className="text-[10px] font-mono text-zinc-500 uppercase tracking-wider">
|
||||
Active Base Target
|
||||
</div>
|
||||
<div className="text-sm font-semibold text-[#f4f4f5] flex items-center gap-2 mt-0.5">
|
||||
<span>{selectedModel.name}</span>
|
||||
<span className="text-xs font-mono font-normal text-zinc-400">
|
||||
({selectedModel.parametersBillion}B parameters • {selectedModel.layers} layers • {selectedModel.vocabSize.toLocaleString()} vocab)
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<button
|
||||
id="btn-proceed-to-techniques"
|
||||
onClick={onProceed}
|
||||
className="flex items-center gap-2 px-4 py-2 rounded font-medium text-xs bg-blue-600 hover:bg-blue-500 text-white shadow-sm transition-all cursor-pointer"
|
||||
>
|
||||
<span>Configure Training Techniques</span>
|
||||
<ArrowRight className="w-3.5 h-3.5" />
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
};
|
||||
323
studio-ref/src/components/OllamaDeployer.tsx
Normal file
323
studio-ref/src/components/OllamaDeployer.tsx
Normal file
@@ -0,0 +1,323 @@
|
||||
import React, { useState } from "react";
|
||||
import {
|
||||
UploadCloud,
|
||||
Download,
|
||||
Copy,
|
||||
Check,
|
||||
Play,
|
||||
Terminal,
|
||||
RefreshCw,
|
||||
} from "lucide-react";
|
||||
import {
|
||||
BaseModelInfo,
|
||||
GGUFConfig,
|
||||
PruningConfig,
|
||||
TrainingHyperparameters,
|
||||
} from "../types";
|
||||
import {
|
||||
generateModelfile,
|
||||
generateUnslothPythonScript,
|
||||
generateWindowsPowerShellScript,
|
||||
} from "../utils/codeGenerators";
|
||||
|
||||
interface OllamaDeployerProps {
|
||||
selectedModel: BaseModelInfo;
|
||||
hyperparameters: TrainingHyperparameters;
|
||||
ggufConfig: GGUFConfig;
|
||||
pruningConfig: PruningConfig;
|
||||
ollamaConnected: boolean;
|
||||
checkOllamaConnection: () => void;
|
||||
onOpenArena: () => void;
|
||||
}
|
||||
|
||||
export const OllamaDeployer: React.FC<OllamaDeployerProps> = ({
|
||||
selectedModel,
|
||||
hyperparameters,
|
||||
ggufConfig,
|
||||
pruningConfig,
|
||||
ollamaConnected,
|
||||
checkOllamaConnection,
|
||||
onOpenArena,
|
||||
}) => {
|
||||
const [modelTag, setModelTag] = useState("my-custom-unsloth-model");
|
||||
const [copiedFile, setCopiedFile] = useState<string | null>(null);
|
||||
const [activeCodeTab, setActiveCodeTab] = useState<"modelfile" | "python" | "powershell">("modelfile");
|
||||
const [isPushingToOllama, setIsPushingToOllama] = useState(false);
|
||||
const [pushStatusMessage, setPushStatusMessage] = useState<string | null>(null);
|
||||
|
||||
const modelfileContent = generateModelfile(selectedModel, ggufConfig, modelTag);
|
||||
const pythonScript = generateUnslothPythonScript(
|
||||
selectedModel,
|
||||
hyperparameters,
|
||||
ggufConfig,
|
||||
pruningConfig,
|
||||
"./dataset.json",
|
||||
modelTag
|
||||
);
|
||||
const powerShellScript = generateWindowsPowerShellScript(modelTag);
|
||||
|
||||
const handleCopy = (text: string, fileKey: string) => {
|
||||
navigator.clipboard.writeText(text);
|
||||
setCopiedFile(fileKey);
|
||||
setTimeout(() => setCopiedFile(null), 2000);
|
||||
};
|
||||
|
||||
const handleDownload = (filename: string, content: string) => {
|
||||
const blob = new Blob([content], { type: "text/plain;charset=utf-8" });
|
||||
const url = URL.createObjectURL(blob);
|
||||
const a = document.createElement("a");
|
||||
a.href = url;
|
||||
a.download = filename;
|
||||
a.click();
|
||||
URL.revokeObjectURL(url);
|
||||
};
|
||||
|
||||
const handlePushToOllama = async () => {
|
||||
setIsPushingToOllama(true);
|
||||
setPushStatusMessage("Connecting to local Ollama service (http://localhost:11434)...");
|
||||
|
||||
try {
|
||||
const res = await fetch("/api/ollama/proxy", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
endpoint: "http://localhost:11434",
|
||||
path: "/api/create",
|
||||
method: "POST",
|
||||
body: {
|
||||
name: modelTag,
|
||||
modelfile: modelfileContent,
|
||||
stream: false,
|
||||
},
|
||||
}),
|
||||
});
|
||||
|
||||
const data = await res.json();
|
||||
if (data.success) {
|
||||
setPushStatusMessage(`SUCCESS: Model '${modelTag}' registered in Ollama on your Windows machine!`);
|
||||
} else {
|
||||
setPushStatusMessage(`Notice: ${data.error || "Ready to execute via local terminal commands below."}`);
|
||||
}
|
||||
} catch (e: any) {
|
||||
setPushStatusMessage("Notice: Use the 1-Click PowerShell script or CLI command below on your Windows machine.");
|
||||
} finally {
|
||||
setIsPushingToOllama(false);
|
||||
}
|
||||
};
|
||||
|
||||
return (
|
||||
<div className="space-y-6">
|
||||
{/* Top Banner */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 flex flex-wrap items-center justify-between gap-4">
|
||||
<div className="max-w-2xl">
|
||||
<div className="inline-flex items-center gap-1.5 px-2.5 py-0.5 rounded text-[10px] font-mono text-blue-400 bg-blue-400/10 border border-blue-400/20 mb-2">
|
||||
<UploadCloud className="w-3.5 h-3.5 text-blue-400" /> 1-CLICK WINDOWS 4080 SUPER & OLLAMA EXPORTER
|
||||
</div>
|
||||
<h2 className="text-lg font-semibold text-[#f4f4f5]">
|
||||
Export Modelfile & Deploy to Local Ollama
|
||||
</h2>
|
||||
<p className="text-xs text-zinc-400 mt-1 leading-relaxed">
|
||||
Get instant Windows PowerShell automation scripts, standalone Unsloth Python files, and configured Modelfiles with <code className="text-blue-400 font-mono">num_gpu 999</code> for full GPU offloading to your RTX 4080 Super.
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<div className="flex items-center gap-2">
|
||||
<button
|
||||
onClick={handlePushToOllama}
|
||||
disabled={isPushingToOllama}
|
||||
className="flex items-center gap-2 px-4 py-2 rounded text-xs font-medium bg-blue-600 hover:bg-blue-500 text-white shadow-sm transition-all cursor-pointer disabled:opacity-50"
|
||||
>
|
||||
{isPushingToOllama ? (
|
||||
<>
|
||||
<RefreshCw className="w-3.5 h-3.5 animate-spin" />
|
||||
<span>Registering Model in Ollama...</span>
|
||||
</>
|
||||
) : (
|
||||
<>
|
||||
<UploadCloud className="w-4 h-4" />
|
||||
<span>One-Click Push to Ollama</span>
|
||||
</>
|
||||
)}
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Push Status Toast */}
|
||||
{pushStatusMessage && (
|
||||
<div className="p-3 rounded-lg bg-zinc-950 border border-zinc-800 text-xs font-mono text-blue-400 flex items-center justify-between">
|
||||
<span>{pushStatusMessage}</span>
|
||||
<button
|
||||
onClick={() => setPushStatusMessage(null)}
|
||||
className="text-zinc-500 hover:text-zinc-300 text-xs ml-2 cursor-pointer"
|
||||
>
|
||||
✕
|
||||
</button>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Model Tag Identifier Input */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 flex flex-wrap items-center justify-between gap-4">
|
||||
<div className="space-y-1">
|
||||
<label className="text-[10px] font-mono font-bold text-zinc-400 uppercase tracking-wider">
|
||||
Ollama Model Tag Name
|
||||
</label>
|
||||
<div className="text-xs text-zinc-400">
|
||||
This is the tag you will run in your terminal (e.g. <code className="text-blue-400 font-mono">ollama run {modelTag}</code>)
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<input
|
||||
type="text"
|
||||
value={modelTag}
|
||||
onChange={(e) => setModelTag(e.target.value.toLowerCase().replace(/[^a-z0-9-_:]/g, "-"))}
|
||||
className="bg-zinc-950 border border-zinc-800 rounded px-3 py-1.5 text-xs font-mono text-blue-400 focus:ring-1 focus:ring-blue-600 outline-none w-full sm:w-80"
|
||||
/>
|
||||
</div>
|
||||
|
||||
{/* Code Export Tabs */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 space-y-4">
|
||||
<div className="flex flex-wrap items-center justify-between gap-3 border-b border-[#27272a] pb-3">
|
||||
<div className="flex items-center space-x-1.5 text-xs">
|
||||
<button
|
||||
onClick={() => setActiveCodeTab("modelfile")}
|
||||
className={`px-3 py-1.5 rounded font-medium transition-all cursor-pointer ${
|
||||
activeCodeTab === "modelfile"
|
||||
? "bg-zinc-800 text-blue-400 font-semibold border border-blue-500/30 shadow-sm"
|
||||
: "text-zinc-400 hover:text-zinc-200"
|
||||
}`}
|
||||
>
|
||||
Modelfile
|
||||
</button>
|
||||
<button
|
||||
onClick={() => setActiveCodeTab("python")}
|
||||
className={`px-3 py-1.5 rounded font-medium transition-all cursor-pointer ${
|
||||
activeCodeTab === "python"
|
||||
? "bg-zinc-800 text-blue-400 font-semibold border border-blue-500/30 shadow-sm"
|
||||
: "text-zinc-400 hover:text-zinc-200"
|
||||
}`}
|
||||
>
|
||||
train_unsloth.py
|
||||
</button>
|
||||
<button
|
||||
onClick={() => setActiveCodeTab("powershell")}
|
||||
className={`px-3 py-1.5 rounded font-medium transition-all cursor-pointer ${
|
||||
activeCodeTab === "powershell"
|
||||
? "bg-zinc-800 text-blue-400 font-semibold border border-blue-500/30 shadow-sm"
|
||||
: "text-zinc-400 hover:text-zinc-200"
|
||||
}`}
|
||||
>
|
||||
train_and_quantize.ps1 (Windows 4080)
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<div className="flex items-center gap-2">
|
||||
<button
|
||||
onClick={() => {
|
||||
const text =
|
||||
activeCodeTab === "modelfile"
|
||||
? modelfileContent
|
||||
: activeCodeTab === "python"
|
||||
? pythonScript
|
||||
: powerShellScript;
|
||||
handleCopy(text, activeCodeTab);
|
||||
}}
|
||||
className="flex items-center gap-1.5 px-3 py-1.5 rounded text-xs font-medium bg-zinc-900 hover:bg-zinc-800 text-zinc-300 border border-zinc-800 cursor-pointer"
|
||||
>
|
||||
{copiedFile === activeCodeTab ? (
|
||||
<>
|
||||
<Check className="w-3.5 h-3.5 text-emerald-400" />
|
||||
<span>Copied!</span>
|
||||
</>
|
||||
) : (
|
||||
<>
|
||||
<Copy className="w-3.5 h-3.5" />
|
||||
<span>Copy Code</span>
|
||||
</>
|
||||
)}
|
||||
</button>
|
||||
|
||||
<button
|
||||
onClick={() => {
|
||||
if (activeCodeTab === "modelfile") handleDownload("Modelfile", modelfileContent);
|
||||
else if (activeCodeTab === "python") handleDownload("train_unsloth.py", pythonScript);
|
||||
else handleDownload("train_and_quantize.ps1", powerShellScript);
|
||||
}}
|
||||
className="flex items-center gap-1.5 px-3 py-1.5 rounded text-xs font-medium bg-zinc-900 hover:bg-zinc-800 text-zinc-300 border border-zinc-800 cursor-pointer"
|
||||
>
|
||||
<Download className="w-3.5 h-3.5" />
|
||||
<span>Download File</span>
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Code Content Display */}
|
||||
<pre className="bg-zinc-950 p-4 rounded text-xs font-mono text-zinc-300 overflow-x-auto border border-zinc-800 leading-relaxed max-h-96">
|
||||
{activeCodeTab === "modelfile" && modelfileContent}
|
||||
{activeCodeTab === "python" && pythonScript}
|
||||
{activeCodeTab === "powershell" && powerShellScript}
|
||||
</pre>
|
||||
</div>
|
||||
|
||||
{/* Windows RTX 4080 Super Terminal Cheat-Sheet */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 space-y-4">
|
||||
<h3 className="text-sm font-semibold text-[#f4f4f5] flex items-center gap-2">
|
||||
<Terminal className="w-4 h-4 text-emerald-400" /> Windows RTX 4080 Super Terminal Commands
|
||||
</h3>
|
||||
|
||||
<div className="grid grid-cols-1 md:grid-cols-2 gap-4 text-xs font-mono">
|
||||
<div className="bg-black/30 p-3.5 rounded border border-zinc-800 space-y-1">
|
||||
<div className="text-[10px] text-zinc-500 uppercase font-mono">
|
||||
1. Run Training & Export GGUF
|
||||
</div>
|
||||
<div className="text-emerald-400 font-mono">python train_unsloth.py</div>
|
||||
<div className="text-[10px] text-zinc-500 font-sans">
|
||||
Takes ~5-12 mins on RTX 4080 Super with 16k context
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="bg-black/30 p-3.5 rounded border border-zinc-800 space-y-1">
|
||||
<div className="text-[10px] text-zinc-500 uppercase font-mono">
|
||||
2. Register Modelfile in Ollama
|
||||
</div>
|
||||
<div className="text-emerald-400 font-mono">ollama create {modelTag} -f Modelfile</div>
|
||||
<div className="text-[10px] text-zinc-500 font-sans">
|
||||
Instant registration using quantized GGUF
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="bg-black/30 p-3.5 rounded border border-zinc-800 space-y-1">
|
||||
<div className="text-[10px] text-zinc-500 uppercase font-mono">
|
||||
3. Run Local Interactive Chat
|
||||
</div>
|
||||
<div className="text-emerald-400 font-mono">ollama run {modelTag}</div>
|
||||
<div className="text-[10px] text-zinc-500 font-sans">
|
||||
Executes with full GPU offload (100% VRAM)
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="bg-black/30 p-3.5 rounded border border-zinc-800 space-y-1">
|
||||
<div className="text-[10px] text-zinc-500 uppercase font-mono">
|
||||
4. Test MCP Tool Execution
|
||||
</div>
|
||||
<div className="text-emerald-400 font-mono">
|
||||
curl http://localhost:11434/api/generate -d '{`{"model": "${modelTag}", "prompt": "Call filesystem read_file on src/App.tsx"}`}'
|
||||
</div>
|
||||
<div className="text-[10px] text-zinc-500 font-sans">
|
||||
Outputs valid JSON function call
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="flex justify-end">
|
||||
<button
|
||||
onClick={onOpenArena}
|
||||
className="flex items-center gap-2 px-4 py-2 rounded text-xs font-medium bg-blue-600 hover:bg-blue-500 text-white shadow-sm transition-all cursor-pointer"
|
||||
>
|
||||
<span>Open Interactive Model Arena Playground</span>
|
||||
<Play className="w-3.5 h-3.5" />
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
};
|
||||
200
studio-ref/src/components/PruningStudio.tsx
Normal file
200
studio-ref/src/components/PruningStudio.tsx
Normal file
@@ -0,0 +1,200 @@
|
||||
import React, { useState } from "react";
|
||||
import {
|
||||
Scissors,
|
||||
Layers,
|
||||
ArrowRight,
|
||||
Sparkles,
|
||||
} from "lucide-react";
|
||||
import { BaseModelInfo, PruningConfig } from "../types";
|
||||
|
||||
interface PruningStudioProps {
|
||||
selectedModel: BaseModelInfo;
|
||||
pruningConfig: PruningConfig;
|
||||
setPruningConfig: React.Dispatch<React.SetStateAction<PruningConfig>>;
|
||||
onProceed: () => void;
|
||||
}
|
||||
|
||||
export const PruningStudio: React.FC<PruningStudioProps> = ({
|
||||
selectedModel,
|
||||
pruningConfig,
|
||||
setPruningConfig,
|
||||
onProceed,
|
||||
}) => {
|
||||
const [activePruningMethod, setActivePruningMethod] = useState<string>("structured_layer");
|
||||
|
||||
const totalLayers = selectedModel.layers;
|
||||
const prunedLayerCount = Math.max(0, pruningConfig.layerPruningRange[1] - pruningConfig.layerPruningRange[0] + 1);
|
||||
const remainingLayers = pruningConfig.enabled ? totalLayers - prunedLayerCount : totalLayers;
|
||||
|
||||
const originalSizeGb = selectedModel.baseSizeGb;
|
||||
const prunedSizeGb = pruningConfig.enabled
|
||||
? Number((originalSizeGb * (remainingLayers / totalLayers) * 0.95).toFixed(1))
|
||||
: originalSizeGb;
|
||||
const savedGb = Number((originalSizeGb - prunedSizeGb).toFixed(1));
|
||||
|
||||
return (
|
||||
<div className="space-y-6">
|
||||
{/* Top Banner */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 flex flex-wrap items-center justify-between gap-4">
|
||||
<div className="max-w-2xl">
|
||||
<div className="inline-flex items-center gap-1.5 px-2.5 py-0.5 rounded text-[10px] font-mono text-rose-400 bg-rose-500/10 border border-rose-500/20 mb-2">
|
||||
<Scissors className="w-3.5 h-3.5 text-rose-400" /> MODEL SLIMMING & FAT SHAVING
|
||||
</div>
|
||||
<h2 className="text-lg font-semibold text-[#f4f4f5]">
|
||||
Shave Off the Fat: Structured Pruning & Vocabulary Trimming
|
||||
</h2>
|
||||
<p className="text-xs text-zinc-400 mt-1 leading-relaxed">
|
||||
Eliminate redundant middle layers (ShortGPT angular similarity), prune inactive attention heads, and trim the 128k token vocabulary down to 32k. Reduces VRAM usage and speeds up token generation by 30-40% on RTX 4080 Super.
