Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
oh okey so we want to MAX out the quality at 2000<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
my laptop crashed. Continue where we left or in case something went corrupt then lets start over<|im_end|>


Prompt:
<|im_start|>system
You are in General Assistant profile. Use the most efficient tool for each request.
<|im_end|>
<|im_start|>user
[IMPORTANT: Background process proc_0a497b8b5935 exited (exit code -15, SIGTERM).
Command: unset PYTHONPATH; /Users/jacobeen/.hermes/hermes-agent/venv/bin/python3 03_train_and_export_unity_agent.py train 2>&1
Output:
4<00:26,  1.25s/it]
Calculating loss...:  20%|██        | 5/25 [00:04<00:21,  1.08s/it]
Calculating loss...:  24%|██▍       | 6/25 [00:05<00:16,  1.18it/s]
Calculating loss...:  28%|██▊       | 7/25 [00:06<00:16,  1.07it/s]
Calculating loss...:  32%|███▏      | 8/25 [00:07<00:15,  1.07it/s]
Calculating loss...:  36%|███▌      | 9/25 [00:08<00:17,  1.10s/it]
Calculating loss...:  40%|████      | 10/25 [00:10<00:19,  1.27s/it]
Calculating loss...:  44%|████▍     | 11/25 [00:11<00:17,  1.28s/it]
Calculating loss...:  48%|████▊     | 12/25 [00:12<00:14,  1.13s/it]
Calculating loss...:  52%|█████▏    | 13/25 [00:14<00:14,  1.24s/it]
Calculating loss...:  56%|█████▌    | 14/25 [00:14<00:10,  1.02it/s]
Calculating loss...:  60%|██████    | 15/25 [00:14<00:08,  1.24it/s]
Calculating loss...:  64%|██████▍   | 16/25 [00:16<00:09,  1.00s/it]
Calculating loss...:  68%|██████▊   | 17/25 [00:16<00:07,  1.13it/s]
Calculating loss...:  72%|███████▏  | 18/25 [00:17<00:06,  1.11it/s]
Calculating loss...:  76%|███████▌  | 19/25 [00:18<00:04,  1.25it/s]
Calculating loss...:  80%|████████  | 20/25 [00:18<00:03,  1.46it/s]
Calculating loss...:  84%|████████▍ | 21/25 [00:19<00:03,  1.33it/s]
Calculating loss...:  88%|████████▊ | 22/25 [00:20<00:02,  1.24it/s]
Calculating loss...:  92%|█████████▏| 23/25 [00:21<00:01,  1.26it/s]
Calculating loss...:  96%|█████████▌| 24/25 [00:22<00:00,  1.27it/s]
Calculating loss...: 100%|██████████| 25/25 [00:24<00:00,  1.10s/it]
Calculating loss...: 100%|██████████| 25/25 [00:24<00:00,  1.04it/s]
Iter 100: Val loss nan, Val took 24.072s
Iter 100: Train loss nan, Learning Rate 1.000e-04, It/sec 0.478, Tokens/sec 10225505.663, Trained Tokens 1068717925, Peak mem 65.672 GB
/Users/jacobeen/.local/share/uv/python/cpython-3.11.15-macos-aarch64-none/lib/python3.11/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '
]<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
execute this project plan

--- Attached Context ---

📄 @file:`QUICKSTART.md` (551 tokens)
```markdown
# Quick Start

## 1. Run the Fine-Tuned Model

### MLX (recommended for inference):
```bash
mlx_lm.generate --model ./fused_model --prompt "Your Unity task..."
```

### Ollama:
Create Modelfile in this directory:
```dockerfile
FROM ./gguf/unity-agent-Q6_K.gguf
TEMPLATE "{{ .Prompt }}"
PARAMETER temperature 0.3
PARAMETER num_ctx 4096
```

Then:
```bash
ollama create unity-agent -f Modelfile
ollama serve
```

### LM Studio:
- Open LM Studio → "My Models" → "Add Model"
- Select ./gguf/unity-agent-Q6_K.gguf
- Start server on port 1234

## 2. Connect to Unity Editor

### Option A: Unity-MCP CLI (recommended)
```bash
npx -y @aigamedeveloper/mcp-server
# Connects Claude Desktop, Cursor, or VS Code to Unity Editor
```

### Option B: Custom Editor Window
- Copy `mcp_config/UnityAgentWindow.cs` to your project's `Assets/Editor/`
- Window → Unity Agent → Chat

### Option C: Python client
```bash
python3 mcp_config/unity_mcp_client.py --list
python3 mcp_config/unity_mcp_client.py --tool gameobject-create \
  --args '{"name":"Cube","primitiveType":"Cube"}'
```

## 3. Example Workflows

**Create a scene from scratch:**
> "Create a medieval village with 5 houses, a central fountain, and a player character"

**Fix bugs interactively:**
> "Read the console logs and fix the NullReferenceException in PlayerHealth.cs"

**Profile and optimize:**
> "Profile the game, find the bottleneck, and suggest optimizations"

**Design a system:**
> "Create a ScriptableObject-based inventory system with item pickup, UI display, and save/load"

## 4. Dataset Pipeline (full run)

```bash
# Generate custom datasets
python3 01_build_unity_mcp_dataset.py
python3 02_build_unity_design_patterns_dataset.py

# Download external datasets
python3 05_download_and_merge_datasets.py
python3 06_extract_unity_repos.py
python3 07_integrate_new_datasets.py

# Train + export
python3 03_train_and_export_unity_agent.py
```

## 5. Fine-Tuning Stages

```bash
# Prepare only (merge datasets)
python3 03_train_and_export_unity_agent.py --stage prepare

