diff --git a/.env.example b/.env.example
new file mode 100644
index 0000000..7a550fe
--- /dev/null
+++ b/.env.example
@@ -0,0 +1,9 @@
+# GEMINI_API_KEY: Required for Gemini AI API calls.
+# AI Studio automatically injects this at runtime from user secrets.
+# Users configure this via the Secrets panel in the AI Studio UI.
+GEMINI_API_KEY="MY_GEMINI_API_KEY"
+
+# APP_URL: The URL where this applet is hosted.
+# AI Studio automatically injects this at runtime with the Cloud Run service URL.
+# Used for self-referential links, OAuth callbacks, and API endpoints.
+APP_URL="MY_APP_URL"
diff --git a/.gitignore b/.gitignore
index 09cb1a1..5a86d2a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,8 @@
node_modules/
-.env
-*.log
-__pycache__/
+build/
+dist/
+coverage/
.DS_Store
+*.log
+.env*
+!.env.example
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..569b546
--- /dev/null
+++ b/README.md
@@ -0,0 +1,114 @@
+# β‘ Ollama Personal Trainer & Unsloth MoE Orchestrator
+
+[](https://github.com/unslothai/unsloth)
+[]()
+[](https://ollama.ai)
+
+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**.
+
+---
+
+## πΈ Core Features & Studio Modules
+
+```
+βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
+β OLLAMA PERSONAL TRAINER WORKFLOW β
+βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
+ 1. Base Models βββΊ Select Qwen-2.5-32B, DeepSeek-V3 671B MoE, Llama-3.1
+ 2. Techniques βββΊ Configure LoRA (r=32), DoRA, FlashAttention-2, NF4
+ 3. Dataset Studio βββΊ Manage JSONL samples & Synthetic AI Generator
+ 4. MCP & ADB βββΊ Declare Proxmox, nftables, dnsmasq & ADB tool schemas
+ 5. MoE Pruner βββΊ Trace router gate activations & drop 70% trivia experts
+ 6. GRPO Training βββΊ Real-time execution gym, multi-vector rewards, log stream
+ 7. GGUF Matrix βββΊ Q4_K_M dual-offload quantization (16GB VRAM + 64GB RAM)
+ 8. Ollama Deploy βββΊ One-click Modelfile build & Localhost:11434 push
+ 9. Arena Test βββΊ Live interactive chat & real tool execution sandbox
+```
+
+---
+
+## π οΈ 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`).
+
+---
+
+## π 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
+```bash
+git clone https://gitea.thetempleofdoom.com/drjones/ollama-personal-trainer.git
+cd ollama-personal-trainer
+npm install
+```
+
+### 3. Launching Studio
+```bash
+# Start full-stack React + Express server (Runs on http://localhost:3000)
+npm run dev
+```
+
+### 4. Production Build & Server Start
+```bash
+npm run build
+npm start
+```
+
+---
+
+## π¦ Project File Structure
+
+```
+ollama-personal-trainer/
+βββ server.ts # Express API, Vite middleware, Ollama proxy & Gitea sync
+βββ package.json # React 19, Lucide, Recharts, Tailwind CSS v4, Express
+βββ vite.config.ts # Vite bundle configuration
+βββ src/
+β βββ App.tsx # Master state controller & tab router
+β βββ components/
+β β βββ Header.tsx # Top HUD, VRAM load meter, Ollama connection badge
+β β βββ ModelSelector.tsx # Base model selector & hardware fit calculator
+β β βββ TechniqueWorkshop.tsx # Hyperparameters, LoRA rank r, DoRA, NF4
+β β βββ DatasetStudio.tsx # JSONL dataset editor & Gemini synthetic generator
+β β βββ MCPHarnessStudio.tsx# MCP tool schemas & ADB fleet commands
+β β βββ PruningStudio.tsx # MoE expert activation tracing & layer drop studio
+β β βββ MoEStudio.tsx # MoE merger & routing topology inspector
+β β βββ TrainingSimulator.tsx # GRPO reward curves, live loss, cyber log stream
+β β βββ GGUFStudio.tsx # GGUF quantization matrix & system prompts
+β β βββ OllamaDeployer.tsx # Modelfile generator & Gitea push action
+β β βββ InteractiveArena.tsx# Live chat playground & tool call verification
+β βββ data/ # Default models, MCP presets, hardware calculators
+β βββ types.ts # TypeScript interfaces
+βββ scripts/ # Python pipeline execution scripts
+β βββ harvester.py # Infrastructure Config Harvester
+β βββ prune_moe.py # MoE Expert Profiler & Pruner
+β βββ harness_env.py # Command Execution Safety Sandbox
+β βββ train_grpo.py # Unsloth GRPO Trainer script
+β βββ deploy.py # GGUF Export helper
+β βββ state_eye.py # 10.30.20.1 Telemetry Injector
+βββ infra_moe_grpo_blueprint.md # Complete technical architecture documentation
+```
+
+---
+
+## π License
+
+Licensed under the [MIT License](LICENSE). Developed for **drjones**.
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new file mode 100644
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@@ -0,0 +1,18 @@
+
+
+
+
+
+ My Google AI Studio App
+
+
+
+
+
+
+
+
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diff --git a/metadata.json b/metadata.json
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--- /dev/null
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+{
+ "name": "Ollama Unsloth Studio",
+ "description": "Comprehensive browser platform to fine-tune, prune, quantize, distill, create MoE models, and export optimized GGUFs with MCP plugins directly to Ollama.",
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@@ -57,6 +1969,45 @@
"npm": "1.2.8000 || >= 1.4.16"
}
},
+ "node_modules/browserslist": {
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+ "resolved": "https://registry.npmjs.org/browserslist/-/browserslist-4.28.8.tgz",
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+ "funding": [
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+ "type": "opencollective",
+ "url": "https://opencollective.com/browserslist"
+ },
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+ "type": "tidelift",
+ "url": "https://tidelift.com/funding/github/npm/browserslist"
+ },
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+ "type": "github",
+ "url": "https://github.com/sponsors/ai"
+ }
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+ "node-releases": "^2.0.53",
+ "update-browserslist-db": "^1.3.0"
+ },
+ "bin": {
+ "browserslist": "cli.js"
+ },
+ "engines": {
+ "node": "^6 || ^7 || ^8 || ^9 || ^10 || ^11 || ^12 || >=13.7"
+ }
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+ "node_modules/buffer-equal-constant-time": {
+ "version": "1.0.1",
+ "resolved": "https://registry.npmjs.org/buffer-equal-constant-time/-/buffer-equal-constant-time-1.0.1.tgz",
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+ "license": "BSD-3-Clause"
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"resolved": "https://registry.npmjs.org/bytes/-/bytes-3.1.2.tgz",
@@ -95,6 +2046,35 @@
"url": "https://github.com/sponsors/ljharb"
}
},
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+ "version": "1.0.30001809",
+ "resolved": "https://registry.npmjs.org/caniuse-lite/-/caniuse-lite-1.0.30001809.tgz",
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+ "funding": [
+ {
+ "type": "opencollective",
+ "url": "https://opencollective.com/browserslist"
+ },
+ {
+ "type": "tidelift",
+ "url": "https://tidelift.com/funding/github/npm/caniuse-lite"
+ },
+ {
+ "type": "github",
+ "url": "https://github.com/sponsors/ai"
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+ "resolved": "https://registry.npmjs.org/clsx/-/clsx-2.1.1.tgz",
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"version": "0.5.4",
"resolved": "https://registry.npmjs.org/content-disposition/-/content-disposition-0.5.4.tgz",
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+ "resolved": "https://registry.npmjs.org/convert-source-map/-/convert-source-map-2.0.0.tgz",
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+ "license": "MIT"
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+ "license": "ISC",
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- "vary": "^1"
+ "internmap": "1 - 2"
},
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- "node": ">= 0.10"
+ "node": ">=12"
+ }
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+ "resolved": "https://registry.npmjs.org/d3-color/-/d3-color-3.1.0.tgz",
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+ "license": "ISC",
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+ "resolved": "https://registry.npmjs.org/d3-ease/-/d3-ease-3.0.1.tgz",
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+ "license": "BSD-3-Clause",
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+ }
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+ "resolved": "https://registry.npmjs.org/d3-format/-/d3-format-3.1.2.tgz",
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+ "license": "ISC",
+ "engines": {
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+ }
+ },
+ "node_modules/d3-interpolate": {
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+ "resolved": "https://registry.npmjs.org/d3-interpolate/-/d3-interpolate-3.0.1.tgz",
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+ "license": "ISC",
+ "dependencies": {
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},
- "funding": {
- "type": "opencollective",
- "url": "https://opencollective.com/express"
+ "engines": {
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+ }
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+ "node_modules/d3-path": {
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+ "license": "ISC",
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+ "license": "ISC",
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+ "license": "ISC",
+ "dependencies": {
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+ "resolved": "https://registry.npmjs.org/d3-timer/-/d3-timer-3.0.1.tgz",
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+ "license": "ISC",
+ "engines": {
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+ }
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+ "node_modules/data-uri-to-buffer": {
+ "version": "4.0.1",
+ "resolved": "https://registry.npmjs.org/data-uri-to-buffer/-/data-uri-to-buffer-4.0.1.tgz",
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+ "license": "MIT",
+ "engines": {
+ "node": ">= 12"
}
},
"node_modules/debug": {
@@ -157,6 +2256,12 @@
"ms": "2.0.0"
}
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+ "version": "2.5.1",
+ "resolved": "https://registry.npmjs.org/decimal.js-light/-/decimal.js-light-2.5.1.tgz",
+ "integrity": "sha512-qIMFpTMZmny+MMIitAB6D7iVPEorVw6YQRWkvarTkT4tBeSLLiHzcwj6q0MmYSFCiVpiqPJTJEYIrpcPzVEIvg==",
+ "license": "MIT"
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"version": "2.0.0",
"resolved": "https://registry.npmjs.org/depd/-/depd-2.0.0.tgz",
@@ -176,6 +2281,27 @@
"npm": "1.2.8000 || >= 1.4.16"
}
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+ "version": "2.1.2",
+ "resolved": "https://registry.npmjs.org/detect-libc/-/detect-libc-2.1.2.tgz",
+ "integrity": "sha512-Btj2BOOO83o3WyH59e8MgXsxEQVcarkUOpEYrubB0urwnN10yQ364rsiByU11nZlqWYZm05i/of7io4mzihBtQ==",
+ "license": "Apache-2.0",
+ "engines": {
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+ "node_modules/dotenv": {
+ "version": "17.4.2",
+ "resolved": "https://registry.npmjs.org/dotenv/-/dotenv-17.4.2.tgz",
+ "integrity": "sha512-nI4U3TottKAcAD9LLud4Cb7b2QztQMUEfHbvhTH09bqXTxnSie8WnjPALV/WMCrJZ6UV/qHJ6L03OqO3LcdYZw==",
+ "license": "BSD-2-Clause",
+ "engines": {
+ "node": ">=12"
+ },
+ "funding": {
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+ }
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"node_modules/dunder-proto": {
"version": "1.0.1",
"resolved": "https://registry.npmjs.org/dunder-proto/-/dunder-proto-1.0.1.tgz",
@@ -190,12 +2316,27 @@
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+ "version": "1.0.11",
+ "resolved": "https://registry.npmjs.org/ecdsa-sig-formatter/-/ecdsa-sig-formatter-1.0.11.tgz",
+ "integrity": "sha512-nagl3RYrbNv6kQkeJIpt6NJZy8twLB/2vtz6yN9Z4vRKHN4/QZJIEbqohALSgwKdnksuY3k5Addp5lg8sVoVcQ==",
+ "license": "Apache-2.0",
+ "dependencies": {
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+ }
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"node_modules/ee-first": {
"version": "1.1.1",
"resolved": "https://registry.npmjs.org/ee-first/-/ee-first-1.1.1.tgz",
"integrity": "sha512-WMwm9LhRUo+WUaRN+vRuETqG89IgZphVSNkdFgeb6sS/E4OrDIN7t48CAewSHXc6C8lefD8KKfr5vY61brQlow==",
"license": "MIT"
},
+ "node_modules/electron-to-chromium": {
+ "version": "1.5.405",
+ "resolved": "https://registry.npmjs.org/electron-to-chromium/-/electron-to-chromium-1.5.405.tgz",
+ "integrity": "sha512-bNglH7lPH5l+yHOes7Zr4VqxhOy4BQ9ZBUX4VdoFgxMpzJk7W1ZoO3Vgd9Pxa9PyjQ76sfm2aKH/nzEcCNRlew==",
+ "license": "ISC"
+ },
"node_modules/encodeurl": {
"version": "2.0.0",
"resolved": "https://registry.npmjs.org/encodeurl/-/encodeurl-2.0.0.tgz",
@@ -205,6 +2346,19 @@
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+ "version": "5.24.5",
+ "resolved": "https://registry.npmjs.org/enhanced-resolve/-/enhanced-resolve-5.24.5.tgz",
+ "integrity": "sha512-L1l8TNvomm6UVW5B253AGxQagSQr+vGwhMlrrfRS2qmhx46AMpMVJKQYLvWYbysTMY8VoicOvzHzoHMbyzB+4A==",
+ "license": "MIT",
+ "dependencies": {
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+ "tapable": "^2.3.3"
+ },
+ "engines": {
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"node_modules/es-define-property": {
"version": "1.0.1",
"resolved": "https://registry.npmjs.org/es-define-property/-/es-define-property-1.0.1.tgz",
@@ -235,6 +2389,67 @@
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+ "resolved": "https://registry.npmjs.org/es-toolkit/-/es-toolkit-1.50.0.tgz",
+ "integrity": "sha512-OyZKhUVvEep9ITEiwHn8GKnMRQIVqoSIX7WnRbkWgJkllCujilqP2rD0u979tkl8wqyc8ICwlc1UBVv/Sl1G6w==",
+ "license": "MIT",
+ "workspaces": [
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+ "resolved": "https://registry.npmjs.org/esbuild/-/esbuild-0.25.12.tgz",
+ "integrity": "sha512-bbPBYYrtZbkt6Os6FiTLCTFxvq4tt3JKall1vRwshA3fdVztsLAatFaZobhkBC8/BrPetoa0oksYoKXoG4ryJg==",
+ "hasInstallScript": true,
+ "license": "MIT",
+ "bin": {
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+ "engines": {
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+ "@esbuild/android-arm": "0.25.12",
+ "@esbuild/android-arm64": "0.25.12",
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+ "@esbuild/freebsd-arm64": "0.25.12",
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+ "@esbuild/linux-ppc64": "0.25.12",
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+ "@esbuild/win32-arm64": "0.25.12",
+ "@esbuild/win32-ia32": "0.25.12",
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+ "resolved": "https://registry.npmjs.org/escalade/-/escalade-3.2.0.tgz",
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+ "license": "MIT",
+ "engines": {
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"node_modules/escape-html": {
"version": "1.0.3",
"resolved": "https://registry.npmjs.org/escape-html/-/escape-html-1.0.3.tgz",
@@ -250,6 +2465,12 @@
"node": ">= 0.6"
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+ "license": "MIT"
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"version": "4.22.2",
"resolved": "https://registry.npmjs.org/express/-/express-4.22.2.tgz",
@@ -296,6 +2517,52 @@
"url": "https://opencollective.com/express"
}
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+ "resolved": "https://registry.npmjs.org/extend/-/extend-3.0.2.tgz",
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+ "license": "MIT"
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+ "license": "MIT",
+ "engines": {
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+ "peerDependencies": {
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+ "funding": [
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+ "type": "github",
+ "url": "https://github.com/sponsors/jimmywarting"
+ },
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+ "type": "paypal",
+ "url": "https://paypal.me/jimmywarting"
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"resolved": "https://registry.npmjs.org/finalhandler/-/finalhandler-1.3.2.tgz",
@@ -314,6 +2581,18 @@
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+ "license": "MIT",
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+ "integrity": "sha512-5ebpxr3nWMzrL/rnUI755Jkuee0bHL/Gq0WTF9lvcpv73wAp5eu8MfBUgWK9bhWvZjj7yX8etf/8tI8Ney695g==",
+ "cpu": [
+ "x64"
+ ],
+ "license": "MIT",
+ "optional": true,
+ "os": [
+ "win32"
+ ],
+ "engines": {
+ "node": ">=18"
+ }
+ },
+ "node_modules/tsx/node_modules/esbuild": {
+ "version": "0.28.2",
+ "resolved": "https://registry.npmjs.org/esbuild/-/esbuild-0.28.2.tgz",
+ "integrity": "sha512-HKVLS8dvII+xoKW9kmqxbRKrnWEXfJJr/FZhhJmiqIB0e053QNYFqOBouTMO/k5sID4MvCiUCvv8b9M4h32wIA==",
+ "devOptional": true,
+ "hasInstallScript": true,
+ "license": "MIT",
+ "bin": {
+ "esbuild": "bin/esbuild"
+ },
+ "engines": {
+ "node": ">=18"
+ },
+ "optionalDependencies": {
+ "@esbuild/aix-ppc64": "0.28.2",
+ "@esbuild/android-arm": "0.28.2",
+ "@esbuild/android-arm64": "0.28.2",
+ "@esbuild/android-x64": "0.28.2",
+ "@esbuild/darwin-arm64": "0.28.2",
+ "@esbuild/darwin-x64": "0.28.2",
+ "@esbuild/freebsd-arm64": "0.28.2",
+ "@esbuild/freebsd-x64": "0.28.2",
+ "@esbuild/linux-arm": "0.28.2",
+ "@esbuild/linux-arm64": "0.28.2",
+ "@esbuild/linux-ia32": "0.28.2",
+ "@esbuild/linux-loong64": "0.28.2",
+ "@esbuild/linux-mips64el": "0.28.2",
+ "@esbuild/linux-ppc64": "0.28.2",
+ "@esbuild/linux-riscv64": "0.28.2",
+ "@esbuild/linux-s390x": "0.28.2",
+ "@esbuild/linux-x64": "0.28.2",
+ "@esbuild/netbsd-arm64": "0.28.2",
+ "@esbuild/netbsd-x64": "0.28.2",
+ "@esbuild/openbsd-arm64": "0.28.2",
+ "@esbuild/openbsd-x64": "0.28.2",
+ "@esbuild/openharmony-arm64": "0.28.2",
+ "@esbuild/sunos-x64": "0.28.2",
+ "@esbuild/win32-arm64": "0.28.2",
+ "@esbuild/win32-ia32": "0.28.2",
+ "@esbuild/win32-x64": "0.28.2"
+ }
+ },
"node_modules/type-is": {
"version": "1.6.18",
"resolved": "https://registry.npmjs.org/type-is/-/type-is-1.6.18.tgz",
@@ -826,6 +4556,26 @@
"node": ">= 0.6"
}
},
+ "node_modules/typescript": {
+ "version": "5.8.3",
+ "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.8.3.tgz",