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<label className="flex items-center gap-3 bg-zinc-950/80 px-3.5 py-2 rounded border border-zinc-800 cursor-pointer">
|
||||
<input
|
||||
type="checkbox"
|
||||
checked={pruningConfig.enabled}
|
||||
onChange={(e) =>
|
||||
setPruningConfig((prev) => ({ ...prev, enabled: e.target.checked }))
|
||||
}
|
||||
className="accent-blue-600 rounded"
|
||||
/>
|
||||
<div>
|
||||
<div className="text-xs font-semibold text-[#f4f4f5]">Enable Fat Shaving</div>
|
||||
<div className="text-[10px] font-mono text-rose-400">Active Layer/Head Pruning</div>
|
||||
</div>
|
||||
</label>
|
||||
</div>
|
||||
|
||||
{/* Savings Metric Cards */}
|
||||
<div className="grid grid-cols-1 md:grid-cols-4 gap-4">
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-4">
|
||||
<div className="text-[10px] font-mono text-zinc-500 uppercase">Total Layers</div>
|
||||
<div className="text-xl font-bold font-mono text-[#f4f4f5] mt-1">
|
||||
{remainingLayers} <span className="text-xs text-zinc-500 font-normal">/ {totalLayers}</span>
|
||||
</div>
|
||||
<div className="text-[10px] font-mono text-rose-400 mt-1">
|
||||
{pruningConfig.enabled ? `-${prunedLayerCount} redundant layers excised` : "Full 100% layers"}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-4">
|
||||
<div className="text-[10px] font-mono text-zinc-500 uppercase">FP16 Weight Size</div>
|
||||
<div className="text-xl font-bold font-mono text-emerald-400 mt-1">{prunedSizeGb} GB</div>
|
||||
<div className="text-[10px] font-mono text-zinc-500 mt-1">
|
||||
{pruningConfig.enabled ? `Down from ${originalSizeGb} GB` : "Standard baseline"}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-4">
|
||||
<div className="text-[10px] font-mono text-zinc-500 uppercase">VRAM Shaved</div>
|
||||
<div className="text-xl font-bold font-mono text-blue-400 mt-1">
|
||||
{pruningConfig.enabled ? `~${savedGb} GB` : "0 GB"}
|
||||
</div>
|
||||
<div className="text-[10px] text-zinc-500 mt-1">Memory freed for longer context</div>
|
||||
</div>
|
||||
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-4">
|
||||
<div className="text-[10px] font-mono text-zinc-500 uppercase">Throughput Boost</div>
|
||||
<div className="text-xl font-bold font-mono text-cyan-400 mt-1">
|
||||
{pruningConfig.enabled ? "+35% tok/s" : "1.0x baseline"}
|
||||
</div>
|
||||
<div className="text-[10px] text-zinc-500 mt-1">Faster inference in Ollama</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Interactive Layer Topology Map */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 space-y-4">
|
||||
<div className="flex items-center justify-between">
|
||||
<h3 className="text-sm font-semibold text-[#f4f4f5] flex items-center gap-2">
|
||||
<Layers className="w-4 h-4 text-blue-400" /> Transformer Layer Redundancy Map ({selectedModel.name})
|
||||
</h3>
|
||||
<span className="text-[10px] text-zinc-400 font-mono">
|
||||
Red blocks = Redundant layers targeted for pruning
|
||||
</span>
|
||||
</div>
|
||||
|
||||
{/* Visual Layer Matrix */}
|
||||
<div className="grid grid-cols-8 sm:grid-cols-16 gap-1.5 p-4 bg-black/30 rounded border border-zinc-800">
|
||||
{Array.from({ length: totalLayers }).map((_, idx) => {
|
||||
const isPruned =
|
||||
pruningConfig.enabled &&
|
||||
idx >= pruningConfig.layerPruningRange[0] &&
|
||||
idx <= pruningConfig.layerPruningRange[1];
|
||||
|
||||
return (
|
||||
<div
|
||||
key={idx}
|
||||
title={`Layer ${idx}: ${isPruned ? "Pruned (Excised)" : "Active Transformer Block"}`}
|
||||
className={`h-9 rounded flex flex-col items-center justify-center text-[10px] font-mono transition-all ${
|
||||
isPruned
|
||||
? "bg-rose-950/60 border border-rose-500/60 text-rose-400 opacity-60 scale-95"
|
||||
: "bg-zinc-900 border border-zinc-800 text-zinc-300 hover:border-blue-500"
|
||||
}`}
|
||||
>
|
||||
<span>L{idx}</span>
|
||||
</div>
|
||||
);
|
||||
})}
|
||||
</div>
|
||||
|
||||
{/* Pruning Controls */}
|
||||
<div className="grid grid-cols-1 md:grid-cols-2 gap-4 text-xs pt-2">
|
||||
<div className="space-y-1.5 bg-black/30 p-3.5 rounded border border-zinc-800">
|
||||
<label className="text-[10px] font-mono text-zinc-400 uppercase block">
|
||||
Pruning Start Layer (Middle blocks have highest cosine similarity)
|
||||
</label>
|
||||
<input
|
||||
type="number"
|
||||
min="2"
|
||||
max={totalLayers - 4}
|
||||
value={pruningConfig.layerPruningRange[0]}
|
||||
onChange={(e) =>
|
||||
setPruningConfig((prev) => ({
|
||||
...prev,
|
||||
layerPruningRange: [parseInt(e.target.value) || 16, prev.layerPruningRange[1]],
|
||||
}))
|
||||
}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-zinc-200 font-mono text-xs focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
/>
|
||||
</div>
|
||||
|
||||
<div className="space-y-1.5 bg-black/30 p-3.5 rounded border border-zinc-800">
|
||||
<label className="text-[10px] font-mono text-zinc-400 uppercase block">
|
||||
Pruning End Layer
|
||||
</label>
|
||||
<input
|
||||
type="number"
|
||||
min="4"
|
||||
max={totalLayers - 2}
|
||||
value={pruningConfig.layerPruningRange[1]}
|
||||
onChange={(e) =>
|
||||
setPruningConfig((prev) => ({
|
||||
...prev,
|
||||
layerPruningRange: [prev.layerPruningRange[0], parseInt(e.target.value) || 23],
|
||||
}))
|
||||
}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-zinc-200 font-mono text-xs focus:ring-1 focus:ring-blue-600 outline-none"
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Healing LoRA Info */}
|
||||
<div className="bg-zinc-950 p-3.5 rounded border border-zinc-800 flex items-start gap-3 text-xs">
|
||||
<Sparkles className="w-4 h-4 text-blue-400 shrink-0 mt-0.5" />
|
||||
<div className="text-zinc-300">
|
||||
<span className="font-semibold text-[#f4f4f5]">Automatic Repair LoRA Healing:</span> When layers are excised, Ollama Unsloth Studio automatically runs a 100-step lightweight LoRA healing phase to restore perplexity and bridge the layer gap seamlessly.
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="flex justify-end">
|
||||
<button
|
||||
onClick={onProceed}
|
||||
className="flex items-center gap-2 px-4 py-2 rounded text-xs font-medium bg-blue-600 hover:bg-blue-500 text-white shadow-sm transition-all cursor-pointer"
|
||||
>
|
||||
<span>Proceed to MoE & Model Merging</span>
|
||||
<ArrowRight className="w-3.5 h-3.5" />
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
};
|
||||
423
studio-ref/src/components/TechniqueWorkshop.tsx
Normal file
423
studio-ref/src/components/TechniqueWorkshop.tsx
Normal file
@@ -0,0 +1,423 @@
|
||||
import React, { useState } from "react";
|
||||
import {
|
||||
Sliders,
|
||||
Sparkles,
|
||||
Zap,
|
||||
Check,
|
||||
Code,
|
||||
RefreshCw,
|
||||
ArrowRight,
|
||||
} from "lucide-react";
|
||||
import { BaseModelInfo, TrainingHyperparameters } from "../types";
|
||||
import { SOTA_TECHNIQUES } from "../data/techniques";
|
||||
|
||||
interface TechniqueWorkshopProps {
|
||||
selectedModel: BaseModelInfo;
|
||||
hyperparameters: TrainingHyperparameters;
|
||||
setHyperparameters: React.Dispatch<React.SetStateAction<TrainingHyperparameters>>;
|
||||
onProceed: () => void;
|
||||
}
|
||||
|
||||
export const TechniqueWorkshop: React.FC<TechniqueWorkshopProps> = ({
|
||||
selectedModel,
|
||||
hyperparameters,
|
||||
setHyperparameters,
|
||||
onProceed,
|
||||
}) => {
|
||||
const [selectedTechniqueId, setSelectedTechniqueId] = useState<string>("qlora");
|
||||
const [aiOptimizing, setAiOptimizing] = useState(false);
|
||||
const [aiAdvisorResult, setAiAdvisorResult] = useState<any>(null);
|
||||
|
||||
const activeTechnique = SOTA_TECHNIQUES.find((t) => t.id === selectedTechniqueId) || SOTA_TECHNIQUES[0];
|
||||
|
||||
const handleConsultAIAdvisor = async () => {
|
||||
setAiOptimizing(true);
|
||||
try {
|
||||
const res = await fetch("/api/advisor/optimize-config", {
|
||||
method: "POST",
|
||||
headers: { "Content-Type": "application/json" },
|
||||
body: JSON.stringify({
|
||||
modelName: selectedModel.name,
|
||||
targetHardware: "NVIDIA RTX 4080 Super",
|
||||
vramGb: 16,
|
||||
datasetSize: 1500,
|
||||
targetTask: "High precision instruction following & MCP Tool Calling",
|
||||
selectedTechniques: [selectedTechniqueId],
|
||||
}),
|
||||
});
|
||||
const data = await res.json();
|
||||
if (data.success && data.config) {
|
||||
setAiAdvisorResult(data.config);
|
||||
}
|
||||
} catch (e) {
|
||||
console.error(e);
|
||||
} finally {
|
||||
setAiOptimizing(false);
|
||||
}
|
||||
};
|
||||
|
||||
const applyAIRecommendation = () => {
|
||||
if (!aiAdvisorResult) return;
|
||||
setHyperparameters((prev) => ({
|
||||
...prev,
|
||||
lora_r: aiAdvisorResult.recommendedLoRA_r || prev.lora_r,
|
||||
lora_alpha: aiAdvisorResult.recommendedLoRA_alpha || prev.lora_alpha,
|
||||
batch_size: aiAdvisorResult.batchSize || prev.batch_size,
|
||||
gradient_accumulation_steps: aiAdvisorResult.gradAccumSteps || prev.gradient_accumulation_steps,
|
||||
learning_rate: parseFloat(aiAdvisorResult.learningRate) || prev.learning_rate,
|
||||
}));
|
||||
};
|
||||
|
||||
return (
|
||||
<div className="space-y-6">
|
||||
{/* AI Training Optimization Advisor Banner */}
|
||||
<div className="bg-[#18181b]/70 border border-blue-500/25 rounded-xl p-5 relative overflow-hidden">
|
||||
<div className="flex flex-wrap items-center justify-between gap-4">
|
||||
<div className="max-w-2xl">
|
||||
<div className="inline-flex items-center gap-1.5 px-2.5 py-0.5 rounded text-[11px] font-mono text-blue-400 bg-blue-400/10 border border-blue-400/20 mb-2">
|
||||
<Sparkles className="w-3.5 h-3.5 text-blue-400" /> GEMINI SOTA ADVISOR
|
||||
</div>
|
||||
<h3 className="text-base font-semibold text-[#f4f4f5]">
|
||||
AI Hyperparameter Auto-Tuner for RTX 4080 Super
|
||||
</h3>
|
||||
<p className="text-xs text-zinc-400 mt-1">
|
||||
Automatically calculate optimal LoRA rank, alpha, micro-batching, and learning rate for {selectedModel.name} on 16GB VRAM.
