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{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nwhich custom made model on HuggingFace is extraordinarly smart?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nfind me best Llama 3.3 70B custom fine-tuned model for coding<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\ncan unsloth fine tune and train MLX?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nexecute this project plan\n\n--- Attached Context ---\n\n\ud83d\udcc4 @file:`QUICKSTART.md` (551 tokens)\n```markdown\n# Quick Start\n\n## 1. Run the Fine-Tuned Model\n\n### MLX (recommended for inference):\n```bash\nmlx_lm.generate --model ./fused_model --prompt \"Your Unity task...\"\n```\n\n### Ollama:\nCreate Modelfile in this directory:\n```dockerfile\nFROM ./gguf/unity-agent-Q6_K.gguf\nTEMPLATE \"{{ .Prompt }}\"\nPARAMETER temperature 0.3\nPARAMETER num_ctx 4096\n```\n\nThen:\n```bash\nollama create unity-agent -f Modelfile\nollama serve\n```\n\n### LM Studio:\n- Open LM Studio \u2192 \"My Models\" \u2192 \"Add Model\"\n- Select ./gguf/unity-agent-Q6_K.gguf\n- Start server on port 1234\n\n## 2. Connect to Unity Editor\n\n### Option A: Unity-MCP CLI (recommended)\n```bash\nnpx -y @aigamedeveloper/mcp-server\n# Connects Claude Desktop, Cursor, or VS Code to Unity Editor\n```\n\n### Option B: Custom Editor Window\n- Copy `mcp_config/UnityAgentWindow.cs` to your project's `Assets/Editor/`\n- Window \u2192 Unity Agent \u2192 Chat\n\n### Option C: Python client\n```bash\npython3 mcp_config/unity_mcp_client.py --list\npython3 mcp_config/unity_mcp_client.py --tool gameobject-create \\\n --args '{\"name\":\"Cube\",\"primitiveType\":\"Cube\"}'\n```\n\n## 3. Example Workflows\n\n**Create a scene from scratch:**\n> \"Create a medieval village with 5 houses, a central fountain, and a player character\"\n\n**Fix bugs interactively:**\n> \"Read the console logs and fix the NullReferenceException in PlayerHealth.cs\"\n\n**Profile and optimize:**\n> \"Profile the game, find the bottleneck, and suggest optimizations\"\n\n**Design a system:**\n> \"Create a ScriptableObject-based inventory system with item pickup, UI display, and save/load\"\n\n## 4. Dataset Pipeline (full run)\n\n```bash\n# Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# Download external datasets\npython3 05_download_and_merge_datasets.py\npython3 06_extract_unity_repos.py\npython3 07_integrate_new_datasets.py\n\n# Train + export\npython3 03_train_and_export_unity_agent.py\n```\n\n## 5. Fine-Tuning Stages\n\n```bash\n# Prepare only (merge datasets)\npython3 03_train_and_export_unity_agent.py --stage prepare\n\n# Train only (QLoRA)\npython3 03_train_and_export_unity_agent.py --stage train\n\n# Fuse adapters and export GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n```\n\n```\n\n\ud83d\udcc4 @file:`README.md` (1740 tokens)\n```markdown\n# Unity Agent Developer \u2014 Model Training Pipeline\n\nBuild and deploy a Unity game development agent model on Apple Silicon (M5 Pro).\n\n## Quick Start\n\n```bash\n# 1. Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# 2. Download + merge everything (internet needed)\npython3 05_download_and_merge_datasets.py # vishnuOI + function-calling\npython3 06_extract_unity_repos.py # ML-Agents, ECS, uGUI, netcode\npython3 07_integrate_new_datasets.py # Erocal Q&A + Mix & Jam mechanics\n\n# 3. Fine-tune (MLX QLoRA on Apple Silicon)\npython3 03_train_and_export_unity_agent.py\n\n# 4. Export to GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n\n# 5. Run\nollama create unity-agent -f Modelfile\nollama run unity-agent\n```\n\n## Components\n\n| # | Script | What It Does | Output |\n|---|---|---|---|\n| 01 | `01_build_unity_mcp_dataset.py` | Generates ~128 MCP tool-calling examples from real game-dev tasks | `datasets/unity_mcp_tool_calls_*.jsonl` |\n| 02 | `02_build_unity_design_patterns_dataset.py` | Generates ~75 design pattern + bug-fix examples | `datasets/unity_design_patterns_*.jsonl` |\n| 03 | `03_train_and_export_unity_agent.py` | MLX QLoRA train \u2192 fuse \u2192 GGUF export \u2192 eval | `adapters/`, `fused_model/`, `gguf/` |\n| 04 | `04_setup_unity_mcp_bridge.py` | Sets up Unity-MCP bridge, configs for Claude/Cursor/VS Code | `mcp_config/` |\n| 05 | `05_download_and_merge_datasets.py` | Downloads `vishnuOI/unity-dev-instructions` (46k) + `hermes-function-calling-v1` (2k), merges everything | `datasets/train.jsonl` |\n| 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` |\n| 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` |\n\n## Dataset Composition (after all merges)\n\n| Source | Rows | Coverage |\n|---|---|---|\n| `vishnuOI/unity-dev-instructions` | ~42k (after dedup) | Unity API docs, Stack Overflow, GitHub code \u2014 scripting, rendering, physics, editor, XR, UI, animation, networking, math, performance, input |\n| `NousResearch/hermes-function-calling-v1` | ~1.8k | Generic tool-calling / JSON-mode structured outputs |\n| `Erocal/Unity_ResolvedQuestions` | ~400 | Real resolved Q&A \u2014 physics, ECS, URP/HDRP, UI Toolkit, editor tooling, platform issues, animation rigging |\n| `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 |\n| ML-Agents example agents | ~29 | RL agent patterns (Crawler, Walker, FoodCollector, Soccer, Pyramids, WallJump, Worm, Hallway, 3DBall, PushBlock, GridWorld, Sorter) |\n| ML-Agents core + sensors | ~5 | Agent.cs lifecycle, ray perception, camera sensor, vector sensor, grid sensor |\n| ECS/DOTS samples | ~10 | Jobs, SystemBase, ISystem, IComponentData, IAspect, Baker patterns |\n| uGUI | ~10 | Graphic, Image, Text, Button, Slider, Toggle, ScrollRect, Dropdown, InputField, LayoutGroup |\n| Netcode for GameObjects | ~5 | NetworkBehaviour, NetworkVariable, RPC, NetworkObject patterns |\n| Unity MCP tool calls (custom) | ~128 | Agentic Unity tool use: scene setup, scripting, prefabs, debugging, profiling, testing, shaders, reflection |\n| Design patterns (custom) | ~75 | ScriptableObject architecture, Object Pooling, Singleton, State Machine, Observer, Strategy, Command, Factory, Flyweight, Save/Load, ServiceLocator, EventBus |\n| **Total (deduped)** | **~45k** | |\n\n### Datasets considered and rejected\n\n| Source | Why skipped |\n|---|---|\n| `ibranze/codellama_unity3d_v1/v2` | 535 + 2k rows, wrong format, overlaps with vishnuOI |\n| `ZorraZabb/code25pile75_shuffle_filtered_unity3d_dedup` | 1M rows of noise \u2014 substring \"unity3d\" filter catches Python, JS, news articles, medical papers |\n| `supergoose/buzz_sources_244_unity3d-asset` | 101 rows of git diff YAML for .meta files |\n| `robertzeng/unity3ddoc` | 113k rows but shallow/trivial responses (5-15 word generic definitions) |\n| `DLMveloper/DLM_DataSet` | 3.6M rows but only ~3,570 \"Unity\"-tagged, all trivial (variable declarations, basic array ops) |\n| `Saibalaji25/unity-csharp-scripts` | Requires login + T&C agreement, unknown quality, 11 downloads/mo |\n\n## Training Configuration (M5 Pro 64GB)\n\n```\nModel: Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)\nBatch size: 1\nLayers: 16/64 (25% LoRA)\nLR: 1e-4\nMax seq: 4096\nIters: 2000\nPeak mem: ~25GB\nDuration: ~45-60 min\n```\n\n## GGUF Output Quantization\n\n| Type | Size | Best For |\n|---|---|---|\n| Q4_K_M | ~20 GB | 24GB+ systems |\n| Q5_K_M | ~23 GB | 32GB+ systems |\n| **Q6_K** | **~27 GB** | **M5 Pro 64GB (recommended)** |\n| Q8_0 | ~34 GB | 64GB+ systems (max quality) |\n\n## Unity-MCP Bridge\n\nRun the bridge setup to configure connections to Claude Desktop, Cursor, VS Code, or the included Python client:\n\n```bash\npython3 04_setup_unity_mcp_bridge.py\n```\n\nThen connect any MCP-compatible AI client (Claude Desktop, Cursor, VS Code with Cline/Copilot, Codex) to the Unity Editor.\n\n## Project Structure\n\n```\nunity-agent-training/\n\u251c\u2500\u2500 01_build_unity_mcp_dataset.py # MCP tool-calling dataset\n\u251c\u2500\u2500 02_build_unity_design_patterns_dataset.py # Design patterns dataset\n\u251c\u2500\u2500 03_train_and_export_unity_agent.py # MLX training + GGUF export\n\u251c\u2500\u2500 04_setup_unity_mcp_bridge.py # Unity-MCP integration\n\u251c\u2500\u2500 05_download_and_merge_datasets.py # HF dataset download + merge\n\u251c\u2500\u2500 06_extract_unity_repos.py # Unity org repo C# extraction\n\u251c\u2500\u2500 07_integrate_new_datasets.py # Newly discovered datasets\n\u251c\u2500\u2500 Modelfile # Ollama model definition\n\u251c\u2500\u2500 QUICKSTART.md # Quick start guide\n\u251c\u2500\u2500 datasets/\n\u2502 \u251c\u2500\u2500 unity_mcp_tool_calls_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_design_patterns_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_api_instructions.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 unity_repos_{train,valid,test}.jsonl # (from repo extraction)\n\u2502 \u251c\u2500\u2500 function_calling_full.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 train.jsonl # Merged MLX-ready\n\u2502 \u251c\u2500\u2500 valid.jsonl # Merged MLX-ready\n\u2502 \u2514\u2500\u2500 test.jsonl # Merged MLX-ready\n\u251c\u2500\u2500 adapters/ # LoRA adapters (after training)\n\u251c\u2500\u2500 fused_model/ # Fused MLX model\n\u251c\u2500\u2500 gguf/ # GGUF quantized models\n\u2514\u2500\u2500 mcp_config/ # Bridge configuration\n \u251c\u2500\u2500 claude_desktop_config.json\n \u251c\u2500\u2500 vscode_mcp.json\n \u251c\u2500\u2500 unity_mcp_server_config.json\n \u251c\u2500\u2500 unity_mcp_client.py\n \u2514\u2500\u2500 UnityAgentWindow.cs\n```\n\n```<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nmy laptop crashed. Continue where we left or in case something went corrupt then lets start over<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\n[IMPORTANT: Background process proc_0a497b8b5935 exited (exit code -15, SIGTERM).