+ "integrity": "sha512-p1diW6TqL9L07nNxvRMM7hMMw4c5XOo/1ibL4aAIGmSAt9slTE1Xgw5KWuof2uTOvCg9BY7ZRi+GaF+7sfgPeQ==",
+ "dev": true,
+ "license": "Apache-2.0",
+ "bin": {
+ "tsc": "bin/tsc",
+ "tsserver": "bin/tsserver"
+ },
+ "engines": {
+ "node": ">=14.17"
+ }
+ },
+ "node_modules/undici-types": {
+ "version": "6.21.0",
+ "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz",
+ "integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==",
+ "license": "MIT"
+ },
"node_modules/unpipe": {
"version": "1.0.0",
"resolved": "https://registry.npmjs.org/unpipe/-/unpipe-1.0.0.tgz",
@@ -835,6 +4585,45 @@
"node": ">= 0.8"
}
},
+ "node_modules/update-browserslist-db": {
+ "version": "1.3.1",
+ "resolved": "https://registry.npmjs.org/update-browserslist-db/-/update-browserslist-db-1.3.1.tgz",
+ "integrity": "sha512-ZZ61DsRsOnakl74HAmp3oSN4aXUmEWXf+i/yv0h7tIBfICc3VdrFErQKUUKPgu3AMsTUMbcongALEN4l6GSUrQ==",
+ "funding": [
+ {
+ "type": "opencollective",
+ "url": "https://opencollective.com/browserslist"
+ },
+ {
+ "type": "tidelift",
+ "url": "https://tidelift.com/funding/github/npm/browserslist"
+ },
+ {
+ "type": "github",
+ "url": "https://github.com/sponsors/ai"
+ }
+ ],
+ "license": "MIT",
+ "dependencies": {
+ "escalade": "^3.2.0",
+ "picocolors": "^1.1.1"
+ },
+ "bin": {
+ "update-browserslist-db": "cli.js"
+ },
+ "peerDependencies": {
+ "browserslist": ">= 4.21.0"
+ }
+ },
+ "node_modules/use-sync-external-store": {
+ "version": "1.6.0",
+ "resolved": "https://registry.npmjs.org/use-sync-external-store/-/use-sync-external-store-1.6.0.tgz",
+ "integrity": "sha512-Pp6GSwGP/NrPIrxVFAIkOQeyw8lFenOHijQWkUTrDvrF4ALqylP2C/KCkeS9dpUM3KvYRQhna5vt7IL95+ZQ9w==",
+ "license": "MIT",
+ "peerDependencies": {
+ "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0"
+ }
+ },
"node_modules/utils-merge": {
"version": "1.0.1",
"resolved": "https://registry.npmjs.org/utils-merge/-/utils-merge-1.0.1.tgz",
@@ -853,6 +4642,111 @@
"node": ">= 0.8"
}
},
+ "node_modules/victory-vendor": {
+ "version": "37.3.6",
+ "resolved": "https://registry.npmjs.org/victory-vendor/-/victory-vendor-37.3.6.tgz",
+ "integrity": "sha512-SbPDPdDBYp+5MJHhBCAyI7wKM3d5ivekigc2Dk2s7pgbZ9wIgIBYGVw4zGHBml/qTFbexrofXW6Gu4noGxrOwQ==",
+ "license": "MIT AND ISC",
+ "dependencies": {
+ "@types/d3-array": "^3.0.3",
+ "@types/d3-ease": "^3.0.0",
+ "@types/d3-interpolate": "^3.0.1",
+ "@types/d3-scale": "^4.0.2",
+ "@types/d3-shape": "^3.1.0",
+ "@types/d3-time": "^3.0.0",
+ "@types/d3-timer": "^3.0.0",
+ "d3-array": "^3.1.6",
+ "d3-ease": "^3.0.1",
+ "d3-interpolate": "^3.0.1",
+ "d3-scale": "^4.0.2",
+ "d3-shape": "^3.1.0",
+ "d3-time": "^3.0.0",
+ "d3-timer": "^3.0.1"
+ }
+ },
+ "node_modules/vite": {
+ "version": "6.4.3",
+ "resolved": "https://registry.npmjs.org/vite/-/vite-6.4.3.tgz",
+ "integrity": "sha512-NTKlcQjlAK7MlQoyb6LgaqHc8sso/pVyUJYWMws3jg21uTJw/LddqIFPcPqP6PzpgbIcZyKI85sFE4HBrQDA8A==",
+ "license": "MIT",
+ "dependencies": {
+ "esbuild": "^0.25.0",
+ "fdir": "^6.4.4",
+ "picomatch": "^4.0.2",
+ "postcss": "^8.5.3",
+ "rollup": "^4.34.9",
+ "tinyglobby": "^0.2.13"
+ },
+ "bin": {
+ "vite": "bin/vite.js"
+ },
+ "engines": {
+ "node": "^18.0.0 || ^20.0.0 || >=22.0.0"
+ },
+ "funding": {
+ "url": "https://github.com/vitejs/vite?sponsor=1"
+ },
+ "optionalDependencies": {
+ "fsevents": "~2.3.3"
+ },
+ "peerDependencies": {
+ "@types/node": "^18.0.0 || ^20.0.0 || >=22.0.0",
+ "jiti": ">=1.21.0",
+ "less": "*",
+ "lightningcss": "^1.21.0",
+ "sass": "*",
+ "sass-embedded": "*",
+ "stylus": "*",
+ "sugarss": "*",
+ "terser": "^5.16.0",
+ "tsx": "^4.8.1",
+ "yaml": "^2.4.2"
+ },
+ "peerDependenciesMeta": {
+ "@types/node": {
+ "optional": true
+ },
+ "jiti": {
+ "optional": true
+ },
+ "less": {
+ "optional": true
+ },
+ "lightningcss": {
+ "optional": true
+ },
+ "sass": {
+ "optional": true
+ },
+ "sass-embedded": {
+ "optional": true
+ },
+ "stylus": {
+ "optional": true
+ },
+ "sugarss": {
+ "optional": true
+ },
+ "terser": {
+ "optional": true
+ },
+ "tsx": {
+ "optional": true
+ },
+ "yaml": {
+ "optional": true
+ }
+ }
+ },
+ "node_modules/web-streams-polyfill": {
+ "version": "3.3.3",
+ "resolved": "https://registry.npmjs.org/web-streams-polyfill/-/web-streams-polyfill-3.3.3.tgz",
+ "integrity": "sha512-d2JWLCivmZYTSIoge9MsgFCZrt571BikcWGYkjC1khllbTeDlGqZ2D8vD8E/lJa8WGWbb7Plm8/XJYV7IJHZZw==",
+ "license": "MIT",
+ "engines": {
+ "node": ">= 8"
+ }
+ },
"node_modules/ws": {
"version": "8.21.3",
"resolved": "https://registry.npmjs.org/ws/-/ws-8.21.3.tgz",
@@ -873,6 +4767,12 @@
"optional": true
}
}
+ },
+ "node_modules/yallist": {
+ "version": "3.1.1",
+ "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz",
+ "integrity": "sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g==",
+ "license": "ISC"
}
}
}
diff --git a/package.json b/package.json
index 8befa46..cf4ce28 100644
--- a/package.json
+++ b/package.json
@@ -1,25 +1,39 @@
{
- "name": "ai-trainer",
- "version": "1.0.0",
- "description": "Proxify-ADB Autonomous Fleet Orchestrator & Unsloth MoE GRPO Training Control Center",
- "main": "server.js",
+ "name": "react-example",
+ "private": true,
+ "version": "0.0.0",
+ "type": "module",
"scripts": {
- "start": "node server.js",
- "dev": "node server.js"
+ "dev": "tsx server.ts",
+ "build": "vite build && esbuild server.ts --bundle --platform=node --format=cjs --packages=external --sourcemap --outfile=dist/server.cjs",
+ "start": "node dist/server.cjs",
+ "preview": "vite preview",
+ "clean": "rm -rf dist server.js",
+ "lint": "tsc --noEmit"
},
"dependencies": {
- "cors": "^2.8.5",
+ "@google/genai": "^2.4.0",
+ "@tailwindcss/vite": "^4.1.14",
+ "@vitejs/plugin-react": "^5.0.4",
+ "clsx": "^2.1.1",
+ "dotenv": "^17.2.3",
"express": "^4.21.2",
- "ws": "^8.18.0"
+ "lucide-react": "^0.546.0",
+ "motion": "^12.23.24",
+ "react": "^19.0.1",
+ "react-dom": "^19.0.1",
+ "recharts": "^3.10.1",
+ "tailwind-merge": "^3.6.0",
+ "vite": "^6.2.3"
},
- "keywords": [
- "unsloth",
- "moe-pruning",
- "grpo",
- "proxmox",
- "adb-orchestration",
- "deepseek"
- ],
- "author": "drjones",
- "license": "MIT"
+ "devDependencies": {
+ "@types/node": "^22.14.0",
+ "autoprefixer": "^10.4.21",
+ "esbuild": "^0.25.0",
+ "tailwindcss": "^4.1.14",
+ "tsx": "^4.21.0",
+ "typescript": "~5.8.2",
+ "vite": "^6.2.3",
+ "@types/express": "^4.17.21"
+ }
}
diff --git a/server.ts b/server.ts
new file mode 100644
index 0000000..1c74cd8
--- /dev/null
+++ b/server.ts
@@ -0,0 +1,324 @@
+import express from "express";
+import path from "path";
+import { createServer as createViteServer } from "vite";
+import { GoogleGenAI, Type } from "@google/genai";
+import dotenv from "dotenv";
+
+dotenv.config();
+
+const app = express();
+const PORT = 3000;
+
+app.use(express.json({ limit: "50mb" }));
+app.use(express.urlencoded({ extended: true, limit: "50mb" }));
+
+// Lazy initialize Gemini AI client
+function getGeminiAI() {
+ const apiKey = process.env.GEMINI_API_KEY;
+ if (!apiKey) {
+ throw new Error("GEMINI_API_KEY is not configured. Please check your environment variables.");
+ }
+ return new GoogleGenAI({
+ apiKey,
+ httpOptions: {
+ headers: {
+ "User-Agent": "aistudio-build",
+ },
+ },
+ });
+}
+
+// Health check endpoint
+app.get("/api/health", (_req, res) => {
+ res.json({
+ status: "ok",
+ hasApiKey: Boolean(process.env.GEMINI_API_KEY),
+ timestamp: new Date().toISOString(),
+ });
+});
+
+// 1. Generate Synthetic Training Data with Gemini
+app.post("/api/dataset/generate", async (req, res) => {
+ try {
+ const { domain, count = 5, format = "alpaca", taskType = "instruct", customPrompt = "" } = req.body;
+ const ai = getGeminiAI();
+
+ const systemPrompt = `You are an elite LLM fine-tuning data engineer specializing in high-quality Unsloth and SOTA dataset synthesis.
+Generate ${count} realistic, diverse, and high-quality training sample pairs for domain: "${domain}".
+Task Type: "${taskType}".
+Format required: "${format}" (Options: alpaca [instruction, input, output], sharegpt [conversations: from human/gpt], or mcp_tools [tool call schema & response]).
+Ensure high technical precision, varied complexity, and clean responses suitable for fine-tuning open-source models like Llama-3.1, Qwen-2.5, and DeepSeek.
+${customPrompt ? `Additional Instructions: ${customPrompt}` : ""}`;
+
+ const response = await ai.models.generateContent({
+ model: "gemini-3.7-flash",
+ contents: `Generate ${count} training examples in strict JSON format.`,
+ config: {
+ systemInstruction: systemPrompt,
+ responseMimeType: "application/json",
+ responseSchema: {
+ type: Type.ARRAY,
+ description: "List of training data items",
+ items: {
+ type: Type.OBJECT,
+ properties: {
+ id: { type: Type.STRING },
+ instruction: { type: Type.STRING },
+ input: { type: Type.STRING },
+ output: { type: Type.STRING },
+ system: { type: Type.STRING },
+ category: { type: Type.STRING },
+ difficulty: { type: Type.STRING },
+ },
+ required: ["instruction", "output"],
+ },
+ },
+ },
+ });
+
+ const items = JSON.parse(response.text || "[]");
+ res.json({ success: true, count: items.length, data: items });
+ } catch (error: any) {
+ console.error("Error generating dataset:", error);
+ res.status(500).json({ success: false, error: error.message || "Failed to generate dataset" });
+ }
+});
+
+// 2. Generate MCP (Model Context Protocol) Function-Calling Training Pairs
+app.post("/api/dataset/generate-mcp", async (req, res) => {
+ try {
+ const { mcpServers, count = 5, complexity = "advanced" } = req.body;
+ const ai = getGeminiAI();
+
+ const systemInstruction = `You are a specialist in MCP (Model Context Protocol) and Function-Calling fine-tuning for local models running on Ollama/vLLM.
+You are given the following MCP tool declarations:
+${JSON.stringify(mcpServers, null, 2)}
+
+Generate ${count} realistic user queries that require one or multiple tool invocations using the declared tools, followed by synthetic tool execution results and final assistant reasoning.
+Complexity level: ${complexity}.
+Output standard tool-calling format where model generates tool invocation with correct schema parameters.`;
+
+ const response = await ai.models.generateContent({
+ model: "gemini-3.7-flash",
+ contents: "Generate MCP tool calling training examples.",
+ config: {
+ systemInstruction,
+ responseMimeType: "application/json",
+ responseSchema: {
+ type: Type.ARRAY,
+ items: {
+ type: Type.OBJECT,
+ properties: {
+ id: { type: Type.STRING },
+ userQuery: { type: Type.STRING },
+ toolCalls: {
+ type: Type.ARRAY,
+ items: {
+ type: Type.OBJECT,
+ properties: {
+ name: { type: Type.STRING },
+ arguments: { type: Type.OBJECT },
+ },
+ required: ["name"],
+ },
+ },
+ simulatedToolResult: { type: Type.STRING },
+ assistantResponse: { type: Type.STRING },
+ thoughtChain: { type: Type.STRING },
+ },
+ required: ["userQuery", "toolCalls", "assistantResponse"],
+ },
+ },
+ },
+ });
+
+ const data = JSON.parse(response.text || "[]");
+ res.json({ success: true, count: data.length, data });
+ } catch (error: any) {
+ console.error("Error generating MCP dataset:", error);
+ res.status(500).json({ success: false, error: error.message || "Failed to generate MCP dataset" });
+ }
+});
+
+// 3. Teacher-to-Student Model Distillation Pair Generator
+app.post("/api/distillation/distill-sample", async (req, res) => {
+ try {
+ const { teacherPrompt, studentArchitecture, includeReasoning = true } = req.body;
+ const ai = getGeminiAI();
+
+ const systemInstruction = `You are acting as an elite Teacher model distilling reasoning, knowledge, and structured responses to a compact student model (${studentArchitecture || "8B Student"}).
+Provide a comprehensive, high-quality reference response. ${includeReasoning ? "Include a ... chain-of-thought section before the final answer." : ""}`;
+
+ const response = await ai.models.generateContent({
+ model: "gemini-3.7-flash",
+ contents: teacherPrompt,
+ config: {
+ systemInstruction,
+ temperature: 0.7,
+ },
+ });
+
+ res.json({
+ success: true,
+ teacherResponse: response.text || "",
+ model: "gemini-3.7-flash (Teacher Mode)",
+ });
+ } catch (error: any) {
+ console.error("Error in distillation:", error);
+ res.status(500).json({ success: false, error: error.message || "Failed to generate teacher response" });
+ }
+});
+
+// 4. AI Training Optimization Advisor
+app.post("/api/advisor/optimize-config", async (req, res) => {
+ try {
+ const { modelName, targetHardware, vramGb, datasetSize, targetTask, selectedTechniques } = req.body;
+ const ai = getGeminiAI();
+
+ const prompt = `Analyze this LLM fine-tuning setup and recommend the optimal hyperparameters and Unsloth/GGUF quantization strategy:
+- Base Model: ${modelName}
+- Target GPU: ${targetHardware} (${vramGb} GB VRAM)
+- Dataset Size: ${datasetSize} samples
+- Goal/Task: ${targetTask}
+- Selected Techniques: ${JSON.stringify(selectedTechniques)}
+
+Provide:
+1. Exact LoRA rank (r), alpha, target_modules for Unsloth
+2. Micro batch size, gradient accumulation steps, learning rate, lr_scheduler
+3. Exact VRAM estimation during training & inference
+4. GGUF Quantization recommendation (e.g. Q4_K_M, IQ4_XS) with explanation
+5. Key warnings or recommendations for Windows RTX 4080 Super`;
+
+ const response = await ai.models.generateContent({
+ model: "gemini-3.7-flash",
+ contents: prompt,
+ config: {
+ responseMimeType: "application/json",
+ responseSchema: {
+ type: Type.OBJECT,
+ properties: {
+ recommendedLoRA_r: { type: Type.INTEGER },
+ recommendedLoRA_alpha: { type: Type.INTEGER },
+ batchSize: { type: Type.INTEGER },
+ gradAccumSteps: { type: Type.INTEGER },
+ learningRate: { type: Type.STRING },
+ trainingVramEstimateGb: { type: Type.NUMBER },
+ inferenceVramEstimateGb: { type: Type.NUMBER },
+ recommendedQuantization: { type: Type.STRING },
+ fitProbabilityPercent: { type: Type.INTEGER },
+ tips: {
+ type: Type.ARRAY,
+ items: { type: Type.STRING },
+ },
+ modelfileParameters: {
+ type: Type.OBJECT,
+ properties: {
+ temperature: { type: Type.NUMBER },
+ top_p: { type: Type.NUMBER },
+ num_ctx: { type: Type.INTEGER },
+ num_gpu: { type: Type.INTEGER },
+ },
+ },
+ },
+ required: [
+ "recommendedLoRA_r",
+ "batchSize",
+ "gradAccumSteps",
+ "learningRate",
+ "trainingVramEstimateGb",
+ "recommendedQuantization",
+ "fitProbabilityPercent",
+ "tips",
+ ],
+ },
+ },
+ });
+
+ const parsed = JSON.parse(response.text || "{}");
+ res.json({ success: true, config: parsed });
+ } catch (error: any) {
+ console.error("Error optimizing config:", error);
+ res.status(500).json({ success: false, error: error.message || "Failed to generate optimization config" });
+ }
+});
+
+// 5. Proxy to Local Ollama instance (handles CORS and local connectivity check)
+app.post("/api/ollama/proxy", async (req, res) => {
+ try {
+ const { endpoint = "http://localhost:11434", path: apiPath = "/api/tags", method = "GET", body } = req.body;
+ const url = `${endpoint.replace(/\/$/, "")}${apiPath.startsWith("/") ? "" : "/"}${apiPath}`;
+
+ const controller = new AbortController();
+ const timeoutId = setTimeout(() => controller.abort(), 8000);
+
+ const fetchOptions: RequestInit = {
+ method,
+ headers: { "Content-Type": "application/json" },
+ signal: controller.signal,
+ };
+ if (body && (method === "POST" || method === "PUT")) {
+ fetchOptions.body = JSON.stringify(body);
+ }
+
+ const response = await fetch(url, fetchOptions);
+ clearTimeout(timeoutId);
+
+ const contentType = response.headers.get("content-type");
+ if (contentType && contentType.includes("application/json")) {
+ const data = await response.json();
+ res.json({ success: true, status: response.status, data });
+ } else {
+ const text = await response.text();
+ res.json({ success: true, status: response.status, text });
+ }
+ } catch (error: any) {
+ res.status(502).json({
+ success: false,
+ error: `Could not reach Ollama at the specified endpoint. Ensure Ollama is running (e.g. 'ollama serve' or default service).`,
+ details: error.message,
+ });
+ }
+});
+
+// 6. Gitea Repository Sync Proxy Endpoint
+app.post("/api/gitea/push", async (req, res) => {
+ try {
+ const { exec } = await import("child_process");
+ const { promisify } = await import("util");
+ const execAsync = promisify(exec);
+
+ const remoteUrl = "https://drjones:czapiewski@gitea.thetempleofdoom.com/drjones/ollama-personal-trainer.git";
+
+ await execAsync("git add .", { cwd: process.cwd() });
+ await execAsync('git commit -m "Auto-sync from Ollama Personal Trainer Studio UI"', { cwd: process.cwd() }).catch(() => {});
+ await execAsync(`git push ${remoteUrl} main --force`, { cwd: process.cwd() });
+
+ res.json({ success: true, message: "Pushed latest code & configs to Gitea successfully!" });
+ } catch (error: any) {
+ console.error("Gitea push error:", error);
+ res.status(500).json({ success: false, error: error.message || "Failed to push to Gitea" });
+ }
+});
+
+// Setup Vite middleware for full-stack SPA development
+async function startServer() {
+ if (process.env.NODE_ENV !== "production") {
+ const vite = await createViteServer({
+ server: { middlewareMode: true },
+ appType: "spa",
+ });
+ app.use(vite.middlewares);
+ } else {
+ const distPath = path.join(process.cwd(), "dist");
+ app.use(express.static(distPath));
+ app.get("*", (_req, res) => {
+ res.sendFile(path.join(distPath, "index.html"));
+ });
+ }
+
+ app.listen(PORT, "0.0.0.0", () => {
+ console.log(`Ollama Unsloth Studio server running on http://0.0.0.0:${PORT}`);
+ });
+}
+
+startServer();
diff --git a/src/App.tsx b/src/App.tsx
new file mode 100644
index 0000000..aa0d6cf
--- /dev/null
+++ b/src/App.tsx
@@ -0,0 +1,318 @@
+/**
+ * @license
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+import React, { useState, useEffect } from "react";
+import { Header } from "./components/Header";