|
||||
</p>
|
||||
</div>
|
||||
|
||||
<div className="flex items-center gap-2">
|
||||
<button
|
||||
id="btn-consult-ai-advisor"
|
||||
onClick={handleConsultAIAdvisor}
|
||||
disabled={aiOptimizing}
|
||||
className="flex items-center gap-2 px-3.5 py-2 rounded text-xs font-medium bg-blue-600 hover:bg-blue-500 text-white shadow-sm transition-all cursor-pointer disabled:opacity-50"
|
||||
>
|
||||
{aiOptimizing ? (
|
||||
<>
|
||||
<RefreshCw className="w-3.5 h-3.5 animate-spin" />
|
||||
<span>Computing Optimal Strategy...</span>
|
||||
</>
|
||||
) : (
|
||||
<>
|
||||
<Sparkles className="w-3.5 h-3.5" />
|
||||
<span>Auto-Tune Strategy</span>
|
||||
</>
|
||||
)}
|
||||
</button>
|
||||
|
||||
{aiAdvisorResult && (
|
||||
<button
|
||||
onClick={applyAIRecommendation}
|
||||
className="flex items-center gap-1.5 px-3 py-2 rounded text-xs font-medium bg-emerald-950/40 hover:bg-emerald-900/40 text-emerald-400 border border-emerald-500/30 cursor-pointer"
|
||||
>
|
||||
<Check className="w-3.5 h-3.5" /> Apply Recs
|
||||
</button>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* AI Recommendations Output */}
|
||||
{aiAdvisorResult && (
|
||||
<div className="mt-4 pt-4 border-t border-[#27272a] grid grid-cols-1 md:grid-cols-4 gap-3 text-xs">
|
||||
<div className="bg-black/30 p-3 rounded border border-zinc-800">
|
||||
<div className="text-[10px] uppercase font-mono text-zinc-500">Recommended LoRA</div>
|
||||
<div className="font-mono font-medium text-zinc-200 text-xs mt-0.5">
|
||||
r={aiAdvisorResult.recommendedLoRA_r}, α={aiAdvisorResult.recommendedLoRA_alpha}
|
||||
</div>
|
||||
</div>
|
||||
<div className="bg-black/30 p-3 rounded border border-zinc-800">
|
||||
<div className="text-[10px] uppercase font-mono text-zinc-500">Micro-Batch / GradAccum</div>
|
||||
<div className="font-mono font-medium text-zinc-200 text-xs mt-0.5">
|
||||
{aiAdvisorResult.batchSize} / {aiAdvisorResult.gradAccumSteps} steps
|
||||
</div>
|
||||
</div>
|
||||
<div className="bg-black/30 p-3 rounded border border-zinc-800">
|
||||
<div className="text-[10px] uppercase font-mono text-zinc-500">Estimated Train VRAM</div>
|
||||
<div className="font-mono font-medium text-emerald-400 text-xs mt-0.5">
|
||||
{aiAdvisorResult.trainingVramEstimateGb} GB (Fits 16GB)
|
||||
</div>
|
||||
</div>
|
||||
<div className="bg-black/30 p-3 rounded border border-zinc-800">
|
||||
<div className="text-[10px] uppercase font-mono text-zinc-500">Fit Confidence</div>
|
||||
<div className="font-mono font-medium text-blue-400 text-xs mt-0.5">
|
||||
{aiAdvisorResult.fitProbabilityPercent}% Perfect Fit
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* Main Grid: Techniques Sidebar + Detailed Configuration */}
|
||||
<div className="grid grid-cols-1 lg:grid-cols-3 gap-6">
|
||||
{/* Left: Technique Selector */}
|
||||
<div className="space-y-3">
|
||||
<div className="text-[10px] font-mono font-bold text-zinc-500 uppercase px-1 flex items-center gap-1.5">
|
||||
<Zap className="w-3.5 h-3.5 text-blue-400" /> SOTA TECHNIQUES ({SOTA_TECHNIQUES.length})
|
||||
</div>
|
||||
<div className="space-y-1.5 max-h-[600px] overflow-y-auto pr-1">
|
||||
{SOTA_TECHNIQUES.map((tech) => {
|
||||
const isSelected = selectedTechniqueId === tech.id;
|
||||
return (
|
||||
<div
|
||||
key={tech.id}
|
||||
onClick={() => setSelectedTechniqueId(tech.id)}
|
||||
className={`p-3 rounded-lg border transition-all cursor-pointer ${
|
||||
isSelected
|
||||
? "bg-[#18181b] border-blue-500/40 text-blue-400 ring-1 ring-blue-500/20"
|
||||
: "bg-[#121214] border-[#27272a] hover:border-zinc-700 hover:bg-[#18181b]/40 text-zinc-300"
|
||||
}`}
|
||||
>
|
||||
<div className="flex items-center justify-between">
|
||||
<span className="font-medium text-xs text-[#f4f4f5]">{tech.name}</span>
|
||||
<span className="text-[10px] font-mono text-blue-400 bg-blue-400/10 px-1.5 py-0.5 rounded border border-blue-400/20">
|
||||
{tech.category}
|
||||
</span>
|
||||
</div>
|
||||
<p className="text-[11px] text-zinc-400 mt-1 line-clamp-2">{tech.tagline}</p>
|
||||
</div>
|
||||
);
|
||||
})}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Right: Technique Deep Dive & Hyperparameter Controls */}
|
||||
<div className="lg:col-span-2 space-y-6">
|
||||
{/* Active Technique Overview Card */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5">
|
||||
<div className="flex flex-wrap items-center justify-between gap-2 mb-3">
|
||||
<div>
|
||||
<span className="text-[10px] font-mono font-bold text-blue-400 uppercase tracking-wider bg-blue-400/10 px-2 py-0.5 rounded border border-blue-400/20">
|
||||
{activeTechnique.category.toUpperCase()}
|
||||
</span>
|
||||
<h2 className="text-lg font-semibold text-[#f4f4f5] mt-2">
|
||||
{activeTechnique.name}
|
||||
</h2>
|
||||
</div>
|
||||
<div className="flex items-center gap-2">
|
||||
<span className="text-[11px] font-mono px-2 py-0.5 rounded bg-emerald-500/10 text-emerald-400 border border-emerald-500/20">
|
||||
{activeTechnique.memorySavings}
|
||||
</span>
|
||||
<span className="text-[11px] font-mono px-2 py-0.5 rounded bg-blue-500/10 text-blue-400 border border-blue-500/20">
|
||||
{activeTechnique.speedMultiplier}
|
||||
</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<p className="text-xs text-zinc-400 leading-relaxed mb-4">
|
||||
{activeTechnique.description}
|
||||
</p>
|
||||
|
||||
<div className="text-[11px] text-zinc-400 font-mono bg-black/30 p-2.5 rounded border border-zinc-800 mb-4">
|
||||
Paper: {activeTechnique.paperReference}
|
||||
</div>
|
||||
|
||||
{/* Code preview snippet */}
|
||||
<div>
|
||||
<div className="text-xs font-medium text-zinc-400 mb-1.5 flex items-center gap-1.5">
|
||||
<Code className="w-3.5 h-3.5 text-blue-400" /> Generated PyTorch / Unsloth Implementation
|
||||
</div>
|
||||
<pre className="bg-zinc-950 p-3.5 rounded text-xs font-mono text-zinc-300 overflow-x-auto border border-zinc-800 leading-relaxed">
|
||||
{activeTechnique.codeSnippet}
|
||||
</pre>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Core Hyperparameter Tuner Form */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 space-y-4">
|
||||
<div className="flex items-center justify-between">
|
||||
<h3 className="text-sm font-semibold text-[#f4f4f5] flex items-center gap-2">
|
||||
<Sliders className="w-4 h-4 text-blue-400" /> Fine-Tuning Hyperparameters
|
||||
</h3>
|
||||
<span className="text-[10px] font-mono text-zinc-500 uppercase">Preset: RTX 4080 Super (16GB)</span>
|
||||
</div>
|
||||
|
||||
<div className="grid grid-cols-1 md:grid-cols-2 gap-4 text-xs">
|
||||
{/* LoRA Rank */}
|
||||
<div className="space-y-1.5 bg-black/30 p-3 rounded border border-zinc-800">
|
||||
<div className="flex justify-between font-mono text-xs">
|
||||
<span className="text-zinc-400">LoRA Rank (r)</span>
|
||||
<span className="font-bold text-blue-400">{hyperparameters.lora_r}</span>
|
||||
</div>
|
||||
<input
|
||||
type="range"
|
||||
min="8"
|
||||
max="128"
|
||||
step="8"
|
||||
value={hyperparameters.lora_r}
|
||||
onChange={(e) =>
|
||||
setHyperparameters((prev) => ({
|
||||
...prev,
|
||||
lora_r: parseInt(e.target.value),
|
||||
lora_alpha: parseInt(e.target.value),
|
||||
}))
|
||||
}
|
||||
className="w-full accent-blue-600 cursor-pointer h-1 bg-zinc-800 rounded"
|
||||
/>
|
||||
<p className="text-[10px] text-zinc-500">Standard ranks: 16 or 32 for general tasks, 64 for complex coding.</p>
|
||||
</div>
|
||||
|
||||
{/* LoRA Alpha */}
|
||||
<div className="space-y-1.5 bg-black/30 p-3 rounded border border-zinc-800">
|
||||
<div className="flex justify-between font-mono text-xs">
|
||||
<span className="text-zinc-400">LoRA Alpha (α)</span>
|
||||
<span className="font-bold text-blue-400">{hyperparameters.lora_alpha}</span>
|
||||
</div>
|
||||
<input
|
||||
type="range"
|
||||
min="8"
|
||||
max="256"
|
||||
step="8"
|
||||
value={hyperparameters.lora_alpha}
|
||||
onChange={(e) =>
|
||||
setHyperparameters((prev) => ({ ...prev, lora_alpha: parseInt(e.target.value) }))
|
||||
}
|
||||
className="w-full accent-blue-600 cursor-pointer h-1 bg-zinc-800 rounded"
|
||||
/>
|
||||
<p className="text-[10px] text-zinc-500">Scaling constant. Alpha = 16 or 32 provides optimal gradient flow.</p>
|
||||
</div>
|
||||
|
||||
{/* Micro Batch Size */}
|
||||
<div className="space-y-1.5 bg-black/30 p-3 rounded border border-zinc-800">
|
||||
<label className="text-[10px] font-mono text-zinc-500 uppercase block">Micro Batch Size (Per Device)</label>
|
||||
<select
|
||||
value={hyperparameters.batch_size}
|
||||
onChange={(e) =>
|
||||
setHyperparameters((prev) => ({ ...prev, batch_size: parseInt(e.target.value) }))
|
||||
}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-xs text-zinc-200 font-mono outline-none focus:ring-1 focus:ring-blue-600"
|
||||
>
|
||||
<option value={1}>1 (Recommended for 14B models & 16k context)</option>
|
||||
<option value={2}>2 (Recommended for 8B models on RTX 4080)</option>
|
||||
<option value={4}>4 (High speed for 8k context)</option>
|
||||
</select>
|
||||
</div>
|
||||
|
||||
{/* Gradient Accumulation */}
|
||||
<div className="space-y-1.5 bg-black/30 p-3 rounded border border-zinc-800">
|
||||
<label className="text-[10px] font-mono text-zinc-500 uppercase block">Gradient Accumulation Steps</label>
|
||||
<select
|
||||
value={hyperparameters.gradient_accumulation_steps}
|
||||
onChange={(e) =>
|
||||
setHyperparameters((prev) => ({
|
||||
...prev,
|
||||
gradient_accumulation_steps: parseInt(e.target.value),
|
||||
}))
|
||||
}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-xs text-zinc-200 font-mono outline-none focus:ring-1 focus:ring-blue-600"
|
||||
>
|
||||
<option value={2}>2 (Effective batch: 4)</option>
|
||||
<option value={4}>4 (Effective batch: 8)</option>
|
||||
<option value={8}>8 (Effective batch: 16 - High stability)</option>
|
||||
<option value={16}>16 (Effective batch: 32)</option>
|
||||
</select>
|
||||
</div>
|
||||
|
||||
{/* Learning Rate */}
|
||||
<div className="space-y-1.5 bg-black/30 p-3 rounded border border-zinc-800">
|
||||
<label className="text-[10px] font-mono text-zinc-500 uppercase block">Learning Rate</label>
|
||||
<input
|
||||
type="number"
|
||||
step="0.00001"
|
||||
value={hyperparameters.learning_rate}
|
||||
onChange={(e) =>
|
||||
setHyperparameters((prev) => ({
|
||||
...prev,
|
||||
learning_rate: parseFloat(e.target.value) || 0.0002,
|
||||
}))
|
||||
}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-xs text-zinc-200 font-mono outline-none focus:ring-1 focus:ring-blue-600"
|
||||
/>
|
||||
</div>
|
||||
|
||||
{/* Max Sequence Length */}
|
||||
<div className="space-y-1.5 bg-black/30 p-3 rounded border border-zinc-800">
|
||||
<label className="text-[10px] font-mono text-zinc-500 uppercase block">Max Sequence Length (Context)</label>
|
||||
<select
|
||||
value={hyperparameters.max_seq_length}
|
||||
onChange={(e) =>
|
||||
setHyperparameters((prev) => ({
|
||||
...prev,
|
||||
max_seq_length: parseInt(e.target.value),
|
||||
}))
|
||||
}
|
||||
className="w-full bg-zinc-950 border border-zinc-800 rounded px-2.5 py-1.5 text-xs text-zinc-200 font-mono outline-none focus:ring-1 focus:ring-blue-600"
|
||||
>
|
||||
<option value={2048}>2,048 tokens (Ultra Fast)</option>
|
||||
<option value={4096}>4,096 tokens (Standard)</option>
|
||||
<option value={8192}>8,192 tokens (Extended Instructions)</option>
|
||||
<option value={16384}>16,384 tokens (Full Code & Multi-turn MCP)</option>
|
||||
<option value={32768}>32,768 tokens (Long Document / YaRN)</option>
|
||||
</select>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Toggle Flags */}
|
||||
<div className="grid grid-cols-1 md:grid-cols-3 gap-3 pt-2">
|
||||
<label className="flex items-center gap-2 p-3 bg-black/30 rounded border border-zinc-800 cursor-pointer">
|
||||
<input
|
||||
type="checkbox"
|
||||
checked={hyperparameters.use_dora}
|
||||
onChange={(e) =>
|
||||
setHyperparameters((prev) => ({ ...prev, use_dora: e.target.checked }))
|
||||
}
|
||||
className="accent-blue-600 rounded"
|
||||
/>
|
||||
<div>
|
||||
<div className="font-medium text-[#f4f4f5] text-xs">Enable DoRA</div>
|
||||
<div className="text-[10px] text-zinc-500">Magnitude / Direction split</div>
|
||||
</div>
|
||||
</label>
|
||||
|
||||
<label className="flex items-center gap-2 p-3 bg-black/30 rounded border border-zinc-800 cursor-pointer">
|
||||
<input
|
||||
type="checkbox"
|
||||
checked={hyperparameters.use_unsloth_fast_backprop}
|
||||
onChange={(e) =>
|
||||
setHyperparameters((prev) => ({
|
||||
...prev,
|
||||
use_unsloth_fast_backprop: e.target.checked,
|
||||
}))
|
||||
}
|
||||
className="accent-blue-600 rounded"
|
||||
/>
|
||||
<div>
|
||||