# Train only (QLoRA)
python3 03_train_and_export_unity_agent.py --stage train

# Fuse adapters and export GGUF
python3 03_train_and_export_unity_agent.py --stage export
```

```

📄 @file:`README.md` (1740 tokens)
```markdown
# Unity Agent Developer — Model Training Pipeline

Build and deploy a Unity game development agent model on Apple Silicon (M5 Pro).

## Quick Start

```bash
# 1. Generate custom datasets
python3 01_build_unity_mcp_dataset.py
python3 02_build_unity_design_patterns_dataset.py

# 2. Download + merge everything (internet needed)
python3 05_download_and_merge_datasets.py       # vishnuOI + function-calling
python3 06_extract_unity_repos.py                # ML-Agents, ECS, uGUI, netcode
python3 07_integrate_new_datasets.py             # Erocal Q&A + Mix & Jam mechanics

# 3. Fine-tune (MLX QLoRA on Apple Silicon)
python3 03_train_and_export_unity_agent.py

# 4. Export to GGUF
python3 03_train_and_export_unity_agent.py --stage export

# 5. Run
ollama create unity-agent -f Modelfile
ollama run unity-agent
```

## Components

| # | Script | What It Does | Output |
|---|---|---|---|
| 01 | `01_build_unity_mcp_dataset.py` | Generates ~128 MCP tool-calling examples from real game-dev tasks | `datasets/unity_mcp_tool_calls_*.jsonl` |
| 02 | `02_build_unity_design_patterns_dataset.py` | Generates ~75 design pattern + bug-fix examples | `datasets/unity_design_patterns_*.jsonl` |
| 03 | `03_train_and_export_unity_agent.py` | MLX QLoRA train → fuse → GGUF export → eval | `adapters/`, `fused_model/`, `gguf/` |
| 04 | `04_setup_unity_mcp_bridge.py` | Sets up Unity-MCP bridge, configs for Claude/Cursor/VS Code | `mcp_config/` |
| 05 | `05_download_and_merge_datasets.py` | Downloads `vishnuOI/unity-dev-instructions` (46k) + `hermes-function-calling-v1` (2k), merges everything | `datasets/train.jsonl` |
| 06 | `06_extract_unity_repos.py` | Extracts C# code from Unity repos: ML-Agents (29), ECS (10), uGUI (10), Netcode (5) | `datasets/unity_repos_*.jsonl` |
| 07 | `07_integrate_new_datasets.py` | Downloads + integrates `Erocal/Unity_ResolvedQuestions` (400 real Q&A) and `MixAndJam/GameMechanics` (32 game mechanic C# codes) | Rebuilds `datasets/{train,valid,test}.jsonl` |

## Dataset Composition (after all merges)

| Source | Rows | Coverage |
|---|---|---|
| `vishnuOI/unity-dev-instructions` | ~42k (after dedup) | Unity API docs, Stack Overflow, GitHub code — scripting, rendering, physics, editor, XR, UI, animation, networking, math, performance, input |
| `NousResearch/hermes-function-calling-v1` | ~1.8k | Generic tool-calling / JSON-mode structured outputs |
| `Erocal/Unity_ResolvedQuestions` | ~400 | Real resolved Q&A — physics, ECS, URP/HDRP, UI Toolkit, editor tooling, platform issues, animation rigging |
| `AmnaHassan/MixAndJam-GameMechanics` | ~32 | Full game mechanic C# implementations: Celeste movement, Splatoon ink, Arkham combat, God of War axe throw, Balatro feel, AC dialogue, Luigi's Mansion ghost capture, Mario Galaxy launch stars, Pikmin gameplay |
| ML-Agents example agents | ~29 | RL agent patterns (Crawler, Walker, FoodCollector, Soccer, Pyramids, WallJump, Worm, Hallway, 3DBall, PushBlock, GridWorld, Sorter) |
| ML-Agents core + sensors | ~5 | Agent.cs lifecycle, ray perception, camera sensor, vector sensor, grid sensor |
| ECS/DOTS samples | ~10 | Jobs, SystemBase, ISystem, IComponentData, IAspect, Baker patterns |
| uGUI | ~10 | Graphic, Image, Text, Button, Slider, Toggle, ScrollRect, Dropdown, InputField, LayoutGroup |
| Netcode for GameObjects | ~5 | NetworkBehaviour, NetworkVariable, RPC, NetworkObject patterns |
| Unity MCP tool calls (custom) | ~128 | Agentic Unity tool use: scene setup, scripting, prefabs, debugging, profiling, testing, shaders, reflection |
| Design patterns (custom) | ~75 | ScriptableObject architecture, Object Pooling, Singleton, State Machine, Observer, Strategy, Command, Factory, Flyweight, Save/Load, ServiceLocator, EventBus |
| **Total (deduped)** | **~45k** | |

### Datasets considered and rejected

| Source | Why skipped |
|---|---|
| `ibranze/codellama_unity3d_v1/v2` | 535 + 2k rows, wrong format, overlaps with vishnuOI |
| `ZorraZabb/code25pile75_shuffle_filtered_unity3d_dedup` | 1M rows of noise — substring "unity3d" filter catches Python, JS, news articles, medical papers |
| `supergoose/buzz_sources_244_unity3d-asset` | 101 rows of git diff YAML for .meta files |
| `robertzeng/unity3ddoc` | 113k rows but shallow/trivial responses (5-15 word generic definitions) |
| `DLMveloper/DLM_DataSet` | 3.6M rows but only ~3,570 "Unity"-tagged, all trivial (variable declarations, basic array ops) |
| `Saibalaji25/unity-csharp-scripts` | Requires login + T&C agreement, unknown quality, 11 downloads/mo |

## Training Configuration (M5 Pro 64GB)