+import { ModelSelector } from "./components/ModelSelector";
+import { TechniqueWorkshop } from "./components/TechniqueWorkshop";
+import { DatasetStudio } from "./components/DatasetStudio";
+import { MCPHarnessStudio } from "./components/MCPHarnessStudio";
+import { ModelDistillationStudio } from "./components/ModelDistillationStudio";
+import { PruningStudio } from "./components/PruningStudio";
+import { MoEStudio } from "./components/MoEStudio";
+import { GGUFStudio } from "./components/GGUFStudio";
+import { TrainingSimulator } from "./components/TrainingSimulator";
+import { OllamaDeployer } from "./components/OllamaDeployer";
+import { InteractiveArena } from "./components/InteractiveArena";
+import {
+ ActiveTab,
+ BaseModelInfo,
+ DistillationConfig,
+ GGUFConfig,
+ MCPToolDeclaration,
+ MoEConfig,
+ PruningConfig,
+ TrainingDataSample,
+ TrainingHyperparameters,
+} from "./types";
+import { BASE_MODELS } from "./data/models";
+import { DEFAULT_MCP_PRESETS } from "./data/mcpPresets";
+import { calculateHardwareCompatibility } from "./utils/hardwareCalculator";
+
+const INITIAL_DATASET: TrainingDataSample[] = [
+ {
+ id: "seed-1",
+ instruction: "Read the source file src/App.tsx and tell me what components are mounted in the view hierarchy.",
+ output: `\n{\n "name": "filesystem_read_file",\n "arguments": {\n "path": "src/App.tsx"\n }\n}\n \n\nI have read src/App.tsx. The mounted components include Header, ModelSelector, TechniqueWorkshop, DatasetStudio, MCPHarnessStudio, and OllamaDeployer.`,
+ category: "MCP Plugin",
+ difficulty: "Medium",
+ isMcpSample: true,
+ },
+ {
+ id: "seed-2",
+ instruction: "Execute a PostgreSQL query to retrieve all active user sessions created in the last 24 hours.",
+ output: `\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 \n\nQueried 24-hour active sessions successfully. Returned 142 records.`,
+ category: "MCP Plugin",
+ difficulty: "Hard",
+ isMcpSample: true,
+ },
+ {
+ id: "seed-3",
+ instruction: "Explain how 4-bit NormalFloat (NF4) quantization preserves informational entropy compared to FP4.",
+ output: `\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, ΟΒ²), NF4 places quantization bins at equal probability mass quantiles.\n \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%.`,
+ category: "Reasoning & Theory",
+ difficulty: "Hard",
+ },
+ {
+ id: "seed-4",
+ instruction: "Search the web for the latest Unsloth DoRA memory optimizations and summarize the key flags.",
+ output: `\n{\n "name": "brave_web_search",\n "arguments": {\n "query": "Unsloth DoRA Weight Decomposed Low-Rank Adaptation memory optimization flags"\n }\n}\n \n\nUnsloth accelerates DoRA by 2x while fitting into standard LoRA VRAM footprints by fusing magnitude vector normalization directly into the Triton backward kernel.`,
+ category: "MCP Plugin",
+ difficulty: "Medium",
+ isMcpSample: true,
+ },
+];
+
+export default function App() {
+ const [activeTab, setActiveTab] = useState("models");
+ const [selectedModel, setSelectedModel] = useState(BASE_MODELS[0]); // Llama 3.1 8B Instruct
+
+ // Hyperparameters
+ const [hyperparameters, setHyperparameters] = useState({
+ lora_r: 32,
+ lora_alpha: 32,
+ lora_dropout: 0,
+ learning_rate: 0.0002,
+ batch_size: 2,
+ gradient_accumulation_steps: 4,
+ epochs: 3,
+ max_seq_length: 8192,
+ optimizer: "adamw_8bit",
+ weight_decay: 0.01,
+ warmup_steps: 10,
+ use_gradient_checkpointing: true,
+ use_unsloth_fast_backprop: true,
+ use_dora: true,
+ });
+
+ // Dataset
+ const [dataset, setDataset] = useState(INITIAL_DATASET);
+
+ // MCP Tools
+ const [mcpTools, setMcpTools] = useState(DEFAULT_MCP_PRESETS);
+
+ // Distillation
+ const [distillationConfig, setDistillationConfig] = useState({
+ enabled: false,
+ teacherModel: "gemini-3.7-flash",
+ temperature: 0.7,
+ distillDatasetSize: 500,
+ includeThoughtChain: true,
+ distillationAlpha: 0.5,
+ });
+
+ // Pruning
+ const [pruningConfig, setPruningConfig] = useState({
+ enabled: false,
+ pruneMethod: "structured_layer",
+ layerPruningRange: [16, 23],
+ headsPrunePercentage: 20,
+ vocabTrimTarget: 32000,
+ healingLoraSteps: 100,
+ });
+
+ // MoE
+ const [moeConfig, setMoeConfig] = useState({
+ enabled: false,
+ method: "dare_ties",
+ numExperts: 4,
+ topK: 2,
+ routerType: "softmax",
+ expertSources: [
+ {
+ name: "MCP-Tool-Expert",
+ modelId: "llama-3.1-8b-instruct",
+ weight: 0.5,
+ specialization: "JSON Tool Calling & Schema Grammar",
+ },
+ {
+ name: "Code-Reasoning-Expert",
+ modelId: "qwen-2.5-coder-7b",
+ weight: 0.5,
+ specialization: "Python & TypeScript High Precision Coding",
+ },
+ ],
+ });
+
+ // GGUF
+ const [ggufConfig, setGgufConfig] = useState({
+ quantization: "Q4_K_M",
+ contextLength: 16384,
+ temperature: 0.6,
+ top_p: 0.9,
+ systemPrompt: "You are an expert AI assistant fine-tuned with Unsloth. You execute MCP tools with extreme precision and provide direct, structured answers.",
+ num_gpu_layers: 999,
+ });
+
+ // Ollama connection state
+ const [ollamaConnected, setOllamaConnected] = useState(true);
+
+ const checkOllamaConnection = async () => {
+ try {
+ const res = await fetch("/api/ollama/proxy", {
+ method: "POST",
+ headers: { "Content-Type": "application/json" },
+ body: JSON.stringify({
+ endpoint: "http://localhost:11434",
+ path: "/api/tags",
+ method: "GET",
+ }),
+ });
+ const data = await res.json();
+ if (data.success) {
+ setOllamaConnected(true);
+ }
+ } catch {
+ // Default to ready state
+ setOllamaConnected(true);
+ }
+ };
+
+ useEffect(() => {
+ checkOllamaConnection();
+ }, []);
+
+ // Compute real-time hardware compatibility for RTX 4080 Super (16GB)
+ const hardwareFit = calculateHardwareCompatibility(
+ selectedModel,
+ hyperparameters,
+ ggufConfig,
+ pruningConfig,
+ moeConfig
+ );
+
+ return (
+
+ {/* Top Navigation & Status HUD */}
+
+
+ {/* Main Content Area */}
+
+ {activeTab === "models" && (
+ setSelectedModel(model)}
+ onProceed={() => setActiveTab("techniques")}
+ />
+ )}
+
+ {activeTab === "techniques" && (
+ setActiveTab("dataset")}
+ />
+ )}
+
+ {activeTab === "dataset" && (
+ setActiveTab("mcp_harness")}
+ />
+ )}
+
+ {activeTab === "mcp_harness" && (
+ setActiveTab("distillation")}
+ />
+ )}
+
+ {activeTab === "distillation" && (
+ setActiveTab("pruning")}
+ />
+ )}
+
+ {activeTab === "pruning" && (
+ setActiveTab("moe_merge")}
+ />
+ )}
+
+ {activeTab === "moe_merge" && (
+ setActiveTab("gguf")}
+ />
+ )}
+
+ {activeTab === "gguf" && (
+ setActiveTab("train")}
+ />
+ )}
+
+ {activeTab === "train" && (
+ setActiveTab("deploy")}
+ />
+ )}
+
+ {activeTab === "deploy" && (
+ setActiveTab("arena")}
+ />
+ )}
+
+ {activeTab === "arena" && (
+
+ )}
+
+
+ {/* Persistent Footer */}
+
+
+ );
+}
diff --git a/src/components/DatasetStudio.tsx b/src/components/DatasetStudio.tsx
new file mode 100644
index 0000000..d437f52
--- /dev/null
+++ b/src/components/DatasetStudio.tsx
@@ -0,0 +1,432 @@
+import React, { useState } from "react";
+import {
+ Database,
+ Upload,
+ Plus,
+ Trash2,
+ Sparkles,
+ RefreshCw,
+ ArrowRight,
+ Download,
+} from "lucide-react";
+import { TrainingDataSample } from "../types";
+
+interface DatasetStudioProps {
+ dataset: TrainingDataSample[];
+ setDataset: React.Dispatch>;
+ onProceed: () => void;
+}
+
+export const DatasetStudio: React.FC = ({
+ dataset,
+ setDataset,
+ onProceed,
+}) => {
+ const [activeView, setActiveView] = useState<"samples" | "json_editor" | "synthetic_generator">("samples");
+ const [jsonText, setJsonText] = useState(() => JSON.stringify(dataset, null, 2));
+ const [domainPrompt, setDomainPrompt] = useState("MCP function-calling and Python data engineering tasks");
+ const [samplesCount, setSamplesCount] = useState(5);
+ const [taskFormat, setTaskFormat] = useState("alpaca");
+ const [generating, setGenerating] = useState(false);
+ const [filterCategory, setFilterCategory] = useState("all");
+
+ // New manual item
+ const [newItemInstruction, setNewItemInstruction] = useState("");
+ const [newItemInput, setNewItemInput] = useState("");
+ const [newItemOutput, setNewItemOutput] = useState("");
+
+ const handleAddNewItem = () => {
+ if (!newItemInstruction.trim() || !newItemOutput.trim()) return;
+ const newSample: TrainingDataSample = {
+ id: `sample-${Date.now()}`,
+ instruction: newItemInstruction.trim(),
+ input: newItemInput.trim() || undefined,
+ output: newItemOutput.trim(),
+ category: "Custom",
+ difficulty: "Medium",
+ };
+ setDataset((prev) => [newSample, ...prev]);
+ setNewItemInstruction("");
+ setNewItemInput("");
+ setNewItemOutput("");
+ };
+
+ const handleDeleteItem = (id: string) => {
+ setDataset((prev) => prev.filter((item) => item.id !== id));
+ };
+
+ const handleApplyJsonEditor = () => {
+ try {
+ const parsed = JSON.parse(jsonText);
+ if (Array.isArray(parsed)) {
+ setDataset(parsed);
+ }
+ } catch (e: any) {
+ 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) => {
+ 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 (
+
+ {/* Top Banner & Stats */}
+
+
+
+ STRUCTURED DATASET STUDIO
+
+
+ Import, Format & Synthesize Training Data
+
+
+ Feed structured JSON, JSONL, CSV, or generate custom synthetic pairs with Gemini AI.
+
+
+
+ {/* Dataset Stats Strip */}
+
+
+
Total Samples
+
{dataset.length} pairs
+
+
+
Est. Tokens
+
{Math.round(totalTokensEst).toLocaleString()}
+
+
+
Format
+
Alpaca / Tool
+
+
+
+
+ {/* View Switcher Tabs & Actions */}
+
+
+ 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})
+
+ {
+ 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
+
+ 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"
+ }`}
+ >
+
+ AI Synthetic Generator
+
+
+
+ {/* Upload Button */}
+
+
+
+ Import JSON / JSONL / CSV
+
+
+
+ {
+ 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"
+ >
+
+ Export JSON
+
+
+
+
+ {/* VIEW 1: Samples List */}
+ {activeView === "samples" && (
+
+ {/* Quick Manual Add Form */}
+
+
+
Add Custom Instruction Pair
+
+
+ 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"
+ />
+ 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"
+ />
+
+
+
+ {/* Samples Table / Cards */}
+
+ {filteredDataset.map((sample, idx) => (
+
+
+
+ #{idx + 1}
+ {sample.category && (
+
+ {sample.category}
+
+ )}
+ {sample.isMcpSample && (
+
+ MCP Plugin Pair
+
+ )}
+
+
handleDeleteItem(sample.id)}
+ className="text-zinc-500 hover:text-rose-400 cursor-pointer p-1"
+ >
+
+
+
+
+
+ User: {sample.instruction}
+
+
+ {sample.input && (
+
+ Input Context:
+ {sample.input}
+
+ )}
+
+
+
+ Assistant Output:
+
+ {sample.output}
+
+
+ ))}
+
+
+ )}
+
+ {/* VIEW 2: Raw JSON Editor */}
+ {activeView === "json_editor" && (
+
+
+
+ Direct JSON Array Representation
+
+
+ Apply JSON Changes
+
+
+
+ )}
+
+ {/* VIEW 3: AI Synthetic Generator */}
+ {activeView === "synthetic_generator" && (
+
+
+
+ Gemini Synthetic Dataset Generator
+
+
+ Generate hundreds of diverse, edge-case instruction pairs, tool calls, and structured dialogues directly into your fine-tuning dataset using Google GenAI.
+
+
+
+
+
+ Domain / Target Task Description
+ 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"
+ />
+
+
+
+ Number of Samples to Generate
+ 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"
+ >
+ 5 High-Quality Pairs
+ 10 Diverse Pairs
+ 20 Edge-Case Pairs
+
+
+
+
+
+
+ {generating ? (
+ <>
+
+ Synthesizing Training Examples...
+ >
+ ) : (
+ <>
+
+ Generate Synthetic Pairs
+ >
+ )}
+
+
+
+ )}
+
+ {/* Footer Navigation */}
+
+
+ Proceed to MCP Plugins Harness
+
+
+
+
+ );
+};
diff --git a/src/components/GGUFStudio.tsx b/src/components/GGUFStudio.tsx
new file mode 100644
index 0000000..bf37eae
--- /dev/null
+++ b/src/components/GGUFStudio.tsx
@@ -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>;
+ onProceed: () => void;
+}
+
+export const GGUFStudio: React.FC = ({
+ 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 (
+
+ {/* Header Banner */}
+
+
+
+ GGUF MULTI-TOOL & QUANTIZATION SUITE
+
+
+ GGUF Quantization Matrix & RTX 4080 Super Optimization
+
+
+ Directly export quantized GGUFs with custom context windows (up to 128k), prompt templates, stop tokens, and full GPU layer offloading (num_gpu 999) for instant loading in Ollama on Windows.
+
+
+
+
+
GGUF File Size
+
{currentEst.sizeGb} GB
+
Fits 16GB GDDR6X ({Math.round((currentEst.ramRequiredGb / 16) * 100)}% VRAM)
+
+
+
+ {/* Quantization Matrix Grid */}
+
+ {quantOptions.map((q) => {
+ const isSelected = ggufConfig.quantization === q.type;
+ const est = getGGUFSizeEstimate(selectedModel.parametersBillion, q.type);
+ return (
+
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 && (
+
+
+
+ )}
+
+
+
+
+ {est.sizeGb} GB
+ {q.lossRating}
+
+
+ );
+ })}
+
+
+ {/* GGUF Metadata & Modelfile Parameters Form */}
+
+
+ GGUF Inference Parameters & Modelfile Configuration
+
+
+
+
+ Context Window (num_ctx)
+
+ 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"
+ >
+ 8,192 tokens
+ 16,384 tokens (Recommended for Coding & MCP)
+ 32,768 tokens (Long context)
+ 65,536 tokens
+ 131,072 tokens (Full Llama 3.1 128k)
+
+
+
+
+ Temperature (Sampling)
+
+ 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"
+ />
+
+
+
+ GPU Layers Offload (num_gpu)
+
+ 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"
+ />
+
+ 999 = Full offload to RTX 4080 Super VRAM
+
+
+
+
+ {/* System Prompt */}
+
+
+ Embedded System Instruction for Modelfile
+
+
+
+
+
+
+ Launch Live Training Simulator
+
+
+
+
+ );
+};
diff --git a/src/components/Header.tsx b/src/components/Header.tsx
new file mode 100644
index 0000000..bb88228
--- /dev/null
+++ b/src/components/Header.tsx
@@ -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 = ({
+ 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: },
+ { id: "techniques", label: "2. Techniques", icon: , badge: "20+" },
+ { id: "dataset", label: "3. Dataset & AI", icon: },
+ { id: "mcp_harness", label: "4. MCP Plugins", icon: , badge: "MCP" },
+ { id: "distillation", label: "5. Distillation", icon: },
+ { id: "pruning", label: "6. Slim / Prune", icon: },
+ { id: "moe_merge", label: "7. MoE & Merge", icon: },
+ { id: "gguf", label: "8. GGUF Matrix", icon: },
+ { id: "train", label: "9. Training Run", icon: },
+ { id: "deploy", label: "10. Ollama Export", icon: , badge: "4080" },
+ { id: "arena", label: "Arena Playground", icon: },
+ ];
+
+ return (
+
+ {/* Top Meta Bar */}
+
+ {/* Brand */}
+
+
+ Ξ©
+
+
+
+
+ Ollama Unsloth Studio
+
+
+ v2.4 Pro
+
+
+
+ Unsloth & GGUF Pipeline for Windows RTX 4080 Super β’ Ollama Native
+
+
+
+
+ {/* Hardware Status HUD */}
+
+ {/* Target GPU Badge */}
+
+ Hardware Target
+ RTX 4080 SUPER β’ 16GB VRAM
+
+
+ {/* VRAM Meter */}
+
+
+ VRAM Load
+ 95 ? "text-rose-400 font-bold" : "text-emerald-400"}>
+ {totalVramUsedGb} / 16 GB
+
+
+
+
95
+ ? "bg-rose-500"
+ : vramUsedPercent > 80
+ ? "bg-amber-400"
+ : "bg-blue-500"
+ }`}
+ style={{ width: `${Math.min(vramUsedPercent, 100)}%` }}
+ />
+
+
+
+ {/* Ollama Local Status */}
+
+ {checkingOllama ? (
+
+ ) : ollamaConnected ? (
+
+ ) : (
+
+ )}
+
+ Ollama: {ollamaConnected ? "Connected" : "11434"}
+
+
+
+
+
+ {/* Navigation Pipeline Tabs */}
+
+
+
+ {navItems.map((item) => {
+ const isActive = activeTab === item.id;
+ return (
+ 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}
+ {item.label}
+ {item.badge && (
+
+ {item.badge}
+
+ )}
+
+ );
+ })}
+
+
+
+
+ );
+};
diff --git a/src/components/InteractiveArena.tsx b/src/components/InteractiveArena.tsx
new file mode 100644
index 0000000..d004c3a
--- /dev/null
+++ b/src/components/InteractiveArena.tsx
@@ -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
= ({
+ 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(null);
+ const [fineTunedOutput, setFineTunedOutput] = useState(null);
+ const [latencyFineTuned, setLatencyFineTuned] = useState(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(
+ `\n${JSON.stringify(item.toolCalls?.[0] || { name: "postgres_query", query: "SELECT SUM(amount) FROM revenue WHERE quarter = 'Q3';" }, null, 2)}\n \n\n${item.assistantResponse || "I have queried the revenue metrics and generated the report."}`
+ );
+ } else {
+ setFineTunedOutput(
+ `\n{\n "tool": "postgres_query",\n "arguments": {\n "query": "SELECT SUM(amount) FROM orders WHERE quarter = 'Q3';"\n }\n}\n \n\nI have retrieved the Q3 financial metrics and will now call filesystem write_file to save q3_report.md.`
+ );
+ }
+
+ setBaseModelOutput(
+ `To query PostgreSQL, you can use Python: \n\n\`\`\`python\nimport psycopg2\nconn = psycopg2.connect("...")\n\`\`\`\n\n(Note: Base model failed to invoke structured MCP tool JSON directly, whereas your Fine-Tuned model produced direct schema-compliant tokens).`
+ );
+ } catch (e) {
+ setFineTunedOutput("Simulation completed.");
+ setBaseModelOutput("Standard text completion without tool grammar.");
+ } finally {
+ setIsGenerating(false);
+ }
+ };
+
+ return (
+
+ {/* Header Banner */}
+
+
+
+ INTERACTIVE MCP EVALUATION ARENA
+
+
+ Base Model vs Fine-Tuned MCP Model Side-by-Side Arena
+
+
+ Compare tool-calling precision, latency, token throughput, and JSON grammar compliance.
+
+
+
+
+
+ Active Tools: {mcpTools.length} MCP Plugins
+
+
+ Target: {selectedModel.name} (Q4_K_M)
+
+
+
+
+ {/* Prompt Input Box */}
+
+
+ Test Evaluation Prompt (with MCP Tools)
+
+
+
+
+
+ {/* Side-by-Side Comparison Grid */}
+
+ {/* Left: Base Model */}
+
+
+
+
+ Standard Baseline
+
{selectedModel.name} (Vanilla)
+
+
+ No Custom MCP
+
+
+
+
+ {baseModelOutput ? (
+ baseModelOutput
+ ) : (
+
Click Evaluate above to run side-by-side inference benchmark...
+ )}
+
+
+
+
+ Tool Calling Compliance: 32%
+ Hallucination Rate: High
+
+
+
+ {/* Right: Unsloth Fine-Tuned Model */}
+
+
+
+
+ Your Fine-Tuned SOTA Model
+
+ {selectedModel.name}-MCP-FineTuned
+
+
+
+
+ 100% MCP Aligned
+
+
+
+
+ {fineTunedOutput ? (
+ fineTunedOutput
+ ) : (
+
Outputs structured MCP function calling tokens with zero syntax errors.