\nCommand: unset PYTHONPATH; /Users/jacobeen/.hermes/hermes-agent/venv/bin/python3 03_train_and_export_unity_agent.py train 2>&1\nOutput:\n4<00:26, 1.25s/it]\rCalculating loss...: 20%|\u2588\u2588 | 5/25 [00:04<00:21, 1.08s/it]\rCalculating loss...: 24%|\u2588\u2588\u258d | 6/25 [00:05<00:16, 1.18it/s]\rCalculating loss...: 28%|\u2588\u2588\u258a | 7/25 [00:06<00:16, 1.07it/s]\rCalculating loss...: 32%|\u2588\u2588\u2588\u258f | 8/25 [00:07<00:15, 1.07it/s]\rCalculating loss...: 36%|\u2588\u2588\u2588\u258c | 9/25 [00:08<00:17, 1.10s/it]\rCalculating loss...: 40%|\u2588\u2588\u2588\u2588 | 10/25 [00:10<00:19, 1.27s/it]\rCalculating loss...: 44%|\u2588\u2588\u2588\u2588\u258d | 11/25 [00:11<00:17, 1.28s/it]\rCalculating loss...: 48%|\u2588\u2588\u2588\u2588\u258a | 12/25 [00:12<00:14, 1.13s/it]\rCalculating loss...: 52%|\u2588\u2588\u2588\u2588\u2588\u258f | 13/25 [00:14<00:14, 1.24s/it]\rCalculating loss...: 56%|\u2588\u2588\u2588\u2588\u2588\u258c | 14/25 [00:14<00:10, 1.02it/s]\rCalculating loss...: 60%|\u2588\u2588\u2588\u2588\u2588\u2588 | 15/25 [00:14<00:08, 1.24it/s]\rCalculating loss...: 64%|\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 16/25 [00:16<00:09, 1.00s/it]\rCalculating loss...: 68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 17/25 [00:16<00:07, 1.13it/s]\rCalculating loss...: 72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 18/25 [00:17<00:06, 1.11it/s]\rCalculating loss...: 76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 19/25 [00:18<00:04, 1.25it/s]\rCalculating loss...: 80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 20/25 [00:18<00:03, 1.46it/s]\rCalculating loss...: 84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 21/25 [00:19<00:03, 1.33it/s]\rCalculating loss...: 88%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 22/25 [00:20<00:02, 1.24it/s]\rCalculating loss...: 92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 23/25 [00:21<00:01, 1.26it/s]\rCalculating loss...: 96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 24/25 [00:22<00:00, 1.27it/s]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.10s/it]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.04it/s]\nIter 100: Val loss nan, Val took 24.072s\nIter 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\n/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\n warnings.warn('resource_tracker: There appear to be %d '\n]<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\ni need to know if you are making a GGUF model or MLX model?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nlower iter so we won't hit OOM<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\noh okey so we want to MAX out the quality at 2000<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nprogress?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nexecute this project plan\n\n--- Attached Context ---\n\n\ud83d\udcc4 @file:`QUICKSTART.md` (551 tokens)\n```markdown\n# Quick Start\n\n## 1. Run the Fine-Tuned Model\n\n### MLX (recommended for inference):\n```bash\nmlx_lm.generate --model ./fused_model --prompt \"Your Unity task...\"\n```\n\n### Ollama:\nCreate Modelfile in this directory:\n```dockerfile\nFROM ./gguf/unity-agent-Q6_K.gguf\nTEMPLATE \"{{ .Prompt }}\"\nPARAMETER temperature 0.3\nPARAMETER num_ctx 4096\n```\n\nThen:\n```bash\nollama create unity-agent -f Modelfile\nollama serve\n```\n\n### LM Studio:\n- Open LM Studio \u2192 \"My Models\" \u2192 \"Add Model\"\n- Select ./gguf/unity-agent-Q6_K.gguf\n- Start server on port 1234\n\n## 2. Connect to Unity Editor\n\n### Option A: Unity-MCP CLI (recommended)\n```bash\nnpx -y @aigamedeveloper/mcp-server\n# Connects Claude Desktop, Cursor, or VS Code to Unity Editor\n```\n\n### Option B: Custom Editor Window\n- Copy `mcp_config/UnityAgentWindow.cs` to your project's `Assets/Editor/`\n- Window \u2192 Unity Agent \u2192 Chat\n\n### Option C: Python client\n```bash\npython3 mcp_config/unity_mcp_client.py --list\npython3 mcp_config/unity_mcp_client.py --tool gameobject-create \\\n --args '{\"name\":\"Cube\",\"primitiveType\":\"Cube\"}'\n```\n\n## 3. Example Workflows\n\n**Create a scene from scratch:**\n> \"Create a medieval village with 5 houses, a central fountain, and a player character\"\n\n**Fix bugs interactively:**\n> \"Read the console logs and fix the NullReferenceException in PlayerHealth.cs\"\n\n**Profile and optimize:**\n> \"Profile the game, find the bottleneck, and suggest optimizations\"\n\n**Design a system:**\n> \"Create a ScriptableObject-based inventory system with item pickup, UI display, and save/load\"\n\n## 4. Dataset Pipeline (full run)\n\n```bash\n# Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# Download external datasets\npython3 05_download_and_merge_datasets.py\npython3 06_extract_unity_repos.py\npython3 07_integrate_new_datasets.py\n\n# Train + export\npython3 03_train_and_export_unity_agent.py\n```\n\n## 5. Fine-Tuning Stages\n\n```bash\n# Prepare only (merge datasets)\npython3 03_train_and_export_unity_agent.py --stage prepare\n\n# Train only (QLoRA)\npython3 03_train_and_export_unity_agent.py --stage train\n\n# Fuse adapters and export GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n```\n\n```\n\n\ud83d\udcc4 @file:`README.md` (1740 tokens)\n```markdown\n# Unity Agent Developer \u2014 Model Training Pipeline\n\nBuild and deploy a Unity game development agent model on Apple Silicon (M5 Pro).\n\n## Quick Start\n\n```bash\n# 1. Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# 2. Download + merge everything (internet needed)\npython3 05_download_and_merge_datasets.py # vishnuOI + function-calling\npython3 06_extract_unity_repos.py # ML-Agents, ECS, uGUI, netcode\npython3 07_integrate_new_datasets.py # Erocal Q&A + Mix & Jam mechanics\n\n# 3. Fine-tune (MLX QLoRA on Apple Silicon)\npython3 03_train_and_export_unity_agent.py\n\n# 4. Export to GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n\n# 5. Run\nollama create unity-agent -f Modelfile\nollama run unity-agent\n```\n\n## Components\n\n| # | Script | What It Does | Output |\n|---|---|---|---|\n| 01 | `01_build_unity_mcp_dataset.py` | Generates ~128 MCP tool-calling examples from real game-dev tasks | `datasets/unity_mcp_tool_calls_*.jsonl` |\n| 02 | `02_build_unity_design_patterns_dataset.py` | Generates ~75 design pattern + bug-fix examples | `datasets/unity_design_patterns_*.jsonl` |\n| 03 | `03_train_and_export_unity_agent.py` | MLX QLoRA train \u2192 fuse \u2192 GGUF export \u2192 eval | `adapters/`, `fused_model/`, `gguf/` |\n| 04 | `04_setup_unity_mcp_bridge.py` | Sets up Unity-MCP bridge, configs for Claude/Cursor/VS Code | `mcp_config/` |\n| 05 | `05_download_and_merge_datasets.py` | Downloads `vishnuOI/unity-dev-instructions` (46k) + `hermes-function-calling-v1` (2k), merges everything | `datasets/train.jsonl` |\n| 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` |\n| 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` |\n\n## Dataset Composition (after all merges)\n\n| Source | Rows | Coverage |\n|---|---|---|\n| `vishnuOI/unity-dev-instructions` | ~42k (after dedup) | Unity API docs, Stack Overflow, GitHub code \u2014 scripting, rendering, physics, editor, XR, UI, animation, networking, math, performance, input |\n| `NousResearch/hermes-function-calling-v1` | ~1.8k | Generic tool-calling / JSON-mode structured outputs |\n| `Erocal/Unity_ResolvedQuestions` | ~400 | Real resolved Q&A \u2014 physics, ECS, URP/HDRP, UI Toolkit, editor tooling, platform issues, animation rigging |\n| `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 |\n| ML-Agents example agents | ~29 | RL agent patterns (Crawler, Walker, FoodCollector, Soccer, Pyramids, WallJump, Worm, Hallway, 3DBall, PushBlock, GridWorld, Sorter) |\n| ML-Agents core + sensors | ~5 | Agent.cs lifecycle, ray perception, camera sensor, vector sensor, grid sensor |\n| ECS/DOTS samples | ~10 | Jobs, SystemBase, ISystem, IComponentData, IAspect, Baker patterns |\n| uGUI | ~10 | Graphic, Image, Text, Button, Slider, Toggle, ScrollRect, Dropdown, InputField, LayoutGroup |\n| Netcode for GameObjects | ~5 | NetworkBehaviour, NetworkVariable, RPC, NetworkObject patterns |\n| Unity MCP tool calls (custom) | ~128 | Agentic Unity tool use: scene setup, scripting, prefabs, debugging, profiling, testing, shaders, reflection |\n| Design patterns (custom) | ~75 | ScriptableObject architecture, Object Pooling, Singleton, State Machine, Observer, Strategy, Command, Factory, Flyweight, Save/Load, ServiceLocator, EventBus |\n| **Total (deduped)** | **~45k** | |\n\n### Datasets considered and rejected\n\n| Source | Why skipped |\n|---|---|\n| `ibranze/codellama_unity3d_v1/v2` | 535 + 2k rows, wrong format, overlaps with vishnuOI |\n| `ZorraZabb/code25pile75_shuffle_filtered_unity3d_dedup` | 1M rows of noise \u2014 substring \"unity3d\" filter catches Python, JS, news articles, medical papers |\n| `supergoose/buzz_sources_244_unity3d-asset` | 101 rows of git diff YAML for .meta files |\n| `robertzeng/unity3ddoc` | 113k rows but shallow/trivial responses (5-15 word generic definitions) |\n| `DLMveloper/DLM_DataSet` | 3.6M rows but only ~3,570 \"Unity\"-tagged, all trivial (variable declarations, basic array ops) |\n| `Saibalaji25/unity-csharp-scripts` | Requires login + T&C agreement, unknown quality, 11 downloads/mo |\n\n## Training Configuration (M5 Pro 64GB)\n\n```\nModel: Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)\nBatch size: 1\nLayers: 16/64 (25% LoRA)\nLR: 1e-4\nMax seq: 4096\nIters: 2000\nPeak mem: ~25GB\nDuration: ~45-60 min\n```\n\n## GGUF Output Quantization\n\n| Type | Size | Best For |\n|---|---|---|\n| Q4_K_M | ~20 GB | 24GB+ systems |\n| Q5_K_M | ~23 GB | 32GB+ systems |\n| **Q6_K** | **~27 GB** | **M5 Pro 64GB (recommended)** |\n| Q8_0 | ~34 GB | 64GB+ systems (max quality) |\n\n## Unity-MCP Bridge\n\nRun the bridge setup to configure connections to Claude Desktop, Cursor, VS Code, or the included Python client:\n\n```bash\npython3 04_setup_unity_mcp_bridge.py\n```\n\nThen connect any MCP-compatible AI client (Claude Desktop, Cursor, VS Code with Cline/Copilot, Codex) to the Unity Editor.