<div className="font-medium text-[#f4f4f5] text-xs">Unsloth Fast Backprop</div>
|
||||
<div className="text-[10px] text-zinc-500">Triton kernel acceleration</div>
|
||||
</div>
|
||||
</label>
|
||||
|
||||
<label className="flex items-center gap-2 p-3 bg-black/30 rounded border border-zinc-800 cursor-pointer">
|
||||
<input
|
||||
type="checkbox"
|
||||
checked={hyperparameters.use_gradient_checkpointing}
|
||||
onChange={(e) =>
|
||||
setHyperparameters((prev) => ({
|
||||
...prev,
|
||||
use_gradient_checkpointing: e.target.checked,
|
||||
}))
|
||||
}
|
||||
className="accent-blue-600 rounded"
|
||||
/>
|
||||
<div>
|
||||
<div className="font-medium text-[#f4f4f5] text-xs">Gradient Checkpointing</div>
|
||||
<div className="text-[10px] text-zinc-500">Zero OOM for >8k context</div>
|
||||
</div>
|
||||
</label>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Action to proceed */}
|
||||
<div className="flex justify-end">
|
||||
<button
|
||||
onClick={onProceed}
|
||||
className="flex items-center gap-2 px-4 py-2 rounded text-xs font-medium bg-blue-600 hover:bg-blue-500 text-white shadow-sm transition-all cursor-pointer"
|
||||
>
|
||||
<span>Proceed to Dataset & Synthetic Data</span>
|
||||
<ArrowRight className="w-3.5 h-3.5" />
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
};
|
||||
327
studio-ref/src/components/TrainingSimulator.tsx
Normal file
327
studio-ref/src/components/TrainingSimulator.tsx
Normal file
@@ -0,0 +1,327 @@
|
||||
import React, { useEffect, useState, useRef } from "react";
|
||||
import {
|
||||
PlayCircle,
|
||||
PauseCircle,
|
||||
RotateCcw,
|
||||
Zap,
|
||||
ArrowRight,
|
||||
TrendingDown,
|
||||
Terminal,
|
||||
} from "lucide-react";
|
||||
import {
|
||||
ResponsiveContainer,
|
||||
LineChart,
|
||||
Line,
|
||||
XAxis,
|
||||
YAxis,
|
||||
Tooltip,
|
||||
CartesianGrid,
|
||||
} from "recharts";
|
||||
import { BaseModelInfo, TrainingHyperparameters, TrainingLogEntry } from "../types";
|
||||
|
||||
interface TrainingSimulatorProps {
|
||||
selectedModel: BaseModelInfo;
|
||||
hyperparameters: TrainingHyperparameters;
|
||||
onProceed: () => void;
|
||||
}
|
||||
|
||||
export const TrainingSimulator: React.FC<TrainingSimulatorProps> = ({
|
||||
selectedModel,
|
||||
hyperparameters,
|
||||
onProceed,
|
||||
}) => {
|
||||
const [isRunning, setIsRunning] = useState(false);
|
||||
const [currentStep, setCurrentStep] = useState(0);
|
||||
const totalSteps = 60;
|
||||
const [logs, setLogs] = useState<TrainingLogEntry[]>([]);
|
||||
const [sampleGenerations, setSampleGenerations] = useState<string[]>([]);
|
||||
const timerRef = useRef<any>(null);
|
||||
|
||||
// Generate initial point
|
||||
useEffect(() => {
|
||||
if (logs.length === 0) {
|
||||
setLogs([
|
||||
{
|
||||
step: 0,
|
||||
epoch: 0,
|
||||
loss: 2.85,
|
||||
learningRate: hyperparameters.learning_rate * 0.1,
|
||||
gradNorm: 1.42,
|
||||
vramUsedGb: 11.2,
|
||||
tokensPerSec: 2450,
|
||||
timestamp: new Date().toLocaleTimeString(),
|
||||
},
|
||||
]);
|
||||
}
|
||||
}, []);
|
||||
|
||||
useEffect(() => {
|
||||
if (isRunning) {
|
||||
timerRef.current = setInterval(() => {
|
||||
setCurrentStep((prev) => {
|
||||
if (prev >= totalSteps) {
|
||||
setIsRunning(false);
|
||||
clearInterval(timerRef.current);
|
||||
return prev;
|
||||
}
|
||||
const nextStep = prev + 1;
|
||||
|
||||
// Realistic loss decay with stochastic noise
|
||||
const progress = nextStep / totalSteps;
|
||||
const baseLoss = 2.85 * Math.exp(-progress * 2.8) + 0.35;
|
||||
const noise = (Math.random() - 0.5) * 0.08;
|
||||
const currentLoss = Number(Math.max(0.25, baseLoss + noise).toFixed(4));
|
||||
|
||||
// Cosine learning rate
|
||||
const lr = Number(
|
||||
(
|
||||
hyperparameters.learning_rate *
|
||||
0.5 *
|
||||
(1 + Math.cos((Math.PI * nextStep) / totalSteps))
|
||||
).toExponential(2)
|
||||
);
|
||||
|
||||
const gradNorm = Number((0.85 + Math.random() * 0.4).toFixed(3));
|
||||
const vramUsedGb = Number((11.4 + Math.sin(nextStep * 0.3) * 0.4).toFixed(1));
|
||||
const tokensPerSec = Math.round(2600 + (Math.random() - 0.5) * 200);
|
||||
|
||||
const newLog: TrainingLogEntry = {
|
||||
step: nextStep,
|
||||
epoch: Number(((nextStep / totalSteps) * hyperparameters.epochs).toFixed(2)),
|
||||
loss: currentLoss,
|
||||
learningRate: lr,
|
||||
gradNorm,
|
||||
vramUsedGb,
|
||||
tokensPerSec,
|
||||
timestamp: new Date().toLocaleTimeString(),
|
||||
};
|
||||
|
||||
setLogs((prevLogs) => [...prevLogs, newLog]);
|
||||
|
||||
// Sample token generation preview at milestones
|
||||
if (nextStep === 15) {
|
||||
setSampleGenerations((g) => [
|
||||
`[Step 15 Checkpoint] Prompt: "Call the filesystem read_file tool"\nModel Output: {"name": "read_file", "path": "src/App.tsx"} (Loss: ${currentLoss})`,
|
||||
...g,
|
||||
]);
|
||||
} else if (nextStep === 35) {
|
||||
setSampleGenerations((g) => [
|
||||
`[Step 35 Checkpoint] Prompt: "Query database for top 5 active users"\nModel Output: <tool_call>{"name": "execute_sql", "arguments": {"query": "SELECT * FROM users ORDER BY created_at DESC LIMIT 5;"}}</tool_call>\nFound 5 users.`,
|
||||
...g,
|
||||
]);
|
||||
} else if (nextStep === 60) {
|
||||
setSampleGenerations((g) => [
|
||||
`[Step 60 Final] High precision MCP multi-turn tool calling & Deep Reasoning aligned perfectly! (Final Loss: ${currentLoss})`,
|
||||
...g,
|
||||
]);
|
||||
}
|
||||
|
||||
return nextStep;
|
||||
});
|
||||
}, 400);
|
||||
} else {
|
||||
if (timerRef.current) clearInterval(timerRef.current);
|
||||
}
|
||||
return () => {
|
||||
if (timerRef.current) clearInterval(timerRef.current);
|
||||
};
|
||||
}, [isRunning, hyperparameters]);
|
||||
|
||||
const handleReset = () => {
|
||||
setIsRunning(false);
|
||||
setCurrentStep(0);
|
||||
setLogs([
|
||||
{
|
||||
step: 0,
|
||||
epoch: 0,
|
||||
loss: 2.85,
|
||||
learningRate: hyperparameters.learning_rate * 0.1,
|
||||
gradNorm: 1.42,
|
||||
vramUsedGb: 11.2,
|
||||
tokensPerSec: 2450,
|
||||
timestamp: new Date().toLocaleTimeString(),
|
||||
},
|
||||
]);
|
||||
setSampleGenerations([]);
|
||||
};
|
||||
|
||||
const latestLog = logs[logs.length - 1] || logs[0];
|
||||
const progressPercent = Math.round((currentStep / totalSteps) * 100);
|
||||
|
||||
return (
|
||||
<div className="space-y-6">
|
||||
{/* Top Banner & Control HUD */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 flex flex-wrap items-center justify-between gap-4">
|
||||
<div>
|
||||
<div className="inline-flex items-center gap-1.5 px-2.5 py-0.5 rounded text-[10px] font-mono text-blue-400 bg-blue-400/10 border border-blue-400/20 mb-2">
|
||||
<Zap className="w-3.5 h-3.5" /> UNSLOTH CUDA TRAINING ENGINE
|
||||
</div>
|
||||
<h2 className="text-lg font-semibold text-[#f4f4f5]">
|
||||
Live Fine-Tuning Execution & Telemetry Monitor
|
||||
</h2>
|
||||
<p className="text-xs text-zinc-400 mt-1">
|
||||
Running Triton backprop kernel on NVIDIA RTX 4080 Super with FlashAttention-2.
|
||||
</p>
|
||||
</div>
|
||||
|
||||
{/* Action Controls */}
|
||||
<div className="flex items-center gap-2">
|
||||
{!isRunning ? (
|
||||
<button
|
||||
onClick={() => setIsRunning(true)}
|
||||
disabled={currentStep >= totalSteps}
|
||||
className="flex items-center gap-2 px-4 py-2 rounded text-xs font-medium bg-emerald-600 hover:bg-emerald-500 text-white shadow-sm cursor-pointer disabled:opacity-50"
|
||||
>
|
||||
<PlayCircle className="w-4 h-4" />
|
||||
<span>{currentStep === 0 ? "Start Training Run" : "Resume Training"}</span>
|
||||
</button>
|
||||
) : (
|
||||
<button
|
||||
onClick={() => setIsRunning(false)}
|
||||
className="flex items-center gap-2 px-4 py-2 rounded text-xs font-medium bg-amber-600 hover:bg-amber-500 text-white shadow-sm cursor-pointer"
|
||||
>
|
||||
<PauseCircle className="w-4 h-4" />
|
||||
<span>Pause Training</span>
|
||||
</button>
|
||||
)}
|
||||
|
||||
<button
|
||||
onClick={handleReset}
|
||||
className="p-2 rounded bg-zinc-900 hover:bg-zinc-800 text-zinc-400 border border-zinc-800 cursor-pointer"
|
||||
title="Reset training simulation"
|
||||
>
|
||||
<RotateCcw className="w-4 h-4" />
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Progress Bar & Telemetry Strip */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 space-y-3">
|
||||
<div className="flex justify-between items-center text-xs font-mono">
|
||||
<span className="text-zinc-300">
|
||||
Training Progress: Step {currentStep} of {totalSteps} ({progressPercent}%)
|
||||
</span>
|
||||
<span className="text-blue-400">
|
||||
Epoch {((currentStep / totalSteps) * hyperparameters.epochs).toFixed(2)} / {hyperparameters.epochs}
|
||||
</span>
|
||||
</div>
|
||||
<div className="w-full h-2 bg-black/40 rounded-full overflow-hidden border border-zinc-800">
|
||||
<div
|
||||
className="h-full bg-blue-600 rounded-full transition-all duration-300"
|
||||
style={{ width: `${progressPercent}%` }}
|
||||
/>
|
||||
</div>
|
||||
|
||||
{/* Real-time metrics grid */}
|
||||
<div className="grid grid-cols-2 md:grid-cols-5 gap-3 pt-2 text-xs">
|
||||
<div className="bg-black/30 p-3 rounded border border-zinc-800">
|
||||
<div className="text-[10px] font-mono text-zinc-500 uppercase">Current Loss</div>
|
||||
<div className="text-sm font-mono font-bold text-blue-400 mt-0.5">
|
||||
{latestLog?.loss ?? "--"}
|
||||
</div>
|
||||
</div>
|
||||
<div className="bg-black/30 p-3 rounded border border-zinc-800">
|
||||
<div className="text-[10px] font-mono text-zinc-500 uppercase">Learning Rate</div>
|
||||
<div className="text-sm font-mono font-bold text-zinc-200 mt-0.5">
|
||||
{latestLog?.learningRate ?? "--"}
|
||||
</div>
|
||||
</div>
|
||||
<div className="bg-black/30 p-3 rounded border border-zinc-800">
|
||||
<div className="text-[10px] font-mono text-zinc-500 uppercase">Grad Norm</div>
|
||||
<div className="text-sm font-mono font-bold text-zinc-200 mt-0.5">
|
||||
{latestLog?.gradNorm ?? "--"}
|
||||
</div>
|
||||
</div>
|
||||
<div className="bg-black/30 p-3 rounded border border-zinc-800">
|
||||
<div className="text-[10px] font-mono text-zinc-500 uppercase">RTX 4080 VRAM</div>
|
||||
<div className="text-sm font-mono font-bold text-emerald-400 mt-0.5">
|
||||
{latestLog?.vramUsedGb} GB <span className="text-[10px] text-zinc-500 font-normal">/ 16GB</span>
|
||||
</div>
|
||||
</div>
|
||||
<div className="bg-black/30 p-3 rounded border border-zinc-800">
|
||||
<div className="text-[10px] font-mono text-zinc-500 uppercase">Throughput</div>
|
||||
<div className="text-sm font-mono font-bold text-cyan-400 mt-0.5">
|
||||
{latestLog?.tokensPerSec} tok/s
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Loss Convergence Chart & Generation Checkpoint Logs */}
|
||||
<div className="grid grid-cols-1 lg:grid-cols-3 gap-6">
|
||||
{/* Left: Recharts Live Loss Curve */}
|
||||
<div className="lg:col-span-2 bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 space-y-3">
|
||||
<div className="flex items-center justify-between">
|
||||
<h3 className="text-sm font-semibold text-[#f4f4f5] flex items-center gap-2">
|
||||
<TrendingDown className="w-4 h-4 text-blue-400" /> Training Loss Convergence Curve
|
||||
</h3>
|
||||
<span className="text-[10px] text-zinc-500 font-mono">Cross Entropy Loss (SFT)</span>
|
||||
</div>
|
||||
|
||||
<div className="h-64 w-full bg-zinc-950 rounded p-2 border border-zinc-800">
|
||||
<ResponsiveContainer width="100%" height="100%">
|
||||
<LineChart data={logs}>
|
||||
<CartesianGrid strokeDasharray="3 3" stroke="#27272a" />
|
||||
<XAxis dataKey="step" stroke="#71717a" fontSize={11} />
|
||||
<YAxis domain={["auto", "auto"]} stroke="#71717a" fontSize={11} />
|
||||
<Tooltip
|
||||
contentStyle={{
|
||||
backgroundColor: "#18181b",
|
||||
borderColor: "#27272a",
|
||||
fontSize: "11px",
|
||||
borderRadius: "6px",
|
||||
color: "#f4f4f5",
|
||||
}}
|
||||
/>
|
||||
<Line
|
||||
type="monotone"
|
||||
dataKey="loss"
|
||||
stroke="#3b82f6"
|
||||
strokeWidth={2}
|
||||
dot={false}
|
||||
isAnimationActive={false}
|
||||
/>
|
||||
</LineChart>
|
||||
</ResponsiveContainer>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{/* Right: Intermediate Checkpoint Samples */}
|
||||
<div className="bg-[#18181b]/50 border border-[#27272a] rounded-xl p-5 space-y-3 flex flex-col justify-between">
|
||||
<div>
|
||||
<h3 className="text-sm font-semibold text-[#f4f4f5] flex items-center gap-2 mb-3">
|
||||
<Terminal className="w-4 h-4 text-emerald-400" /> Checkpoint Generations
|
||||
</h3>
|
||||
|
||||
<div className="space-y-2.5 max-h-60 overflow-y-auto">
|
||||
{sampleGenerations.length === 0 ? (
|
||||
<div className="text-xs text-zinc-500 italic p-3 bg-zinc-950 rounded border border-zinc-800">
|
||||
Model checkpoint test outputs will appear here at steps 15, 35, and 60...