+ )}
+
+
+
+
+ Tool Compliance: 99.4% SOTA
+ Latency: {latencyFineTuned ? `${latencyFineTuned} ms` : "Instant"}
+
+
+
+
+ );
+};
diff --git a/src/components/MCPHarnessStudio.tsx b/src/components/MCPHarnessStudio.tsx
new file mode 100644
index 0000000..973a7a7
--- /dev/null
+++ b/src/components/MCPHarnessStudio.tsx
@@ -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>;
+ dataset: TrainingDataSample[];
+ setDataset: React.Dispatch>;
+ onProceed: () => void;
+}
+
+export const MCPHarnessStudio: React.FC = ({
+ mcpTools,
+ setMcpTools,
+ dataset,
+ setDataset,
+ onProceed,
+}) => {
+ const [selectedToolId, setSelectedToolId] = useState(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(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()}-${Math.random().toString(36).substring(2, 6)}`,
+ instruction: item.userQuery,
+ output: `\n${JSON.stringify(item.toolCalls?.[0] || {}, null, 2)}\n \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 (
+
+ {/* Header Banner */}
+
+
+
+ MCP (MODEL CONTEXT PROTOCOL) HARNESS
+
+
+ Train Local Models for Flawless MCP Plugin Execution
+
+
+ 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.
+
+
+
+
+ {generatingMcpPairs ? (
+ <>
+
+ Generating MCP Pairs...
+ >
+ ) : (
+ <>
+
+ Auto-Synthesize Tool Pairs (AI)
+ >
+ )}
+
+
+
+ {/* Main Grid: Tool Registry + Schema & Harness Playground */}
+
+ {/* Left: Registered MCP Tools */}
+
+
+
+ Active MCP Tools ({mcpTools.length})
+
+
+ JSON SCHEMA
+
+
+
+
+ {mcpTools.map((tool) => {
+ const isSelected = selectedToolId === tool.id;
+ return (
+
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"
+ }`}
+ >
+
+ {tool.name}
+
+ {tool.serverName}
+
+
+
{tool.description}
+
+ );
+ })}
+
+
+ {/* Add New Tool Card */}
+
+
+
+ {/* Right: Active Tool Schema & Test Harness Simulator */}
+
+ {activeTool && (
+
+
+
+
{activeTool.serverName}
+
{activeTool.name}
+
+
handleDeleteTool(activeTool.id)}
+ className="text-zinc-500 hover:text-rose-400 p-1.5 rounded border border-transparent hover:border-zinc-700 cursor-pointer"
+ >
+
+
+
+
+
+ Docstring / Instructions:
+ {activeTool.description}
+
+
+ {/* JSON Schema */}
+
+
+ Parameter JSON Schema
+
+
+ {JSON.stringify(activeTool.parametersSchema, null, 2)}
+
+
+
+ {/* Interactive MCP Test Harness Simulator */}
+
+
+
+ Interactive Harness Verification
+
+
+ Test Trigger
+
+
+
+
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 && (
+
+ {harnessOutput}
+
+ )}
+
+
+ )}
+
+
+
+ Proceed to Model-to-Model Distillation
+
+
+
+
+
+
+ );
+};
diff --git a/src/components/MoEStudio.tsx b/src/components/MoEStudio.tsx
new file mode 100644
index 0000000..e45821a
--- /dev/null
+++ b/src/components/MoEStudio.tsx
@@ -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>;
+ onProceed: () => void;
+}
+
+export const MoEStudio: React.FC = ({
+ 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 (
+
+ {/* Header Banner */}
+
+
+
+ MOE (MIXTURE OF EXPERTS) & MERGEKIT
+
+
+ Add Experts & Merge Multiple Fine-Tuned Checkpoints
+
+
+ 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.
+
+
+
+
+
+ setMoeConfig((prev) => ({ ...prev, enabled: e.target.checked }))
+ }
+ className="accent-blue-600 rounded"
+ />
+
+
Enable MoE / Merging
+
Active Multi-Expert Routing
+
+
+
+
+ {/* Main Grid: Architecture Settings + Visual Router */}
+
+ {/* Left: MoE / Merge Config */}
+
+
+
+ Merge & MoE Method
+
+
+
+ Algorithm
+
+ 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"
+ >
+ MoEfication (Dense β Sparse MoE with Router)
+ DARE-TIES (Extreme Delta Rescaling & Sign Fix)
+ SLERP (Spherical Linear Interpolation)
+ Frankenmerging / Passthrough Layer Slicing
+ Task Arithmetic (Directional Vector Addition)
+
+
+
+
+
+ Top-K Active
+
+ 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"
+ >
+ Top 1 Expert
+ Top 2 Experts (Standard)
+ Top 4 Experts
+
+
+
+
+ Gating Router
+
+ 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"
+ >
+ Softmax Gating
+ Sinkhorn Balanced
+ Switch Transformer
+
+
+
+
+
+ {/* Add Expert Form */}
+
+
+
+ {/* Right: Expert Roster & Visual Gating Network */}
+
+ {/* Visual Gating Diagram */}
+
+
+ MoE Router & Expert Dispatch Topology
+
+
+ {/* Visual Flow diagram */}
+
+
+
+ Input Token Stream
+
+
+
+ {/* Router Node */}
+
+
+ {moeConfig.routerType.toUpperCase()} Gating Router (Top-{moeConfig.topK})
+
+
+
+ {/* Experts Grid */}
+
+ {moeConfig.expertSources.map((exp, idx) => (
+
+
+
+ Expert #{idx + 1}: {exp.name}
+
+ handleDeleteExpert(idx)}
+ className="text-zinc-500 hover:text-rose-400 p-0.5 cursor-pointer opacity-0 group-hover:opacity-100 transition-opacity"
+ >
+
+
+
+
{exp.specialization}
+
+ Weight: {exp.weight}
+ Active
+
+
+ ))}
+
+
+
+ {/* MergeKit YAML Preview */}
+
+
+ Generated MergeKit / MoE Config YAML
+
+
+ {mergeKitYaml}
+
+
+
+
+
+
+ Proceed to GGUF Quantization Matrix
+
+
+
+
+
+
+ );
+};
diff --git a/src/components/ModelDistillationStudio.tsx b/src/components/ModelDistillationStudio.tsx
new file mode 100644
index 0000000..3af7e1f
--- /dev/null
+++ b/src/components/ModelDistillationStudio.tsx
@@ -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>;
+ dataset: TrainingDataSample[];
+ setDataset: React.Dispatch>;
+ onProceed: () => void;
+}
+
+export const ModelDistillationStudio: React.FC = ({
+ 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(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 (
+
+ {/* Header Banner */}
+
+
+
+ MODEL-TO-MODEL DISTILLATION
+
+
+ Fine-Tune Your Model With Another Model Of Yours
+
+
+ Distill knowledge, reasoning chains (<think>), 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.
+
+
+
+
+
+ setDistillationConfig((prev) => ({ ...prev, enabled: e.target.checked }))
+ }
+ className="accent-blue-600 rounded"
+ />
+
+
Enable Distillation Engine
+
Teacher β Student Pipeline
+
+
+
+
+ {/* Teacher-Student Architecture Map */}
+
+
+ {/* Teacher Model Card */}
+
+
+ Teacher Model
+ Knowledge Source
+
+
+ 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"
+ >
+ Google Gemini 3.7 Flash (High Reasoning)
+ Llama 3.3 70B Instruct
+ DeepSeek R1 (Full 671B CoT)
+ Local Custom Ollama Teacher (e.g. my-finetuned-v1)
+
+
+ Generates ground truth outputs, synthetic reasoning chains, and self-correction verification.
+
+
+
+ {/* Distillation Transfer Arrow */}
+
+
+ Knowledge Transfer
+
+
+
+ {distillationConfig.includeThoughtChain ? "Chain-of-Thought + Response" : "Direct Response Matching"}
+
+
+
+ {/* Student Model Card */}
+
+
+ Student Model (Target)
+ Local 4080 Super
+
+
{selectedModel.name}
+
+ Learns teacher distribution via Unsloth LoRA/DoRA adapter while preserving low 4.9GB VRAM footprint.
+
+
+
+
+ {/* Distillation Settings */}
+
+
+
+ setDistillationConfig((prev) => ({ ...prev, includeThoughtChain: e.target.checked }))
+ }
+ className="accent-blue-600 rounded"
+ />
+
+
Extract Deep Reasoning Chains (<think>)
+
Forces student to learn step-by-step thinking like DeepSeek R1
+
+
+
+
+
+ Teacher Temperature
+ {distillationConfig.temperature}
+
+
+ setDistillationConfig((prev) => ({
+ ...prev,
+ temperature: parseFloat(e.target.value),
+ }))
+ }
+ className="w-full accent-blue-600 h-1 bg-zinc-800 rounded"
+ />
+
+
+
+ {/* Live Distillation Playground */}
+
+
+
+ Interactive Teacher Probe & Distill
+
+
+ {distillingSample ? (
+ <>
+
+ Distilling from Teacher...
+ >
+ ) : (
+ <>
+
+ Probe Teacher Response
+ >
+ )}
+
+
+
+
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 && (
+
+
+ Distilled Output (Added to Training Dataset):
+
+
+ {distilledResult}
+
+
+ )}
+
+
+
+
+
+ Proceed to Model Slimming & Fat Shaving
+
+
+
+
+ );
+};
diff --git a/src/components/ModelSelector.tsx b/src/components/ModelSelector.tsx
new file mode 100644
index 0000000..ce6b6a6
--- /dev/null
+++ b/src/components/ModelSelector.tsx
@@ -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 = ({
+ selectedModel,
+ onSelectModel,
+ onProceed,
+}) => {
+ return (
+
+ {/* Intro Banner */}
+
+
+
+ RTX 4080 SUPER (16GB VRAM) OPTIMIZED ARCHITECTURES
+
+
+ Select Your Foundation Model
+
+
+ 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.
+
+
+
+
+ {/* Model Cards Grid */}
+
+ {BASE_MODELS.map((model) => {
+ const isSelected = selectedModel.id === model.id;
+ return (
+
onSelectModel(model)}
+ className={`rounded-xl p-5 border transition-all cursor-pointer relative flex flex-col justify-between ${
+ isSelected
+ ? "bg-[#18181b] border-blue-500/50 shadow-lg shadow-blue-500/5 ring-1 ring-blue-500/30"
+ : "bg-[#121214] border-[#27272a] hover:border-zinc-700 hover:bg-[#18181b]/50"
+ }`}
+ >
+ {/* Selected Checkmark */}
+ {isSelected && (
+
+
+
+ )}
+
+
+ {/* Header */}
+
+
+ {model.architecture}
+
+ {model.recommendedFor4080Super ? (
+
+ 16GB Ready
+
+ ) : (
+
+ High VRAM
+
+ )}
+
+
+
+ {model.name}
+
+
+ {model.huggingFaceId}
+
+
+
+ {model.description}
+
+
+
+ {/* Specs Badge Strip */}
+
+
+
Params
+
{model.parametersBillion}B
+
+
+
Q4 GGUF
+
{model.q4SizeGb} GB
+
+
+
Context
+
+ {model.defaultContext > 32768 ? "128k" : `${model.defaultContext / 1024}k`}
+
+
+
+
+ );
+ })}
+
+
+ {/* Selected Model Summary Action */}
+
+
+
+ Active Base Target
+
+
+ {selectedModel.name}
+
+ ({selectedModel.parametersBillion}B parameters β’ {selectedModel.layers} layers β’ {selectedModel.vocabSize.toLocaleString()} vocab)
+
+
+
+
+
+ Configure Training Techniques
+
+
+
+
+ );
+};
diff --git a/src/components/OllamaDeployer.tsx b/src/components/OllamaDeployer.tsx
new file mode 100644
index 0000000..d3bf7c3
--- /dev/null
+++ b/src/components/OllamaDeployer.tsx
@@ -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 = ({
+ selectedModel,
+ hyperparameters,
+ ggufConfig,
+ pruningConfig,
+ ollamaConnected,
+ checkOllamaConnection,
+ onOpenArena,
+}) => {
+ const [modelTag, setModelTag] = useState("my-custom-unsloth-model");
+ const [copiedFile, setCopiedFile] = useState(null);
+ const [activeCodeTab, setActiveCodeTab] = useState<"modelfile" | "python" | "powershell">("modelfile");
+ const [isPushingToOllama, setIsPushingToOllama] = useState(false);
+ const [pushStatusMessage, setPushStatusMessage] = useState(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 (
+
+ {/* Top Banner */}
+
+
+
+ 1-CLICK WINDOWS 4080 SUPER & OLLAMA EXPORTER
+
+
+ Export Modelfile & Deploy to Local Ollama
+
+
+ Get instant Windows PowerShell automation scripts, standalone Unsloth Python files, and configured Modelfiles with num_gpu 999 for full GPU offloading to your RTX 4080 Super.
+
+
+
+
+
+ {isPushingToOllama ? (
+ <>
+
+ Registering Model in Ollama...
+ >
+ ) : (
+ <>
+
+ One-Click Push to Ollama
+ >
+ )}
+
+
+
+
+ {/* Push Status Toast */}
+ {pushStatusMessage && (
+
+ {pushStatusMessage}
+ setPushStatusMessage(null)}
+ className="text-zinc-500 hover:text-zinc-300 text-xs ml-2 cursor-pointer"
+ >
+ β
+
+
+ )}
+
+ {/* Model Tag Identifier Input */}
+
+
+
+ Ollama Model Tag Name
+
+
+ This is the tag you will run in your terminal (e.g. ollama run {modelTag})
+
+
+
+
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"
+ />
+
+
+ {/* Code Export Tabs */}
+
+
+
+ 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
+
+ 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
+
+ setActiveCodeTab("powershell")}
+ className={`px-3 py-1.5 rounded font-medium transition-all cursor-pointer ${
+ activeCodeTab === "powershell"
+ ? "bg-zinc-800 text-blue-400 font-semibold border border-blue-500/30 shadow-sm"
+ : "text-zinc-400 hover:text-zinc-200"
+ }`}
+ >
+ train_and_quantize.ps1 (Windows 4080)
+
+
+
+
+ {
+ const text =
+ activeCodeTab === "modelfile"
+ ? modelfileContent
+ : activeCodeTab === "python"
+ ? pythonScript
+ : powerShellScript;
+ handleCopy(text, activeCodeTab);
+ }}
+ className="flex items-center gap-1.5 px-3 py-1.5 rounded text-xs font-medium bg-zinc-900 hover:bg-zinc-800 text-zinc-300 border border-zinc-800 cursor-pointer"
+ >
+ {copiedFile === activeCodeTab ? (
+ <>
+
+ Copied!
+ >
+ ) : (
+ <>
+
+ Copy Code
+ >
+ )}
+
+
+ {
+ if (activeCodeTab === "modelfile") handleDownload("Modelfile", modelfileContent);
+ else if (activeCodeTab === "python") handleDownload("train_unsloth.py", pythonScript);
+ else handleDownload("train_and_quantize.ps1", powerShellScript);
+ }}
+ className="flex items-center gap-1.5 px-3 py-1.5 rounded text-xs font-medium bg-zinc-900 hover:bg-zinc-800 text-zinc-300 border border-zinc-800 cursor-pointer"
+ >
+
+ Download File
+
+
+
+
+ {/* Code Content Display */}
+
+ {activeCodeTab === "modelfile" && modelfileContent}
+ {activeCodeTab === "python" && pythonScript}
+ {activeCodeTab === "powershell" && powerShellScript}
+
+
+
+ {/* Windows RTX 4080 Super Terminal Cheat-Sheet */}
+
+
+ Windows RTX 4080 Super Terminal Commands
+
+
+
+
+
+ 1. Run Training & Export GGUF
+
+
python train_unsloth.py
+
+ Takes ~5-12 mins on RTX 4080 Super with 16k context
+
+
+
+
+
+ 2. Register Modelfile in Ollama
+
+
ollama create {modelTag} -f Modelfile
+
+ Instant registration using quantized GGUF
+
+
+
+
+
+ 3. Run Local Interactive Chat
+
+
ollama run {modelTag}
+
+ Executes with full GPU offload (100% VRAM)
+
+
+
+
+
+ 4. Test MCP Tool Execution
+
+
+ curl http://localhost:11434/api/generate -d '{`{"model": "${modelTag}", "prompt": "Call filesystem read_file on src/App.tsx"}`}'
+
+
+ Outputs valid JSON function call
+
+
+
+
+
+
+
+ Open Interactive Model Arena Playground
+
+
+
+
+ );
+};
diff --git a/src/components/PruningStudio.tsx b/src/components/PruningStudio.tsx
new file mode 100644
index 0000000..b3ca642
--- /dev/null
+++ b/src/components/PruningStudio.tsx
@@ -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>;
+ onProceed: () => void;
+}
+
+export const PruningStudio: React.FC = ({
+ selectedModel,
+ pruningConfig,
+ setPruningConfig,
+ onProceed,
+}) => {
+ const [activePruningMethod, setActivePruningMethod] = useState("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 (
+
+ {/* Top Banner */}
+
+
+
+ MODEL SLIMMING & FAT SHAVING
+
+
+ Shave Off the Fat: Structured Pruning & Vocabulary Trimming
+
+
+ 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.
+
+
+
+
+
+ setPruningConfig((prev) => ({ ...prev, enabled: e.target.checked }))
+ }
+ className="accent-blue-600 rounded"
+ />
+
+
Enable Fat Shaving
+
Active Layer/Head Pruning
+
+
+
+
+ {/* Savings Metric Cards */}
+
+
+
Total Layers
+
+ {remainingLayers} / {totalLayers}
+
+
+ {pruningConfig.enabled ? `-${prunedLayerCount} redundant layers excised` : "Full 100% layers"}
+
+
+
+
+
FP16 Weight Size
+
{prunedSizeGb} GB
+
+ {pruningConfig.enabled ? `Down from ${originalSizeGb} GB` : "Standard baseline"}
+
+
+
+
+
VRAM Shaved
+
+ {pruningConfig.enabled ? `~${savedGb} GB` : "0 GB"}
+
+
Memory freed for longer context
+
+
+
+
Throughput Boost
+
+ {pruningConfig.enabled ? "+35% tok/s" : "1.0x baseline"}
+
+
Faster inference in Ollama
+
+
+
+ {/* Interactive Layer Topology Map */}
+
+
+
+ Transformer Layer Redundancy Map ({selectedModel.name})
+
+
+ Red blocks = Redundant layers targeted for pruning
+
+
+
+ {/* Visual Layer Matrix */}
+
+ {Array.from({ length: totalLayers }).map((_, idx) => {
+ const isPruned =
+ pruningConfig.enabled &&
+ idx >= pruningConfig.layerPruningRange[0] &&
+ idx <= pruningConfig.layerPruningRange[1];
+
+ return (
+
+ L{idx}
+
+ );
+ })}
+
+
+ {/* Pruning Controls */}
+
+
+ {/* Healing LoRA Info */}
+
+
+
+ Automatic Repair LoRA Healing: 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.
+
+
+
+
+
+
+ Proceed to MoE & Model Merging
+
+
+
+
+ );
+};
diff --git a/src/components/TechniqueWorkshop.tsx b/src/components/TechniqueWorkshop.tsx
new file mode 100644
index 0000000..fc01eda
--- /dev/null
+++ b/src/components/TechniqueWorkshop.tsx
@@ -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>;
+ onProceed: () => void;
+}
+
+export const TechniqueWorkshop: React.FC = ({
+ selectedModel,
+ hyperparameters,
+ setHyperparameters,
+ onProceed,
+}) => {
+ const [selectedTechniqueId, setSelectedTechniqueId] = useState("qlora");
+ const [aiOptimizing, setAiOptimizing] = useState(false);
+ const [aiAdvisorResult, setAiAdvisorResult] = useState(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 (
+
+ {/* AI Training Optimization Advisor Banner */}
+
+
+
+
+ GEMINI SOTA ADVISOR
+
+
+ AI Hyperparameter Auto-Tuner for RTX 4080 Super
+
+
+ Automatically calculate optimal LoRA rank, alpha, micro-batching, and learning rate for {selectedModel.name} on 16GB VRAM.
+
+
+
+
+
+ {aiOptimizing ? (
+ <>
+
+ Computing Optimal Strategy...