\n\n## Project Structure\n\n```\nunity-agent-training/\n\u251c\u2500\u2500 01_build_unity_mcp_dataset.py # MCP tool-calling dataset\n\u251c\u2500\u2500 02_build_unity_design_patterns_dataset.py # Design patterns dataset\n\u251c\u2500\u2500 03_train_and_export_unity_agent.py # MLX training + GGUF export\n\u251c\u2500\u2500 04_setup_unity_mcp_bridge.py # Unity-MCP integration\n\u251c\u2500\u2500 05_download_and_merge_datasets.py # HF dataset download + merge\n\u251c\u2500\u2500 06_extract_unity_repos.py # Unity org repo C# extraction\n\u251c\u2500\u2500 07_integrate_new_datasets.py # Newly discovered datasets\n\u251c\u2500\u2500 Modelfile # Ollama model definition\n\u251c\u2500\u2500 QUICKSTART.md # Quick start guide\n\u251c\u2500\u2500 datasets/\n\u2502 \u251c\u2500\u2500 unity_mcp_tool_calls_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_design_patterns_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_api_instructions.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 unity_repos_{train,valid,test}.jsonl # (from repo extraction)\n\u2502 \u251c\u2500\u2500 function_calling_full.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 train.jsonl # Merged MLX-ready\n\u2502 \u251c\u2500\u2500 valid.jsonl # Merged MLX-ready\n\u2502 \u2514\u2500\u2500 test.jsonl # Merged MLX-ready\n\u251c\u2500\u2500 adapters/ # LoRA adapters (after training)\n\u251c\u2500\u2500 fused_model/ # Fused MLX model\n\u251c\u2500\u2500 gguf/ # GGUF quantized models\n\u2514\u2500\u2500 mcp_config/ # Bridge configuration\n \u251c\u2500\u2500 claude_desktop_config.json\n \u251c\u2500\u2500 vscode_mcp.json\n \u251c\u2500\u2500 unity_mcp_server_config.json\n \u251c\u2500\u2500 unity_mcp_client.py\n \u2514\u2500\u2500 UnityAgentWindow.cs\n```\n\n```<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nmy laptop crashed. Continue where we left or in case something went corrupt then lets start over<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\n[IMPORTANT: Background process proc_0a497b8b5935 exited (exit code -15, SIGTERM).\nCommand: unset PYTHONPATH; /Users/jacobeen/.hermes/hermes-agent/venv/bin/python3 03_train_and_export_unity_agent.py train 2>&1\nOutput:\n4<00:26, 1.25s/it]\rCalculating loss...: 20%|\u2588\u2588 | 5/25 [00:04<00:21, 1.08s/it]\rCalculating loss...: 24%|\u2588\u2588\u258d | 6/25 [00:05<00:16, 1.18it/s]\rCalculating loss...: 28%|\u2588\u2588\u258a | 7/25 [00:06<00:16, 1.07it/s]\rCalculating loss...: 32%|\u2588\u2588\u2588\u258f | 8/25 [00:07<00:15, 1.07it/s]\rCalculating loss...: 36%|\u2588\u2588\u2588\u258c | 9/25 [00:08<00:17, 1.10s/it]\rCalculating loss...: 40%|\u2588\u2588\u2588\u2588 | 10/25 [00:10<00:19, 1.27s/it]\rCalculating loss...: 44%|\u2588\u2588\u2588\u2588\u258d | 11/25 [00:11<00:17, 1.28s/it]\rCalculating loss...: 48%|\u2588\u2588\u2588\u2588\u258a | 12/25 [00:12<00:14, 1.13s/it]\rCalculating loss...: 52%|\u2588\u2588\u2588\u2588\u2588\u258f | 13/25 [00:14<00:14, 1.24s/it]\rCalculating loss...: 56%|\u2588\u2588\u2588\u2588\u2588\u258c | 14/25 [00:14<00:10, 1.02it/s]\rCalculating loss...: 60%|\u2588\u2588\u2588\u2588\u2588\u2588 | 15/25 [00:14<00:08, 1.24it/s]\rCalculating loss...: 64%|\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 16/25 [00:16<00:09, 1.00s/it]\rCalculating loss...: 68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 17/25 [00:16<00:07, 1.13it/s]\rCalculating loss...: 72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 18/25 [00:17<00:06, 1.11it/s]\rCalculating loss...: 76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 19/25 [00:18<00:04, 1.25it/s]\rCalculating loss...: 80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 20/25 [00:18<00:03, 1.46it/s]\rCalculating loss...: 84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 21/25 [00:19<00:03, 1.33it/s]\rCalculating loss...: 88%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 22/25 [00:20<00:02, 1.24it/s]\rCalculating loss...: 92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 23/25 [00:21<00:01, 1.26it/s]\rCalculating loss...: 96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 24/25 [00:22<00:00, 1.27it/s]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.10s/it]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.04it/s]\nIter 100: Val loss nan, Val took 24.072s\nIter 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\n/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\n warnings.warn('resource_tracker: There appear to be %d '\n]<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\ni need to know if you are making a GGUF model or MLX model?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nlower iter so we won't hit OOM<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\noh okey so we want to MAX out the quality at 2000<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nprogress?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nexecute this project plan\n\n--- Attached Context ---\n\n\ud83d\udcc4 @file:`QUICKSTART.md` (551 tokens)\n```markdown\n# Quick Start\n\n## 1. Run the Fine-Tuned Model\n\n### MLX (recommended for inference):\n```bash\nmlx_lm.generate --model ./fused_model --prompt \"Your Unity task...\"\n```\n\n### Ollama:\nCreate Modelfile in this directory:\n```dockerfile\nFROM ./gguf/unity-agent-Q6_K.gguf\nTEMPLATE \"{{ .Prompt }}\"\nPARAMETER temperature 0.3\nPARAMETER num_ctx 4096\n```\n\nThen:\n```bash\nollama create unity-agent -f Modelfile\nollama serve\n```\n\n### LM Studio:\n- Open LM Studio \u2192 \"My Models\" \u2192 \"Add Model\"\n- Select ./gguf/unity-agent-Q6_K.gguf\n- Start server on port 1234\n\n## 2. Connect to Unity Editor\n\n### Option A: Unity-MCP CLI (recommended)\n```bash\nnpx -y @aigamedeveloper/mcp-server\n# Connects Claude Desktop, Cursor, or VS Code to Unity Editor\n```\n\n### Option B: Custom Editor Window\n- Copy `mcp_config/UnityAgentWindow.cs` to your project's `Assets/Editor/`\n- Window \u2192 Unity Agent \u2192 Chat\n\n### Option C: Python client\n```bash\npython3 mcp_config/unity_mcp_client.py --list\npython3 mcp_config/unity_mcp_client.py --tool gameobject-create \\\n --args '{\"name\":\"Cube\",\"primitiveType\":\"Cube\"}'\n```\n\n## 3. Example Workflows\n\n**Create a scene from scratch:**\n> \"Create a medieval village with 5 houses, a central fountain, and a player character\"\n\n**Fix bugs interactively:**\n> \"Read the console logs and fix the NullReferenceException in PlayerHealth.cs\"\n\n**Profile and optimize:**\n> \"Profile the game, find the bottleneck, and suggest optimizations\"\n\n**Design a system:**\n> \"Create a ScriptableObject-based inventory system with item pickup, UI display, and save/load\"\n\n## 4. Dataset Pipeline (full run)\n\n```bash\n# Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# Download external datasets\npython3 05_download_and_merge_datasets.py\npython3 06_extract_unity_repos.py\npython3 07_integrate_new_datasets.py\n\n# Train + export\npython3 03_train_and_export_unity_agent.py\n```\n\n## 5. Fine-Tuning Stages\n\n```bash\n# Prepare only (merge datasets)\npython3 03_train_and_export_unity_agent.py --stage prepare\n\n# Train only (QLoRA)\npython3 03_train_and_export_unity_agent.py --stage train\n\n# Fuse adapters and export GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n```\n\n```\n\n\ud83d\udcc4 @file:`README.md` (1740 tokens)\n```markdown\n# Unity Agent Developer \u2014 Model Training Pipeline\n\nBuild and deploy a Unity game development agent model on Apple Silicon (M5 Pro).\n\n## Quick Start\n\n```bash\n# 1. Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# 2. Download + merge everything (internet needed)\npython3 05_download_and_merge_datasets.py # vishnuOI + function-calling\npython3 06_extract_unity_repos.py # ML-Agents, ECS, uGUI, netcode\npython3 07_integrate_new_datasets.py # Erocal Q&A + Mix & Jam mechanics\n\n# 3. Fine-tune (MLX QLoRA on Apple Silicon)\npython3 03_train_and_export_unity_agent.py\n\n# 4. Export to GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n\n# 5. Run\nollama create unity-agent -f Modelfile\nollama run unity-agent\n```\n\n## Components\n\n| # | Script | What It Does | Output |\n|---|---|---|---|\n| 01 | `01_build_unity_mcp_dataset.py` | Generates ~128 MCP tool-calling examples from real game-dev tasks | `datasets/unity_mcp_tool_calls_*.jsonl` |\n| 02 | `02_build_unity_design_patterns_dataset.py` | Generates ~75 design pattern + bug-fix examples | `datasets/unity_design_patterns_*.jsonl` |\n| 03 | `03_train_and_export_unity_agent.py` | MLX QLoRA train \u2192 fuse \u2192 GGUF export \u2192 eval | `adapters/`, `fused_model/`, `gguf/` |\n| 04 | `04_setup_unity_mcp_bridge.py` | Sets up Unity-MCP bridge, configs for Claude/Cursor/VS Code | `mcp_config/` |\n| 05 | `05_download_and_merge_datasets.py` | Downloads `vishnuOI/unity-dev-instructions` (46k) + `hermes-function-calling-v1` (2k), merges everything | `datasets/train.jsonl` |\n| 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` |\n| 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` |\n\n## Dataset Composition (after all merges)\n\n| Source | Rows | Coverage |\n|---|---|---|\n| `vishnuOI/unity-dev-instructions` | ~42k (after dedup) | Unity API docs, Stack Overflow, GitHub code \u2014 scripting, rendering, physics, editor, XR, UI, animation, networking, math, performance, input |\n| `NousResearch/hermes-function-calling-v1` | ~1.8k | Generic tool-calling / JSON-mode structured outputs |\n| `Erocal/Unity_ResolvedQuestions` | ~400 | Real resolved Q&A \u2014 physics, ECS, URP/HDRP, UI Toolkit, editor tooling, platform issues, animation rigging |\n| `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 |\n| ML-Agents example agents | ~29 | RL agent patterns (Crawler, Walker, FoodCollector, Soccer, Pyramids, WallJump, Worm, Hallway, 3DBall, PushBlock, GridWorld, Sorter) |\n| ML-Agents core + sensors | ~5 | Agent.cs lifecycle, ray perception, camera sensor, vector sensor, grid sensor |\n| ECS/DOTS samples | ~10 | Jobs, SystemBase, ISystem, IComponentData, IAspect, Baker patterns |\n| uGUI | ~10 | Graphic, Image, Text, Button, Slider, Toggle, ScrollRect, Dropdown, InputField, LayoutGroup |\n| Netcode for GameObjects | ~5 | NetworkBehaviour, NetworkVariable, RPC, NetworkObject patterns |\n| Unity MCP tool calls (custom) | ~128 | Agentic Unity tool use: scene setup, scripting, prefabs, debugging, profiling, testing, shaders, reflection |\n| Design patterns (custom) | ~75 | ScriptableObject architecture, Object Pooling, Singleton, State Machine, Observer, Strategy, Command, Factory, Flyweight, Save/Load, ServiceLocator, EventBus |\n| **Total (deduped)** | **~45k** | |\n\n### Datasets considered and rejected\n\n| Source | Why skipped |\n|---|---|\n| `ibranze/codellama_unity3d_v1/v2` | 535 + 2k rows, wrong format, overlaps with vishnuOI |\n| `ZorraZabb/code25pile75_shuffle_filtered_unity3d_dedup` | 1M rows of noise \u2014 substring \"unity3d\" filter catches Python, JS, news articles, medical papers |\n| `supergoose/buzz_sources_244_unity3d-asset` | 101 rows of git diff YAML for .meta files |\n| `robertzeng/unity3ddoc` | 113k rows but shallow/trivial responses (5-15 word generic definitions) |\n| `DLMveloper/DLM_DataSet` | 3.6M rows but only ~3,570 \"Unity\"-tagged, all trivial (variable declarations, basic array ops) |\n| `Saibalaji25/unity-csharp-scripts` | Requires login + T&C agreement, unknown quality, 11 downloads/mo |\n\n## Training Configuration (M5 Pro 64GB)\n\n```\nModel: Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)\nBatch size: 1\nLayers: 16/64 (25% LoRA)\nLR: 1e-4\nMax seq: 4096\nIters: 2000\nPeak mem: ~25GB\nDuration: ~45-60 min\n```\n\n## GGUF Output Quantization\n\n| Type | Size | Best For |\n|---|---|---|\n| Q4_K_M | ~20 GB | 24GB+ systems |\n| Q5_K_M | ~23 GB | 32GB+ systems |\n| **Q6_K** | **~27 GB** | **M5 Pro 64GB (recommended)** |\n| Q8_0 | ~34 GB | 64GB+ systems (max quality) |\n\n## Unity-MCP Bridge\n\nRun the bridge setup to configure connections to Claude Desktop, Cursor, VS Code, or the included Python client:\n\n```bash\npython3 04_setup_unity_mcp_bridge.py\n```\n\nThen connect any MCP-compatible AI client (Claude Desktop, Cursor, VS Code with Cline/Copilot, Codex) to the Unity Editor.\n\n## Project Structure\n\n```\nunity-agent-training/\n\u251c\u2500\u2500 01_build_unity_mcp_dataset.py # MCP tool-calling dataset\n\u251c\u2500\u2500 02_build_unity_design_patterns_dataset.py # Design patterns dataset\n\u251c\u2500\u2500 03_train_and_export_unity_agent.py # MLX training + GGUF export\n\u251c\u2500\u2500 04_setup_unity_mcp_bridge.py # Unity-MCP integration\n\u251c\u2500\u2500 05_download_and_merge_datasets.py # HF dataset download + merge\n\u251c\u2500\u2500 06_extract_unity_repos.py # Unity org repo C# extraction\n\u251c\u2500\u2500 07_integrate_new_datasets.py # Newly discovered datasets\n\u251c\u2500\u2500 Modelfile # Ollama model definition\n\u251c\u2500\u2500 QUICKSTART.md # Quick start guide\n\u251c\u2500\u2500 datasets/\n\u2502 \u251c\u2500\u2500 unity_mcp_tool_calls_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_design_patterns_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_api_instructions.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 unity_repos_{train,valid,test}.jsonl # (from repo extraction)\n\u2502 \u251c\u2500\u2500 function_calling_full.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 train.jsonl # Merged MLX-ready\n\u2502 \u251c\u2500\u2500 valid.jsonl # Merged MLX-ready\n\u2502 \u2514\u2500\u2500 test.jsonl # Merged MLX-ready\n\u251c\u2500\u2500 adapters/ # LoRA adapters (after training)\n\u251c\u2500\u2500 fused_model/ # Fused MLX model\n\u251c\u2500\u2500 gguf/ # GGUF quantized models\n\u2514\u2500\u2500 mcp_config/ # Bridge configuration\n \u251c\u2500\u2500 claude_desktop_config.json\n \u251c\u2500\u2500 vscode_mcp.json\n \u251c\u2500\u2500 unity_mcp_server_config.json\n \u251c\u2500\u2500 unity_mcp_client.py\n \u2514\u2500\u2500 UnityAgentWindow.cs\n```\n\n```<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nmy laptop crashed. Continue where we left or in case something went corrupt then lets start over<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\n[IMPORTANT: Background process proc_0a497b8b5935 exited (exit code -15, SIGTERM).\nCommand: unset PYTHONPATH; /Users/jacobeen/.hermes/hermes-agent/venv/bin/python3 03_train_and_export_unity_agent.py train 2>&1\nOutput:\n4<00:26, 1.25s/it]\rCalculating loss...: 20%|\u2588\u2588 | 5/25 [00:04<00:21, 1.08s/it]\rCalculating loss...: 24%|\u2588\u2588\u258d | 6/25 [00:05<00:16, 1.18it/s]\rCalculating loss...: 28%|\u2588\u2588\u258a | 7/25 [00:06<00:16, 1.07it/s]\rCalculating loss...: 32%|\u2588\u2588\u2588\u258f | 8/25 [00:07<00:15, 1.07it/s]\rCalculating loss...: 36%|\u2588\u2588\u2588\u258c | 9/25 [00:08<00:17, 1.10s/it]\rCalculating loss...: 40%|\u2588\u2588\u2588\u2588 | 10/25 [00:10<00:19, 1.27s/it]\rCalculating loss...: 44%|\u2588\u2588\u2588\u2588\u258d | 11/25 [00:11<00:17, 1.28s/it]\rCalculating loss...: 48%|\u2588\u2588\u2588\u2588\u258a | 12/25 [00:12<00:14, 1.13s/it]\rCalculating loss...: 52%|\u2588\u2588\u2588\u2588\u2588\u258f | 13/25 [00:14<00:14, 1.24s/it]\rCalculating loss...: 56%|\u2588\u2588\u2588\u2588\u2588\u258c | 14/25 [00:14<00:10, 1.02it/s]\rCalculating loss...: 60%|\u2588\u2588\u2588\u2588\u2588\u2588 | 15/25 [00:14<00:08, 1.24it/s]\rCalculating loss...: 64%|\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 16/25 [00:16<00:09, 1.00s/it]\rCalculating loss...: 68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 17/25 [00:16<00:07, 1.13it/s]\rCalculating loss...: 72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 18/25 [00:17<00:06, 1.11it/s]\rCalculating loss...: 76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 19/25 [00:18<00:04, 1.25it/s]\rCalculating loss...: 80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 20/25 [00:18<00:03, 1.46it/s]\rCalculating loss...: 84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 21/25 [00:19<00:03, 1.33it/s]\rCalculating loss...: 88%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 22/25 [00:20<00:02, 1.24it/s]\rCalculating loss...: 92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 23/25 [00:21<00:01, 1.26it/s]\rCalculating loss...: 96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 24/25 [00:22<00:00, 1.27it/s]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.10s/it]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.04it/s]\nIter 100: Val loss nan, Val took 24.072s\nIter 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\n/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\n warnings.warn('resource_tracker: There appear to be %d '\n]<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\ni need to know if you are making a GGUF model or MLX model?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nlower iter so we won't hit OOM<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\noh okey so we want to MAX out the quality at 2000<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nprogress?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\n[ASYNC DELEGATION BATCH COMPLETE \u2014 deleg_2d07ecd6]\nA 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 \u2014 act on these or re-dispatch if things have changed.\n\nDispatched: 2026-07-13 23:59:18 (11m48s ago)\nContext 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.\nRole: leaf Model: ? Total duration: 708.72s\n\n--- \u2713 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) ---\nContext length exceeded (379 tokens). Cannot compress further.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome: 10/10 repos cloned successfully, 0 failures.**\n\nAll 10 OpenClaw repositories are now present at `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | clawdinators | \u2705 |\n| 2 | openclaw-ansible | \u2705 |\n| 3 | casa | \u2705 |\n| 4 | spogo | \u2705 |\n| 5 | goplaces | \u2705 |\n| 6 | voice-community | \u2705 |\n| 7 | acpx | \u2705 |\n| 8 | community | \u2705 |\n| 9 | caclawphony | \u2705 |\n| 10 | discrawl | \u2705 |\n\nNo issues encountered \u2014 all clones completed cleanly with exit code 0.