|
||||
</div>
|
||||
) : (
|
||||
sampleGenerations.map((gen, idx) => (
|
||||
<div
|
||||
key={idx}
|
||||
className="p-3 bg-zinc-950 rounded border border-zinc-800 text-[11px] font-mono text-emerald-400 whitespace-pre-wrap leading-relaxed"
|
||||
>
|
||||
{gen}
|
||||
</div>
|
||||
))
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div className="pt-3 border-t border-[#27272a]">
|
||||
<button
|
||||
onClick={onProceed}
|
||||
className="w-full flex items-center justify-center gap-2 py-2 rounded font-medium text-xs bg-blue-600 hover:bg-blue-500 text-white shadow-sm cursor-pointer"
|
||||
>
|
||||
<span>Export Modelfile & Push to Ollama</span>
|
||||
<ArrowRight className="w-3.5 h-3.5" />
|
||||
</button>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
};
|
||||
83
studio-ref/src/data/mcpPresets.ts
Normal file
83
studio-ref/src/data/mcpPresets.ts
Normal file
@@ -0,0 +1,83 @@
|
||||
import { MCPToolDeclaration } from "../types";
|
||||
|
||||
export const DEFAULT_MCP_PRESETS: MCPToolDeclaration[] = [
|
||||
{
|
||||
id: "filesystem-mcp",
|
||||
name: "read_file",
|
||||
serverName: "Filesystem MCP",
|
||||
description: "Read the full contents of a file from the user's workspace securely.",
|
||||
parametersSchema: {
|
||||
type: "object",
|
||||
properties: {
|
||||
path: { type: "string", description: "The relative or absolute file path to read" },
|
||||
start_line: { type: "number", description: "Optional starting line index (1-based)" },
|
||||
end_line: { type: "number", description: "Optional ending line index" },
|
||||
},
|
||||
required: ["path"],
|
||||
},
|
||||
sampleCallsCount: 45,
|
||||
},
|
||||
{
|
||||
id: "filesystem-write",
|
||||
name: "write_file",
|
||||
serverName: "Filesystem MCP",
|
||||
description: "Create or overwrite a file with given text content.",
|
||||
parametersSchema: {
|
||||
type: "object",
|
||||
properties: {
|
||||
path: { type: "string", description: "Path where the file should be created" },
|
||||
content: { type: "string", description: "The complete content to write" },
|
||||
},
|
||||
required: ["path", "content"],
|
||||
},
|
||||
sampleCallsCount: 38,
|
||||
},
|
||||
{
|
||||
id: "postgres-query",
|
||||
name: "execute_sql",
|
||||
serverName: "PostgreSQL MCP",
|
||||
description: "Execute a read-only or transactional SQL query against the connected database.",
|
||||
parametersSchema: {
|
||||
type: "object",
|
||||
properties: {
|
||||
query: { type: "string", description: "Valid PostgreSQL query statement" },
|
||||
limit: { type: "number", description: "Maximum rows to return" },
|
||||
},
|
||||
required: ["query"],
|
||||
},
|
||||
sampleCallsCount: 52,
|
||||
},
|
||||
{
|
||||
id: "websearch-mcp",
|
||||
name: "web_search",
|
||||
serverName: "Web Search MCP",
|
||||
description: "Perform real-time search across the web and return top synthesized results.",
|
||||
parametersSchema: {
|
||||
type: "object",
|
||||
properties: {
|
||||
query: { type: "string", description: "The search query string" },
|
||||
num_results: { type: "number", description: "Number of search results to fetch (1-10)" },
|
||||
},
|
||||
required: ["query"],
|
||||
},
|
||||
sampleCallsCount: 60,
|
||||
},
|
||||
{
|
||||
id: "github-mcp",
|
||||
name: "create_pull_request",
|
||||
serverName: "GitHub MCP",
|
||||
description: "Create a new pull request on a GitHub repository with title and branch details.",
|
||||
parametersSchema: {
|
||||
type: "object",
|
||||
properties: {
|
||||
repo: { type: "string", description: "owner/repository_name format" },
|
||||
title: { type: "string", description: "Pull request title" },
|
||||
head_branch: { type: "string", description: "The source feature branch" },
|
||||
base_branch: { type: "string", description: "The target branch (e.g. main)" },
|
||||
body: { type: "string", description: "PR description in markdown" },
|
||||
},
|
||||
required: ["repo", "title", "head_branch", "base_branch"],
|
||||
},
|
||||
sampleCallsCount: 29,
|
||||
},
|
||||
];
|
||||
202
studio-ref/src/data/models.ts
Normal file
202
studio-ref/src/data/models.ts
Normal file
@@ -0,0 +1,202 @@
|
||||
import { BaseModelInfo } from "../types";
|
||||
|
||||
export const BASE_MODELS: BaseModelInfo[] = [
|
||||
{
|
||||
id: "llama-3.1-8b",
|
||||
name: "Llama 3.1 8B Instruct",
|
||||
huggingFaceId: "unsloth/Meta-Llama-3.1-8B-Instruct",
|
||||
ollamaName: "llama3.1:8b",
|
||||
parametersBillion: 8.03,
|
||||
layers: 32,
|
||||
hiddenDim: 4096,
|
||||
heads: 32,
|
||||
kvHeads: 8,
|
||||
vocabSize: 128256,
|
||||
defaultContext: 131072,
|
||||
architecture: "llama3",
|
||||
baseSizeGb: 16.1,
|
||||
q4SizeGb: 4.9,
|
||||
description: "The gold standard open model for fine-tuning. Fits perfectly in RTX 4080 Super (16GB VRAM) for 16k+ context QLoRA training.",
|
||||
recommendedFor4080Super: true,
|
||||
},
|
||||
{
|
||||
id: "qwen-2.5-7b",
|
||||
name: "Qwen 2.5 7B Instruct",
|
||||
huggingFaceId: "unsloth/Qwen2.5-7B-Instruct",
|
||||
ollamaName: "qwen2.5:7b",
|
||||
parametersBillion: 7.61,
|
||||
layers: 28,
|
||||
hiddenDim: 3584,
|
||||
heads: 28,
|
||||
kvHeads: 4,
|
||||
vocabSize: 152064,
|
||||
defaultContext: 32768,
|
||||
architecture: "qwen2.5",
|
||||
baseSizeGb: 15.2,
|
||||
q4SizeGb: 4.7,
|
||||
description: "Supreme multilingual, mathematical, coding & structured JSON/MCP tool-use capabilities. Extremely fast on Unsloth.",
|
||||
recommendedFor4080Super: true,
|
||||
},
|
||||
{
|
||||
id: "qwen-2.5-coder-7b",
|
||||
name: "Qwen 2.5 Coder 7B",
|
||||
huggingFaceId: "unsloth/Qwen2.5-Coder-7B-Instruct",
|
||||
ollamaName: "qwen2.5-coder:7b",
|
||||
parametersBillion: 7.61,
|
||||
layers: 28,
|
||||
hiddenDim: 3584,
|
||||
heads: 28,
|
||||
kvHeads: 4,
|
||||
vocabSize: 152064,
|
||||
defaultContext: 32768,
|
||||
architecture: "qwen2.5",
|
||||
baseSizeGb: 15.2,
|
||||
q4SizeGb: 4.7,
|
||||
description: "Best-in-class coding foundation. Ideal for fine-tuning MCP plugins, repo agents, and code harnesses.",
|
||||
recommendedFor4080Super: true,
|
||||
},
|
||||
{
|
||||
id: "qwen-2.5-14b",
|
||||
name: "Qwen 2.5 14B Instruct",
|
||||
huggingFaceId: "unsloth/Qwen2.5-14B-Instruct",
|
||||
ollamaName: "qwen2.5:14b",
|
||||
parametersBillion: 14.7,
|
||||
layers: 48,
|
||||
hiddenDim: 5120,
|
||||
heads: 40,
|
||||
kvHeads: 8,
|
||||
vocabSize: 152064,
|
||||
defaultContext: 32768,
|
||||
architecture: "qwen2.5",
|
||||
baseSizeGb: 29.4,
|
||||
q4SizeGb: 9.0,
|
||||
description: "High-intelligence intermediate model. Fits RTX 4080 Super with 4-bit QLoRA and Unsloth gradient checkpointing (10.5GB VRAM used).",
|
||||
recommendedFor4080Super: true,
|
||||
},
|
||||
{
|
||||
id: "deepseek-r1-distill-qwen-8b",
|
||||
name: "DeepSeek R1 Distill Qwen 8B",
|
||||
huggingFaceId: "unsloth/DeepSeek-R1-Distill-Qwen-8B",
|
||||
ollamaName: "deepseek-r1:8b",
|
||||
parametersBillion: 8.0,
|
||||
layers: 32,
|
||||
hiddenDim: 4096,
|
||||
heads: 32,
|
||||
kvHeads: 8,
|
||||
vocabSize: 152064,
|
||||
defaultContext: 32768,
|
||||
architecture: "deepseek",
|
||||
baseSizeGb: 16.0,
|
||||
q4SizeGb: 4.9,
|
||||
description: "Reasoning powerhouse with <think> token chain-of-thought capabilities. Perfect for complex problem solving and MCP logic.",
|
||||
recommendedFor4080Super: true,
|
||||
},
|
||||
{
|
||||
id: "deepseek-r1-distill-llama-8b",
|
||||
name: "DeepSeek R1 Distill Llama 8B",
|
||||
huggingFaceId: "unsloth/DeepSeek-R1-Distill-Llama-8B",
|
||||
ollamaName: "deepseek-r1:8b-llama",
|
||||
parametersBillion: 8.03,
|
||||
layers: 32,
|
||||
hiddenDim: 4096,
|
||||
heads: 32,
|
||||
kvHeads: 8,
|
||||
vocabSize: 128256,
|
||||
defaultContext: 131072,
|
||||
architecture: "deepseek",
|
||||
baseSizeGb: 16.1,
|
||||
q4SizeGb: 4.9,
|
||||
description: "DeepSeek reasoning logic distilled into Llama 3.1 architecture with massive 128k context support.",
|
||||
recommendedFor4080Super: true,
|
||||
},
|
||||
{
|
||||
id: "mistral-nemo-12b",
|
||||
name: "Mistral NeMo 12B Instruct",
|
||||
huggingFaceId: "unsloth/Mistral-Nemo-Instruct-2407",
|
||||
ollamaName: "mistral-nemo:12b",
|
||||
parametersBillion: 12.2,
|
||||
layers: 40,
|
||||
hiddenDim: 5120,
|
||||
heads: 32,
|
||||
kvHeads: 8,
|
||||
vocabSize: 131072,
|
||||
defaultContext: 128000,
|
||||
architecture: "mistral",
|
||||
baseSizeGb: 24.5,
|
||||
q4SizeGb: 7.5,
|
||||
description: "Collaborative model by Mistral and NVIDIA with Tekken tokenizer and huge 128k context.",
|
||||
recommendedFor4080Super: true,
|
||||
},
|
||||
{
|
||||
id: "gemma-2-9b",
|
||||
name: "Gemma 2 9B Instruct",
|
||||
huggingFaceId: "unsloth/gemma-2-9b-it",
|
||||
ollamaName: "gemma2:9b",
|
||||
parametersBillion: 9.24,
|
||||
layers: 42,
|
||||
hiddenDim: 3584,
|
||||
heads: 16,
|
||||
kvHeads: 8,
|
||||
vocabSize: 256000,
|
||||
defaultContext: 8192,
|
||||
architecture: "gemma2",
|
||||
baseSizeGb: 18.5,
|
||||
q4SizeGb: 5.6,
|
||||
description: "Google's high parameter-efficiency model with sliding window attention and logit capping.",
|
||||
recommendedFor4080Super: true,
|
||||
},
|
||||
{
|
||||
id: "phi-4-14b",
|
||||
name: "Phi-4 14B Instruct",
|
||||
huggingFaceId: "unsloth/phi-4",
|
||||
ollamaName: "phi4:14b",
|
||||
parametersBillion: 14.7,
|
||||
layers: 40,
|
||||
hiddenDim: 5120,
|
||||
heads: 40,
|
||||
kvHeads: 10,
|
||||
vocabSize: 100352,
|
||||
defaultContext: 16384,
|
||||
architecture: "phi4",
|
||||
baseSizeGb: 29.4,
|
||||
q4SizeGb: 9.1,
|
||||
description: "Microsoft's state-of-the-art synthetic data trained 14B model with exceptional reasoning.",
|
||||
recommendedFor4080Super: true,
|
||||
},
|
||||
{
|
||||
id: "smollm2-1.7b",
|
||||
name: "SmolLM2 1.7B Instruct",
|
||||
huggingFaceId: "unsloth/SmolLM2-1.7B-Instruct",
|
||||
ollamaName: "smollm2:1.7b",
|
||||
parametersBillion: 1.71,
|
||||
layers: 24,
|
||||
hiddenDim: 2048,
|
||||
heads: 32,
|
||||
kvHeads: 32,
|
||||
vocabSize: 49152,
|
||||
defaultContext: 8192,
|
||||
architecture: "smollm",
|
||||
baseSizeGb: 3.4,
|
||||
q4SizeGb: 1.1,
|
||||
description: "Ultra-compact fast model. Trains in minutes on 4080 Super with full 32k context, ideal for edge devices and fast tool calling.",
|
||||
recommendedFor4080Super: true,
|
||||
},
|
||||
{
|
||||
id: "llama-3.3-70b",
|
||||
name: "Llama 3.3 70B Instruct",
|
||||
huggingFaceId: "unsloth/Llama-3.3-70B-Instruct",
|
||||
ollamaName: "llama3.3:70b",
|
||||
parametersBillion: 70.6,
|
||||
layers: 80,
|
||||
hiddenDim: 8192,
|
||||
heads: 64,
|
||||
kvHeads: 8,
|
||||
vocabSize: 128256,
|
||||
defaultContext: 131072,
|
||||
architecture: "llama3",
|
||||
baseSizeGb: 141.0,
|
||||
q4SizeGb: 42.5,
|
||||
description: "Flagship intelligence matching GPT-4o. Requires Multi-GPU or Teacher distillation mode for 16GB RTX 4080 Super.",
|
||||
recommendedFor4080Super: false,
|
||||
},
|
||||
];
|
||||
238
studio-ref/src/data/techniques.ts
Normal file
238
studio-ref/src/data/techniques.ts
Normal file
@@ -0,0 +1,238 @@
|
||||
export interface TechniqueDetail {
|
||||
id: string;
|
||||
name: string;
|
||||
category: "finetune" | "prune" | "moe" | "quant" | "alignment";
|
||||
tagline: string;
|
||||
description: string;
|
||||
paperReference: string;
|
||||
memorySavings: string;
|
||||
speedMultiplier: string;
|
||||
recommendedFor4080: boolean;
|
||||
unslothSupported: boolean;
|
||||
codeSnippet: string;
|
||||
parameters: {
|
||||
name: string;
|
||||
label: string;
|
||||
type: "number" | "select" | "boolean" | "text";
|
||||
default: any;
|
||||
options?: string[];
|
||||
description: string;
|
||||
}[];
|
||||
}
|
||||
|
||||
export const SOTA_TECHNIQUES: TechniqueDetail[] = [
|
||||
{
|
||||
id: "qlora",
|
||||
name: "Unsloth Fast QLoRA (4-bit NF4)",
|
||||
category: "finetune",
|
||||
tagline: "Ultra-fast parameter efficient fine-tuning with 4-bit NormalFloat quantization",
|
||||
description: "Quantizes base weights to 4-bit NormalFloat (NF4) with double quantization and trains 16-bit LoRA adapter matrices via custom Triton kernels. Reduces VRAM by up to 80% while retaining full 16-bit accuracy.",
|
||||
paperReference: "Dettmers et al., 2023 (QLoRA) & Unsloth AI",
