Initial commit: Ollama Personal Trainer & Unsloth MoE Orchestrator Studio
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README.md
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# ⚡ Ollama Personal Trainer & Unsloth MoE Orchestrator
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[](https://github.com/unslothai/unsloth)
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[]()
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[](https://ollama.ai)
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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).
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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**.
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---
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## 📸 Core Features & Studio Modules
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```
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ OLLAMA PERSONAL TRAINER WORKFLOW │
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└─────────────────────────────────────────────────────────────────────────────┘
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1. Base Models ──► Select Qwen-2.5-32B, DeepSeek-V3 671B MoE, Llama-3.1
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2. Techniques ──► Configure LoRA (r=32), DoRA, FlashAttention-2, NF4
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3. Dataset Studio ──► Manage JSONL samples & Synthetic AI Generator
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4. MCP & ADB ──► Declare Proxmox, nftables, dnsmasq & ADB tool schemas
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5. MoE Pruner ──► Trace router gate activations & drop 70% trivia experts
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6. GRPO Training ──► Real-time execution gym, multi-vector rewards, log stream
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7. GGUF Matrix ──► Q4_K_M dual-offload quantization (16GB VRAM + 64GB RAM)
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8. Ollama Deploy ──► One-click Modelfile build & Localhost:11434 push
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9. Arena Test ──► Live interactive chat & real tool execution sandbox
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```
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---
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## 🛠️ Deep Subsystem Integration
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### 1. MoE Router Activation Profiling & Expert Weight Pruning
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- **Domain-Specific Tracing**: Traces top-k router gate selection frequencies across all 61 transformer layers during calibration forward passes.
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- **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).
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### 2. Harness-Grounded GRPO (Execution-in-the-Loop RL)
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- **Hard Execution Rewards ($R_{\text{exec}}$)**: $+3.0$ for exit code 0, $-1.5$ for runtime exceptions, $-10.0$ for safety violations.
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- **Anti-Hesitation Penalty ($R_{\text{anti\_hesit}}$)**: $+2.0$ if the command code block is initiated within 25 tokens, suppressing natural language chatter.
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- **Rolling Cyber Terminal**: Live WebSocket log console with level filtering (`INFO`, `HARNESS`, `REWARD`, `WARN`, `ERROR`) and auto-scroll controls.
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### 3. Proxify-ADB Fleet Telemetry & MCP Plugins
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- 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`).
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---
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## 🚀 Quickstart Guide
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### 1. Requirements
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- **OS**: Windows 11 / Linux (WSL2 Ubuntu 24.04 recommended for Triton acceleration)
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- **Node.js**: v18.x or higher (v24 tested)
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- **Ollama**: Running locally on `http://localhost:11434`
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- **GPU**: NVIDIA RTX 4080 Super (16 GB VRAM) + 64 GB System RAM
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### 2. Installation
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```bash
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git clone https://gitea.thetempleofdoom.com/drjones/ollama-personal-trainer.git
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cd ollama-personal-trainer
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npm install
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```
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### 3. Launching Studio
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```bash
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# Start full-stack React + Express server (Runs on http://localhost:3000)
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npm run dev
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```
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### 4. Production Build & Server Start
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```bash
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npm run build
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npm start
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```
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---
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## 📦 Project File Structure
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```
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ollama-personal-trainer/
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├── server.ts # Express API, Vite middleware, Ollama proxy & Gitea sync
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├── package.json # React 19, Lucide, Recharts, Tailwind CSS v4, Express
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├── vite.config.ts # Vite bundle configuration
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├── src/
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│ ├── App.tsx # Master state controller & tab router
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│ ├── components/
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│ │ ├── Header.tsx # Top HUD, VRAM load meter, Ollama connection badge
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│ │ ├── ModelSelector.tsx # Base model selector & hardware fit calculator
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│ │ ├── TechniqueWorkshop.tsx # Hyperparameters, LoRA rank r, DoRA, NF4
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│ │ ├── DatasetStudio.tsx # JSONL dataset editor & Gemini synthetic generator
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│ │ ├── MCPHarnessStudio.tsx# MCP tool schemas & ADB fleet commands
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│ │ ├── PruningStudio.tsx # MoE expert activation tracing & layer drop studio
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│ │ ├── MoEStudio.tsx # MoE merger & routing topology inspector
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│ │ ├── TrainingSimulator.tsx # GRPO reward curves, live loss, cyber log stream
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│ │ ├── GGUFStudio.tsx # GGUF quantization matrix & system prompts
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│ │ ├── OllamaDeployer.tsx # Modelfile generator & Gitea push action
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│ │ └── InteractiveArena.tsx# Live chat playground & tool call verification
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│ ├── data/ # Default models, MCP presets, hardware calculators
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│ └── types.ts # TypeScript interfaces
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├── scripts/ # Python pipeline execution scripts
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│ ├── harvester.py # Infrastructure Config Harvester
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│ ├── prune_moe.py # MoE Expert Profiler & Pruner
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│ ├── harness_env.py # Command Execution Safety Sandbox
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│ ├── train_grpo.py # Unsloth GRPO Trainer script
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│ ├── deploy.py # GGUF Export helper
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│ └── state_eye.py # 10.30.20.1 Telemetry Injector
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└── infra_moe_grpo_blueprint.md # Complete technical architecture documentation
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```
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---
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## 🔒 License
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Licensed under the [MIT License](LICENSE). Developed for **drjones**.
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