```
Model:      Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)
Batch size: 1
Layers:     16/64 (25% LoRA)
LR:         1e-4
Max seq:    4096
Iters:      2000
Peak mem:   ~25GB
Duration:   ~45-60 min
```

## GGUF Output Quantization

| Type | Size | Best For |
|---|---|---|
| Q4_K_M | ~20 GB | 24GB+ systems |
| Q5_K_M | ~23 GB | 32GB+ systems |
| **Q6_K** | **~27 GB** | **M5 Pro 64GB (recommended)** |
| Q8_0 | ~34 GB | 64GB+ systems (max quality) |

## Unity-MCP Bridge

Run the bridge setup to configure connections to Claude Desktop, Cursor, VS Code, or the included Python client:

```bash
python3 04_setup_unity_mcp_bridge.py
```

Then connect any MCP-compatible AI client (Claude Desktop, Cursor, VS Code with Cline/Copilot, Codex) to the Unity Editor.

## Project Structure

```
unity-agent-training/
├── 01_build_unity_mcp_dataset.py            # MCP tool-calling dataset
├── 02_build_unity_design_patterns_dataset.py  # Design patterns dataset
├── 03_train_and_export_unity_agent.py        # MLX training + GGUF export
├── 04_setup_unity_mcp_bridge.py              # Unity-MCP integration
├── 05_download_and_merge_datasets.py         # HF dataset download + merge
├── 06_extract_unity_repos.py                 # Unity org repo C# extraction
├── 07_integrate_new_datasets.py              # Newly discovered datasets
├── Modelfile                                 # Ollama model definition
├── QUICKSTART.md                             # Quick start guide
├── datasets/
│   ├── unity_mcp_tool_calls_{train,valid,test}.jsonl
│   ├── unity_design_patterns_{train,valid,test}.jsonl
│   ├── unity_api_instructions.jsonl          # (downloaded)
│   ├── unity_repos_{train,valid,test}.jsonl  # (from repo extraction)
│   ├── function_calling_full.jsonl           # (downloaded)
│   ├── train.jsonl                           # Merged MLX-ready
│   ├── valid.jsonl                           # Merged MLX-ready
│   └── test.jsonl                            # Merged MLX-ready
├── adapters/                                 # LoRA adapters (after training)
├── fused_model/                              # Fused MLX model
├── gguf/                                     # GGUF quantized models
└── mcp_config/                               # Bridge configuration
    ├── claude_desktop_config.json
    ├── vscode_mcp.json
    ├── unity_mcp_server_config.json
    ├── unity_mcp_client.py
    └── UnityAgentWindow.cs
```

```<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
execute this project plan

--- Attached Context ---

📄 @file:`QUICKSTART.md` (551 tokens)
```markdown
# Quick Start

## 1. Run the Fine-Tuned Model

### MLX (recommended for inference):
```bash
mlx_lm.generate --model ./fused_model --prompt "Your Unity task..."
```

### Ollama:
Create Modelfile in this directory:
```dockerfile
FROM ./gguf/unity-agent-Q6_K.gguf
TEMPLATE "{{ .Prompt }}"
PARAMETER temperature 0.3
PARAMETER num_ctx 4096
```

Then:
```bash
ollama create unity-agent -f Modelfile
ollama serve
```

### LM Studio:
- Open LM Studio → "My Models" → "Add Model"
- Select ./gguf/unity-agent-Q6_K.gguf
- Start server on port 1234

## 2. Connect to Unity Editor

### Option A: Unity-MCP CLI (recommended)
```bash
npx -y @aigamedeveloper/mcp-server
# Connects Claude Desktop, Cursor, or VS Code to Unity Editor
```

### Option B: Custom Editor Window
- Copy `mcp_config/UnityAgentWindow.cs` to your project's `Assets/Editor/`
- Window → Unity Agent → Chat

### Option C: Python client
```bash
python3 mcp_config/unity_mcp_client.py --list
python3 mcp_config/unity_mcp_client.py --tool gameobject-create \
  --args '{"name":"Cube","primitiveType":"Cube"}'
```

## 3. Example Workflows

**Create a scene from scratch:**
> "Create a medieval village with 5 houses, a central fountain, and a player character"

**Fix bugs interactively:**
> "Read the console logs and fix the NullReferenceException in PlayerHealth.cs"

**Profile and optimize:**
> "Profile the game, find the bottleneck, and suggest optimizations"

**Design a system:**
> "Create a ScriptableObject-based inventory system with item pickup, UI display, and save/load"

## 4. Dataset Pipeline (full run)

```bash
# Generate custom datasets
python3 01_build_unity_mcp_dataset.py
python3 02_build_unity_design_patterns_dataset.py

# Download external datasets
python3 05_download_and_merge_datasets.py
python3 06_extract_unity_repos.py
python3 07_integrate_new_datasets.py

# Train + export
python3 03_train_and_export_unity_agent.py
```

## 5. Fine-Tuning Stages

```bash
# Prepare only (merge datasets)
python3 03_train_and_export_unity_agent.py --stage prepare

# Train only (QLoRA)
python3 03_train_and_export_unity_agent.py --stage train

# Fuse adapters and export GGUF
python3 03_train_and_export_unity_agent.py --stage export
```

```

📄 @file:`README.md` (1740 tokens)
```markdown
# Unity Agent Developer — Model Training Pipeline

Build and deploy a Unity game development agent model on Apple Silicon (M5 Pro).

## Quick Start

```bash
# 1. Generate custom datasets
python3 01_build_unity_mcp_dataset.py
python3 02_build_unity_design_patterns_dataset.py