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Result: 10/10 repos cloned with zero failures.**\n\nCloned to `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | `slacrawl` | \u2705 Cloned |\n| 2 | `crabline` | \u2705 Cloned |\n| 3 | `releases` | \u2705 Cloned (15,075 files) |\n| 4 | `docs` | \u2705 Cloned |\n| 5 | `esp-openclaw-node` | \u2705 Cloned |\n| 6 | `shellbench` | \u2705 Cloned (23,628 files) |\n| 7 | `notcrawl` | \u2705 Cloned |\n| 8 | `clawsweeper` | \u2705 Cloned |\n| 9 | `clownfish` | \u2705 Cloned |\n| 10 | `crabpot` | \u2705 Cloned |\n\nNo issues encountered. All 10 repositories are present and fully cloned.\n\n--- \u2713 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) ---\nContext length exceeded: max compression attempts (3) reached.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome:** All 10 OpenClaw repositories cloned successfully into `/Volumes/this_and_that/projects/multi-repo_0/`.\n\n**Cloned repos:**\n1. \u2705 crawlbar\n2. \u2705 clawgrit-reports (previously cloned)\n3. \u2705 fs-safe\n4. \u2705 ask-molty\n5. \u2705 graincrawl\n6. \u2705 clickclack\n7. \u2705 clawdex\n8. \u2705 proxyline\n9. \u2705 telecrawl\n10. \u2705 clawpatch\n\n**Issues:** None. All 10 clones completed with exit code 0.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome: 10/10 repos cloned successfully, 0 failures.**\n\nCloned to `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | crabfleet | \u2705 |\n| 2 | agent-skills | \u2705 |\n| 3 | rfcs | \u2705 |\n| 4 | rastermill | \u2705 |\n| 5 | libopus-wasm | \u2705 |\n| 6 | octopool | \u2705 |\n| 7 | clawpdf | \u2705 |\n| 8 | photoscrawl | \u2705 |\n| 9 | clawrouter | \u2705 |\n| 10 | imsgcrawl | \u2705 |\n\nNo issues encountered \u2014 all clones completed cleanly with no errors.\n\n--- \u2713 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) ---\nContext length exceeded (435 tokens). Cannot compress further.\n\n--- \u2713 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) ---\nContext length exceeded (1,277 tokens). Cannot compress further.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\n[ASYNC DELEGATION BATCH COMPLETE \u2014 deleg_2d07ecd6]\nA 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 \u2014 act on these or re-dispatch if things have changed.\n\nDispatched: 2026-07-13 23:59:18 (11m48s ago)\nContext 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.\nRole: leaf Model: ? Total duration: 708.72s\n\n--- \u2713 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) ---\nContext length exceeded (379 tokens). Cannot compress further.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome: 10/10 repos cloned successfully, 0 failures.**\n\nAll 10 OpenClaw repositories are now present at `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | clawdinators | \u2705 |\n| 2 | openclaw-ansible | \u2705 |\n| 3 | casa | \u2705 |\n| 4 | spogo | \u2705 |\n| 5 | goplaces | \u2705 |\n| 6 | voice-community | \u2705 |\n| 7 | acpx | \u2705 |\n| 8 | community | \u2705 |\n| 9 | caclawphony | \u2705 |\n| 10 | discrawl | \u2705 |\n\nNo issues encountered \u2014 all clones completed cleanly with exit code 0.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Result: 10/10 repos cloned with zero failures.**\n\nCloned to `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | `slacrawl` | \u2705 Cloned |\n| 2 | `crabline` | \u2705 Cloned |\n| 3 | `releases` | \u2705 Cloned (15,075 files) |\n| 4 | `docs` | \u2705 Cloned |\n| 5 | `esp-openclaw-node` | \u2705 Cloned |\n| 6 | `shellbench` | \u2705 Cloned (23,628 files) |\n| 7 | `notcrawl` | \u2705 Cloned |\n| 8 | `clawsweeper` | \u2705 Cloned |\n| 9 | `clownfish` | \u2705 Cloned |\n| 10 | `crabpot` | \u2705 Cloned |\n\nNo issues encountered. All 10 repositories are present and fully cloned.\n\n--- \u2713 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) ---\nContext length exceeded: max compression attempts (3) reached.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome:** All 10 OpenClaw repositories cloned successfully into `/Volumes/this_and_that/projects/multi-repo_0/`.\n\n**Cloned repos:**\n1. \u2705 crawlbar\n2. \u2705 clawgrit-reports (previously cloned)\n3. \u2705 fs-safe\n4. \u2705 ask-molty\n5. \u2705 graincrawl\n6. \u2705 clickclack\n7. \u2705 clawdex\n8. \u2705 proxyline\n9. \u2705 telecrawl\n10. \u2705 clawpatch\n\n**Issues:** None. All 10 clones completed with exit code 0.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome: 10/10 repos cloned successfully, 0 failures.**\n\nCloned to `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | crabfleet | \u2705 |\n| 2 | agent-skills | \u2705 |\n| 3 | rfcs | \u2705 |\n| 4 | rastermill | \u2705 |\n| 5 | libopus-wasm | \u2705 |\n| 6 | octopool | \u2705 |\n| 7 | clawpdf | \u2705 |\n| 8 | photoscrawl | \u2705 |\n| 9 | clawrouter | \u2705 |\n| 10 | imsgcrawl | \u2705 |\n\nNo issues encountered \u2014 all clones completed cleanly with no errors.\n\n--- \u2713 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) ---\nContext length exceeded (435 tokens). Cannot compress further.\n\n--- \u2713 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) ---\nContext length exceeded (1,277 tokens). Cannot compress further.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\ncan you make sure that every single profile is utilizing our Obsidian.md, Hindsight and othr plugins?\n\n[System: The active model for this chat has changed to Ornith-1.0-35B-MTPLX via provider omlx-mbp. From this point forward, use this runtime metadata when answering questions about what model/provider is active.]\n\ncan you make sure that every single profile is utilizing our Obsidian.md, Hindsight and othr plugins?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nmake a research on internet what will be best setup for each profile i have<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for a 'game-dev' profile focused on Unity game development, 3D graphics, game logic, shaders, and game engine workflows. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant for a game developer. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for a 'cybersec' profile focused on security auditing, penetration testing, vulnerability research, and security analysis. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant for a security-focused agent. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for an 'office-manager' profile focused on office productivity, document management, spreadsheets, presentations, email management, scheduling, and administrative tasks. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for an 'image-generator' profile focused on AI image generation, image editing, design workflows, and visual content creation. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant for an image generation agent. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nI need to execute this project plan --- Attached. Please help.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nI need to process this. Please help.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\n[IMPORTANT: Background process proc_0a497b8b5935 exited (exit code -15, SIGTERM).\nCommand: unset PYTHONPATH; /Users/jacobeen/.hermes/hermes-agent/venv/bin/python3 03_train_and_export_unity_agent.py train 2>&1\nOutput:\n4<00:26, 1.25s/it]\rCalculating loss...: 20%|\u2588\u2588 | 5/25 [00:04<00:21, 1.08s/it]\rCalculating loss...: 24%|\u2588\u2588\u258d | 6/25 [00:05<00:16, 1.18it/s]\rCalculating loss...: 28%|\u2588\u2588\u258a | 7/25 [00:06<00:16, 1.07it/s]\rCalculating loss...: 32%|\u2588\u2588\u2588\u258f | 8/25 [00:07<00:15, 1.07it/s]\rCalculating loss...: 36%|\u2588\u2588\u2588\u258c | 9/25 [00:08<00:17, 1.10s/it]\rCalculating loss...: 40%|\u2588\u2588\u2588\u2588 | 10/25 [00:10<00:19, 1.27s/it]\rCalculating loss...: 44%|\u2588\u2588\u2588\u2588\u258d | 11/25 [00:11<00:17, 1.28s/it]\rCalculating loss...: 48%|\u2588\u2588\u2588\u2588\u258a | 12/25 [00:12<00:14, 1.13s/it]\rCalculating loss...: 52%|\u2588\u2588\u2588\u2588\u2588\u258f | 13/25 [00:14<00:14, 1.24s/it]\rCalculating loss...: 56%|\u2588\u2588\u2588\u2588\u2588\u258c | 14/25 [00:14<00:10, 1.02it/s]\rCalculating loss...: 60%|\u2588\u2588\u2588\u2588\u2588\u2588 | 15/25 [00:14<00:08, 1.24it/s]\rCalculating loss...: 64%|\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 16/25 [00:16<00:09, 1.00s/it]\rCalculating loss...: 68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 17/25 [00:16<00:07, 1.13it/s]\rCalculating loss...: 72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 18/25 [00:17<00:06, 1.11it/s]\rCalculating loss...: 76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 19/25 [00:18<00:04, 1.25it/s]\rCalculating loss...: 80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 20/25 [00:18<00:03, 1.46it/s]\rCalculating loss...: 84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 21/25 [00:19<00:03, 1.33it/s]\rCalculating loss...: 88%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 22/25 [00:20<00:02, 1.24it/s]\rCalculating loss...: 92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 23/25 [00:21<00:01, 1.26it/s]\rCalculating loss...: 96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 24/25 [00:22<00:00, 1.27it/s]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.10s/it]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.04it/s]\nIter 100: Val loss nan, Val took 24.072s\nIter 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\n/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\n warnings.warn('resource_tracker: There appear to be %d '\n]<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nCan you process this?