⚡ Ollama Personal Trainer & Unsloth MoE Orchestrator

An elite, full-stack Studio Dashboard & Fine-Tuning Environment for local Large Language Models (Qwen 2.5 Coder 32B, DeepSeek V3 671B MoE, Llama 3.1 8B).

Designed specifically for Hardware-Aware MoE Expert Pruning, Harness-Grounded GRPO (Execution-in-the-Loop Reinforcement Learning), MCP & ADB Plugin Synthesis, and Direct Ollama GGUF Deployment.

📸 Core Features & Studio Modules

┌─────────────────────────────────────────────────────────────────────────────┐
│                       OLLAMA PERSONAL TRAINER WORKFLOW                       │
└─────────────────────────────────────────────────────────────────────────────┘
  1. Base Models    ──► Select Qwen-2.5-32B, DeepSeek-V3 671B MoE, Llama-3.1
  2. Techniques     ──► Configure LoRA (r=32), DoRA, FlashAttention-2, NF4
  3. Dataset Studio ──► Manage JSONL samples & Synthetic AI Generator
  4. MCP & ADB      ──► Declare Proxmox, nftables, dnsmasq & ADB tool schemas
  5. MoE Pruner     ──► Trace router gate activations & drop 70% trivia experts
  6. GRPO Training  ──► Real-time execution gym, multi-vector rewards, log stream
  7. GGUF Matrix    ──► Q4_K_M dual-offload quantization (16GB VRAM + 64GB RAM)
  8. Ollama Deploy  ──► One-click Modelfile build & Localhost:11434 push
  9. Arena Test     ──► Live interactive chat & real tool execution sandbox

🛠️ Deep Subsystem Integration

1. MoE Router Activation Profiling & Expert Weight Pruning

Domain-Specific Tracing: Traces top-k router gate selection frequencies across all 61 transformer layers during calibration forward passes.
Surgical Expert Dropping: Drops dormant experts holding botany, literature, or trivia knowledge (256 experts \rightarrow 64 experts), saving ~68.75% parameter weight (~34.5 GB System RAM + 6.2 GB VRAM in Q4_K_M).

2. Harness-Grounded GRPO (Execution-in-the-Loop RL)

Hard Execution Rewards (R_{\text{exec}}): +3.0 for exit code 0, -1.5 for runtime exceptions, -10.0 for safety violations.
Anti-Hesitation Penalty (R_{\text{anti\_hesit}}): +2.0 if the command code block is initiated within 25 tokens, suppressing natural language chatter.
Rolling Cyber Terminal: Live WebSocket log console with level filtering (INFO, HARNESS, REWARD, WARN, ERROR) and auto-scroll controls.

3. Proxify-ADB Fleet Telemetry & MCP Plugins

Pre-configured tool declarations for Proxmox Control Gateway (10.30.20.1), isolated bridge vmbr1, dnsmasq leases, nftables proxy routing rules, and ADB phone endpoints (5555).

🚀 Quickstart Guide

1. Requirements

OS: Windows 11 / Linux (WSL2 Ubuntu 24.04 recommended for Triton acceleration)
Node.js: v18.x or higher (v24 tested)
Ollama: Running locally on http://localhost:11434
GPU: NVIDIA RTX 4080 Super (16 GB VRAM) + 64 GB System RAM

2. Installation

git clone https://gitea.thetempleofdoom.com/drjones/ollama-personal-trainer.git
cd ollama-personal-trainer
npm install

3. Launching Studio

# Start full-stack React + Express server (Runs on http://localhost:3000)
npm run dev

4. Production Build & Server Start

npm run build
npm start

📦 Project File Structure

ollama-personal-trainer/
├── server.ts                   # Express API, Vite middleware, Ollama proxy & Gitea sync
├── package.json                # React 19, Lucide, Recharts, Tailwind CSS v4, Express
├── vite.config.ts              # Vite bundle configuration
├── src/
│   ├── App.tsx                 # Master state controller & tab router
│   ├── components/
│   │   ├── Header.tsx          # Top HUD, VRAM load meter, Ollama connection badge
│   │   ├── ModelSelector.tsx   # Base model selector & hardware fit calculator
│   │   ├── TechniqueWorkshop.tsx # Hyperparameters, LoRA rank r, DoRA, NF4
│   │   ├── DatasetStudio.tsx   # JSONL dataset editor & Gemini synthetic generator
│   │   ├── MCPHarnessStudio.tsx# MCP tool schemas & ADB fleet commands
│   │   ├── PruningStudio.tsx   # MoE expert activation tracing & layer drop studio
│   │   ├── MoEStudio.tsx       # MoE merger & routing topology inspector
│   │   ├── TrainingSimulator.tsx # GRPO reward curves, live loss, cyber log stream
│   │   ├── GGUFStudio.tsx      # GGUF quantization matrix & system prompts
│   │   ├── OllamaDeployer.tsx  # Modelfile generator & Gitea push action
│   │   └── InteractiveArena.tsx# Live chat playground & tool call verification
│   ├── data/                   # Default models, MCP presets, hardware calculators
│   └── types.ts                # TypeScript interfaces
├── scripts/                    # Python pipeline execution scripts
│   ├── harvester.py            # Infrastructure Config Harvester
│   ├── prune_moe.py            # MoE Expert Profiler & Pruner
│   ├── harness_env.py          # Command Execution Safety Sandbox
│   ├── train_grpo.py           # Unsloth GRPO Trainer script
│   ├── deploy.py               # GGUF Export helper
│   └── state_eye.py            # 10.30.20.1 Telemetry Injector
└── infra_moe_grpo_blueprint.md # Complete technical architecture documentation

🔒 License

Licensed under the MIT License. Developed for drjones.

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