# 2. Download + merge everything (internet needed)
python3 05_download_and_merge_datasets.py       # vishnuOI + function-calling
python3 06_extract_unity_repos.py                # ML-Agents, ECS, uGUI, netcode
python3 07_integrate_new_datasets.py             # Erocal Q&A + Mix & Jam mechanics

# 3. Fine-tune (MLX QLoRA on Apple Silicon)
python3 03_train_and_export_unity_agent.py

# 4. Export to GGUF
python3 03_train_and_export_unity_agent.py --stage export

# 5. Run
ollama create unity-agent -f Modelfile
ollama run unity-agent
```

## Components

| # | Script | What It Does | Output |
|---|---|---|---|
| 01 | `01_build_unity_mcp_dataset.py` | Generates ~128 MCP tool-calling examples from real game-dev tasks | `datasets/unity_mcp_tool_calls_*.jsonl` |
| 02 | `02_build_unity_design_patterns_dataset.py` | Generates ~75 design pattern + bug-fix examples | `datasets/unity_design_patterns_*.jsonl` |
| 03 | `03_train_and_export_unity_agent.py` | MLX QLoRA train → fuse → GGUF export → eval | `adapters/`, `fused_model/`, `gguf/` |
| 04 | `04_setup_unity_mcp_bridge.py` | Sets up Unity-MCP bridge, configs for Claude/Cursor/VS Code | `mcp_config/` |
| 05 | `05_download_and_merge_datasets.py` | Downloads `vishnuOI/unity-dev-instructions` (46k) + `hermes-function-calling-v1` (2k), merges everything | `datasets/train.jsonl` |
| 06 | `06_extract_unity_repos.py` | Extracts C# code from Unity repos: ML-Agents (29), ECS (10), uGUI (10), Netcode (5) | `datasets/unity_repos_*.jsonl` |
| 07 | `07_integrate_new_datasets.py` | Downloads + integrates `Erocal/Unity_ResolvedQuestions` (400 real Q&A) and `MixAndJam/GameMechanics` (32 game mechanic C# codes) | Rebuilds `datasets/{train,valid,test}.jsonl` |

## Dataset Composition (after all merges)

| Source | Rows | Coverage |
|---|---|---|
| `vishnuOI/unity-dev-instructions` | ~42k (after dedup) | Unity API docs, Stack Overflow, GitHub code — scripting, rendering, physics, editor, XR, UI, animation, networking, math, performance, input |
| `NousResearch/hermes-function-calling-v1` | ~1.8k | Generic tool-calling / JSON-mode structured outputs |
| `Erocal/Unity_ResolvedQuestions` | ~400 | Real resolved Q&A — physics, ECS, URP/HDRP, UI Toolkit, editor tooling, platform issues, animation rigging |
| `AmnaHassan/MixAndJam-GameMechanics` | ~32 | Full game mechanic C# implementations: Celeste movement, Splatoon ink, Arkham combat, God of War axe throw, Balatro feel, AC dialogue, Luigi's Mansion ghost capture, Mario Galaxy launch stars, Pikmin gameplay |
| ML-Agents example agents | ~29 | RL agent patterns (Crawler, Walker, FoodCollector, Soccer, Pyramids, WallJump, Worm, Hallway, 3DBall, PushBlock, GridWorld, Sorter) |
| ML-Agents core + sensors | ~5 | Agent.cs lifecycle, ray perception, camera sensor, vector sensor, grid sensor |
| ECS/DOTS samples | ~10 | Jobs, SystemBase, ISystem, IComponentData, IAspect, Baker patterns |
| uGUI | ~10 | Graphic, Image, Text, Button, Slider, Toggle, ScrollRect, Dropdown, InputField, LayoutGroup |
| Netcode for GameObjects | ~5 | NetworkBehaviour, NetworkVariable, RPC, NetworkObject patterns |
| Unity MCP tool calls (custom) | ~128 | Agentic Unity tool use: scene setup, scripting, prefabs, debugging, profiling, testing, shaders, reflection |
| Design patterns (custom) | ~75 | ScriptableObject architecture, Object Pooling, Singleton, State Machine, Observer, Strategy, Command, Factory, Flyweight, Save/Load, ServiceLocator, EventBus |
| **Total (deduped)** | **~45k** | |

### Datasets considered and rejected

| Source | Why skipped |
|---|---|
| `ibranze/codellama_unity3d_v1/v2` | 535 + 2k rows, wrong format, overlaps with vishnuOI |
| `ZorraZabb/code25pile75_shuffle_filtered_unity3d_dedup` | 1M rows of noise — substring "unity3d" filter catches Python, JS, news articles, medical papers |
| `supergoose/buzz_sources_244_unity3d-asset` | 101 rows of git diff YAML for .meta files |
| `robertzeng/unity3ddoc` | 113k rows but shallow/trivial responses (5-15 word generic definitions) |
| `DLMveloper/DLM_DataSet` | 3.6M rows but only ~3,570 "Unity"-tagged, all trivial (variable declarations, basic array ops) |
| `Saibalaji25/unity-csharp-scripts` | Requires login + T&C agreement, unknown quality, 11 downloads/mo |

## Training Configuration (M5 Pro 64GB)

```
Model:      Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)
Batch size: 1
Layers:     16/64 (25% LoRA)
LR:         1e-4
Max seq:    4096
Iters:      2000
Peak mem:   ~25GB
Duration:   ~45-60 min
```

## GGUF Output Quantization

| Type | Size | Best For |
|---|---|---|
| Q4_K_M | ~20 GB | 24GB+ systems |
| Q5_K_M | ~23 GB | 32GB+ systems |
| **Q6_K** | **~27 GB** | **M5 Pro 64GB (recommended)** |
| Q8_0 | ~34 GB | 64GB+ systems (max quality) |

## Unity-MCP Bridge

Run the bridge setup to configure connections to Claude Desktop, Cursor, VS Code, or the included Python client:

```bash
python3 04_setup_unity_mcp_bridge.py
```

Then connect any MCP-compatible AI client (Claude Desktop, Cursor, VS Code with Cline/Copilot, Codex) to the Unity Editor.

## Project Structure

```
unity-agent-training/
├── 01_build_unity_mcp_dataset.py            # MCP tool-calling dataset
├── 02_build_unity_design_patterns_dataset.py  # Design patterns dataset
├── 03_train_and_export_unity_agent.py        # MLX training + GGUF export
├── 04_setup_unity_mcp_bridge.py              # Unity-MCP integration
├── 05_download_and_merge_datasets.py         # HF dataset download + merge
├── 06_extract_unity_repos.py                 # Unity org repo C# extraction
├── 07_integrate_new_datasets.py              # Newly discovered datasets
├── Modelfile                                 # Ollama model definition
├── QUICKSTART.md                             # Quick start guide
├── datasets/
│   ├── unity_mcp_tool_calls_{train,valid,test}.jsonl
│   ├── unity_design_patterns_{train,valid,test}.jsonl
│   ├── unity_api_instructions.jsonl          # (downloaded)
│   ├── unity_repos_{train,valid,test}.jsonl  # (from repo extraction)
│   ├── function_calling_full.jsonl           # (downloaded)
│   ├── train.jsonl                           # Merged MLX-ready
│   ├── valid.jsonl                           # Merged MLX-ready
│   └── test.jsonl                            # Merged MLX-ready
├── adapters/                                 # LoRA adapters (after training)
├── fused_model/                              # Fused MLX model
├── gguf/                                     # GGUF quantized models
└── mcp_config/                               # Bridge configuration
    ├── claude_desktop_config.json
    ├── vscode_mcp.json
    ├── unity_mcp_server_config.json
    ├── unity_mcp_client.py
    └── UnityAgentWindow.cs
```

```<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
[ASYNC DELEGATION BATCH COMPLETE — deleg_2d07ecd6]
A background fan-out of 8 subagent(s) you dispatched earlier has finished. All ran in parallel and waited on each other; their consolidated results are below. You may have moved on since dispatching — act on these or re-dispatch if things have changed.

Dispatched: 2026-07-13 23:59:18 (11m48s ago)
Context you provided: Clone these 10 repos to /Volumes/this_and_that/projects/multi-repo_0/ using `git clone https://github.com/openclaw/<repo>.git`. Run from /Volumes/this_and_that/projects/multi-repo_0/. Report any failures.
Role: leaf   Model: ?   Total duration: 708.72s

--- ✓ TASK 1/8: Clone 10 OpenClaw repos: openclaw, docs, clawhub, clawgo, homebrew-tap, openclaw.ai, nix-openclaw, nix-openclaw-tools, lobster, hermit  (status=completed, api_calls=3, 356.23s) ---
Context length exceeded (379 tokens). Cannot compress further.

--- ✓ TASK 2/8: Clone 10 OpenClaw repos: clawdinators, openclaw-ansible, casa, spogo, goplaces, voice-community, acpx, community, caclawphony, discrawl  (status=completed, api_calls=4, 525.97s) ---
All 10 repos cloned successfully. Here's the summary:

**Outcome: 10/10 repos cloned successfully, 0 failures.**

All 10 OpenClaw repositories are now present at `/Volumes/this_and_that/projects/multi-repo_0/`:

| # | Repo | Status |
|---|------|--------|
| 1 | clawdinators | ✅ |
| 2 | openclaw-ansible | ✅ |
| 3 | casa | ✅ |
| 4 | spogo | ✅ |
| 5 | goplaces | ✅ |
| 6 | voice-community | ✅ |
| 7 | acpx | ✅ |
| 8 | community | ✅ |
| 9 | caclawphony | ✅ |
| 10 | discrawl | ✅ |

No issues encountered — all clones completed cleanly with exit code 0.

--- ✓ TASK 3/8: Clone 10 OpenClaw repos: slacrawl, crabline, releases, docs, esp-openclaw-node, shellbench, notcrawl, clawsweeper, clownfish, crabpot  (status=completed, api_calls=3, 394.02s) ---
All 10 repos cloned successfully. Here's the summary:

**Result: 10/10 repos cloned with zero failures.**

Cloned to `/Volumes/this_and_that/projects/multi-repo_0/`:

| # | Repo | Status |
|---|------|--------|
| 1 | `slacrawl` | ✅ Cloned |
| 2 | `crabline` | ✅ Cloned |
| 3 | `releases` | ✅ Cloned (15,075 files) |
| 4 | `docs` | ✅ Cloned |
| 5 | `esp-openclaw-node` | ✅ Cloned |
| 6 | `shellbench` | ✅ Cloned (23,628 files) |
| 7 | `notcrawl` | ✅ Cloned |
| 8 | `clawsweeper` | ✅ Cloned |
| 9 | `clownfish` | ✅ Cloned |
| 10 | `crabpot` | ✅ Cloned |

No issues encountered. All 10 repositories are present and fully cloned.

--- ✓ TASK 4/8: Clone 10 OpenClaw repos: wacrawl, plugin-inspector, kitchen-sink, gitcrawl, Kova, cookbook, clawsweeper.bot, clawbox, openclaw-rtt, crawlkit  (status=completed, api_calls=6, 621.05s) ---
Context length exceeded: max compression attempts (3) reached.