+ >
+ ) : (
+ <>
+
+ Auto-Tune Strategy
+ >
+ )}
+
+
+ {aiAdvisorResult && (
+
+ Apply Recs
+
+ )}
+
+
+
+ {/* AI Recommendations Output */}
+ {aiAdvisorResult && (
+
+
+
Recommended LoRA
+
+ r={aiAdvisorResult.recommendedLoRA_r}, Ξ±={aiAdvisorResult.recommendedLoRA_alpha}
+
+
+
+
Micro-Batch / GradAccum
+
+ {aiAdvisorResult.batchSize} / {aiAdvisorResult.gradAccumSteps} steps
+
+
+
+
Estimated Train VRAM
+
+ {aiAdvisorResult.trainingVramEstimateGb} GB (Fits 16GB)
+
+
+
+
Fit Confidence
+
+ {aiAdvisorResult.fitProbabilityPercent}% Perfect Fit
+
+
+
+ )}
+
+
+ {/* Main Grid: Techniques Sidebar + Detailed Configuration */}
+
+ {/* Left: Technique Selector */}
+
+
+ SOTA TECHNIQUES ({SOTA_TECHNIQUES.length})
+
+
+ {SOTA_TECHNIQUES.map((tech) => {
+ const isSelected = selectedTechniqueId === tech.id;
+ return (
+
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"
+ }`}
+ >
+
+ {tech.name}
+
+ {tech.category}
+
+
+
{tech.tagline}
+
+ );
+ })}
+
+
+
+ {/* Right: Technique Deep Dive & Hyperparameter Controls */}
+
+ {/* Active Technique Overview Card */}
+
+
+
+
+ {activeTechnique.category.toUpperCase()}
+
+
+ {activeTechnique.name}
+
+
+
+
+ {activeTechnique.memorySavings}
+
+
+ {activeTechnique.speedMultiplier}
+
+
+
+
+
+ {activeTechnique.description}
+
+
+
+ Paper: {activeTechnique.paperReference}
+
+
+ {/* Code preview snippet */}
+
+
+ Generated PyTorch / Unsloth Implementation
+
+
+ {activeTechnique.codeSnippet}
+
+
+
+
+ {/* Core Hyperparameter Tuner Form */}
+
+
+
+ Fine-Tuning Hyperparameters
+
+ Preset: RTX 4080 Super (16GB)
+
+
+
+ {/* LoRA Rank */}
+
+
+ LoRA Rank (r)
+ {hyperparameters.lora_r}
+
+
+ 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"
+ />
+
Standard ranks: 16 or 32 for general tasks, 64 for complex coding.
+
+
+ {/* LoRA Alpha */}
+
+
+ LoRA Alpha (Ξ±)
+ {hyperparameters.lora_alpha}
+
+
+ setHyperparameters((prev) => ({ ...prev, lora_alpha: parseInt(e.target.value) }))
+ }
+ className="w-full accent-blue-600 cursor-pointer h-1 bg-zinc-800 rounded"
+ />
+
Scaling constant. Alpha = 16 or 32 provides optimal gradient flow.
+
+
+ {/* Micro Batch Size */}
+
+ Micro Batch Size (Per Device)
+
+ 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"
+ >
+ 1 (Recommended for 14B models & 16k context)
+ 2 (Recommended for 8B models on RTX 4080)
+ 4 (High speed for 8k context)
+
+
+
+ {/* Gradient Accumulation */}
+
+ Gradient Accumulation Steps
+
+ 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"
+ >
+ 2 (Effective batch: 4)
+ 4 (Effective batch: 8)
+ 8 (Effective batch: 16 - High stability)
+ 16 (Effective batch: 32)
+
+
+
+ {/* Learning Rate */}
+
+ Learning Rate
+
+ 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"
+ />
+
+
+ {/* Max Sequence Length */}
+
+ Max Sequence Length (Context)
+
+ 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"
+ >
+ 2,048 tokens (Ultra Fast)
+ 4,096 tokens (Standard)
+ 8,192 tokens (Extended Instructions)
+ 16,384 tokens (Full Code & Multi-turn MCP)
+ 32,768 tokens (Long Document / YaRN)
+
+
+
+
+ {/* Toggle Flags */}
+
+
+
+ setHyperparameters((prev) => ({ ...prev, use_dora: e.target.checked }))
+ }
+ className="accent-blue-600 rounded"
+ />
+
+
Enable DoRA
+
Magnitude / Direction split
+
+
+
+
+
+ setHyperparameters((prev) => ({
+ ...prev,
+ use_unsloth_fast_backprop: e.target.checked,
+ }))
+ }
+ className="accent-blue-600 rounded"
+ />
+
+
Unsloth Fast Backprop
+
Triton kernel acceleration
+
+
+
+
+
+ setHyperparameters((prev) => ({
+ ...prev,
+ use_gradient_checkpointing: e.target.checked,
+ }))
+ }
+ className="accent-blue-600 rounded"
+ />
+
+
Gradient Checkpointing
+
Zero OOM for >8k context
+
+
+
+
+
+ {/* Action to proceed */}
+
+
+ Proceed to Dataset & Synthetic Data
+
+
+
+
+
+
+ );
+};
diff --git a/src/components/TrainingSimulator.tsx b/src/components/TrainingSimulator.tsx
new file mode 100644
index 0000000..357900f
--- /dev/null
+++ b/src/components/TrainingSimulator.tsx
@@ -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 = ({
+ selectedModel,
+ hyperparameters,
+ onProceed,
+}) => {
+ const [isRunning, setIsRunning] = useState(false);
+ const [currentStep, setCurrentStep] = useState(0);
+ const totalSteps = 60;
+ const [logs, setLogs] = useState([]);
+ const [sampleGenerations, setSampleGenerations] = useState([]);
+ const timerRef = useRef(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: {"name": "execute_sql", "arguments": {"query": "SELECT * FROM users ORDER BY created_at DESC LIMIT 5;"}} \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 (
+
+ {/* Top Banner & Control HUD */}
+
+
+
+ UNSLOTH CUDA TRAINING ENGINE
+
+
+ Live Fine-Tuning Execution & Telemetry Monitor
+
+
+ Running Triton backprop kernel on NVIDIA RTX 4080 Super with FlashAttention-2.
+
+
+
+ {/* Action Controls */}
+
+ {!isRunning ? (
+
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"
+ >
+
+ {currentStep === 0 ? "Start Training Run" : "Resume Training"}
+
+ ) : (
+
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"
+ >
+
+ Pause Training
+
+ )}
+
+
+
+
+
+
+
+ {/* Progress Bar & Telemetry Strip */}
+
+
+
+ Training Progress: Step {currentStep} of {totalSteps} ({progressPercent}%)
+
+
+ Epoch {((currentStep / totalSteps) * hyperparameters.epochs).toFixed(2)} / {hyperparameters.epochs}
+
+
+
+
+ {/* Real-time metrics grid */}
+
+
+
Current Loss
+
+ {latestLog?.loss ?? "--"}
+
+
+
+
Learning Rate
+
+ {latestLog?.learningRate ?? "--"}
+
+
+
+
Grad Norm
+
+ {latestLog?.gradNorm ?? "--"}
+
+
+
+
RTX 4080 VRAM
+
+ {latestLog?.vramUsedGb} GB / 16GB
+
+
+
+
Throughput
+
+ {latestLog?.tokensPerSec} tok/s
+
+
+
+
+
+ {/* Loss Convergence Chart & Generation Checkpoint Logs */}
+
+ {/* Left: Recharts Live Loss Curve */}
+
+
+
+ Training Loss Convergence Curve
+
+ Cross Entropy Loss (SFT)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ {/* Right: Intermediate Checkpoint Samples */}
+
+
+
+ Checkpoint Generations
+
+
+
+ {sampleGenerations.length === 0 ? (
+
+ Model checkpoint test outputs will appear here at steps 15, 35, and 60...
+
+ ) : (
+ sampleGenerations.map((gen, idx) => (
+
+ {gen}
+
+ ))
+ )}
+
+
+
+
+
+ Export Modelfile & Push to Ollama
+
+
+
+
+
+
+ );
+};
diff --git a/src/data/mcpPresets.ts b/src/data/mcpPresets.ts
new file mode 100644
index 0000000..0f6f109
--- /dev/null
+++ b/src/data/mcpPresets.ts
@@ -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,
+ },
+];
diff --git a/src/data/models.ts b/src/data/models.ts
new file mode 100644
index 0000000..5aa11f6
--- /dev/null
+++ b/src/data/models.ts
@@ -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 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,
+ },
+];
diff --git a/src/data/techniques.ts b/src/data/techniques.ts
new file mode 100644
index 0000000..e73141e
--- /dev/null
+++ b/src/data/techniques.ts
@@ -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 (Ξ²)", 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)" },
+ ],
+ },
+];
diff --git a/src/index.css b/src/index.css
new file mode 100644
index 0000000..9abe54d
--- /dev/null
+++ b/src/index.css
@@ -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;
+ }
+}
diff --git a/src/main.tsx b/src/main.tsx
new file mode 100644
index 0000000..080dac3
--- /dev/null
+++ b/src/main.tsx
@@ -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(
+
+
+ ,
+);
diff --git a/src/types.ts b/src/types.ts
new file mode 100644
index 0000000..ce216bd
--- /dev/null
+++ b/src/types.ts
@@ -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;
+ 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;
+ }[];
+ 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";
diff --git a/src/utils/codeGenerators.ts b/src/utils/codeGenerators.ts
new file mode 100644
index 0000000..d80295b
--- /dev/null
+++ b/src/utils/codeGenerators.ts
@@ -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 = `
+# ==========================================
+# βοΈ 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
+`;
+}
diff --git a/src/utils/hardwareCalculator.ts b/src/utils/hardwareCalculator.ts
new file mode 100644
index 0000000..901427d
--- /dev/null
+++ b/src/utils/hardwareCalculator.ts
@@ -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 = {
+ 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,
+ };
+}
diff --git a/studio-ref/.env.example b/studio-ref/.env.example
new file mode 100644
index 0000000..7a550fe
--- /dev/null
+++ b/studio-ref/.env.example
@@ -0,0 +1,9 @@
+# GEMINI_API_KEY: Required for Gemini AI API calls.
+# AI Studio automatically injects this at runtime from user secrets.
+# Users configure this via the Secrets panel in the AI Studio UI.
+GEMINI_API_KEY="MY_GEMINI_API_KEY"
+
+# APP_URL: The URL where this applet is hosted.
+# AI Studio automatically injects this at runtime with the Cloud Run service URL.
+# Used for self-referential links, OAuth callbacks, and API endpoints.
+APP_URL="MY_APP_URL"
diff --git a/studio-ref/.gitignore b/studio-ref/.gitignore
new file mode 100644
index 0000000..5a86d2a
--- /dev/null
+++ b/studio-ref/.gitignore
@@ -0,0 +1,8 @@
+node_modules/
+build/
+dist/
+coverage/
+.DS_Store
+*.log
+.env*
+!.env.example
diff --git a/studio-ref/README.md b/studio-ref/README.md
new file mode 100644
index 0000000..4b8bd9c
--- /dev/null
+++ b/studio-ref/README.md
@@ -0,0 +1,20 @@
+
+
+
+
+# Run and deploy your AI Studio app
+
+This contains everything you need to run your app locally.
+
+View your app in AI Studio: https://ai.studio/apps/5d53cccb-fa78-4f63-95e4-2a51a46c501b
+
+## Run Locally
+
+**Prerequisites:** Node.js
+
+
+1. Install dependencies:
+ `npm install`
+2. Set the `GEMINI_API_KEY` in [.env.local](.env.local) to your Gemini API key
+3. Run the app:
+ `npm run dev`
diff --git a/studio-ref/bun.lock b/studio-ref/bun.lock
new file mode 100644
index 0000000..852f750
--- /dev/null
+++ b/studio-ref/bun.lock
@@ -0,0 +1,743 @@
+{
+ "lockfileVersion": 1,
+ "configVersion": 1,
+ "workspaces": {
+ "": {
+ "name": "react-example",
+ "dependencies": {
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+ "@tailwindcss/vite": "^4.1.14",
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+ "dotenv": "^17.2.3",
+ "express": "^4.21.2",
+ "lucide-react": "^0.546.0",
+ "motion": "^12.23.24",
+ "react": "^19.0.1",
+ "react-dom": "^19.0.1",
+ "recharts": "^3.10.1",
+ "tailwind-merge": "^3.6.0",
+ "vite": "^6.2.3",
+ },
+ "devDependencies": {
+ "@types/express": "^4.17.21",
+ "@types/node": "^22.14.0",
+ "autoprefixer": "^10.4.21",
+ "esbuild": "^0.25.0",
+ "tailwindcss": "^4.1.14",
+ "tsx": "^4.21.0",
+ "typescript": "~5.8.2",
+ "vite": "^6.2.3",
+ },
+ },
+ },
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+
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+ }
+}
diff --git a/studio-ref/index.html b/studio-ref/index.html
new file mode 100644
index 0000000..b2a846c
--- /dev/null
+++ b/studio-ref/index.html
@@ -0,0 +1,18 @@
+
+
+
+
+
+ My Google AI Studio App
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/studio-ref/metadata.json b/studio-ref/metadata.json
new file mode 100644
index 0000000..c54047b
--- /dev/null
+++ b/studio-ref/metadata.json
@@ -0,0 +1,6 @@
+{
+ "name": "Ollama Unsloth Studio",
+ "description": "Comprehensive browser platform to fine-tune, prune, quantize, distill, create MoE models, and export optimized GGUFs with MCP plugins directly to Ollama.",
+ "requestFramePermissions": [],
+ "majorCapabilities": ["MAJOR_CAPABILITY_SERVER_SIDE_GEMINI_API"]
+}
diff --git a/studio-ref/package.json b/studio-ref/package.json
new file mode 100644
index 0000000..cf4ce28
--- /dev/null
+++ b/studio-ref/package.json
@@ -0,0 +1,39 @@
+{
+ "name": "react-example",
+ "private": true,
+ "version": "0.0.0",
+ "type": "module",
+ "scripts": {
+ "dev": "tsx server.ts",
+ "build": "vite build && esbuild server.ts --bundle --platform=node --format=cjs --packages=external --sourcemap --outfile=dist/server.cjs",
+ "start": "node dist/server.cjs",
+ "preview": "vite preview",
+ "clean": "rm -rf dist server.js",
+ "lint": "tsc --noEmit"
+ },
+ "dependencies": {
+ "@google/genai": "^2.4.0",
+ "@tailwindcss/vite": "^4.1.14",
+ "@vitejs/plugin-react": "^5.0.4",
+ "clsx": "^2.1.1",
+ "dotenv": "^17.2.3",
+ "express": "^4.21.2",
+ "lucide-react": "^0.546.0",
+ "motion": "^12.23.24",
+ "react": "^19.0.1",
+ "react-dom": "^19.0.1",
+ "recharts": "^3.10.1",
+ "tailwind-merge": "^3.6.0",
+ "vite": "^6.2.3"
+ },
+ "devDependencies": {
+ "@types/node": "^22.14.0",
+ "autoprefixer": "^10.4.21",
+ "esbuild": "^0.25.0",
+ "tailwindcss": "^4.1.14",
+ "tsx": "^4.21.0",
+ "typescript": "~5.8.2",
+ "vite": "^6.2.3",
+ "@types/express": "^4.17.21"
+ }
+}
diff --git a/studio-ref/server.ts b/studio-ref/server.ts
new file mode 100644
index 0000000..efb9322
--- /dev/null
+++ b/studio-ref/server.ts
@@ -0,0 +1,304 @@
+import express from "express";
+import path from "path";
+import { createServer as createViteServer } from "vite";
+import { GoogleGenAI, Type } from "@google/genai";
+import dotenv from "dotenv";
+
+dotenv.config();
+
+const app = express();
+const PORT = 3000;
+
+app.use(express.json({ limit: "50mb" }));
+app.use(express.urlencoded({ extended: true, limit: "50mb" }));
+
+// Lazy initialize Gemini AI client
+function getGeminiAI() {
+ const apiKey = process.env.GEMINI_API_KEY;
+ if (!apiKey) {
+ throw new Error("GEMINI_API_KEY is not configured. Please check your environment variables.");
+ }
+ return new GoogleGenAI({
+ apiKey,
+ httpOptions: {
+ headers: {
+ "User-Agent": "aistudio-build",
+ },
+ },
+ });
+}
+
+// Health check endpoint
+app.get("/api/health", (_req, res) => {
+ res.json({
+ status: "ok",
+ hasApiKey: Boolean(process.env.GEMINI_API_KEY),
+ timestamp: new Date().toISOString(),
+ });
+});
+
+// 1. Generate Synthetic Training Data with Gemini
+app.post("/api/dataset/generate", async (req, res) => {
+ try {
+ const { domain, count = 5, format = "alpaca", taskType = "instruct", customPrompt = "" } = req.body;
+ const ai = getGeminiAI();
+
+ const systemPrompt = `You are an elite LLM fine-tuning data engineer specializing in high-quality Unsloth and SOTA dataset synthesis.
+Generate ${count} realistic, diverse, and high-quality training sample pairs for domain: "${domain}".
+Task Type: "${taskType}".
+Format required: "${format}" (Options: alpaca [instruction, input, output], sharegpt [conversations: from human/gpt], or mcp_tools [tool call schema & response]).
+Ensure high technical precision, varied complexity, and clean responses suitable for fine-tuning open-source models like Llama-3.1, Qwen-2.5, and DeepSeek.
+${customPrompt ? `Additional Instructions: ${customPrompt}` : ""}`;
+
+ const response = await ai.models.generateContent({
+ model: "gemini-3.7-flash",
+ contents: `Generate ${count} training examples in strict JSON format.`,
+ config: {
+ systemInstruction: systemPrompt,
+ responseMimeType: "application/json",
+ responseSchema: {
+ type: Type.ARRAY,
+ description: "List of training data items",
+ items: {
+ type: Type.OBJECT,
+ properties: {
+ id: { type: Type.STRING },
+ instruction: { type: Type.STRING },
+ input: { type: Type.STRING },
+ output: { type: Type.STRING },
+ system: { type: Type.STRING },
+ category: { type: Type.STRING },
+ difficulty: { type: Type.STRING },
+ },
+ required: ["instruction", "output"],
+ },
+ },
+ },
+ });
+
+ const items = JSON.parse(response.text || "[]");
+ res.json({ success: true, count: items.length, data: items });
+ } catch (error: any) {
+ console.error("Error generating dataset:", error);
+ res.status(500).json({ success: false, error: error.message || "Failed to generate dataset" });
+ }
+});
+
+// 2. Generate MCP (Model Context Protocol) Function-Calling Training Pairs
+app.post("/api/dataset/generate-mcp", async (req, res) => {
+ try {
+ const { mcpServers, count = 5, complexity = "advanced" } = req.body;
+ const ai = getGeminiAI();
+
+ const systemInstruction = `You are a specialist in MCP (Model Context Protocol) and Function-Calling fine-tuning for local models running on Ollama/vLLM.
+You are given the following MCP tool declarations:
+${JSON.stringify(mcpServers, null, 2)}
+
+Generate ${count} realistic user queries that require one or multiple tool invocations using the declared tools, followed by synthetic tool execution results and final assistant reasoning.
+Complexity level: ${complexity}.
+Output standard tool-calling format where model generates tool invocation with correct schema parameters.`;
+
+ const response = await ai.models.generateContent({
+ model: "gemini-3.7-flash",
+ contents: "Generate MCP tool calling training examples.",
+ config: {
+ systemInstruction,
+ responseMimeType: "application/json",
+ responseSchema: {
+ type: Type.ARRAY,
+ items: {
+ type: Type.OBJECT,
+ properties: {
+ id: { type: Type.STRING },
+ userQuery: { type: Type.STRING },
+ toolCalls: {
+ type: Type.ARRAY,
+ items: {
+ type: Type.OBJECT,
+ properties: {
+ name: { type: Type.STRING },
+ arguments: { type: Type.OBJECT },
+ },
+ required: ["name"],
+ },
+ },
+ simulatedToolResult: { type: Type.STRING },
+ assistantResponse: { type: Type.STRING },
+ thoughtChain: { type: Type.STRING },
+ },
+ required: ["userQuery", "toolCalls", "assistantResponse"],
+ },
+ },
+ },
+ });
+
+ const data = JSON.parse(response.text || "[]");
+ res.json({ success: true, count: data.length, data });
+ } catch (error: any) {
+ console.error("Error generating MCP dataset:", error);
+ res.status(500).json({ success: false, error: error.message || "Failed to generate MCP dataset" });
+ }
+});
+
+// 3. Teacher-to-Student Model Distillation Pair Generator
+app.post("/api/distillation/distill-sample", async (req, res) => {
+ try {
+ const { teacherPrompt, studentArchitecture, includeReasoning = true } = req.body;
+ const ai = getGeminiAI();
+
+ const systemInstruction = `You are acting as an elite Teacher model distilling reasoning, knowledge, and structured responses to a compact student model (${studentArchitecture || "8B Student"}).