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in Developer profile. Prioritize code execution, file editing, and terminal access.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for a 'game-dev' profile focused on Unity game development, 3D graphics, game logic, shaders, and game engine workflows. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant for a game developer. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nCan you process this?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nmy laptop crashed. Continue where we left or in case something went corrupt then lets start over<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nCan you process proc_0a497b8b5935 exited (exit code -15,?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nmy laptop crashed. Continue where we left or in case something went corrupt then lets start over<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\noh okey so we want to MAX out the quality at 2000<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nexecute this project plan\n\n--- Attached Context ---\n\n\ud83d\udcc4 @file:`QUICKSTART.md` (551 tokens)\n```markdown\n# Quick Start\n\n## 1. Run the Fine-Tuned Model\n\n### MLX (recommended for inference):\n```bash\nmlx_lm.generate --model ./fused_model --prompt \"Your Unity task...\"\n```\n\n### Ollama:\nCreate Modelfile in this directory:\n```dockerfile\nFROM ./gguf/unity-agent-Q6_K.gguf\nTEMPLATE \"{{ .Prompt }}\"\nPARAMETER temperature 0.3\nPARAMETER num_ctx 4096\n```\n\nThen:\n```bash\nollama create unity-agent -f Modelfile\nollama serve\n```\n\n### LM Studio:\n- Open LM Studio \u2192 \"My Models\" \u2192 \"Add Model\"\n- Select ./gguf/unity-agent-Q6_K.gguf\n- Start server on port 1234\n\n## 2. Connect to Unity Editor\n\n### Option A: Unity-MCP CLI (recommended)\n```bash\nnpx -y @aigamedeveloper/mcp-server\n# Connects Claude Desktop, Cursor, or VS Code to Unity Editor\n```\n\n### Option B: Custom Editor Window\n- Copy `mcp_config/UnityAgentWindow.cs` to your project's `Assets/Editor/`\n- Window \u2192 Unity Agent \u2192 Chat\n\n### Option C: Python client\n```bash\npython3 mcp_config/unity_mcp_client.py --list\npython3 mcp_config/unity_mcp_client.py --tool gameobject-create \\\n --args '{\"name\":\"Cube\",\"primitiveType\":\"Cube\"}'\n```\n\n## 3. Example Workflows\n\n**Create a scene from scratch:**\n> \"Create a medieval village with 5 houses, a central fountain, and a player character\"\n\n**Fix bugs interactively:**\n> \"Read the console logs and fix the NullReferenceException in PlayerHealth.cs\"\n\n**Profile and optimize:**\n> \"Profile the game, find the bottleneck, and suggest optimizations\"\n\n**Design a system:**\n> \"Create a ScriptableObject-based inventory system with item pickup, UI display, and save/load\"\n\n## 4. Dataset Pipeline (full run)\n\n```bash\n# Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# Download external datasets\npython3 05_download_and_merge_datasets.py\npython3 06_extract_unity_repos.py\npython3 07_integrate_new_datasets.py\n\n# Train + export\npython3 03_train_and_export_unity_agent.py\n```\n\n## 5. Fine-Tuning Stages\n\n```bash\n# Prepare only (merge datasets)\npython3 03_train_and_export_unity_agent.py --stage prepare\n\n# Train only (QLoRA)\npython3 03_train_and_export_unity_agent.py --stage train\n\n# Fuse adapters and export GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n```\n\n```\n\n\ud83d\udcc4 @file:`README.md` (1740 tokens)\n```markdown\n# Unity Agent Developer \u2014 Model Training Pipeline\n\nBuild and deploy a Unity game development agent model on Apple Silicon (M5 Pro).\n\n## Quick Start\n\n```bash\n# 1. Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# 2. Download + merge everything (internet needed)\npython3 05_download_and_merge_datasets.py # vishnuOI + function-calling\npython3 06_extract_unity_repos.py # ML-Agents, ECS, uGUI, netcode\npython3 07_integrate_new_datasets.py # Erocal Q&A + Mix & Jam mechanics\n\n# 3. Fine-tune (MLX QLoRA on Apple Silicon)\npython3 03_train_and_export_unity_agent.py\n\n# 4. Export to GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n\n# 5. Run\nollama create unity-agent -f Modelfile\nollama run unity-agent\n```\n\n## Components\n\n| # | Script | What It Does | Output |\n|---|---|---|---|\n| 01 | `01_build_unity_mcp_dataset.py` | Generates ~128 MCP tool-calling examples from real game-dev tasks | `datasets/unity_mcp_tool_calls_*.jsonl` |\n| 02 | `02_build_unity_design_patterns_dataset.py` | Generates ~75 design pattern + bug-fix examples | `datasets/unity_design_patterns_*.jsonl` |\n| 03 | `03_train_and_export_unity_agent.py` | MLX QLoRA train \u2192 fuse \u2192 GGUF export \u2192 eval | `adapters/`, `fused_model/`, `gguf/` |\n| 04 | `04_setup_unity_mcp_bridge.py` | Sets up Unity-MCP bridge, configs for Claude/Cursor/VS Code | `mcp_config/` |\n| 05 | `05_download_and_merge_datasets.py` | Downloads `vishnuOI/unity-dev-instructions` (46k) + `hermes-function-calling-v1` (2k), merges everything | `datasets/train.jsonl` |\n| 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` |\n| 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` |\n\n## Dataset Composition (after all merges)\n\n| Source | Rows | Coverage |\n|---|---|---|\n| `vishnuOI/unity-dev-instructions` | ~42k (after dedup) | Unity API docs, Stack Overflow, GitHub code \u2014 scripting, rendering, physics, editor, XR, UI, animation, networking, math, performance, input |\n| `NousResearch/hermes-function-calling-v1` | ~1.8k | Generic tool-calling / JSON-mode structured outputs |\n| `Erocal/Unity_ResolvedQuestions` | ~400 | Real resolved Q&A \u2014 physics, ECS, URP/HDRP, UI Toolkit, editor tooling, platform issues, animation rigging |\n| `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 |\n| ML-Agents example agents | ~29 | RL agent patterns (Crawler, Walker, FoodCollector, Soccer, Pyramids, WallJump, Worm, Hallway, 3DBall, PushBlock, GridWorld, Sorter) |\n| ML-Agents core + sensors | ~5 | Agent.cs lifecycle, ray perception, camera sensor, vector sensor, grid sensor |\n| ECS/DOTS samples | ~10 | Jobs, SystemBase, ISystem, IComponentData, IAspect, Baker patterns |\n| uGUI | ~10 | Graphic, Image, Text, Button, Slider, Toggle, ScrollRect, Dropdown, InputField, LayoutGroup |\n| Netcode for GameObjects | ~5 | NetworkBehaviour, NetworkVariable, RPC, NetworkObject patterns |\n| Unity MCP tool calls (custom) | ~128 | Agentic Unity tool use: scene setup, scripting, prefabs, debugging, profiling, testing, shaders, reflection |\n| Design patterns (custom) | ~75 | ScriptableObject architecture, Object Pooling, Singleton, State Machine, Observer, Strategy, Command, Factory, Flyweight, Save/Load, ServiceLocator, EventBus |\n| **Total (deduped)** | **~45k** | |\n\n### Datasets considered and rejected\n\n| Source | Why skipped |\n|---|---|\n| `ibranze/codellama_unity3d_v1/v2` | 535 + 2k rows, wrong format, overlaps with vishnuOI |\n| `ZorraZabb/code25pile75_shuffle_filtered_unity3d_dedup` | 1M rows of noise \u2014 substring \"unity3d\" filter catches Python, JS, news articles, medical papers |\n| `supergoose/buzz_sources_244_unity3d-asset` | 101 rows of git diff YAML for .meta files |\n| `robertzeng/unity3ddoc` | 113k rows but shallow/trivial responses (5-15 word generic definitions) |\n| `DLMveloper/DLM_DataSet` | 3.6M rows but only ~3,570 \"Unity\"-tagged, all trivial (variable declarations, basic array ops) |\n| `Saibalaji25/unity-csharp-scripts` | Requires login + T&C agreement, unknown quality, 11 downloads/mo |\n\n## Training Configuration (M5 Pro 64GB)\n\n```\nModel: Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)\nBatch size: 1\nLayers: 16/64 (25% LoRA)\nLR: 1e-4\nMax seq: 4096\nIters: 2000\nPeak mem: ~25GB\nDuration: ~45-60 min\n```\n\n## GGUF Output Quantization\n\n| Type | Size | Best For |\n|---|---|---|\n| Q4_K_M | ~20 GB | 24GB+ systems |\n| Q5_K_M | ~23 GB | 32GB+ systems |\n| **Q6_K** | **~27 GB** | **M5 Pro 64GB (recommended)** |\n| Q8_0 | ~34 GB | 64GB+ systems (max quality) |\n\n## Unity-MCP Bridge\n\nRun the bridge setup to configure connections to Claude Desktop, Cursor, VS Code, or the included Python client:\n\n```bash\npython3 04_setup_unity_mcp_bridge.py\n```\n\nThen connect any MCP-compatible AI client (Claude Desktop, Cursor, VS Code with Cline/Copilot, Codex) to the Unity Editor.\n\n## Project Structure\n\n```\nunity-agent-training/\n\u251c\u2500\u2500 01_build_unity_mcp_dataset.py # MCP tool-calling dataset\n\u251c\u2500\u2500 02_build_unity_design_patterns_dataset.py # Design patterns dataset\n\u251c\u2500\u2500 03_train_and_export_unity_agent.py # MLX training + GGUF export\n\u251c\u2500\u2500 04_setup_unity_mcp_bridge.py # Unity-MCP integration\n\u251c\u2500\u2500 05_download_and_merge_datasets.py # HF dataset download + merge\n\u251c\u2500\u2500 06_extract_unity_repos.py # Unity org repo C# extraction\n\u251c\u2500\u2500 07_integrate_new_datasets.py # Newly discovered datasets\n\u251c\u2500\u2500 Modelfile # Ollama model definition\n\u251c\u2500\u2500 QUICKSTART.md # Quick start guide\n\u251c\u2500\u2500 datasets/\n\u2502 \u251c\u2500\u2500 unity_mcp_tool_calls_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_design_patterns_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_api_instructions.