--- ✓ TASK 5/8: Clone 10 OpenClaw repos: crawlbar, clawgrit-reports, fs-safe, ask-molty, graincrawl, clickclack, clawdex, proxyline, telecrawl, clawpatch  (status=completed, api_calls=12, 708.31s) ---
All 10 repos cloned successfully. Here's the summary:

**Outcome:** All 10 OpenClaw repositories cloned successfully into `/Volumes/this_and_that/projects/multi-repo_0/`.

**Cloned repos:**
1. ✅ crawlbar
2. ✅ clawgrit-reports (previously cloned)
3. ✅ fs-safe
4. ✅ ask-molty
5. ✅ graincrawl
6. ✅ clickclack
7. ✅ clawdex
8. ✅ proxyline
9. ✅ telecrawl
10. ✅ clawpatch

**Issues:** None. All 10 clones completed with exit code 0.

--- ✓ TASK 6/8: Clone 10 OpenClaw repos: crabfleet, agent-skills, rfcs, rastermill, libopus-wasm, octopool, clawpdf, photoscrawl, clawrouter, imsgcrawl  (status=completed, api_calls=2, 318.11s) ---
All 10 repos cloned successfully. Here's the summary:

**Outcome: 10/10 repos cloned successfully, 0 failures.**

Cloned to `/Volumes/this_and_that/projects/multi-repo_0/`:

| # | Repo | Status |
|---|------|--------|
| 1 | crabfleet | ✅ |
| 2 | agent-skills | ✅ |
| 3 | rfcs | ✅ |
| 4 | rastermill | ✅ |
| 5 | libopus-wasm | ✅ |
| 6 | octopool | ✅ |
| 7 | clawpdf | ✅ |
| 8 | photoscrawl | ✅ |
| 9 | clawrouter | ✅ |
| 10 | imsgcrawl | ✅ |

No issues encountered — all clones completed cleanly with no errors.

--- ✓ TASK 7/8: Clone 10 OpenClaw repos: ffmpeg-wasm, libterminal, clawscan, uirouter, babelfish, crabhelm, turnwire, design-system, reef, AXorcist  (status=completed, api_calls=3, 326.1s) ---
Context length exceeded (435 tokens). Cannot compress further.

--- ✓ TASK 8/8: Clone remaining 8 OpenClaw repos: Peekaboo, Tachikoma, mcporter, Swabble, imsg, gogcli, wacli, songsee, butter.bot  (status=completed, api_calls=3, 355.6s) ---
Context length exceeded (1,277 tokens). Cannot compress further.<|im_end|>


Prompt:
<|im_start|>system
You are in General Assistant profile. Use the most efficient tool for each request.
<|im_end|>
<|im_start|>user
can unsloth fine tune and train MLX?<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
which custom made model on HuggingFace is extraordinarly smart?<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
i need to know if you are making a GGUF model or MLX model?<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
my laptop crashed. Continue where we left or in case something went corrupt then lets start over<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
find me best Llama 3.3 70B custom fine-tuned model for coding<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
progress?<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
I need to process this. Please help.<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
[IMPORTANT: Background process proc_0a497b8b5935 exited (exit code -15, SIGTERM).
Command: unset PYTHONPATH; /Users/jacobeen/.hermes/hermes-agent/venv/bin/python3 03_train_and_export_unity_agent.py train 2>&1
Output:
4<00:26,  1.25s/it]
Calculating loss...:  20%|██        | 5/25 [00:04<00:21,  1.08s/it]
Calculating loss...:  24%|██▍       | 6/25 [00:05<00:16,  1.18it/s]
Calculating loss...:  28%|██▊       | 7/25 [00:06<00:16,  1.07it/s]
Calculating loss...:  32%|███▏      | 8/25 [00:07<00:15,  1.07it/s]
Calculating loss...:  36%|███▌      | 9/25 [00:08<00:17,  1.10s/it]
Calculating loss...:  40%|████      | 10/25 [00:10<00:19,  1.27s/it]
Calculating loss...:  44%|████▍     | 11/25 [00:11<00:17,  1.28s/it]
Calculating loss...:  48%|████▊     | 12/25 [00:12<00:14,  1.13s/it]
Calculating loss...:  52%|█████▏    | 13/25 [00:14<00:14,  1.24s/it]
Calculating loss...:  56%|█████▌    | 14/25 [00:14<00:10,  1.02it/s]
Calculating loss...:  60%|██████    | 15/25 [00:14<00:08,  1.24it/s]
Calculating loss...:  64%|██████▍   | 16/25 [00:16<00:09,  1.00s/it]
Calculating loss...:  68%|██████▊   | 17/25 [00:16<00:07,  1.13it/s]
Calculating loss...:  72%|███████▏  | 18/25 [00:17<00:06,  1.11it/s]
Calculating loss...:  76%|███████▌  | 19/25 [00:18<00:04,  1.25it/s]
Calculating loss...:  80%|████████  | 20/25 [00:18<00:03,  1.46it/s]
Calculating loss...:  84%|████████▍ | 21/25 [00:19<00:03,  1.33it/s]
Calculating loss...:  88%|████████▊ | 22/25 [00:20<00:02,  1.24it/s]
Calculating loss...:  92%|█████████▏| 23/25 [00:21<00:01,  1.26it/s]
Calculating loss...:  96%|█████████▌| 24/25 [00:22<00:00,  1.27it/s]
Calculating loss...: 100%|██████████| 25/25 [00:24<00:00,  1.10s/it]
Calculating loss...: 100%|██████████| 25/25 [00:24<00:00,  1.04it/s]
Iter 100: Val loss nan, Val took 24.072s
Iter 100: Train loss nan, Learning Rate 1.000e-04, It/sec 0.478, Tokens/sec 10225505.663, Trained Tokens 1068717925, Peak mem 65.672 GB
/Users/jacobeen/.local/share/uv/python/cpython-3.11.15-macos-aarch64-none/lib/python3.11/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '
]<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
I need to process this. Please help.<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
I need to process this. Please help.<|im_end|>