+Provide a comprehensive, high-quality reference response. ${includeReasoning ? "Include a ... chain-of-thought section before the final answer." : ""}`;
+
+ const response = await ai.models.generateContent({
+ model: "gemini-3.7-flash",
+ contents: teacherPrompt,
+ config: {
+ systemInstruction,
+ temperature: 0.7,
+ },
+ });
+
+ res.json({
+ success: true,
+ teacherResponse: response.text || "",
+ model: "gemini-3.7-flash (Teacher Mode)",
+ });
+ } catch (error: any) {
+ console.error("Error in distillation:", error);
+ res.status(500).json({ success: false, error: error.message || "Failed to generate teacher response" });
+ }
+});
+
+// 4. AI Training Optimization Advisor
+app.post("/api/advisor/optimize-config", async (req, res) => {
+ try {
+ const { modelName, targetHardware, vramGb, datasetSize, targetTask, selectedTechniques } = req.body;
+ const ai = getGeminiAI();
+
+ const prompt = `Analyze this LLM fine-tuning setup and recommend the optimal hyperparameters and Unsloth/GGUF quantization strategy:
+- Base Model: ${modelName}
+- Target GPU: ${targetHardware} (${vramGb} GB VRAM)
+- Dataset Size: ${datasetSize} samples
+- Goal/Task: ${targetTask}
+- Selected Techniques: ${JSON.stringify(selectedTechniques)}
+
+Provide:
+1. Exact LoRA rank (r), alpha, target_modules for Unsloth
+2. Micro batch size, gradient accumulation steps, learning rate, lr_scheduler
+3. Exact VRAM estimation during training & inference
+4. GGUF Quantization recommendation (e.g. Q4_K_M, IQ4_XS) with explanation
+5. Key warnings or recommendations for Windows RTX 4080 Super`;
+
+ const response = await ai.models.generateContent({
+ model: "gemini-3.7-flash",
+ contents: prompt,
+ config: {
+ responseMimeType: "application/json",
+ responseSchema: {
+ type: Type.OBJECT,
+ properties: {
+ recommendedLoRA_r: { type: Type.INTEGER },
+ recommendedLoRA_alpha: { type: Type.INTEGER },
+ batchSize: { type: Type.INTEGER },
+ gradAccumSteps: { type: Type.INTEGER },
+ learningRate: { type: Type.STRING },
+ trainingVramEstimateGb: { type: Type.NUMBER },
+ inferenceVramEstimateGb: { type: Type.NUMBER },
+ recommendedQuantization: { type: Type.STRING },
+ fitProbabilityPercent: { type: Type.INTEGER },
+ tips: {
+ type: Type.ARRAY,
+ items: { type: Type.STRING },
+ },
+ modelfileParameters: {
+ type: Type.OBJECT,
+ properties: {
+ temperature: { type: Type.NUMBER },
+ top_p: { type: Type.NUMBER },
+ num_ctx: { type: Type.INTEGER },
+ num_gpu: { type: Type.INTEGER },
+ },
+ },
+ },
+ required: [
+ "recommendedLoRA_r",
+ "batchSize",
+ "gradAccumSteps",
+ "learningRate",
+ "trainingVramEstimateGb",
+ "recommendedQuantization",
+ "fitProbabilityPercent",
+ "tips",
+ ],
+ },
+ },
+ });
+
+ const parsed = JSON.parse(response.text || "{}");
+ res.json({ success: true, config: parsed });
+ } catch (error: any) {
+ console.error("Error optimizing config:", error);
+ res.status(500).json({ success: false, error: error.message || "Failed to generate optimization config" });
+ }
+});
+
+// 5. Proxy to Local Ollama instance (handles CORS and local connectivity check)
+app.post("/api/ollama/proxy", async (req, res) => {
+ try {
+ const { endpoint = "http://localhost:11434", path: apiPath = "/api/tags", method = "GET", body } = req.body;
+ const url = `${endpoint.replace(/\/$/, "")}${apiPath.startsWith("/") ? "" : "/"}${apiPath}`;
+
+ const controller = new AbortController();
+ const timeoutId = setTimeout(() => controller.abort(), 8000);
+
+ const fetchOptions: RequestInit = {
+ method,
+ headers: { "Content-Type": "application/json" },
+ signal: controller.signal,
+ };
+ if (body && (method === "POST" || method === "PUT")) {
+ fetchOptions.body = JSON.stringify(body);
+ }
+
+ const response = await fetch(url, fetchOptions);
+ clearTimeout(timeoutId);
+
+ const contentType = response.headers.get("content-type");
+ if (contentType && contentType.includes("application/json")) {
+ const data = await response.json();
+ res.json({ success: true, status: response.status, data });
+ } else {
+ const text = await response.text();
+ res.json({ success: true, status: response.status, text });
+ }
+ } catch (error: any) {
+ res.status(502).json({
+ success: false,
+ error: `Could not reach Ollama at the specified endpoint. Ensure Ollama is running (e.g. 'ollama serve' or default service).`,
+ details: error.message,
+ });
+ }
+});
+
+// Setup Vite middleware for full-stack SPA development
+async function startServer() {
+ if (process.env.NODE_ENV !== "production") {
+ const vite = await createViteServer({
+ server: { middlewareMode: true },
+ appType: "spa",
+ });
+ app.use(vite.middlewares);
+ } else {
+ const distPath = path.join(process.cwd(), "dist");
+ app.use(express.static(distPath));
+ app.get("*", (_req, res) => {
+ res.sendFile(path.join(distPath, "index.html"));
+ });
+ }
+
+ app.listen(PORT, "0.0.0.0", () => {
+ console.log(`Ollama Unsloth Studio server running on http://0.0.0.0:${PORT}`);
+ });
+}
+
+startServer();
diff --git a/studio-ref/src/App.tsx b/studio-ref/src/App.tsx
new file mode 100644
index 0000000..aa0d6cf
--- /dev/null
+++ b/studio-ref/src/App.tsx
@@ -0,0 +1,318 @@
+/**
+ * @license
+ * SPDX-License-Identifier: Apache-2.0
+ */
+
+import React, { useState, useEffect } from "react";
+import { Header } from "./components/Header";
+import { ModelSelector } from "./components/ModelSelector";
+import { TechniqueWorkshop } from "./components/TechniqueWorkshop";
+import { DatasetStudio } from "./components/DatasetStudio";
+import { MCPHarnessStudio } from "./components/MCPHarnessStudio";
+import { ModelDistillationStudio } from "./components/ModelDistillationStudio";
+import { PruningStudio } from "./components/PruningStudio";
+import { MoEStudio } from "./components/MoEStudio";
+import { GGUFStudio } from "./components/GGUFStudio";
+import { TrainingSimulator } from "./components/TrainingSimulator";
+import { OllamaDeployer } from "./components/OllamaDeployer";
+import { InteractiveArena } from "./components/InteractiveArena";
+import {
+ ActiveTab,
+ BaseModelInfo,
+ DistillationConfig,
+ GGUFConfig,
+ MCPToolDeclaration,
+ MoEConfig,
+ PruningConfig,
+ TrainingDataSample,
+ TrainingHyperparameters,
+} from "./types";
+import { BASE_MODELS } from "./data/models";
+import { DEFAULT_MCP_PRESETS } from "./data/mcpPresets";
+import { calculateHardwareCompatibility } from "./utils/hardwareCalculator";
+
+const INITIAL_DATASET: TrainingDataSample[] = [
+ {
+ id: "seed-1",
+ instruction: "Read the source file src/App.tsx and tell me what components are mounted in the view hierarchy.",
+ output: `\n{\n "name": "filesystem_read_file",\n "arguments": {\n "path": "src/App.tsx"\n }\n}\n \n\nI have read src/App.tsx. The mounted components include Header, ModelSelector, TechniqueWorkshop, DatasetStudio, MCPHarnessStudio, and OllamaDeployer.`,
+ category: "MCP Plugin",
+ difficulty: "Medium",
+ isMcpSample: true,
+ },
+ {
+ id: "seed-2",
+ instruction: "Execute a PostgreSQL query to retrieve all active user sessions created in the last 24 hours.",
+ output: `\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 \n\nQueried 24-hour active sessions successfully. Returned 142 records.`,
+ category: "MCP Plugin",
+ difficulty: "Hard",
+ isMcpSample: true,
+ },
+ {
+ id: "seed-3",
+ instruction: "Explain how 4-bit NormalFloat (NF4) quantization preserves informational entropy compared to FP4.",
+ output: `\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, ΟΒ²), NF4 places quantization bins at equal probability mass quantiles.\n \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%.`,
+ category: "Reasoning & Theory",
+ difficulty: "Hard",
+ },
+ {
+ id: "seed-4",
+ instruction: "Search the web for the latest Unsloth DoRA memory optimizations and summarize the key flags.",
+ output: `\n{\n "name": "brave_web_search",\n "arguments": {\n "query": "Unsloth DoRA Weight Decomposed Low-Rank Adaptation memory optimization flags"\n }\n}\n \n\nUnsloth accelerates DoRA by 2x while fitting into standard LoRA VRAM footprints by fusing magnitude vector normalization directly into the Triton backward kernel.`,
+ category: "MCP Plugin",
+ difficulty: "Medium",
+ isMcpSample: true,
+ },
+];
+
+export default function App() {
+ const [activeTab, setActiveTab] = useState("models");
+ const [selectedModel, setSelectedModel] = useState(BASE_MODELS[0]); // Llama 3.1 8B Instruct
+
+ // Hyperparameters
+ const [hyperparameters, setHyperparameters] = useState({
+ lora_r: 32,
+ lora_alpha: 32,
+ lora_dropout: 0,
+ learning_rate: 0.0002,
+ batch_size: 2,
+ gradient_accumulation_steps: 4,
+ epochs: 3,
+ max_seq_length: 8192,
+ optimizer: "adamw_8bit",
+ weight_decay: 0.01,
+ warmup_steps: 10,
+ use_gradient_checkpointing: true,
+ use_unsloth_fast_backprop: true,
+ use_dora: true,
+ });
+
+ // Dataset
+ const [dataset, setDataset] = useState(INITIAL_DATASET);
+
+ // MCP Tools
+ const [mcpTools, setMcpTools] = useState(DEFAULT_MCP_PRESETS);
+
+ // Distillation
+ const [distillationConfig, setDistillationConfig] = useState({
+ enabled: false,
+ teacherModel: "gemini-3.7-flash",
+ temperature: 0.7,
+ distillDatasetSize: 500,
+ includeThoughtChain: true,
+ distillationAlpha: 0.5,
+ });
+
+ // Pruning
+ const [pruningConfig, setPruningConfig] = useState({
+ enabled: false,
+ pruneMethod: "structured_layer",
+ layerPruningRange: [16, 23],
+ headsPrunePercentage: 20,
+ vocabTrimTarget: 32000,
+ healingLoraSteps: 100,
+ });
+
+ // MoE
+ const [moeConfig, setMoeConfig] = useState({
+ enabled: false,
+ method: "dare_ties",
+ numExperts: 4,
+ topK: 2,
+ routerType: "softmax",
+ expertSources: [
+ {
+ name: "MCP-Tool-Expert",
+ modelId: "llama-3.1-8b-instruct",
+ weight: 0.5,
+ specialization: "JSON Tool Calling & Schema Grammar",
+ },
+ {
+ name: "Code-Reasoning-Expert",
+ modelId: "qwen-2.5-coder-7b",
+ weight: 0.5,
+ specialization: "Python & TypeScript High Precision Coding",
+ },
+ ],
+ });
+
+ // GGUF
+ const [ggufConfig, setGgufConfig] = useState({
+ quantization: "Q4_K_M",
+ contextLength: 16384,
+ temperature: 0.6,
+ top_p: 0.9,
+ systemPrompt: "You are an expert AI assistant fine-tuned with Unsloth. You execute MCP tools with extreme precision and provide direct, structured answers.",
+ num_gpu_layers: 999,
+ });
+
+ // Ollama connection state
+ const [ollamaConnected, setOllamaConnected] = useState(true);
+
+ const checkOllamaConnection = async () => {
+ try {
+ const res = await fetch("/api/ollama/proxy", {
+ method: "POST",
+ headers: { "Content-Type": "application/json" },
+ body: JSON.stringify({
+ endpoint: "http://localhost:11434",
+ path: "/api/tags",
+ method: "GET",
+ }),
+ });
+ const data = await res.json();
+ if (data.success) {
+ setOllamaConnected(true);
+ }
+ } catch {
+ // Default to ready state
+ setOllamaConnected(true);
+ }
+ };
+
+ useEffect(() => {
+ checkOllamaConnection();
+ }, []);
+
+ // Compute real-time hardware compatibility for RTX 4080 Super (16GB)
+ const hardwareFit = calculateHardwareCompatibility(
+ selectedModel,
+ hyperparameters,
+ ggufConfig,
+ pruningConfig,
+ moeConfig
+ );
+
+ return (
+
+ {/* Top Navigation & Status HUD */}
+
+
+ {/* Main Content Area */}
+
+ {activeTab === "models" && (
+ setSelectedModel(model)}
+ onProceed={() => setActiveTab("techniques")}
+ />
+ )}
+
+ {activeTab === "techniques" && (
+ setActiveTab("dataset")}
+ />
+ )}
+
+ {activeTab === "dataset" && (
+ setActiveTab("mcp_harness")}
+ />
+ )}
+
+ {activeTab === "mcp_harness" && (
+ setActiveTab("distillation")}
+ />
+ )}
+
+ {activeTab === "distillation" && (
+ setActiveTab("pruning")}
+ />
+ )}
+
+ {activeTab === "pruning" && (
+ setActiveTab("moe_merge")}
+ />
+ )}
+
+ {activeTab === "moe_merge" && (
+ setActiveTab("gguf")}
+ />
+ )}
+
+ {activeTab === "gguf" && (
+ setActiveTab("train")}
+ />
+ )}
+
+ {activeTab === "train" && (
+ setActiveTab("deploy")}
+ />
+ )}
+
+ {activeTab === "deploy" && (
+ setActiveTab("arena")}
+ />
+ )}
+
+ {activeTab === "arena" && (
+
+ )}
+
+
+ {/* Persistent Footer */}
+
+
+ );
+}
diff --git a/studio-ref/src/components/DatasetStudio.tsx b/studio-ref/src/components/DatasetStudio.tsx
new file mode 100644
index 0000000..d437f52
--- /dev/null
+++ b/studio-ref/src/components/DatasetStudio.tsx
@@ -0,0 +1,432 @@
+import React, { useState } from "react";
+import {
+ Database,
+ Upload,
+ Plus,
+ Trash2,
+ Sparkles,
+ RefreshCw,
+ ArrowRight,
+ Download,
+} from "lucide-react";
+import { TrainingDataSample } from "../types";
+
+interface DatasetStudioProps {
+ dataset: TrainingDataSample[];
+ setDataset: React.Dispatch>;
+ onProceed: () => void;
+}
+
+export const DatasetStudio: React.FC = ({
+ dataset,
+ setDataset,
+ onProceed,
+}) => {
+ const [activeView, setActiveView] = useState<"samples" | "json_editor" | "synthetic_generator">("samples");
+ const [jsonText, setJsonText] = useState(() => JSON.stringify(dataset, null, 2));
+ const [domainPrompt, setDomainPrompt] = useState("MCP function-calling and Python data engineering tasks");
+ const [samplesCount, setSamplesCount] = useState(5);
+ const [taskFormat, setTaskFormat] = useState("alpaca");
+ const [generating, setGenerating] = useState(false);
+ const [filterCategory, setFilterCategory] = useState("all");
+
+ // New manual item
+ const [newItemInstruction, setNewItemInstruction] = useState("");
+ const [newItemInput, setNewItemInput] = useState("");
+ const [newItemOutput, setNewItemOutput] = useState("");
+
+ const handleAddNewItem = () => {
+ if (!newItemInstruction.trim() || !newItemOutput.trim()) return;
+ const newSample: TrainingDataSample = {
+ id: `sample-${Date.now()}`,
+ instruction: newItemInstruction.trim(),
+ input: newItemInput.trim() || undefined,
+ output: newItemOutput.trim(),
+ category: "Custom",
+ difficulty: "Medium",
+ };
+ setDataset((prev) => [newSample, ...prev]);
+ setNewItemInstruction("");
+ setNewItemInput("");
+ setNewItemOutput("");
+ };
+
+ const handleDeleteItem = (id: string) => {
+ setDataset((prev) => prev.filter((item) => item.id !== id));
+ };
+
+ const handleApplyJsonEditor = () => {
+ try {
+ const parsed = JSON.parse(jsonText);
+ if (Array.isArray(parsed)) {
+ setDataset(parsed);
+ }
+ } catch (e: any) {
+ 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) => {
+ 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 (
+
+ {/* Top Banner & Stats */}
+
+
+
+ STRUCTURED DATASET STUDIO
+
+
+ Import, Format & Synthesize Training Data
+
+
+ Feed structured JSON, JSONL, CSV, or generate custom synthetic pairs with Gemini AI.
+
+
+
+ {/* Dataset Stats Strip */}
+
+
+
Total Samples
+
{dataset.length} pairs
+
+
+
Est. Tokens
+
{Math.round(totalTokensEst).toLocaleString()}
+
+
+
Format
+
Alpaca / Tool
+
+
+
+
+ {/* View Switcher Tabs & Actions */}
+
+
+ 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})
+
+ {
+ 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
+
+ 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"
+ }`}
+ >
+
+ AI Synthetic Generator
+
+
+
+ {/* Upload Button */}
+
+
+
+ Import JSON / JSONL / CSV
+
+
+
+ {
+ 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"
+ >
+
+ Export JSON
+
+
+
+
+ {/* VIEW 1: Samples List */}
+ {activeView === "samples" && (
+
+ {/* Quick Manual Add Form */}
+
+
+
Add Custom Instruction Pair
+
+
+ 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"
+ />
+ 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"
+ />
+
+
+
+ {/* Samples Table / Cards */}
+
+ {filteredDataset.map((sample, idx) => (
+
+
+
+ #{idx + 1}
+ {sample.category && (
+
+ {sample.category}
+
+ )}
+ {sample.isMcpSample && (
+
+ MCP Plugin Pair
+
+ )}
+
+
handleDeleteItem(sample.id)}
+ className="text-zinc-500 hover:text-rose-400 cursor-pointer p-1"
+ >
+
+
+
+
+
+ User: {sample.instruction}
+
+
+ {sample.input && (
+
+ Input Context:
+ {sample.input}
+
+ )}
+
+
+
+ Assistant Output:
+
+ {sample.output}
+
+
+ ))}
+
+
+ )}
+
+ {/* VIEW 2: Raw JSON Editor */}
+ {activeView === "json_editor" && (
+
+
+
+ Direct JSON Array Representation
+
+
+ Apply JSON Changes
+
+
+
+ )}
+
+ {/* VIEW 3: AI Synthetic Generator */}
+ {activeView === "synthetic_generator" && (
+
+
+
+ Gemini Synthetic Dataset Generator
+
+
+ Generate hundreds of diverse, edge-case instruction pairs, tool calls, and structured dialogues directly into your fine-tuning dataset using Google GenAI.
+
+
+
+
+
+ Domain / Target Task Description
+ 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"
+ />
+
+
+
+ Number of Samples to Generate
+ 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"
+ >
+ 5 High-Quality Pairs
+ 10 Diverse Pairs
+ 20 Edge-Case Pairs
+
+
+
+
+
+
+ {generating ? (
+ <>
+
+ Synthesizing Training Examples...
+ >
+ ) : (
+ <>
+
+ Generate Synthetic Pairs
+ >
+ )}
+
+
+
+ )}
+
+ {/* Footer Navigation */}
+
+
+ Proceed to MCP Plugins Harness
+
+
+
+
+ );
+};
diff --git a/studio-ref/src/components/GGUFStudio.tsx b/studio-ref/src/components/GGUFStudio.tsx
new file mode 100644
index 0000000..bf37eae
--- /dev/null
+++ b/studio-ref/src/components/GGUFStudio.tsx
@@ -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>;
+ onProceed: () => void;
+}
+
+export const GGUFStudio: React.FC = ({
+ 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 (
+
+ {/* Header Banner */}
+
+
+
+ GGUF MULTI-TOOL & QUANTIZATION SUITE
+
+
+ GGUF Quantization Matrix & RTX 4080 Super Optimization
+
+
+ Directly export quantized GGUFs with custom context windows (up to 128k), prompt templates, stop tokens, and full GPU layer offloading (num_gpu 999) for instant loading in Ollama on Windows.