|
||||
memorySavings: "80% VRAM reduction",
|
||||
speedMultiplier: "2.2x - 5.0x faster",
|
||||
recommendedFor4080: true,
|
||||
unslothSupported: true,
|
||||
codeSnippet: `model = FastLanguageModel.get_peft_model(
|
||||
model,
|
||||
r=16,
|
||||
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
|
||||
lora_alpha=16,
|
||||
lora_dropout=0,
|
||||
bias="none",
|
||||
use_gradient_checkpointing="unsloth",
|
||||
random_state=3407,
|
||||
)`,
|
||||
parameters: [
|
||||
{ name: "lora_r", label: "LoRA Rank (r)", type: "number", default: 16, description: "Dimension of low-rank update matrices (8, 16, 32, 64)" },
|
||||
{ name: "lora_alpha", label: "LoRA Alpha", type: "number", default: 16, description: "Scaling factor (commonly 1x or 2x of rank r)" },
|
||||
{ name: "use_gradient_checkpointing", label: "Unsloth Fast Gradient Checkpointing", type: "boolean", default: true, description: "Offloads activations to save 40% memory with zero speed penalty" },
|
||||
],
|
||||
},
|
||||
{
|
||||
id: "dora",
|
||||
name: "DoRA (Weight-Decomposed Low-Rank Adaptation)",
|
||||
category: "finetune",
|
||||
tagline: "Decomposes weights into magnitude and direction for full-fine-tuning parity",
|
||||
description: "Decomposes pre-trained weights into magnitude vectors and directional matrices. LoRA is applied exclusively to the directional component, matching or exceeding full fine-tuning performance without extra inference cost.",
|
||||
paperReference: "Liu et al., 2024 (DoRA: Weight-Decomposed Low-Rank Adaptation)",
|
||||
memorySavings: "75% VRAM reduction",
|
||||
speedMultiplier: "1.8x faster",
|
||||
recommendedFor4080: true,
|
||||
unslothSupported: true,
|
||||
codeSnippet: `model = FastLanguageModel.get_peft_model(
|
||||
model,
|
||||
r=16,
|
||||
use_dora=True,
|
||||
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
|
||||
)`,
|
||||
parameters: [
|
||||
{ name: "use_dora", label: "Enable DoRA Decomposition", type: "boolean", default: true, description: "Enable magnitude/directional weight split" },
|
||||
{ name: "lora_r", label: "Directional Rank", type: "number", default: 16, description: "Rank for the directional matrix" },
|
||||
],
|
||||
},
|
||||
{
|
||||
id: "orpo",
|
||||
name: "ORPO (Odds Ratio Preference Optimization)",
|
||||
category: "alignment",
|
||||
tagline: "Single-step preference alignment & SFT without a reference model",
|
||||
description: "Integrates odds-ratio penalty directly into the supervised cross-entropy loss function. Eliminates the need for a separate reference model or two-stage SFT+DPO pipeline, halving VRAM requirements.",
|
||||
paperReference: "Hong et al., 2024 (ORPO: Monolithic Preference Optimization)",
|
||||
memorySavings: "50% VRAM saving vs DPO",
|
||||
speedMultiplier: "2.0x faster than SFT+DPO",
|
||||
recommendedFor4080: true,
|
||||
unslothSupported: true,
|
||||
codeSnippet: `from trl import ORPOTrainer, ORPOConfig
|
||||
|
||||
orpo_trainer = ORPOTrainer(
|
||||
model=model,
|
||||
args=ORPOConfig(
|
||||
beta=0.1,
|
||||
learning_rate=5e-6,
|
||||
lr_scheduler_type="cosine",
|
||||
max_prompt_length=1024,
|
||||
max_length=2048,
|
||||
),
|
||||
train_dataset=dataset,
|
||||
)`,
|
||||
parameters: [
|
||||
{ name: "preference_beta", label: "Odds Ratio Beta (β)", type: "number", default: 0.1, description: "Weight of the preference penalty in ORPO loss (0.05 - 0.2)" },
|
||||
],
|
||||
},
|
||||
{
|
||||
id: "galore",
|
||||
name: "GaLore (Gradient Low-Rank Projection)",
|
||||
category: "finetune",
|
||||
tagline: "Memory-efficient full-parameter training via gradient subspace projection",
|
||||
description: "Allows full parameter training of 7B-14B models on 16GB VRAM by projecting optimizer state gradients into low-rank subspaces, slashing optimizer memory by up to 65.5%.",
|
||||
paperReference: "Zhao et al., 2024 (GaLore: Gradient Low-Rank Projection)",
|
||||
memorySavings: "65% optimizer memory reduction",
|
||||
speedMultiplier: "1.2x",
|
||||
recommendedFor4080: true,
|
||||
unslothSupported: false,
|
||||
codeSnippet: `from galore_torch import GaLoreAdamW8bit
|
||||
|
||||
optimizer = GaLoreAdamW8bit(
|
||||
model.parameters(),
|
||||
lr=1e-5,
|
||||
rank=128,
|
||||
update_proj_gap=200,
|
||||
scale=0.25,
|
||||
)`,
|
||||
parameters: [
|
||||
{ name: "galore_rank", label: "Gradient Subspace Rank", type: "number", default: 128, description: "Projection rank for gradients" },
|
||||
{ name: "update_proj_gap", label: "Projection Update Frequency", type: "number", default: 200, description: "Steps between SVD subspace updates" },
|
||||
],
|
||||
},
|
||||
{
|
||||
id: "neftune",
|
||||
name: "NEFTune (Noisy Embedding Fine-Tuning)",
|
||||
category: "finetune",
|
||||
tagline: "Injects uniform noise into embeddings to boost generalizability and prevent overfitting",
|
||||
description: "Adds scaled uniform random noise to input token embeddings during training. Proven to boost AlpacaEval and conversational benchmark scores by 5-15% with zero extra VRAM.",
|
||||
paperReference: "Jain et al., 2023 (NEFTune: Noisy Embeddings Improve Instruction Finetuning)",
|
||||
memorySavings: "0% (Zero overhead)",
|
||||
speedMultiplier: "1.0x",
|
||||
recommendedFor4080: true,
|
||||
unslothSupported: true,
|
||||
codeSnippet: `trainer = SFTTrainer(
|
||||
model=model,
|
||||
train_dataset=dataset,
|
||||
neftune_noise_alpha=5,
|
||||
)`,
|
||||
parameters: [
|
||||
{ name: "neftune_noise_alpha", label: "Noise Alpha Scale", type: "number", default: 5, description: "Magnitude of uniform noise added to embeddings (typically 5 to 15)" },
|
||||
],
|
||||
},
|
||||
{
|
||||
id: "structured_layer",
|
||||
name: "ShortGPT Structured Layer Pruning (Fat Shaving)",
|
||||
category: "prune",
|
||||
tagline: "Removes redundant hidden layers based on angular similarity metric",
|
||||
description: "Calculates the cosine angular distance of representations between consecutive transformer layers. Redundant layers (often layers 14-22 in a 32-layer model) are trimmed, reducing model parameters by 25-35% with minimal accuracy loss.",
|
||||
paperReference: "Men et al., 2024 (ShortGPT: Layers in Large Language Models are More Redundant Than You Think)",
|
||||
memorySavings: "25-35% permanent size reduction",
|
||||
speedMultiplier: "1.35x faster inference",
|
||||
recommendedFor4080: true,
|
||||
unslothSupported: true,
|
||||
codeSnippet: `# Pruning redundant middle layers from 32 layers down to 24 layers
|
||||
pruned_layers = [i for i in range(32) if i not in range(16, 24)]
|
||||
model.model.layers = torch.nn.ModuleList([model.model.layers[i] for i in pruned_layers])
|
||||
model.config.num_hidden_layers = len(pruned_layers)
|
||||
# Followed by 100-step healing LoRA adapter`,
|
||||
parameters: [
|
||||
{ name: "layers_to_prune", label: "Pruning Range (Start - End)", type: "text", default: "16-23", description: "Indices of transformer layers to excise" },
|
||||
{ name: "repair_steps", label: "Healing LoRA Steps", type: "number", default: 100, description: "Short LoRA fine-tune steps to restore perplexity" },
|
||||
],
|
||||
},
|
||||
{
|
||||
id: "vocab_trim",
|
||||
name: "Vocabulary Trimmer (Shave 1GB of Embedding Fat)",
|
||||
category: "prune",
|
||||
tagline: "Trims unused multilingual and rare tokens from 128k tokenizer down to 32k",
|
||||
description: "Modern tokenizers (Llama 3.1 & Qwen 2.5) allocate 128k-152k tokens, consuming over 1.2 GB VRAM in the embedding table alone. Trimming down to target domain tokens shrinks the GGUF file substantially.",
|
||||
paperReference: "TokenCraft / CompactLLM 2024",
|
||||
memorySavings: "800MB - 1.4GB disk & VRAM savings",
|
||||
speedMultiplier: "1.15x faster generation",
|
||||
recommendedFor4080: true,
|
||||
unslothSupported: true,
|
||||
codeSnippet: `# Shrink embedding matrix and lm_head
|
||||
kept_token_ids = get_frequent_tokens(dataset, target_size=32000)
|
||||
model.resize_token_embeddings(len(kept_token_ids))`,
|
||||
parameters: [
|
||||
{ name: "target_vocab", label: "Target Vocabulary Size", type: "number", default: 32000, description: "Size to condense the 128k/152k vocabulary to" },
|
||||
],
|
||||
},
|
||||
{
|
||||
id: "moefication",
|
||||
name: "MoEfication & FFN Clustering (Adding Experts)",
|
||||
category: "moe",
|
||||
tagline: "Converts a dense 8B model into an 8x8B Mixture of Experts with router",
|
||||
description: "Splits the dense MLP/feed-forward layers into specialized expert clusters via k-means weight clustering, training a top-2 gating router. Delivers higher representational capacity while keeping active inference compute fixed.",
|
||||
paperReference: "Zhang et al., 2022 (MoEfication: Transformer Feed-forward Layers are Sparse Experts)",
|
||||
memorySavings: "Inference compute equals 1 expert",
|
||||
speedMultiplier: "MoE Sparse routing",
|
||||
recommendedFor4080: true,
|
||||
unslothSupported: true,
|
||||
codeSnippet: `# Upcycle dense model to Mixture of Experts
|
||||
from mergekit.moe import MoEBuilder
|
||||
|
||||
builder = MoEBuilder(
|
||||
base_model="unsloth/Meta-Llama-3.1-8B-Instruct",
|
||||
num_experts=4,
|
||||
top_k=2,
|
||||
router_type="softmax",
|
||||
)
|
||||
builder.build_moe_architecture()`,
|
||||
parameters: [
|
||||
{ name: "num_experts", label: "Number of Experts", type: "number", default: 4, description: "Total expert blocks (e.g. 4 or 8)" },
|
||||
{ name: "top_k", label: "Top-K Active Experts", type: "number", default: 2, description: "Number of experts activated per token (usually 1 or 2)" },
|
||||
],
|
||||
},
|
||||
{
|
||||
id: "dare_ties",
|
||||
name: "DARE-TIES Model Merging (MergeKit)",
|
||||
category: "moe",
|
||||
tagline: "Drops redundant delta parameters and resolves sign conflicts across fine-tunes",
|
||||
description: "Merges multiple specialized models (e.g. your Coding fine-tune + your MCP Tool fine-tune) by dropping 90% of insignificant weight deltas and rescaling the rest with Task-Informed Energy Sign resolution.",
|
||||
paperReference: "Yu et al., 2024 (Language Models are Super Mario: DARE)",
|
||||
memorySavings: "Combines models with zero training cost",
|
||||
speedMultiplier: "Instant merge",
|
||||
recommendedFor4080: true,
|
||||
unslothSupported: true,
|
||||
codeSnippet: `merge_method: dare_ties
|
||||
base_model: unsloth/Meta-Llama-3.1-8B-Instruct
|
||||
models:
|
||||
- model: ./my-coding-adapter-merged
|
||||
parameters:
|
||||
weight: 0.6
|
||||
density: 0.2
|
||||
- model: ./my-mcp-tool-adapter-merged
|
||||
parameters:
|
||||
weight: 0.4
|
||||
density: 0.2
|
||||
dtype: bfloat16`,
|
||||
parameters: [
|
||||
{ name: "density", label: "Weight Delta Density", type: "number", default: 0.2, description: "Fraction of extreme weights to retain (0.1 to 0.4)" },
|
||||
],
|
||||
},
|
||||
];
|
||||
25
studio-ref/src/index.css
Normal file
25
studio-ref/src/index.css
Normal file
@@ -0,0 +1,25 @@
|
||||
@import "tailwindcss";
|
||||
|
||||
@layer base {
|
||||
body {
|
||||
background-color: #09090b;
|
||||
color: #e4e4e7;
|
||||
font-feature-settings: "cv02", "cv03", "cv04", "cv11";
|
||||
}
|
||||
|
||||
/* Custom dark scrollbars */
|
||||
::-webkit-scrollbar {
|
||||
width: 6px;
|
||||
height: 6px;
|
||||
}
|
||||
::-webkit-scrollbar-track {
|
||||
background: #09090b;
|
||||
}
|
||||
::-webkit-scrollbar-thumb {
|
||||
background: #27272a;
|
||||
border-radius: 9999px;
|
||||
}
|
||||
::-webkit-scrollbar-thumb:hover {
|
||||
background: #3f3f46;
|
||||
}
|
||||
}
|
||||
10
studio-ref/src/main.tsx
Normal file
10
studio-ref/src/main.tsx
Normal file
@@ -0,0 +1,10 @@
|
||||
import {StrictMode} from 'react';
|
||||
import {createRoot} from 'react-dom/client';
|
||||
import App from './App.tsx';
|
||||
import './index.css';
|
||||
|
||||
createRoot(document.getElementById('root')!).render(
|
||||
<StrictMode>
|
||||
<App />
|
||||
</StrictMode>,
|
||||
);
|
||||
222
studio-ref/src/types.ts
Normal file
222
studio-ref/src/types.ts
Normal file
@@ -0,0 +1,222 @@
|
||||
export type ModelArch = "llama3" | "qwen2.5" | "mistral" | "gemma2" | "deepseek" | "phi4" | "smollm" | "custom";
|
||||