Prompt:
<|im_start|>system
You are in Data Analyst profile. Prioritize data loading, Python analysis, and charting.
<|im_end|>
<|im_start|>user
which custom made model on HuggingFace is extraordinarly smart?<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
lower iter so we won't hit OOM<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
Can you process proc_0a497b8b5935 exited (exit code -15,?<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
progress?<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
execute this project plan

--- Attached Context ---

📄 @file:`QUICKSTART.md` (551 tokens)
```markdown
# Quick Start

## 1. Run the Fine-Tuned Model

### MLX (recommended for inference):
```bash
mlx_lm.generate --model ./fused_model --prompt "Your Unity task..."
```

### Ollama:
Create Modelfile in this directory:
```dockerfile
FROM ./gguf/unity-agent-Q6_K.gguf
TEMPLATE "{{ .Prompt }}"
PARAMETER temperature 0.3
PARAMETER num_ctx 4096
```

Then:
```bash
ollama create unity-agent -f Modelfile
ollama serve
```

### LM Studio:
- Open LM Studio → "My Models" → "Add Model"
- Select ./gguf/unity-agent-Q6_K.gguf
- Start server on port 1234

## 2. Connect to Unity Editor

### Option A: Unity-MCP CLI (recommended)
```bash
npx -y @aigamedeveloper/mcp-server
# Connects Claude Desktop, Cursor, or VS Code to Unity Editor
```

### Option B: Custom Editor Window
- Copy `mcp_config/UnityAgentWindow.cs` to your project's `Assets/Editor/`
- Window → Unity Agent → Chat

### Option C: Python client
```bash
python3 mcp_config/unity_mcp_client.py --list
python3 mcp_config/unity_mcp_client.py --tool gameobject-create \
  --args '{"name":"Cube","primitiveType":"Cube"}'
```

## 3. Example Workflows

**Create a scene from scratch:**
> "Create a medieval village with 5 houses, a central fountain, and a player character"

**Fix bugs interactively:**
> "Read the console logs and fix the NullReferenceException in PlayerHealth.cs"

**Profile and optimize:**
> "Profile the game, find the bottleneck, and suggest optimizations"

**Design a system:**
> "Create a ScriptableObject-based inventory system with item pickup, UI display, and save/load"

## 4. Dataset Pipeline (full run)

```bash
# Generate custom datasets
python3 01_build_unity_mcp_dataset.py
python3 02_build_unity_design_patterns_dataset.py

# Download external datasets
python3 05_download_and_merge_datasets.py
python3 06_extract_unity_repos.py
python3 07_integrate_new_datasets.py

# Train + export
python3 03_train_and_export_unity_agent.py
```

## 5. Fine-Tuning Stages

```bash
# Prepare only (merge datasets)
python3 03_train_and_export_unity_agent.py --stage prepare

# Train only (QLoRA)
python3 03_train_and_export_unity_agent.py --stage train

# Fuse adapters and export GGUF
python3 03_train_and_export_unity_agent.py --stage export
```

```

📄 @file:`README.md` (1740 tokens)
```markdown
# Unity Agent Developer — Model Training Pipeline

Build and deploy a Unity game development agent model on Apple Silicon (M5 Pro).

## Quick Start

```bash
# 1. Generate custom datasets
python3 01_build_unity_mcp_dataset.py
python3 02_build_unity_design_patterns_dataset.py

# 2. Download + merge everything (internet needed)
python3 05_download_and_merge_datasets.py       # vishnuOI + function-calling
python3 06_extract_unity_repos.py                # ML-Agents, ECS, uGUI, netcode
python3 07_integrate_new_datasets.py             # Erocal Q&A + Mix & Jam mechanics