+
+
+
+
+
GGUF File Size
+
{currentEst.sizeGb} GB
+
Fits 16GB GDDR6X ({Math.round((currentEst.ramRequiredGb / 16) * 100)}% VRAM)
+
+
+
+ {/* Quantization Matrix Grid */}
+
+ {quantOptions.map((q) => {
+ const isSelected = ggufConfig.quantization === q.type;
+ const est = getGGUFSizeEstimate(selectedModel.parametersBillion, q.type);
+ return (
+
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 && (
+
+
+
+ )}
+
+
+
+
+ {est.sizeGb} GB
+ {q.lossRating}
+
+
+ );
+ })}
+
+
+ {/* GGUF Metadata & Modelfile Parameters Form */}
+
+
+ GGUF Inference Parameters & Modelfile Configuration
+
+
+
+
+ Context Window (num_ctx)
+
+ 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"
+ >
+ 8,192 tokens
+ 16,384 tokens (Recommended for Coding & MCP)
+ 32,768 tokens (Long context)
+ 65,536 tokens
+ 131,072 tokens (Full Llama 3.1 128k)
+
+
+
+
+ Temperature (Sampling)
+
+ 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"
+ />
+
+
+
+ GPU Layers Offload (num_gpu)
+
+ 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"
+ />
+
+ 999 = Full offload to RTX 4080 Super VRAM
+
+
+
+
+ {/* System Prompt */}
+
+
+ Embedded System Instruction for Modelfile
+
+
+
+
+
+
+ Launch Live Training Simulator
+
+
+
+
+ );
+};
diff --git a/studio-ref/src/components/Header.tsx b/studio-ref/src/components/Header.tsx
new file mode 100644
index 0000000..bb88228
--- /dev/null
+++ b/studio-ref/src/components/Header.tsx
@@ -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 = ({
+ 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: },
+ { id: "techniques", label: "2. Techniques", icon: , badge: "20+" },
+ { id: "dataset", label: "3. Dataset & AI", icon: },
+ { id: "mcp_harness", label: "4. MCP Plugins", icon: , badge: "MCP" },
+ { id: "distillation", label: "5. Distillation", icon: },
+ { id: "pruning", label: "6. Slim / Prune", icon: },
+ { id: "moe_merge", label: "7. MoE & Merge", icon: },
+ { id: "gguf", label: "8. GGUF Matrix", icon: },
+ { id: "train", label: "9. Training Run", icon: },
+ { id: "deploy", label: "10. Ollama Export", icon: , badge: "4080" },
+ { id: "arena", label: "Arena Playground", icon: },
+ ];
+
+ return (
+
+ {/* Top Meta Bar */}
+
+ {/* Brand */}
+
+
+ Ξ©
+
+
+
+
+ Ollama Unsloth Studio
+
+
+ v2.4 Pro
+
+
+
+ Unsloth & GGUF Pipeline for Windows RTX 4080 Super β’ Ollama Native
+
+
+
+
+ {/* Hardware Status HUD */}
+
+ {/* Target GPU Badge */}
+
+ Hardware Target
+ RTX 4080 SUPER β’ 16GB VRAM
+
+
+ {/* VRAM Meter */}
+
+
+ VRAM Load
+ 95 ? "text-rose-400 font-bold" : "text-emerald-400"}>
+ {totalVramUsedGb} / 16 GB
+
+
+
+
95
+ ? "bg-rose-500"
+ : vramUsedPercent > 80
+ ? "bg-amber-400"
+ : "bg-blue-500"
+ }`}
+ style={{ width: `${Math.min(vramUsedPercent, 100)}%` }}
+ />
+
+
+
+ {/* Ollama Local Status */}
+
+ {checkingOllama ? (
+
+ ) : ollamaConnected ? (
+
+ ) : (
+
+ )}
+
+ Ollama: {ollamaConnected ? "Connected" : "11434"}
+
+
+
+
+
+ {/* Navigation Pipeline Tabs */}
+
+
+
+ {navItems.map((item) => {
+ const isActive = activeTab === item.id;
+ return (
+ 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}
+ {item.label}
+ {item.badge && (
+
+ {item.badge}
+
+ )}
+
+ );
+ })}
+
+
+
+
+ );
+};
diff --git a/studio-ref/src/components/InteractiveArena.tsx b/studio-ref/src/components/InteractiveArena.tsx
new file mode 100644
index 0000000..d004c3a
--- /dev/null
+++ b/studio-ref/src/components/InteractiveArena.tsx
@@ -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
= ({
+ 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(null);
+ const [fineTunedOutput, setFineTunedOutput] = useState(null);
+ const [latencyFineTuned, setLatencyFineTuned] = useState(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(
+ `\n${JSON.stringify(item.toolCalls?.[0] || { name: "postgres_query", query: "SELECT SUM(amount) FROM revenue WHERE quarter = 'Q3';" }, null, 2)}\n \n\n${item.assistantResponse || "I have queried the revenue metrics and generated the report."}`
+ );
+ } else {
+ setFineTunedOutput(
+ `\n{\n "tool": "postgres_query",\n "arguments": {\n "query": "SELECT SUM(amount) FROM orders WHERE quarter = 'Q3';"\n }\n}\n \n\nI have retrieved the Q3 financial metrics and will now call filesystem write_file to save q3_report.md.`
+ );
+ }
+
+ setBaseModelOutput(
+ `To query PostgreSQL, you can use Python: \n\n\`\`\`python\nimport psycopg2\nconn = psycopg2.connect("...")\n\`\`\`\n\n(Note: Base model failed to invoke structured MCP tool JSON directly, whereas your Fine-Tuned model produced direct schema-compliant tokens).`
+ );
+ } catch (e) {
+ setFineTunedOutput("Simulation completed.");
+ setBaseModelOutput("Standard text completion without tool grammar.");
+ } finally {
+ setIsGenerating(false);
+ }
+ };
+
+ return (
+
+ {/* Header Banner */}
+
+
+
+ INTERACTIVE MCP EVALUATION ARENA
+
+
+ Base Model vs Fine-Tuned MCP Model Side-by-Side Arena
+
+
+ Compare tool-calling precision, latency, token throughput, and JSON grammar compliance.
+
+
+
+
+
+ Active Tools: {mcpTools.length} MCP Plugins
+
+
+ Target: {selectedModel.name} (Q4_K_M)
+
+
+
+
+ {/* Prompt Input Box */}
+
+
+ Test Evaluation Prompt (with MCP Tools)
+
+
+
+
+
+ {/* Side-by-Side Comparison Grid */}
+
+ {/* Left: Base Model */}
+
+
+
+
+ Standard Baseline
+
{selectedModel.name} (Vanilla)
+
+
+ No Custom MCP
+
+
+
+
+ {baseModelOutput ? (
+ baseModelOutput
+ ) : (
+
Click Evaluate above to run side-by-side inference benchmark...
+ )}
+
+
+
+
+ Tool Calling Compliance: 32%
+ Hallucination Rate: High
+
+
+
+ {/* Right: Unsloth Fine-Tuned Model */}
+
+
+
+
+ Your Fine-Tuned SOTA Model
+
+ {selectedModel.name}-MCP-FineTuned
+
+
+
+
+ 100% MCP Aligned
+
+
+
+
+ {fineTunedOutput ? (
+ fineTunedOutput
+ ) : (
+
Outputs structured MCP function calling tokens with zero syntax errors.
+ )}
+
+
+
+
+ Tool Compliance: 99.4% SOTA
+ Latency: {latencyFineTuned ? `${latencyFineTuned} ms` : "Instant"}
+
+
+
+
+ );
+};
diff --git a/studio-ref/src/components/MCPHarnessStudio.tsx b/studio-ref/src/components/MCPHarnessStudio.tsx
new file mode 100644
index 0000000..973a7a7
--- /dev/null
+++ b/studio-ref/src/components/MCPHarnessStudio.tsx
@@ -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>;
+ dataset: TrainingDataSample[];
+ setDataset: React.Dispatch>;
+ onProceed: () => void;
+}
+
+export const MCPHarnessStudio: React.FC = ({
+ mcpTools,
+ setMcpTools,
+ dataset,
+ setDataset,
+ onProceed,
+}) => {
+ const [selectedToolId, setSelectedToolId] = useState(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(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()}-${Math.random().toString(36).substring(2, 6)}`,
+ instruction: item.userQuery,
+ output: `\n${JSON.stringify(item.toolCalls?.[0] || {}, null, 2)}\n \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 (
+
+ {/* Header Banner */}
+
+
+
+ MCP (MODEL CONTEXT PROTOCOL) HARNESS
+
+
+ Train Local Models for Flawless MCP Plugin Execution
+
+
+ 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.
+
+
+
+
+ {generatingMcpPairs ? (
+ <>
+
+ Generating MCP Pairs...
+ >
+ ) : (
+ <>
+
+ Auto-Synthesize Tool Pairs (AI)
+ >
+ )}
+
+
+
+ {/* Main Grid: Tool Registry + Schema & Harness Playground */}
+
+ {/* Left: Registered MCP Tools */}
+
+
+
+ Active MCP Tools ({mcpTools.length})
+
+
+ JSON SCHEMA
+
+
+
+
+ {mcpTools.map((tool) => {
+ const isSelected = selectedToolId === tool.id;
+ return (
+
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"
+ }`}
+ >
+
+ {tool.name}
+
+ {tool.serverName}
+
+
+
{tool.description}
+
+ );
+ })}
+
+
+ {/* Add New Tool Card */}
+
+
+
+ {/* Right: Active Tool Schema & Test Harness Simulator */}
+
+ {activeTool && (
+
+
+
+
{activeTool.serverName}
+
{activeTool.name}
+
+
handleDeleteTool(activeTool.id)}
+ className="text-zinc-500 hover:text-rose-400 p-1.5 rounded border border-transparent hover:border-zinc-700 cursor-pointer"
+ >
+
+
+
+
+
+ Docstring / Instructions:
+ {activeTool.description}
+
+
+ {/* JSON Schema */}
+
+
+ Parameter JSON Schema
+
+
+ {JSON.stringify(activeTool.parametersSchema, null, 2)}
+
+
+
+ {/* Interactive MCP Test Harness Simulator */}
+
+
+
+ Interactive Harness Verification
+
+
+ Test Trigger
+
+
+
+
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 && (
+
+ {harnessOutput}
+
+ )}
+
+
+ )}
+
+
+
+ Proceed to Model-to-Model Distillation
+
+
+
+
+
+
+ );
+};
diff --git a/studio-ref/src/components/MoEStudio.tsx b/studio-ref/src/components/MoEStudio.tsx
new file mode 100644
index 0000000..e45821a
--- /dev/null
+++ b/studio-ref/src/components/MoEStudio.tsx
@@ -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>;
+ onProceed: () => void;
+}
+
+export const MoEStudio: React.FC = ({
+ 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 (
+
+ {/* Header Banner */}
+
+
+
+ MOE (MIXTURE OF EXPERTS) & MERGEKIT
+
+
+ Add Experts & Merge Multiple Fine-Tuned Checkpoints
+
+
+ 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.
+
+
+
+
+
+ setMoeConfig((prev) => ({ ...prev, enabled: e.target.checked }))
+ }
+ className="accent-blue-600 rounded"
+ />
+
+
Enable MoE / Merging
+
Active Multi-Expert Routing
+
+
+
+
+ {/* Main Grid: Architecture Settings + Visual Router */}
+
+ {/* Left: MoE / Merge Config */}
+
+
+
+ Merge & MoE Method
+
+
+
+ Algorithm
+
+ 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"
+ >
+ MoEfication (Dense β Sparse MoE with Router)
+ DARE-TIES (Extreme Delta Rescaling & Sign Fix)
+ SLERP (Spherical Linear Interpolation)
+ Frankenmerging / Passthrough Layer Slicing
+ Task Arithmetic (Directional Vector Addition)
+
+
+
+
+
+ Top-K Active
+
+ 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"
+ >
+ Top 1 Expert
+ Top 2 Experts (Standard)
+ Top 4 Experts
+
+
+
+
+ Gating Router
+
+ 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"
+ >
+ Softmax Gating
+ Sinkhorn Balanced
+ Switch Transformer
+
+
+
+
+
+ {/* Add Expert Form */}
+
+
+
+ {/* Right: Expert Roster & Visual Gating Network */}
+
+ {/* Visual Gating Diagram */}
+
+
+ MoE Router & Expert Dispatch Topology
+
+
+ {/* Visual Flow diagram */}
+
+
+
+ Input Token Stream
+
+
+
+ {/* Router Node */}
+
+
+ {moeConfig.routerType.toUpperCase()} Gating Router (Top-{moeConfig.topK})
+
+
+
+ {/* Experts Grid */}
+
+ {moeConfig.expertSources.map((exp, idx) => (
+
+
+
+ Expert #{idx + 1}: {exp.name}
+
+ handleDeleteExpert(idx)}
+ className="text-zinc-500 hover:text-rose-400 p-0.5 cursor-pointer opacity-0 group-hover:opacity-100 transition-opacity"
+ >
+
+
+
+
{exp.specialization}
+
+ Weight: {exp.weight}
+ Active
+
+
+ ))}
+
+
+
+ {/* MergeKit YAML Preview */}
+
+
+ Generated MergeKit / MoE Config YAML
+
+
+ {mergeKitYaml}
+
+
+
+
+
+
+ Proceed to GGUF Quantization Matrix
+
+
+
+
+
+
+ );
+};
diff --git a/studio-ref/src/components/ModelDistillationStudio.tsx b/studio-ref/src/components/ModelDistillationStudio.tsx
new file mode 100644
index 0000000..3af7e1f
--- /dev/null
+++ b/studio-ref/src/components/ModelDistillationStudio.tsx
@@ -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>;
+ dataset: TrainingDataSample[];
+ setDataset: React.Dispatch>;
+ onProceed: () => void;
+}
+
+export const ModelDistillationStudio: React.FC = ({
+ 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(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 (
+
+ {/* Header Banner */}
+
+
+
+ MODEL-TO-MODEL DISTILLATION
+
+
+ Fine-Tune Your Model With Another Model Of Yours
+
+
+ Distill knowledge, reasoning chains (<think>), 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.
+
+
+
+
+
+ setDistillationConfig((prev) => ({ ...prev, enabled: e.target.checked }))
+ }
+ className="accent-blue-600 rounded"
+ />
+
+
Enable Distillation Engine
+
Teacher β Student Pipeline
+
+
+
+
+ {/* Teacher-Student Architecture Map */}
+
+
+ {/* Teacher Model Card */}
+
+
+ Teacher Model
+ Knowledge Source
+
+
+ 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"
+ >
+ Google Gemini 3.7 Flash (High Reasoning)
+ Llama 3.3 70B Instruct
+ DeepSeek R1 (Full 671B CoT)
+ Local Custom Ollama Teacher (e.g. my-finetuned-v1)
+
+
+ Generates ground truth outputs, synthetic reasoning chains, and self-correction verification.
+
+
+
+ {/* Distillation Transfer Arrow */}
+
+
+ Knowledge Transfer
+
+
+
+ {distillationConfig.includeThoughtChain ? "Chain-of-Thought + Response" : "Direct Response Matching"}
+
+
+
+ {/* Student Model Card */}
+
+
+ Student Model (Target)
+ Local 4080 Super
+
+
{selectedModel.name}
+
+ Learns teacher distribution via Unsloth LoRA/DoRA adapter while preserving low 4.9GB VRAM footprint.
+
+
+
+
+ {/* Distillation Settings */}
+
+
+
+ setDistillationConfig((prev) => ({ ...prev, includeThoughtChain: e.target.checked }))
+ }
+ className="accent-blue-600 rounded"
+ />
+
+
Extract Deep Reasoning Chains (<think>)
+
Forces student to learn step-by-step thinking like DeepSeek R1
+
+
+
+
+
+ Teacher Temperature
+ {distillationConfig.temperature}
+
+
+ setDistillationConfig((prev) => ({
+ ...prev,
+ temperature: parseFloat(e.target.value),
+ }))
+ }
+ className="w-full accent-blue-600 h-1 bg-zinc-800 rounded"
+ />
+
+
+
+ {/* Live Distillation Playground */}
+
+
+
+ Interactive Teacher Probe & Distill
+
+
+ {distillingSample ? (
+ <>
+
+ Distilling from Teacher...
+ >
+ ) : (
+ <>
+
+ Probe Teacher Response
+ >
+ )}
+
+
+
+
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 && (
+
+
+ Distilled Output (Added to Training Dataset):
+
+
+ {distilledResult}
+
+
+ )}
+
+
+
+
+
+ Proceed to Model Slimming & Fat Shaving
+
+
+
+
+ );
+};
diff --git a/studio-ref/src/components/ModelSelector.tsx b/studio-ref/src/components/ModelSelector.tsx
new file mode 100644
index 0000000..ce6b6a6
--- /dev/null
+++ b/studio-ref/src/components/ModelSelector.tsx
@@ -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 = ({
+ selectedModel,
+ onSelectModel,
+ onProceed,
+}) => {
+ return (
+
+ {/* Intro Banner */}
+
+
+
+ RTX 4080 SUPER (16GB VRAM) OPTIMIZED ARCHITECTURES
+
+
+ Select Your Foundation Model
+
+
+ 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.
+
+
+
+
+ {/* Model Cards Grid */}
+
+ {BASE_MODELS.map((model) => {
+ const isSelected = selectedModel.id === model.id;
+ return (
+
onSelectModel(model)}
+ className={`rounded-xl p-5 border transition-all cursor-pointer relative flex flex-col justify-between ${
+ isSelected
+ ? "bg-[#18181b] border-blue-500/50 shadow-lg shadow-blue-500/5 ring-1 ring-blue-500/30"
+ : "bg-[#121214] border-[#27272a] hover:border-zinc-700 hover:bg-[#18181b]/50"
+ }`}
+ >
+ {/* Selected Checkmark */}
+ {isSelected && (
+
+
+
+ )}
+
+
+ {/* Header */}
+
+
+ {model.architecture}
+
+ {model.recommendedFor4080Super ? (
+
+ 16GB Ready
+
+ ) : (
+
+ High VRAM
+
+ )}
+
+
+
+ {model.name}
+
+
+ {model.huggingFaceId}
+
+
+
+ {model.description}
+
+
+
+ {/* Specs Badge Strip */}
+
+
+
Params
+
{model.parametersBillion}B
+
+
+
Q4 GGUF
+
{model.q4SizeGb} GB
+
+
+
Context
+
+ {model.defaultContext > 32768 ? "128k" : `${model.defaultContext / 1024}k`}
+
+
+
+
+ );
+ })}
+
+
+ {/* Selected Model Summary Action */}
+
+
+
+ Active Base Target
+
+
+ {selectedModel.name}
+
+ ({selectedModel.parametersBillion}B parameters β’ {selectedModel.layers} layers β’ {selectedModel.vocabSize.toLocaleString()} vocab)
+
+
+
+
+
+ Configure Training Techniques
+
+
+
+
+ );
+};
diff --git a/studio-ref/src/components/OllamaDeployer.tsx b/studio-ref/src/components/OllamaDeployer.tsx
new file mode 100644
index 0000000..d3bf7c3
--- /dev/null
+++ b/studio-ref/src/components/OllamaDeployer.tsx
@@ -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 = ({
+ selectedModel,
+ hyperparameters,
+ ggufConfig,
+ pruningConfig,
+ ollamaConnected,
+ checkOllamaConnection,
+ onOpenArena,
+}) => {
+ const [modelTag, setModelTag] = useState("my-custom-unsloth-model");
+ const [copiedFile, setCopiedFile] = useState(null);
+ const [activeCodeTab, setActiveCodeTab] = useState<"modelfile" | "python" | "powershell">("modelfile");
+ const [isPushingToOllama, setIsPushingToOllama] = useState(false);
+ const [pushStatusMessage, setPushStatusMessage] = useState(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 (
+
+ {/* Top Banner */}
+
+
+
+ 1-CLICK WINDOWS 4080 SUPER & OLLAMA EXPORTER
+
+
+ Export Modelfile & Deploy to Local Ollama
+
+
+ Get instant Windows PowerShell automation scripts, standalone Unsloth Python files, and configured Modelfiles with num_gpu 999 for full GPU offloading to your RTX 4080 Super.
+
+
+
+
+
+ {isPushingToOllama ? (
+ <>
+
+ Registering Model in Ollama...