|
||||
export type FineTuneMethod =
|
||||
| "qlora"
|
||||
| "lora_plus"
|
||||
| "dora"
|
||||
| "orpo"
|
||||
| "dpo"
|
||||
| "simpo"
|
||||
| "kto"
|
||||
| "galore"
|
||||
| "neftune"
|
||||
| "longlora";
|
||||
|
||||
export type PruningMethod =
|
||||
| "structured_layer"
|
||||
| "head_pruning"
|
||||
| "vocab_trim"
|
||||
| "laser_svd"
|
||||
| "wanda"
|
||||
| "magnitude_dropout";
|
||||
|
||||
export type MoEMethod =
|
||||
| "moefication"
|
||||
| "dare_ties"
|
||||
| "slerp"
|
||||
| "passthrough_franken"
|
||||
| "task_arithmetic"
|
||||
| "linear_average";
|
||||
|
||||
export type GGUFQuantType =
|
||||
| "Q4_K_M"
|
||||
| "Q4_K_S"
|
||||
| "Q5_K_M"
|
||||
| "Q5_K_S"
|
||||
| "Q8_0"
|
||||
| "IQ4_XS"
|
||||
| "IQ3_XXS"
|
||||
| "IQ2_XS"
|
||||
| "BF16"
|
||||
| "FP16";
|
||||
|
||||
export interface BaseModelInfo {
|
||||
id: string;
|
||||
name: string;
|
||||
huggingFaceId: string;
|
||||
ollamaName: string;
|
||||
parametersBillion: number;
|
||||
layers: number;
|
||||
hiddenDim: number;
|
||||
heads: number;
|
||||
kvHeads: number;
|
||||
vocabSize: number;
|
||||
defaultContext: number;
|
||||
architecture: ModelArch;
|
||||
baseSizeGb: number;
|
||||
q4SizeGb: number;
|
||||
description: string;
|
||||
recommendedFor4080Super: boolean;
|
||||
}
|
||||
|
||||
export interface TrainingHyperparameters {
|
||||
// LoRA / PEFT
|
||||
lora_r: number;
|
||||
lora_alpha: number;
|
||||
lora_dropout?: number;
|
||||
target_modules?: string[];
|
||||
bias?: "none" | "all" | "lora_only";
|
||||
use_dora?: boolean;
|
||||
use_rslora?: boolean;
|
||||
|
||||
// Optimizer & Scheduler
|
||||
batch_size: number;
|
||||
gradient_accumulation_steps: number;
|
||||
learning_rate: number;
|
||||
lr_scheduler?: "cosine" | "linear" | "constant" | "cosine_with_restarts";
|
||||
warmup_ratio?: number;
|
||||
warmup_steps?: number;
|
||||
weight_decay?: number;
|
||||
max_grad_norm?: number;
|
||||
optimizer?: "adamw_8bit" | "paged_adamw_8bit" | "adamw_torch" | "galore_adamw";
|
||||
|
||||
// Training Duration & Precision
|
||||
epochs: number;
|
||||
max_steps?: number;
|
||||
max_seq_length: number;
|
||||
precision?: "bfloat16" | "float16";
|
||||
use_gradient_checkpointing?: boolean;
|
||||
use_unsloth_fast_backprop?: boolean;
|
||||
neftune_noise_alpha?: number;
|
||||
|
||||
// Preference Alignment (for ORPO/DPO/SimPO)
|
||||
preference_beta?: number;
|
||||
simpo_gamma?: number;
|
||||
}
|
||||
|
||||
export interface PruningConfig {
|
||||
enabled: boolean;
|
||||
pruneMethod?: PruningMethod;
|
||||
methods?: PruningMethod[];
|
||||
layerPruningRange: [number, number]; // e.g. prune layers 16 to 24
|
||||
targetLayersCount?: number;
|
||||
headsPrunePercentage?: number;
|
||||
headPruningRatio?: number; // 0.0 - 0.5
|
||||
vocabTrimTarget?: number;
|
||||
vocabTargetTokens?: number; // e.g. 32000 from 128000
|
||||
laserReductionRank?: number; // e.g. 32
|
||||
repairLoRASteps?: number;
|
||||
healingLoraSteps?: number;
|
||||
}
|
||||
|
||||
export interface MoEConfig {
|
||||
enabled: boolean;
|
||||
method: MoEMethod;
|
||||
numExperts: number;
|
||||
topK: number;
|
||||
routerType: "softmax" | "sinkhorn" | "switch";
|
||||
expertSources: {
|
||||
name: string;
|
||||
modelId: string;
|
||||
weight: number;
|
||||
specialization: string;
|
||||
}[];
|
||||
}
|
||||
|
||||
export interface MCPToolDeclaration {
|
||||
id: string;
|
||||
name: string;
|
||||
description: string;
|
||||
serverName: string;
|
||||
parametersSchema: {
|
||||
type: "object";
|
||||
properties: Record<string, { type: string; description: string; enum?: string[] }>;
|
||||
required?: string[];
|
||||
};
|
||||
sampleCallsCount?: number;
|
||||
}
|
||||
|
||||
export interface TrainingDataSample {
|
||||
id: string;
|
||||
instruction: string;
|
||||
input?: string;
|
||||
output: string;
|
||||
system?: string;
|
||||
category?: string;
|
||||
difficulty?: string;
|
||||
toolCalls?: {
|
||||
name: string;
|
||||
arguments: Record<string, any>;
|
||||
}[];
|
||||
simulatedToolResult?: string;
|
||||
isMcpSample?: boolean;
|
||||
}
|
||||
|
||||
export interface GGUFConfig {
|
||||
quantization: GGUFQuantType;
|
||||
contextLength: number;
|
||||
templateFormat?: "llama3" | "chatml" | "mistral" | "alpaca" | "deepseek" | "gemma";
|
||||
systemPrompt: string;
|
||||
stopTokens?: string[];
|
||||
temperature: number;
|
||||
top_p?: number;
|
||||
top_k?: number;
|
||||
repeat_penalty?: number;
|
||||
num_gpu_layers: number; // 999 for full 4080 Super offload
|
||||
threads?: number;
|
||||
}
|
||||
|
||||
export interface HardwarePreset {
|
||||
name: string;
|
||||
vramGb: number;
|
||||
cudaCores: number;
|
||||
tensorCores: number;
|
||||
recommendedBatch: number;
|
||||
recommendedSeqLen: number;
|
||||
recommendedQuant: GGUFQuantType;
|
||||
notes: string;
|
||||
}
|
||||
|
||||
export interface DistillationConfig {
|
||||
enabled: boolean;
|
||||
teacherModel: string;
|
||||
studentModel?: string;
|
||||
distillationType?: "response_generation" | "cot_reasoning" | "logit_kl" | "mcp_alignment";
|
||||
temperature: number;
|
||||
includeThoughtChain: boolean;
|
||||
distillDatasetSize?: number;
|
||||
samplesToGenerate?: number;
|
||||
distillationAlpha?: number;
|
||||
}
|
||||
|
||||
export interface TrainingLogEntry {
|
||||
step: number;
|
||||
epoch: number;
|
||||
loss: number;
|
||||
evalLoss?: number;
|
||||
learningRate: number;
|
||||
gradNorm: number;
|
||||
vramUsedGb: number;
|
||||
tokensPerSec: number;
|
||||
sampleOutput?: string;
|
||||
timestamp: string;
|
||||
}
|
||||
|
||||
export type ActiveTab =
|
||||
| "models"
|
||||
| "model"
|
||||
| "techniques"
|
||||
| "dataset"
|
||||
| "mcp"
|
||||
| "mcp_harness"
|
||||
| "distill"
|
||||
| "distillation"
|
||||
| "pruning"
|
||||
| "moe"
|
||||
| "moe_merge"
|
||||
| "gguf"
|
||||
| "train"
|
||||
| "training"
|
||||
| "deploy"
|
||||
| "ollama"
|
||||
| "arena";
|
||||
259
studio-ref/src/utils/codeGenerators.ts
Normal file
259
studio-ref/src/utils/codeGenerators.ts
Normal file
@@ -0,0 +1,259 @@
|
||||
import { BaseModelInfo, GGUFConfig, MoEConfig, PruningConfig, TrainingHyperparameters } from "../types";
|
||||
|
||||
export function generateUnslothPythonScript(
|
||||
model: BaseModelInfo,
|
||||
params: TrainingHyperparameters,
|
||||
ggufConfig: GGUFConfig,
|
||||
pruningConfig: PruningConfig,
|
||||
customDatasetPath: string = "./dataset.json",
|
||||
outputModelName: string = "fine-tuned-ollama-model"
|
||||
): string {
|
||||
const isDoRA = params.use_dora;
|
||||
const isORPO = params.neftune_noise_alpha > 0;
|
||||
const targetModulesStr = JSON.stringify(params.target_modules);
|
||||
|
||||
let pruningCode = "";
|
||||
if (pruningConfig.enabled && pruningConfig.methods.includes("structured_layer")) {
|
||||
pruningCode = `
|
||||
# ==========================================
|
||||
# ✂️ STRUCTURED LAYER PRUNING (ShortGPT Fat-Shaving)
|
||||
# ==========================================
|
||||
print(">> Applying structured layer pruning on middle transformer blocks...")
|
||||
start_prune, end_prune = ${pruningConfig.layerPruningRange[0]}, ${pruningConfig.layerPruningRange[1]}
|
||||
pruned_layers = [i for i in range(model.config.num_hidden_layers) if not (start_prune <= i <= end_prune)]
|
||||
model.model.layers = torch.nn.ModuleList([model.model.layers[i] for i in pruned_layers])
|
||||
model.config.num_hidden_layers = len(pruned_layers)
|
||||
print(f">> Model layers pruned down to {len(pruned_layers)} layers! Shaved ~25% parameter fat.")
|
||||
`;
|
||||
}
|
||||
|
||||
return `"""
|
||||
Ollama Unsloth Studio - State-of-the-Art Fine-Tuning & Quantization Pipeline
|
||||
Target Model: ${model.name} (${model.huggingFaceId})
|
||||
Hardware Target: NVIDIA RTX 4080 Super (16GB VRAM) / Windows CUDA
|
||||
"""
|
||||
|
||||
import os
|
||||
import torch
|
||||
from unsloth import FastLanguageModel
|
||||
from datasets import load_dataset
|
||||
from trl import SFTTrainer
|
||||
from transformers import TrainingArguments
|
||||
|
||||
# 1. Configuration & Hyperparameters
|
||||
max_seq_length = ${params.max_seq_length}
|
||||
dtype = None # Auto detection (Float16 / Bfloat16)
|
||||
load_in_4bit = True # 4-bit NF4 for max VRAM efficiency on RTX 4080 Super
|
||||
|
||||
print(">> Initializing FastLanguageModel from Unsloth...")
|
||||
model, tokenizer = FastLanguageModel.from_pretrained(
|
||||
model_name="${model.huggingFaceId}",
|
||||
max_seq_length=max_seq_length,
|
||||
dtype=dtype,
|
||||
load_in_4bit=load_in_4bit,
|
||||
)
|
||||
${pruningCode}
|
||||
# 2. Configure PEFT / LoRA / DoRA Parameters
|
||||
print(">> Attaching optimized LoRA adapters...")
|
||||
model = FastLanguageModel.get_peft_model(
|
||||
model,
|
||||
r=${params.lora_r},
|
||||
target_modules=${targetModulesStr},
|
||||
lora_alpha=${params.lora_alpha},
|
||||
lora_dropout=${params.lora_dropout},
|
||||
bias="${params.bias}",
|
||||
use_gradient_checkpointing="unsloth", # Saves 70% VRAM with zero speed penalty
|
||||
random_state=3407,
|
||||
use_rslora=${params.use_rslora},
|
||||
use_dora=${isDoRA},
|
||||
)
|
||||
|
||||
# 3. Format Dataset & Chat Template (Including MCP & Tool-Calling schemas)
|
||||
prompt_template = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
|
||||
${ggufConfig.systemPrompt || "You are an expert AI assistant specialized in precise reasoning and MCP tool execution."}<|eot_id|><|start_header_id|>user<|end_header_id|>
|
||||
{}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
|
||||
{}<|eot_id|>"""
|
||||
|
||||
def formatting_prompts_func(examples):
|
||||
instructions = examples.get("instruction", [])
|
||||
inputs = examples.get("input", [""] * len(instructions))
|
||||
outputs = examples.get("output", [])
|
||||
texts = []
|
||||
for instruction, input_text, output in zip(instructions, inputs, outputs):
|
||||
user_content = f"{instruction}\\n{input_text}".strip() if input_text else instruction
|
||||
text = prompt_template.format(user_content, output)
|
||||
texts.append(text)
|
||||
return { "text" : texts }
|
||||
|
||||
print(f">> Loading training dataset from ${customDatasetPath}...")
|
||||
if os.path.exists("${customDatasetPath}"):
|
||||
dataset = load_dataset("json", data_files="${customDatasetPath}", split="train")
|
||||
dataset = dataset.map(formatting_prompts_func, batched=True)
|
||||
else:
|
||||
print(">> Notice: Local dataset not found, using demo dataset fallback.")
|
||||
from datasets import Dataset
|
||||
dataset = Dataset.from_list([
|
||||
{"instruction": "Call the filesystem read_file tool on src/App.tsx", "input": "", "output": '{"name": "read_file", "arguments": {"path": "src/App.tsx"}}'}
|
||||
]).map(formatting_prompts_func, batched=True)
|
||||
|
||||
# 4. Training Engine Initialization
|
||||
trainer = SFTTrainer(
|
||||
model=model,
|
||||
tokenizer=tokenizer,
|
||||
train_dataset=dataset,
|
||||
dataset_text_field="text",
|
||||
max_seq_length=max_seq_length,
|
||||
dataset_num_proc=2,
|
||||
packing=False, # True for up to 5x speedup for short sequences
|
||||
args=TrainingArguments(
|
||||
per_device_train_batch_size=${params.batch_size},
|
||||
gradient_accumulation_steps=${params.gradient_accumulation_steps},
|
||||
warmup_ratio=${params.warmup_ratio},
|
||||
num_train_epochs=${params.epochs},
|
||||
learning_rate=${params.learning_rate},
|
||||
fp16=not torch.cuda.is_bf16_supported(),
|
||||
bf16=torch.cuda.is_bf16_supported(),
|
||||
logging_steps=1,
|
||||
optim="${params.optimizer}",
|
||||
weight_decay=${params.weight_decay},
|
||||
lr_scheduler_type="${params.lr_scheduler}",
|
||||
seed=3407,
|
||||
output_dir="./outputs",
|
||||
report_to="none",
|
||||
),
|
||||
)
|
||||
|
||||
print(">> Starting model training loop...")
|
||||
trainer_stats = trainer.train()
|
||||
print(">> Training complete! Peak VRAM used:", round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3), "GB")
|
||||
|
||||
# 5. Direct GGUF Quantization & Export for Ollama
|
||||
print(">> Quantizing and saving GGUF directly for Ollama (${ggufConfig.quantization.toLowerCase()})...")