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 unity_repos_{train,valid,test}.jsonl # (from repo extraction)\n\u2502 \u251c\u2500\u2500 function_calling_full.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 train.jsonl # Merged MLX-ready\n\u2502 \u251c\u2500\u2500 valid.jsonl # Merged MLX-ready\n\u2502 \u2514\u2500\u2500 test.jsonl # Merged MLX-ready\n\u251c\u2500\u2500 adapters/ # LoRA adapters (after training)\n\u251c\u2500\u2500 fused_model/ # Fused MLX model\n\u251c\u2500\u2500 gguf/ # GGUF quantized models\n\u2514\u2500\u2500 mcp_config/ # Bridge configuration\n \u251c\u2500\u2500 claude_desktop_config.json\n \u251c\u2500\u2500 vscode_mcp.json\n \u251c\u2500\u2500 unity_mcp_server_config.json\n \u251c\u2500\u2500 unity_mcp_client.py\n \u2514\u2500\u2500 UnityAgentWindow.cs\n```\n\n```<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\ncan unsloth fine tune and train MLX?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in System Admin profile. Prioritize terminal commands, process management, and system tools.\n<|im_end|>\n<|im_start|>user\nmy laptop crashed. Continue where we left or in case something went corrupt then lets start over<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nexecute this project plan\n\n--- Attached Context ---\n\n\ud83d\udcc4 @file:`QUICKSTART.md` (551 tokens)\n```markdown\n# Quick Start\n\n## 1. Run the Fine-Tuned Model\n\n### MLX (recommended for inference):\n```bash\nmlx_lm.generate --model ./fused_model --prompt \"Your Unity task...\"\n```\n\n### Ollama:\nCreate Modelfile in this directory:\n```dockerfile\nFROM ./gguf/unity-agent-Q6_K.gguf\nTEMPLATE \"{{ .Prompt }}\"\nPARAMETER temperature 0.3\nPARAMETER num_ctx 4096\n```\n\nThen:\n```bash\nollama create unity-agent -f Modelfile\nollama serve\n```\n\n### LM Studio:\n- Open LM Studio \u2192 \"My Models\" \u2192 \"Add Model\"\n- Select ./gguf/unity-agent-Q6_K.gguf\n- Start server on port 1234\n\n## 2. Connect to Unity Editor\n\n### Option A: Unity-MCP CLI (recommended)\n```bash\nnpx -y @aigamedeveloper/mcp-server\n# Connects Claude Desktop, Cursor, or VS Code to Unity Editor\n```\n\n### Option B: Custom Editor Window\n- Copy `mcp_config/UnityAgentWindow.cs` to your project's `Assets/Editor/`\n- Window \u2192 Unity Agent \u2192 Chat\n\n### Option C: Python client\n```bash\npython3 mcp_config/unity_mcp_client.py --list\npython3 mcp_config/unity_mcp_client.py --tool gameobject-create \\\n --args '{\"name\":\"Cube\",\"primitiveType\":\"Cube\"}'\n```\n\n## 3. Example Workflows\n\n**Create a scene from scratch:**\n> \"Create a medieval village with 5 houses, a central fountain, and a player character\"\n\n**Fix bugs interactively:**\n> \"Read the console logs and fix the NullReferenceException in PlayerHealth.cs\"\n\n**Profile and optimize:**\n> \"Profile the game, find the bottleneck, and suggest optimizations\"\n\n**Design a system:**\n> \"Create a ScriptableObject-based inventory system with item pickup, UI display, and save/load\"\n\n## 4. Dataset Pipeline (full run)\n\n```bash\n# Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# Download external datasets\npython3 05_download_and_merge_datasets.py\npython3 06_extract_unity_repos.py\npython3 07_integrate_new_datasets.py\n\n# Train + export\npython3 03_train_and_export_unity_agent.py\n```\n\n## 5. Fine-Tuning Stages\n\n```bash\n# Prepare only (merge datasets)\npython3 03_train_and_export_unity_agent.py --stage prepare\n\n# Train only (QLoRA)\npython3 03_train_and_export_unity_agent.py --stage train\n\n# Fuse adapters and export GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n```\n\n```\n\n\ud83d\udcc4 @file:`README.md` (1740 tokens)\n```markdown\n# Unity Agent Developer \u2014 Model Training Pipeline\n\nBuild and deploy a Unity game development agent model on Apple Silicon (M5 Pro).\n\n## Quick Start\n\n```bash\n# 1. Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# 2. Download + merge everything (internet needed)\npython3 05_download_and_merge_datasets.py # vishnuOI + function-calling\npython3 06_extract_unity_repos.py # ML-Agents, ECS, uGUI, netcode\npython3 07_integrate_new_datasets.py # Erocal Q&A + Mix & Jam mechanics\n\n# 3. Fine-tune (MLX QLoRA on Apple Silicon)\npython3 03_train_and_export_unity_agent.py\n\n# 4. Export to GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n\n# 5. Run\nollama create unity-agent -f Modelfile\nollama run unity-agent\n```\n\n## Components\n\n| # | Script | What It Does | Output |\n|---|---|---|---|\n| 01 | `01_build_unity_mcp_dataset.py` | Generates ~128 MCP tool-calling examples from real game-dev tasks | `datasets/unity_mcp_tool_calls_*.jsonl` |\n| 02 | `02_build_unity_design_patterns_dataset.py` | Generates ~75 design pattern + bug-fix examples | `datasets/unity_design_patterns_*.jsonl` |\n| 03 | `03_train_and_export_unity_agent.py` | MLX QLoRA train \u2192 fuse \u2192 GGUF export \u2192 eval | `adapters/`, `fused_model/`, `gguf/` |\n| 04 | `04_setup_unity_mcp_bridge.py` | Sets up Unity-MCP bridge, configs for Claude/Cursor/VS Code | `mcp_config/` |\n| 05 | `05_download_and_merge_datasets.py` | Downloads `vishnuOI/unity-dev-instructions` (46k) + `hermes-function-calling-v1` (2k), merges everything | `datasets/train.jsonl` |\n| 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` |\n| 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` |\n\n## Dataset Composition (after all merges)\n\n| Source | Rows | Coverage |\n|---|---|---|\n| `vishnuOI/unity-dev-instructions` | ~42k (after dedup) | Unity API docs, Stack Overflow, GitHub code \u2014 scripting, rendering, physics, editor, XR, UI, animation, networking, math, performance, input |\n| `NousResearch/hermes-function-calling-v1` | ~1.8k | Generic tool-calling / JSON-mode structured outputs |\n| `Erocal/Unity_ResolvedQuestions` | ~400 | Real resolved Q&A \u2014 physics, ECS, URP/HDRP, UI Toolkit, editor tooling, platform issues, animation rigging |\n| `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 |\n| ML-Agents example agents | ~29 | RL agent patterns (Crawler, Walker, FoodCollector, Soccer, Pyramids, WallJump, Worm, Hallway, 3DBall, PushBlock, GridWorld, Sorter) |\n| ML-Agents core + sensors | ~5 | Agent.cs lifecycle, ray perception, camera sensor, vector sensor, grid sensor |\n| ECS/DOTS samples | ~10 | Jobs, SystemBase, ISystem, IComponentData, IAspect, Baker patterns |\n| uGUI | ~10 | Graphic, Image, Text, Button, Slider, Toggle, ScrollRect, Dropdown, InputField, LayoutGroup |\n| Netcode for GameObjects | ~5 | NetworkBehaviour, NetworkVariable, RPC, NetworkObject patterns |\n| Unity MCP tool calls (custom) | ~128 | Agentic Unity tool use: scene setup, scripting, prefabs, debugging, profiling, testing, shaders, reflection |\n| Design patterns (custom) | ~75 | ScriptableObject architecture, Object Pooling, Singleton, State Machine, Observer, Strategy, Command, Factory, Flyweight, Save/Load, ServiceLocator, EventBus |\n| **Total (deduped)** | **~45k** | |\n\n### Datasets considered and rejected\n\n| Source | Why skipped |\n|---|---|\n| `ibranze/codellama_unity3d_v1/v2` | 535 + 2k rows, wrong format, overlaps with vishnuOI |\n| `ZorraZabb/code25pile75_shuffle_filtered_unity3d_dedup` | 1M rows of noise \u2014 substring \"unity3d\" filter catches Python, JS, news articles, medical papers |\n| `supergoose/buzz_sources_244_unity3d-asset` | 101 rows of git diff YAML for .meta files |\n| `robertzeng/unity3ddoc` | 113k rows but shallow/trivial responses (5-15 word generic definitions) |\n| `DLMveloper/DLM_DataSet` | 3.6M rows but only ~3,570 \"Unity\"-tagged, all trivial (variable declarations, basic array ops) |\n| `Saibalaji25/unity-csharp-scripts` | Requires login + T&C agreement, unknown quality, 11 downloads/mo |\n\n## Training Configuration (M5 Pro 64GB)\n\n```\nModel: Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)\nBatch size: 1\nLayers: 16/64 (25% LoRA)\nLR: 1e-4\nMax seq: 4096\nIters: 2000\nPeak mem: ~25GB\nDuration: ~45-60 min\n```\n\n## GGUF Output Quantization\n\n| Type | Size | Best For |\n|---|---|---|\n| Q4_K_M | ~20 GB | 24GB+ systems |\n| Q5_K_M | ~23 GB | 32GB+ systems |\n| **Q6_K** | **~27 GB** | **M5 Pro 64GB (recommended)** |\n| Q8_0 | ~34 GB | 64GB+ systems (max quality) |\n\n## Unity-MCP Bridge\n\nRun the bridge setup to configure connections to Claude Desktop, Cursor, VS Code, or the included Python client:\n\n```bash\npython3 04_setup_unity_mcp_bridge.py\n```\n\nThen connect any MCP-compatible AI client (Claude Desktop, Cursor, VS Code with Cline/Copilot, Codex) to the Unity Editor.\n\n## Project Structure\n\n```\nunity-agent-training/\n\u251c\u2500\u2500 01_build_unity_mcp_dataset.py # MCP tool-calling dataset\n\u251c\u2500\u2500 02_build_unity_design_patterns_dataset.py # Design patterns dataset\n\u251c\u2500\u2500 03_train_and_export_unity_agent.py # MLX training + GGUF export\n\u251c\u2500\u2500 04_setup_unity_mcp_bridge.py # Unity-MCP integration\n\u251c\u2500\u2500 05_download_and_merge_datasets.py # HF dataset download + merge\n\u251c\u2500\u2500 06_extract_unity_repos.py # Unity org repo C# extraction\n\u251c\u2500\u2500 07_integrate_new_datasets.py # Newly discovered datasets\n\u251c\u2500\u2500 Modelfile # Ollama model definition\n\u251c\u2500\u2500 QUICKSTART.md # Quick start guide\n\u251c\u2500\u2500 datasets/\n\u2502 \u251c\u2500\u2500 unity_mcp_tool_calls_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_design_patterns_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_api_instructions.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 unity_repos_{train,valid,test}.jsonl # (from repo extraction)\n\u2502 \u251c\u2500\u2500 function_calling_full.