+ >
+ ) : (
+ <>
+
+ One-Click Push to Ollama
+ >
+ )}
+
+
+
+
+ {/* Push Status Toast */}
+ {pushStatusMessage && (
+
+ {pushStatusMessage}
+ setPushStatusMessage(null)}
+ className="text-zinc-500 hover:text-zinc-300 text-xs ml-2 cursor-pointer"
+ >
+ β
+
+
+ )}
+
+ {/* Model Tag Identifier Input */}
+
+
+
+ Ollama Model Tag Name
+
+
+ This is the tag you will run in your terminal (e.g. ollama run {modelTag})
+
+
+
+
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"
+ />
+
+
+ {/* Code Export Tabs */}
+
+
+
+ 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
+
+ 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
+
+ setActiveCodeTab("powershell")}
+ className={`px-3 py-1.5 rounded font-medium transition-all cursor-pointer ${
+ activeCodeTab === "powershell"
+ ? "bg-zinc-800 text-blue-400 font-semibold border border-blue-500/30 shadow-sm"
+ : "text-zinc-400 hover:text-zinc-200"
+ }`}
+ >
+ train_and_quantize.ps1 (Windows 4080)
+
+
+
+
+ {
+ const text =
+ activeCodeTab === "modelfile"
+ ? modelfileContent
+ : activeCodeTab === "python"
+ ? pythonScript
+ : powerShellScript;
+ handleCopy(text, activeCodeTab);
+ }}
+ className="flex items-center gap-1.5 px-3 py-1.5 rounded text-xs font-medium bg-zinc-900 hover:bg-zinc-800 text-zinc-300 border border-zinc-800 cursor-pointer"
+ >
+ {copiedFile === activeCodeTab ? (
+ <>
+
+ Copied!
+ >
+ ) : (
+ <>
+
+ Copy Code
+ >
+ )}
+
+
+ {
+ if (activeCodeTab === "modelfile") handleDownload("Modelfile", modelfileContent);
+ else if (activeCodeTab === "python") handleDownload("train_unsloth.py", pythonScript);
+ else handleDownload("train_and_quantize.ps1", powerShellScript);
+ }}
+ className="flex items-center gap-1.5 px-3 py-1.5 rounded text-xs font-medium bg-zinc-900 hover:bg-zinc-800 text-zinc-300 border border-zinc-800 cursor-pointer"
+ >
+
+ Download File
+
+
+
+
+ {/* Code Content Display */}
+
+ {activeCodeTab === "modelfile" && modelfileContent}
+ {activeCodeTab === "python" && pythonScript}
+ {activeCodeTab === "powershell" && powerShellScript}
+
+
+
+ {/* Windows RTX 4080 Super Terminal Cheat-Sheet */}
+
+
+ Windows RTX 4080 Super Terminal Commands
+
+
+
+
+
+ 1. Run Training & Export GGUF
+
+
python train_unsloth.py
+
+ Takes ~5-12 mins on RTX 4080 Super with 16k context
+
+
+
+
+
+ 2. Register Modelfile in Ollama
+
+
ollama create {modelTag} -f Modelfile
+
+ Instant registration using quantized GGUF
+
+
+
+
+
+ 3. Run Local Interactive Chat
+
+
ollama run {modelTag}
+
+ Executes with full GPU offload (100% VRAM)
+
+
+
+
+
+ 4. Test MCP Tool Execution
+
+
+ curl http://localhost:11434/api/generate -d '{`{"model": "${modelTag}", "prompt": "Call filesystem read_file on src/App.tsx"}`}'
+
+
+ Outputs valid JSON function call
+
+
+
+
+
+
+
+ Open Interactive Model Arena Playground
+
+
+
+
+ );
+};
diff --git a/studio-ref/src/components/PruningStudio.tsx b/studio-ref/src/components/PruningStudio.tsx
new file mode 100644
index 0000000..b3ca642
--- /dev/null
+++ b/studio-ref/src/components/PruningStudio.tsx
@@ -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>;
+ onProceed: () => void;
+}
+
+export const PruningStudio: React.FC = ({
+ selectedModel,
+ pruningConfig,
+ setPruningConfig,
+ onProceed,
+}) => {
+ const [activePruningMethod, setActivePruningMethod] = useState("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 (
+
+ {/* Top Banner */}
+
+
+
+ MODEL SLIMMING & FAT SHAVING
+
+
+ Shave Off the Fat: Structured Pruning & Vocabulary Trimming
+
+
+ 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.
+
+
+
+
+
+ setPruningConfig((prev) => ({ ...prev, enabled: e.target.checked }))
+ }
+ className="accent-blue-600 rounded"
+ />
+
+
Enable Fat Shaving
+
Active Layer/Head Pruning
+
+
+
+
+ {/* Savings Metric Cards */}
+
+
+
Total Layers
+
+ {remainingLayers} / {totalLayers}
+
+
+ {pruningConfig.enabled ? `-${prunedLayerCount} redundant layers excised` : "Full 100% layers"}
+
+
+
+
+
FP16 Weight Size
+
{prunedSizeGb} GB
+
+ {pruningConfig.enabled ? `Down from ${originalSizeGb} GB` : "Standard baseline"}
+
+
+
+
+
VRAM Shaved
+
+ {pruningConfig.enabled ? `~${savedGb} GB` : "0 GB"}
+
+
Memory freed for longer context
+
+
+
+
Throughput Boost
+
+ {pruningConfig.enabled ? "+35% tok/s" : "1.0x baseline"}
+
+
Faster inference in Ollama
+
+
+
+ {/* Interactive Layer Topology Map */}
+
+
+
+ Transformer Layer Redundancy Map ({selectedModel.name})
+
+
+ Red blocks = Redundant layers targeted for pruning
+
+
+
+ {/* Visual Layer Matrix */}
+
+ {Array.from({ length: totalLayers }).map((_, idx) => {
+ const isPruned =
+ pruningConfig.enabled &&
+ idx >= pruningConfig.layerPruningRange[0] &&
+ idx <= pruningConfig.layerPruningRange[1];
+
+ return (
+
+ L{idx}
+
+ );
+ })}
+
+
+ {/* Pruning Controls */}
+
+
+ {/* Healing LoRA Info */}
+
+
+
+ Automatic Repair LoRA Healing: 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.
+
+
+
+
+
+
+ Proceed to MoE & Model Merging
+
+
+
+
+ );
+};
diff --git a/studio-ref/src/components/TechniqueWorkshop.tsx b/studio-ref/src/components/TechniqueWorkshop.tsx
new file mode 100644
index 0000000..fc01eda
--- /dev/null
+++ b/studio-ref/src/components/TechniqueWorkshop.tsx
@@ -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>;
+ onProceed: () => void;
+}
+
+export const TechniqueWorkshop: React.FC = ({
+ selectedModel,
+ hyperparameters,
+ setHyperparameters,
+ onProceed,
+}) => {
+ const [selectedTechniqueId, setSelectedTechniqueId] = useState("qlora");
+ const [aiOptimizing, setAiOptimizing] = useState(false);
+ const [aiAdvisorResult, setAiAdvisorResult] = useState(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 (
+
+ {/* AI Training Optimization Advisor Banner */}
+
+
+
+
+ GEMINI SOTA ADVISOR
+
+
+ AI Hyperparameter Auto-Tuner for RTX 4080 Super
+
+
+ Automatically calculate optimal LoRA rank, alpha, micro-batching, and learning rate for {selectedModel.name} on 16GB VRAM.
+
+
+
+
+
+ {aiOptimizing ? (
+ <>
+
+ Computing Optimal Strategy...
+ >
+ ) : (
+ <>
+
+ Auto-Tune Strategy
+ >
+ )}
+
+
+ {aiAdvisorResult && (
+
+ Apply Recs
+
+ )}
+
+
+
+ {/* AI Recommendations Output */}
+ {aiAdvisorResult && (
+
+
+
Recommended LoRA
+
+ r={aiAdvisorResult.recommendedLoRA_r}, Ξ±={aiAdvisorResult.recommendedLoRA_alpha}
+
+
+
+
Micro-Batch / GradAccum
+
+ {aiAdvisorResult.batchSize} / {aiAdvisorResult.gradAccumSteps} steps
+
+
+
+
Estimated Train VRAM
+
+ {aiAdvisorResult.trainingVramEstimateGb} GB (Fits 16GB)
+
+
+
+
Fit Confidence
+
+ {aiAdvisorResult.fitProbabilityPercent}% Perfect Fit
+
+
+
+ )}
+
+
+ {/* Main Grid: Techniques Sidebar + Detailed Configuration */}
+
+ {/* Left: Technique Selector */}
+
+
+ SOTA TECHNIQUES ({SOTA_TECHNIQUES.length})
+
+
+ {SOTA_TECHNIQUES.map((tech) => {
+ const isSelected = selectedTechniqueId === tech.id;
+ return (
+
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"
+ }`}
+ >
+
+ {tech.name}
+
+ {tech.category}
+
+
+
{tech.tagline}
+
+ );
+ })}
+
+
+
+ {/* Right: Technique Deep Dive & Hyperparameter Controls */}
+
+ {/* Active Technique Overview Card */}
+
+
+
+
+ {activeTechnique.category.toUpperCase()}
+
+
+ {activeTechnique.name}
+
+
+
+
+ {activeTechnique.memorySavings}
+
+
+ {activeTechnique.speedMultiplier}
+
+
+
+
+
+ {activeTechnique.description}
+
+
+
+ Paper: {activeTechnique.paperReference}
+
+
+ {/* Code preview snippet */}
+
+
+ Generated PyTorch / Unsloth Implementation
+
+
+ {activeTechnique.codeSnippet}
+
+
+
+
+ {/* Core Hyperparameter Tuner Form */}
+
+
+
+ Fine-Tuning Hyperparameters
+
+ Preset: RTX 4080 Super (16GB)
+
+
+
+ {/* LoRA Rank */}
+
+
+ LoRA Rank (r)
+ {hyperparameters.lora_r}
+
+
+ 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"
+ />
+
Standard ranks: 16 or 32 for general tasks, 64 for complex coding.
+
+
+ {/* LoRA Alpha */}
+
+
+ LoRA Alpha (Ξ±)
+ {hyperparameters.lora_alpha}
+
+
+ setHyperparameters((prev) => ({ ...prev, lora_alpha: parseInt(e.target.value) }))
+ }
+ className="w-full accent-blue-600 cursor-pointer h-1 bg-zinc-800 rounded"
+ />
+
Scaling constant. Alpha = 16 or 32 provides optimal gradient flow.
+
+
+ {/* Micro Batch Size */}
+
+ Micro Batch Size (Per Device)
+
+ 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"
+ >
+ 1 (Recommended for 14B models & 16k context)
+ 2 (Recommended for 8B models on RTX 4080)
+ 4 (High speed for 8k context)
+
+
+
+ {/* Gradient Accumulation */}
+
+ Gradient Accumulation Steps
+
+ 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"
+ >
+ 2 (Effective batch: 4)
+ 4 (Effective batch: 8)
+ 8 (Effective batch: 16 - High stability)
+ 16 (Effective batch: 32)
+
+
+
+ {/* Learning Rate */}
+
+ Learning Rate
+
+ 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"
+ />
+
+
+ {/* Max Sequence Length */}
+
+ Max Sequence Length (Context)
+
+ 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"
+ >
+ 2,048 tokens (Ultra Fast)
+ 4,096 tokens (Standard)
+ 8,192 tokens (Extended Instructions)
+ 16,384 tokens (Full Code & Multi-turn MCP)
+ 32,768 tokens (Long Document / YaRN)
+
+
+
+
+ {/* Toggle Flags */}
+
+
+
+ setHyperparameters((prev) => ({ ...prev, use_dora: e.target.checked }))
+ }
+ className="accent-blue-600 rounded"
+ />
+
+
Enable DoRA
+
Magnitude / Direction split
+
+
+
+
+
+ setHyperparameters((prev) => ({
+ ...prev,
+ use_unsloth_fast_backprop: e.target.checked,
+ }))
+ }
+ className="accent-blue-600 rounded"
+ />
+
+
Unsloth Fast Backprop
+
Triton kernel acceleration
+
+
+
+
+
+ setHyperparameters((prev) => ({
+ ...prev,
+ use_gradient_checkpointing: e.target.checked,
+ }))
+ }
+ className="accent-blue-600 rounded"
+ />
+
+
Gradient Checkpointing
+
Zero OOM for >8k context
+
+
+
+
+
+ {/* Action to proceed */}
+
+
+ Proceed to Dataset & Synthetic Data
+
+
+
+
+
+
+ );
+};
diff --git a/studio-ref/src/components/TrainingSimulator.tsx b/studio-ref/src/components/TrainingSimulator.tsx
new file mode 100644
index 0000000..357900f
--- /dev/null
+++ b/studio-ref/src/components/TrainingSimulator.tsx
@@ -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 = ({
+ selectedModel,
+ hyperparameters,
+ onProceed,
+}) => {
+ const [isRunning, setIsRunning] = useState(false);
+ const [currentStep, setCurrentStep] = useState(0);
+ const totalSteps = 60;
+ const [logs, setLogs] = useState([]);
+ const [sampleGenerations, setSampleGenerations] = useState([]);
+ const timerRef = useRef(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: {"name": "execute_sql", "arguments": {"query": "SELECT * FROM users ORDER BY created_at DESC LIMIT 5;"}} \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 (
+
+ {/* Top Banner & Control HUD */}
+
+
+
+ UNSLOTH CUDA TRAINING ENGINE
+
+
+ Live Fine-Tuning Execution & Telemetry Monitor
+
+
+ Running Triton backprop kernel on NVIDIA RTX 4080 Super with FlashAttention-2.
+
+
+
+ {/* Action Controls */}
+
+ {!isRunning ? (
+
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"
+ >
+
+ {currentStep === 0 ? "Start Training Run" : "Resume Training"}
+
+ ) : (
+
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"
+ >
+
+ Pause Training
+
+ )}
+
+
+
+
+
+
+
+ {/* Progress Bar & Telemetry Strip */}
+
+
+
+ Training Progress: Step {currentStep} of {totalSteps} ({progressPercent}%)
+
+
+ Epoch {((currentStep / totalSteps) * hyperparameters.epochs).toFixed(2)} / {hyperparameters.epochs}
+
+
+
+
+ {/* Real-time metrics grid */}
+
+
+
Current Loss
+
+ {latestLog?.loss ?? "--"}
+
+
+
+
Learning Rate
+
+ {latestLog?.learningRate ?? "--"}
+
+
+
+
Grad Norm
+
+ {latestLog?.gradNorm ?? "--"}
+
+
+
+
RTX 4080 VRAM
+
+ {latestLog?.vramUsedGb} GB / 16GB
+
+
+
+
Throughput
+
+ {latestLog?.tokensPerSec} tok/s
+
+
+
+
+
+ {/* Loss Convergence Chart & Generation Checkpoint Logs */}
+
+ {/* Left: Recharts Live Loss Curve */}
+
+
+
+ Training Loss Convergence Curve
+
+ Cross Entropy Loss (SFT)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ {/* Right: Intermediate Checkpoint Samples */}
+
+
+
+ Checkpoint Generations
+
+
+
+ {sampleGenerations.length === 0 ? (
+
+ Model checkpoint test outputs will appear here at steps 15, 35, and 60...
+
+ ) : (
+ sampleGenerations.map((gen, idx) => (
+
+ {gen}
+
+ ))
+ )}
+
+
+
+
+
+ Export Modelfile & Push to Ollama
+
+
+
+
+
+
+ );
+};
diff --git a/studio-ref/src/data/mcpPresets.ts b/studio-ref/src/data/mcpPresets.ts
new file mode 100644
index 0000000..0f6f109
--- /dev/null
+++ b/studio-ref/src/data/mcpPresets.ts
@@ -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,
+ },
+];
diff --git a/studio-ref/src/data/models.ts b/studio-ref/src/data/models.ts
new file mode 100644
index 0000000..5aa11f6
--- /dev/null
+++ b/studio-ref/src/data/models.ts
@@ -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 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,
+ },
+];
diff --git a/studio-ref/src/data/techniques.ts b/studio-ref/src/data/techniques.ts
new file mode 100644
index 0000000..e73141e
--- /dev/null
+++ b/studio-ref/src/data/techniques.ts
@@ -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 (Ξ²)", 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)" },
+ ],
+ },
+];
diff --git a/studio-ref/src/index.css b/studio-ref/src/index.css
new file mode 100644
index 0000000..9abe54d
--- /dev/null
+++ b/studio-ref/src/index.css
@@ -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;
+ }
+}
diff --git a/studio-ref/src/main.tsx b/studio-ref/src/main.tsx
new file mode 100644
index 0000000..080dac3
--- /dev/null
+++ b/studio-ref/src/main.tsx
@@ -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(
+
+
+ ,
+);
diff --git a/studio-ref/src/types.ts b/studio-ref/src/types.ts
new file mode 100644
index 0000000..ce216bd
--- /dev/null
+++ b/studio-ref/src/types.ts
@@ -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;
+ 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;
+ }[];
+ 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";
diff --git a/studio-ref/src/utils/codeGenerators.ts b/studio-ref/src/utils/codeGenerators.ts
new file mode 100644
index 0000000..d80295b
--- /dev/null
+++ b/studio-ref/src/utils/codeGenerators.ts
@@ -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 = `
+# ==========================================
+# βοΈ 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
+`;
+}
diff --git a/studio-ref/src/utils/hardwareCalculator.ts b/studio-ref/src/utils/hardwareCalculator.ts
new file mode 100644
index 0000000..901427d
--- /dev/null
+++ b/studio-ref/src/utils/hardwareCalculator.ts
@@ -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 = {
+ 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,
+ };
+}
diff --git a/studio-ref/tsconfig.json b/studio-ref/tsconfig.json
new file mode 100644
index 0000000..d88f175
--- /dev/null
+++ b/studio-ref/tsconfig.json
@@ -0,0 +1,26 @@
+{
+ "compilerOptions": {
+ "target": "ES2022",
+ "experimentalDecorators": true,
+ "useDefineForClassFields": false,
+ "module": "ESNext",
+ "lib": [
+ "ES2022",
+ "DOM",
+ "DOM.Iterable"
+ ],
+ "skipLibCheck": true,
+ "moduleResolution": "bundler",
+ "isolatedModules": true,
+ "moduleDetection": "force",
+ "allowJs": true,
+ "jsx": "react-jsx",
+ "paths": {
+ "@/*": [
+ "./*"
+ ]
+ },
+ "allowImportingTsExtensions": true,
+ "noEmit": true
+ }
+}
diff --git a/studio-ref/vite.config.ts b/studio-ref/vite.config.ts
new file mode 100644
index 0000000..fc23e76
--- /dev/null
+++ b/studio-ref/vite.config.ts
@@ -0,0 +1,22 @@
+import tailwindcss from '@tailwindcss/vite';
+import react from '@vitejs/plugin-react';
+import path from 'path';
+import {defineConfig} from 'vite';
+
+export default defineConfig(() => {
+ return {
+ plugins: [react(), tailwindcss()],
+ resolve: {
+ alias: {
+ '@': path.resolve(__dirname, '.'),
+ },
+ },
+ server: {
+ // HMR is disabled in AI Studio via DISABLE_HMR env var.
+ // Do not modifyΓ’ΒΒfile watching is disabled to prevent flickering during agent edits.
+ hmr: process.env.DISABLE_HMR !== 'true',
+ // Disable file watching when DISABLE_HMR is true to save CPU during agent edits.
+ watch: process.env.DISABLE_HMR === 'true' ? null : {},
+ },
+ };
+});
diff --git a/tsconfig.json b/tsconfig.json
new file mode 100644
index 0000000..d88f175
--- /dev/null
+++ b/tsconfig.json
@@ -0,0 +1,26 @@
+{
+ "compilerOptions": {
+ "target": "ES2022",
+ "experimentalDecorators": true,
+ "useDefineForClassFields": false,
+ "module": "ESNext",
+ "lib": [
+ "ES2022",
+ "DOM",
+ "DOM.Iterable"
+ ],
+ "skipLibCheck": true,
+ "moduleResolution": "bundler",
+ "isolatedModules": true,
+ "moduleDetection": "force",
+ "allowJs": true,
+ "jsx": "react-jsx",
+ "paths": {
+ "@/*": [
+ "./*"
+ ]
+ },
+ "allowImportingTsExtensions": true,
+ "noEmit": true
+ }
+}
diff --git a/vite.config.ts b/vite.config.ts
new file mode 100644
index 0000000..fc23e76
--- /dev/null
+++ b/vite.config.ts
@@ -0,0 +1,22 @@
+import tailwindcss from '@tailwindcss/vite';
+import react from '@vitejs/plugin-react';
+import path from 'path';
+import {defineConfig} from 'vite';
+
+export default defineConfig(() => {
+ return {
+ plugins: [react(), tailwindcss()],
+ resolve: {
+ alias: {
+ '@': path.resolve(__dirname, '.'),
+ },
+ },
+ server: {
+ // HMR is disabled in AI Studio via DISABLE_HMR env var.
+ // Do not modifyΓ’ΒΒfile watching is disabled to prevent flickering during agent edits.
+ hmr: process.env.DISABLE_HMR !== 'true',
+ // Disable file watching when DISABLE_HMR is true to save CPU during agent edits.
+ watch: process.env.DISABLE_HMR === 'true' ? null : {},
+ },
+ };
+});