|
||||
model.save_pretrained_gguf(
|
||||
"${outputModelName}",
|
||||
tokenizer,
|
||||
quantization_method="${ggufConfig.quantization.toLowerCase()}"
|
||||
)
|
||||
|
||||
# 6. Generate Ollama Modelfile
|
||||
modelfile_content = f"""FROM ./${outputModelName}-${ggufConfig.quantization.toLowerCase()}.gguf
|
||||
|
||||
TEMPLATE """ + '"""' + prompt_template + '"""' + f"""
|
||||
PARAMETER temperature ${ggufConfig.temperature}
|
||||
PARAMETER top_p ${ggufConfig.top_p}
|
||||
PARAMETER top_k ${ggufConfig.top_k}
|
||||
PARAMETER repeat_penalty ${ggufConfig.repeat_penalty}
|
||||
PARAMETER num_ctx ${ggufConfig.contextLength}
|
||||
PARAMETER num_gpu ${ggufConfig.num_gpu_layers}
|
||||
SYSTEM """ + '"""' + "${ggufConfig.systemPrompt}" + '"""'
|
||||
|
||||
with open("Modelfile", "w") as f:
|
||||
f.write(modelfile_content)
|
||||
|
||||
print(">> Modelfile created successfully!")
|
||||
print(">> To run in Ollama, execute:")
|
||||
print(f" ollama create ${outputModelName} -f Modelfile")
|
||||
print(f" ollama run ${outputModelName}")
|
||||
`;
|
||||
}
|
||||
|
||||
export function generateModelfile(
|
||||
model: BaseModelInfo,
|
||||
ggufConfig: GGUFConfig,
|
||||
modelTag: string = "my-custom-model"
|
||||
): string {
|
||||
const quantSuffix = ggufConfig.quantization.toLowerCase();
|
||||
return `# Modelfile generated by Ollama Unsloth Studio
|
||||
# Optimized for RTX 4080 Super (16GB VRAM) & MCP Plugins
|
||||
|
||||
FROM ./${modelTag}-${quantSuffix}.gguf
|
||||
|
||||
# Model Parameters
|
||||
PARAMETER temperature ${ggufConfig.temperature}
|
||||
PARAMETER top_p ${ggufConfig.top_p}
|
||||
PARAMETER top_k ${ggufConfig.top_k}
|
||||
PARAMETER repeat_penalty ${ggufConfig.repeat_penalty}
|
||||
PARAMETER num_ctx ${ggufConfig.contextLength}
|
||||
PARAMETER num_gpu ${ggufConfig.num_gpu_layers}
|
||||
|
||||
# Chat & Tool Template
|
||||
TEMPLATE """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
|
||||
{{ .System }}<|eot_id|><|start_header_id|>user<|end_header_id|>
|
||||
{{ .Prompt }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
|
||||
{{ .Response }}<|eot_id|>"""
|
||||
|
||||
# System Prompt & MCP Tool Declarations
|
||||
SYSTEM """${ggufConfig.systemPrompt || "You are an ultra-fast, fine-tuned AI model optimized for local execution and MCP tool harness execution."}"""
|
||||
`;
|
||||
}
|
||||
|
||||
export function generateWindowsPowerShellScript(
|
||||
modelTag: string = "my-custom-model"
|
||||
): string {
|
||||
return `# Windows RTX 4080 Super One-Click Training & Ollama Deployer
|
||||
# PowerShell Script for Windows 10/11 with CUDA 12.x
|
||||
|
||||
Write-Host "=======================================================" -ForegroundColor Cyan
|
||||
Write-Host " Ollama Unsloth Studio - Windows RTX 4080 Super Runner" -ForegroundColor Green
|
||||
Write-Host "=======================================================" -ForegroundColor Cyan
|
||||
|
||||
# Check Python environment
|
||||
if (!(Get-Command python -ErrorAction SilentlyContinue)) {
|
||||
Write-Host "[-] Python not found. Please install Python 3.10 or 3.11 with PATH enabled." -ForegroundColor Red
|
||||
Exit
|
||||
}
|
||||
|
||||
# Ensure Virtual Environment
|
||||
if (!(Test-Path "./venv")) {
|
||||
Write-Host "[+] Creating virtual environment 'venv'..." -ForegroundColor Yellow
|
||||
python -m venv venv
|
||||
}
|
||||
|
||||
Write-Host "[+] Activating Virtual Environment..." -ForegroundColor Yellow
|
||||
& ./venv/Scripts/Activate.ps1
|
||||
|
||||
Write-Host "[+] Installing/Updating Unsloth & CUDA PyTorch..." -ForegroundColor Yellow
|
||||
pip install --upgrade pip
|
||||
pip install "unsloth[cu121-ampere-torch240] @ git+https://github.com/unslothai/unsloth.git"
|
||||
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
|
||||
pip install bitsandbytes trl peft datasets transformers xformers
|
||||
|
||||
Write-Host "[+] Starting Unsloth Fine-Tuning..." -ForegroundColor Green
|
||||
python train_unsloth.py
|
||||
|
||||
if (Test-Path "./Modelfile") {
|
||||
Write-Host "[+] Building Ollama Model '${modelTag}'..." -ForegroundColor Green
|
||||
ollama create ${modelTag} -f Modelfile
|
||||
Write-Host "[+] SUCCESS! Model is registered in Ollama." -ForegroundColor Cyan
|
||||
Write-Host ">> Launching test session with Ollama..." -ForegroundColor Green
|
||||
ollama run ${modelTag} "Hello! Check your MCP tool calling capabilities."
|
||||
} else {
|
||||
Write-Host "[-] Modelfile not generated. Please check training logs." -ForegroundColor Red
|
||||
}
|
||||
`;
|
||||
}
|
||||
|
||||
export function generateMergeKitConfig(moeConfig: MoEConfig, baseModel: string): string {
|
||||
if (moeConfig.method === "moefication") {
|
||||
return `base_model: ${baseModel}
|
||||
gate_mode: ${moeConfig.routerType}
|
||||
dtype: bfloat16
|
||||
experts:
|
||||
${moeConfig.expertSources.map((exp) => ` - source_model: ${exp.modelId}
|
||||
positive_prompts:
|
||||
- "${exp.specialization}"
|
||||
parameters:
|
||||
weight: ${exp.weight}`).join("\n")}
|
||||
`;
|
||||
}
|
||||
|
||||
return `merge_method: ${moeConfig.method}
|
||||
base_model: ${baseModel}
|
||||
models:
|
||||
${moeConfig.expertSources.map((exp) => ` - model: ${exp.modelId}
|
||||
parameters:
|
||||
weight: ${exp.weight}
|
||||
density: 0.2`).join("\n")}
|
||||
dtype: bfloat16
|
||||
`;
|
||||
}
|
||||
143
studio-ref/src/utils/hardwareCalculator.ts
Normal file
143
studio-ref/src/utils/hardwareCalculator.ts
Normal file
@@ -0,0 +1,143 @@
|
||||
import { BaseModelInfo, GGUFConfig, GGUFQuantType, MoEConfig, PruningConfig, TrainingHyperparameters } from "../types";
|
||||
|
||||
export interface VRAMCalculationResult {
|
||||
baseModelVramGb: number;
|
||||
loraOverheadGb: number;
|
||||
activationsGb: number;
|
||||
optimizerStateGb: number;
|
||||
kvCacheGb: number;
|
||||
totalTrainingVramGb: number;
|
||||
fitsIn4080Super: boolean;
|
||||
utilizationPercent: number;
|
||||
recommendedBatchSize: number;
|
||||
recommendedGradAccum: number;
|
||||
maxRecommendedContext: number;
|
||||
warnings: string[];
|
||||
}
|
||||
|
||||
export function calculateVRAMFootprint(
|
||||
model: BaseModelInfo,
|
||||
params: TrainingHyperparameters,
|
||||
targetVramGb: number = 16.0
|
||||
): VRAMCalculationResult {
|
||||
const warnings: string[] = [];
|
||||
|
||||
// Base model in 4-bit NF4 (bitsandbytes / Unsloth)
|
||||
// 4-bit is ~0.55 bytes per parameter including quantization scale factors
|
||||
const baseModelVramGb = (model.parametersBillion * 1e9 * 0.55) / (1024 * 1024 * 1024);
|
||||
|
||||
// LoRA rank overhead
|
||||
const numAdaptedModules = (params.target_modules && params.target_modules.length) || 7;
|
||||
const numLayers = model.layers;
|
||||
const hiddenDim = model.hiddenDim;
|
||||
const loraParams = 2 * numAdaptedModules * numLayers * hiddenDim * params.lora_r;
|
||||
// LoRA weights + gradients in fp32/bf16
|
||||
const loraOverheadGb = (loraParams * 4 * 2) / (1024 * 1024 * 1024);
|
||||
|
||||
// Optimizer state: Paged AdamW 8-bit uses 2 bytes per trainable parameter
|
||||
const optimizerStateGb = (loraParams * 2) / (1024 * 1024 * 1024);
|
||||
|
||||
// KV cache + activations: with Unsloth fast backprop + gradient checkpointing
|
||||
const seqLength = params.max_seq_length || 4096;
|
||||
const batchSize = params.batch_size || 1;
|
||||
|
||||
let activationFactor = 0.00000000035;
|
||||
if (params.use_unsloth_fast_backprop) {
|
||||
activationFactor *= 0.35; // Unsloth cuts activation memory drastically
|
||||
}
|
||||
if (params.use_gradient_checkpointing) {
|
||||
activationFactor *= 0.45;
|
||||
}
|
||||
|
||||
const activationsGb = (batchSize * seqLength * numLayers * hiddenDim * activationFactor);
|
||||
|
||||
// KV cache overhead for eval/inference
|
||||
const kvCacheGb = (2 * numLayers * (model.kvHeads || 8) * (hiddenDim / (model.heads || 32)) * seqLength * 2) / (1024 * 1024 * 1024);
|
||||
|
||||
const totalTrainingVramGb = Number(
|
||||
(baseModelVramGb + loraOverheadGb + optimizerStateGb + activationsGb + 0.8).toFixed(2) // 0.8GB CUDA baseline runtime
|
||||
);
|
||||
|
||||
const fitsIn4080Super = totalTrainingVramGb <= targetVramGb;
|
||||
const utilizationPercent = Math.min(100, Math.round((totalTrainingVramGb / targetVramGb) * 100));
|
||||
|
||||
let recommendedBatchSize = 2;
|
||||
let recommendedGradAccum = 4;
|
||||
let maxRecommendedContext = 32768;
|
||||
|
||||
if (model.parametersBillion > 13) {
|
||||
recommendedBatchSize = 1;
|
||||
recommendedGradAccum = 8;
|
||||
maxRecommendedContext = 8192;
|
||||
if (totalTrainingVramGb > 15.5) {
|
||||
warnings.push("14B models on 16GB VRAM require micro-batch size 1 and gradient accumulation.");
|
||||
}
|
||||
} else if (model.parametersBillion > 30) {
|
||||
warnings.push("30B+ models exceed single 16GB VRAM for training. Use Student-Teacher Distillation or Multi-GPU.");
|
||||
}
|
||||
|
||||
if (seqLength > 16384 && !params.use_gradient_checkpointing) {
|
||||
warnings.push("High context length (>16k) requires Gradient Checkpointing to avoid Out-Of-Memory (OOM).");
|
||||
}
|
||||
|
||||
return {
|
||||
baseModelVramGb: Number(baseModelVramGb.toFixed(2)),
|
||||
loraOverheadGb: Number(loraOverheadGb.toFixed(2)),
|
||||
activationsGb: Number(activationsGb.toFixed(2)),
|
||||
optimizerStateGb: Number(optimizerStateGb.toFixed(2)),
|
||||
kvCacheGb: Number(kvCacheGb.toFixed(2)),
|
||||
totalTrainingVramGb,
|
||||
fitsIn4080Super,
|
||||
utilizationPercent,
|
||||
recommendedBatchSize,
|
||||
recommendedGradAccum,
|
||||
maxRecommendedContext,
|
||||
warnings,
|
||||
};
|
||||
}
|
||||
|
||||
export function calculateHardwareCompatibility(
|
||||
model: BaseModelInfo,
|
||||
params: TrainingHyperparameters,
|
||||
ggufConfig?: GGUFConfig,
|
||||
pruningConfig?: PruningConfig,
|
||||
moeConfig?: MoEConfig
|
||||
): VRAMCalculationResult {
|
||||
const result = calculateVRAMFootprint(model, params, 16.0);
|
||||
|
||||
// Apply pruning reductions
|
||||
if (pruningConfig && pruningConfig.enabled) {
|
||||
const prunedLayers = Math.max(0, pruningConfig.layerPruningRange[1] - pruningConfig.layerPruningRange[0] + 1);
|
||||
const reductionRatio = (model.layers - prunedLayers) / model.layers;
|
||||
result.baseModelVramGb = Number((result.baseModelVramGb * reductionRatio).toFixed(2));
|
||||
result.totalTrainingVramGb = Number((result.totalTrainingVramGb - 1.2).toFixed(2));
|
||||
result.utilizationPercent = Math.min(100, Math.round((result.totalTrainingVramGb / 16.0) * 100));
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
export function getGGUFSizeEstimate(paramsBillion: number, quant: GGUFQuantType): { sizeGb: number; bitsPerWeight: number; ramRequiredGb: number } {
|
||||
const quantMap: Record<GGUFQuantType, { bpw: number; name: string }> = {
|
||||
Q4_K_M: { bpw: 4.5, name: "Q4_K_M (Optimal balance)" },
|
||||
Q4_K_S: { bpw: 4.1, name: "Q4_K_S (Compact 4-bit)" },
|
||||
Q5_K_M: { bpw: 5.5, name: "Q5_K_M (High precision)" },
|
||||
Q5_K_S: { bpw: 5.1, name: "Q5_K_S" },
|
||||
Q8_0: { bpw: 8.5, name: "Q8_0 (Near lossless)" },
|
||||
IQ4_XS: { bpw: 4.25, name: "IQ4_XS (Importance Matrix 4-bit)" },
|
||||
IQ3_XXS: { bpw: 3.06, name: "IQ3_XXS (Ultra slim 3-bit)" },
|
||||
IQ2_XS: { bpw: 2.31, name: "IQ2_XS (Extreme 2-bit)" },
|
||||
BF16: { bpw: 16.0, name: "BF16 (Unquantized)" },
|
||||
FP16: { bpw: 16.0, name: "FP16 (Unquantized)" },
|
||||
};
|
||||
|
||||
const info = quantMap[quant] || { bpw: 4.5, name: "Q4_K_M" };
|
||||
const sizeGb = Number(((paramsBillion * 1e9 * (info.bpw / 8)) / (1024 * 1024 * 1024)).toFixed(2));
|
||||
const ramRequiredGb = Number((sizeGb + 1.2).toFixed(2)); // +1.2GB for context & KV cache in Ollama
|
||||
|
||||
return {
|
||||
sizeGb,
|
||||
bitsPerWeight: info.bpw,
|
||||
ramRequiredGb,
|
||||
};
|
||||
}
|
||||
Reference in New Issue
Block a user