# 3. Fine-tune (MLX QLoRA on Apple Silicon)
python3 03_train_and_export_unity_agent.py

# 4. Export to GGUF
python3 03_train_and_export_unity_agent.py --stage export

# 5. Run
ollama create unity-agent -f Modelfile
ollama run unity-agent
```

## Components

| # | Script | What It Does | Output |
|---|---|---|---|
| 01 | `01_build_unity_mcp_dataset.py` | Generates ~128 MCP tool-calling examples from real game-dev tasks | `datasets/unity_mcp_tool_calls_*.jsonl` |
| 02 | `02_build_unity_design_patterns_dataset.py` | Generates ~75 design pattern + bug-fix examples | `datasets/unity_design_patterns_*.jsonl` |
| 03 | `03_train_and_export_unity_agent.py` | MLX QLoRA train → fuse → GGUF export → eval | `adapters/`, `fused_model/`, `gguf/` |
| 04 | `04_setup_unity_mcp_bridge.py` | Sets up Unity-MCP bridge, configs for Claude/Cursor/VS Code | `mcp_config/` |
| 05 | `05_download_and_merge_datasets.py` | Downloads `vishnuOI/unity-dev-instructions` (46k) + `hermes-function-calling-v1` (2k), merges everything | `datasets/train.jsonl` |
| 06 | `06_extract_unity_repos.py` | Extracts C# code from Unity repos: ML-Agents (29), ECS (10), uGUI (10), Netcode (5) | `datasets/unity_repos_*.jsonl` |
| 07 | `07_integrate_new_datasets.py` | Downloads + integrates `Erocal/Unity_ResolvedQuestions` (400 real Q&A) and `MixAndJam/GameMechanics` (32 game mechanic C# codes) | Rebuilds `datasets/{train,valid,test}.jsonl` |

## Dataset Composition (after all merges)

| Source | Rows | Coverage |
|---|---|---|
| `vishnuOI/unity-dev-instructions` | ~42k (after dedup) | Unity API docs, Stack Overflow, GitHub code — scripting, rendering, physics, editor, XR, UI, animation, networking, math, performance, input |
| `NousResearch/hermes-function-calling-v1` | ~1.8k | Generic tool-calling / JSON-mode structured outputs |
| `Erocal/Unity_ResolvedQuestions` | ~400 | Real resolved Q&A — physics, ECS, URP/HDRP, UI Toolkit, editor tooling, platform issues, animation rigging |
| `AmnaHassan/MixAndJam-GameMechanics` | ~32 | Full game mechanic C# implementations: Celeste movement, Splatoon ink, Arkham combat, God of War axe throw, Balatro feel, AC dialogue, Luigi's Mansion ghost capture, Mario Galaxy launch stars, Pikmin gameplay |
| ML-Agents example agents | ~29 | RL agent patterns (Crawler, Walker, FoodCollector, Soccer, Pyramids, WallJump, Worm, Hallway, 3DBall, PushBlock, GridWorld, Sorter) |
| ML-Agents core + sensors | ~5 | Agent.cs lifecycle, ray perception, camera sensor, vector sensor, grid sensor |
| ECS/DOTS samples | ~10 | Jobs, SystemBase, ISystem, IComponentData, IAspect, Baker patterns |
| uGUI | ~10 | Graphic, Image, Text, Button, Slider, Toggle, ScrollRect, Dropdown, InputField, LayoutGroup |
| Netcode for GameObjects | ~5 | NetworkBehaviour, NetworkVariable, RPC, NetworkObject patterns |
| Unity MCP tool calls (custom) | ~128 | Agentic Unity tool use: scene setup, scripting, prefabs, debugging, profiling, testing, shaders, reflection |
| Design patterns (custom) | ~75 | ScriptableObject architecture, Object Pooling, Singleton, State Machine, Observer, Strategy, Command, Factory, Flyweight, Save/Load, ServiceLocator, EventBus |
| **Total (deduped)** | **~45k** | |

### Datasets considered and rejected

| Source | Why skipped |
|---|---|
| `ibranze/codellama_unity3d_v1/v2` | 535 + 2k rows, wrong format, overlaps with vishnuOI |
| `ZorraZabb/code25pile75_shuffle_filtered_unity3d_dedup` | 1M rows of noise — substring "unity3d" filter catches Python, JS, news articles, medical papers |
| `supergoose/buzz_sources_244_unity3d-asset` | 101 rows of git diff YAML for .meta files |
| `robertzeng/unity3ddoc` | 113k rows but shallow/trivial responses (5-15 word generic definitions) |
| `DLMveloper/DLM_DataSet` | 3.6M rows but only ~3,570 "Unity"-tagged, all trivial (variable declarations, basic array ops) |
| `Saibalaji25/unity-csharp-scripts` | Requires login + T&C agreement, unknown quality, 11 downloads/mo |

## Training Configuration (M5 Pro 64GB)

```
Model:      Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)
Batch size: 1
Layers:     16/64 (25% LoRA)
LR:         1e-4
Max seq:    4096
Iters:      2000
Peak mem:   ~25GB
Duration:   ~45-60 min
```

## GGUF Output Quantization

| Type | Size | Best For |
|---|---|---|
| Q4_K_M | ~20 GB | 24GB+ systems |
| Q5_K_M | ~23 GB | 32GB+ systems |
| **Q6_K** | **~27 GB** | **M5 Pro 64GB (recommended)** |
| Q8_0 | ~34 GB | 64GB+ systems (max quality) |

## Unity-MCP Bridge

Run the bridge setup to configure connections to Claude Desktop, Cursor, VS Code, or the included Python client:

```bash
python3 04_setup_unity_mcp_bridge.py
```

Then connect any MCP-compatible AI client (Claude Desktop, Cursor, VS Code with Cline/Copilot, Codex) to the Unity Editor.

## Project Structure

```
unity-agent-training/
├── 01_build_unity_mcp_dataset.py            # MCP tool-calling dataset
├── 02_build_unity_design_patterns_dataset.py  # Design patterns dataset
├── 03_train_and_export_unity_agent.py        # MLX training + GGUF export
├── 04_setup_unity_mcp_bridge.py              # Unity-MCP integration
├── 05_download_and_merge_datasets.py         # HF dataset download + merge
├── 06_extract_unity_repos.py                 # Unity org repo C# extraction
├── 07_integrate_new_datasets.py              # Newly discovered datasets
├── Modelfile                                 # Ollama model definition
├── QUICKSTART.md                             # Quick start guide
├── datasets/
│   ├── unity_mcp_tool_calls_{train,valid,test}.jsonl
│   ├── unity_design_patterns_{train,valid,test}.jsonl
│   ├── unity_api_instructions.jsonl          # (downloaded)
│   ├── unity_repos_{train,valid,test}.jsonl  # (from repo extraction)
│   ├── function_calling_full.jsonl           # (downloaded)
│   ├── train.jsonl                           # Merged MLX-ready
│   ├── valid.jsonl                           # Merged MLX-ready
│   └── test.jsonl                            # Merged MLX-ready
├── adapters/                                 # LoRA adapters (after training)
├── fused_model/                              # Fused MLX model
├── gguf/                                     # GGUF quantized models
└── mcp_config/                               # Bridge configuration
    ├── claude_desktop_config.json
    ├── vscode_mcp.json
    ├── unity_mcp_server_config.json
    ├── unity_mcp_client.py
    └── UnityAgentWindow.cs
```

```<|im_end|>


Prompt:
<|im_start|>system
You are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.
<|im_end|>
<|im_start|>user
Can you process this?<|im_end|>