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 train.jsonl # Merged MLX-ready\n\u2502 \u251c\u2500\u2500 valid.jsonl # Merged MLX-ready\n\u2502 \u2514\u2500\u2500 test.jsonl # Merged MLX-ready\n\u251c\u2500\u2500 adapters/ # LoRA adapters (after training)\n\u251c\u2500\u2500 fused_model/ # Fused MLX model\n\u251c\u2500\u2500 gguf/ # GGUF quantized models\n\u2514\u2500\u2500 mcp_config/ # Bridge configuration\n \u251c\u2500\u2500 claude_desktop_config.json\n \u251c\u2500\u2500 vscode_mcp.json\n \u251c\u2500\u2500 unity_mcp_server_config.json\n \u251c\u2500\u2500 unity_mcp_client.py\n \u2514\u2500\u2500 UnityAgentWindow.cs\n```\n\n```<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for a 'game-dev' profile focused on Unity game development, 3D graphics, game logic, shaders, and game engine workflows. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant for a game developer. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\noh okey so we want to MAX out the quality at 2000<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nprogress?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nprogress?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in Creative profile. Prioritize content generation and design-oriented tools.\n<|im_end|>\n<|im_start|>user\nmy laptop crashed. Continue where we left or in case something went corrupt then lets start over<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nCan you process this?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nCan you process this?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\n[IMPORTANT: Background process proc_0a497b8b5935 exited (exit code -15, SIGTERM).\nCommand: unset PYTHONPATH; /Users/jacobeen/.hermes/hermes-agent/venv/bin/python3 03_train_and_export_unity_agent.py train 2>&1\nOutput:\n4<00:26, 1.25s/it]\rCalculating loss...: 20%|\u2588\u2588 | 5/25 [00:04<00:21, 1.08s/it]\rCalculating loss...: 24%|\u2588\u2588\u258d | 6/25 [00:05<00:16, 1.18it/s]\rCalculating loss...: 28%|\u2588\u2588\u258a | 7/25 [00:06<00:16, 1.07it/s]\rCalculating loss...: 32%|\u2588\u2588\u2588\u258f | 8/25 [00:07<00:15, 1.07it/s]\rCalculating loss...: 36%|\u2588\u2588\u2588\u258c | 9/25 [00:08<00:17, 1.10s/it]\rCalculating loss...: 40%|\u2588\u2588\u2588\u2588 | 10/25 [00:10<00:19, 1.27s/it]\rCalculating loss...: 44%|\u2588\u2588\u2588\u2588\u258d | 11/25 [00:11<00:17, 1.28s/it]\rCalculating loss...: 48%|\u2588\u2588\u2588\u2588\u258a | 12/25 [00:12<00:14, 1.13s/it]\rCalculating loss...: 52%|\u2588\u2588\u2588\u2588\u2588\u258f | 13/25 [00:14<00:14, 1.24s/it]\rCalculating loss...: 56%|\u2588\u2588\u2588\u2588\u2588\u258c | 14/25 [00:14<00:10, 1.02it/s]\rCalculating loss...: 60%|\u2588\u2588\u2588\u2588\u2588\u2588 | 15/25 [00:14<00:08, 1.24it/s]\rCalculating loss...: 64%|\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 16/25 [00:16<00:09, 1.00s/it]\rCalculating loss...: 68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 17/25 [00:16<00:07, 1.13it/s]\rCalculating loss...: 72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 18/25 [00:17<00:06, 1.11it/s]\rCalculating loss...: 76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 19/25 [00:18<00:04, 1.25it/s]\rCalculating loss...: 80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 20/25 [00:18<00:03, 1.46it/s]\rCalculating loss...: 84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 21/25 [00:19<00:03, 1.33it/s]\rCalculating loss...: 88%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 22/25 [00:20<00:02, 1.24it/s]\rCalculating loss...: 92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 23/25 [00:21<00:01, 1.26it/s]\rCalculating loss...: 96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 24/25 [00:22<00:00, 1.27it/s]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.10s/it]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.04it/s]\nIter 100: Val loss nan, Val took 24.072s\nIter 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\n/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\n warnings.warn('resource_tracker: There appear to be %d '\n]<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nmy laptop crashed. Continue where we left or in case something went corrupt then lets start over<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\noh okey so we want to MAX out the quality at 2000<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for a 'cybersec' profile focused on security auditing, penetration testing, vulnerability research, and security analysis. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant for a security-focused agent. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\noh okey so we want to MAX out the quality at 2000<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in Developer profile. Prioritize code execution, file editing, and terminal access.\n<|im_end|>\n<|im_start|>user\nexecute this project plan\n\n--- Attached Context ---\n\n\ud83d\udcc4 @file:`QUICKSTART.md` (551 tokens)\n```markdown\n# Quick Start\n\n## 1. Run the Fine-Tuned Model\n\n### MLX (recommended for inference):\n```bash\nmlx_lm.generate --model ./fused_model --prompt \"Your Unity task...\"\n```\n\n### Ollama:\nCreate Modelfile in this directory:\n```dockerfile\nFROM ./gguf/unity-agent-Q6_K.gguf\nTEMPLATE \"{{ .Prompt }}\"\nPARAMETER temperature 0.3\nPARAMETER num_ctx 4096\n```\n\nThen:\n```bash\nollama create unity-agent -f Modelfile\nollama serve\n```\n\n### LM Studio:\n- Open LM Studio \u2192 \"My Models\" \u2192 \"Add Model\"\n- Select ./gguf/unity-agent-Q6_K.gguf\n- Start server on port 1234\n\n## 2. Connect to Unity Editor\n\n### Option A: Unity-MCP CLI (recommended)\n```bash\nnpx -y @aigamedeveloper/mcp-server\n# Connects Claude Desktop, Cursor, or VS Code to Unity Editor\n```\n\n### Option B: Custom Editor Window\n- Copy `mcp_config/UnityAgentWindow.cs` to your project's `Assets/Editor/`\n- Window \u2192 Unity Agent \u2192 Chat\n\n### Option C: Python client\n```bash\npython3 mcp_config/unity_mcp_client.py --list\npython3 mcp_config/unity_mcp_client.py --tool gameobject-create \\\n --args '{\"name\":\"Cube\",\"primitiveType\":\"Cube\"}'\n```\n\n## 3. Example Workflows\n\n**Create a scene from scratch:**\n> \"Create a medieval village with 5 houses, a central fountain, and a player character\"\n\n**Fix bugs interactively:**\n> \"Read the console logs and fix the NullReferenceException in PlayerHealth.cs\"\n\n**Profile and optimize:**\n> \"Profile the game, find the bottleneck, and suggest optimizations\"\n\n**Design a system:**\n> \"Create a ScriptableObject-based inventory system with item pickup, UI display, and save/load\"\n\n## 4. Dataset Pipeline (full run)\n\n```bash\n# Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# Download external datasets\npython3 05_download_and_merge_datasets.py\npython3 06_extract_unity_repos.py\npython3 07_integrate_new_datasets.py\n\n# Train + export\npython3 03_train_and_export_unity_agent.py\n```\n\n## 5. Fine-Tuning Stages\n\n```bash\n# Prepare only (merge datasets)\npython3 03_train_and_export_unity_agent.py --stage prepare\n\n# Train only (QLoRA)\npython3 03_train_and_export_unity_agent.py --stage train\n\n# Fuse adapters and export GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n```\n\n```\n\n\ud83d\udcc4 @file:`README.md` (1740 tokens)\n```markdown\n# Unity Agent Developer \u2014 Model Training Pipeline\n\nBuild and deploy a Unity game development agent model on Apple Silicon (M5 Pro).\n\n## Quick Start\n\n```bash\n# 1. Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# 2. Download + merge everything (internet needed)\npython3 05_download_and_merge_datasets.py # vishnuOI + function-calling\npython3 06_extract_unity_repos.py # ML-Agents, ECS, uGUI, netcode\npython3 07_integrate_new_datasets.py # Erocal Q&A + Mix & Jam mechanics\n\n# 3. Fine-tune (MLX QLoRA on Apple Silicon)\npython3 03_train_and_export_unity_agent.py\n\n# 4. Export to GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n\n# 5. Run\nollama create unity-agent -f Modelfile\nollama run unity-agent\n```\n\n## Components\n\n| # | Script | What It Does | Output |\n|---|---|---|---|\n| 01 | `01_build_unity_mcp_dataset.py` | Generates ~128 MCP tool-calling examples from real game-dev tasks | `datasets/unity_mcp_tool_calls_*.jsonl` |\n| 02 | `02_build_unity_design_patterns_dataset.py` | Generates ~75 design pattern + bug-fix examples | `datasets/unity_design_patterns_*.jsonl` |\n| 03 | `03_train_and_export_unity_agent.py` | MLX QLoRA train \u2192 fuse \u2192 GGUF export \u2192 eval | `adapters/`, `fused_model/`, `gguf/` |\n| 04 | `04_setup_unity_mcp_bridge.py` | Sets up Unity-MCP bridge, configs for Claude/Cursor/VS Code | `mcp_config/` |\n| 05 | `05_download_and_merge_datasets.py` | Downloads `vishnuOI/unity-dev-instructions` (46k) + `hermes-function-calling-v1` (2k), merges everything | `datasets/train.jsonl` |\n| 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` |\n| 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` |\n\n## Dataset Composition (after all merges)\n\n| Source | Rows | Coverage |\n|---|---|---|\n| `vishnuOI/unity-dev-instructions` | ~42k (after dedup) | Unity API docs, Stack Overflow, GitHub code \u2014 scripting, rendering, physics, editor, XR, UI, animation, networking, math, performance, input |\n| `NousResearch/hermes-function-calling-v1` | ~1.8k | Generic tool-calling / JSON-mode structured outputs |\n| `Erocal/Unity_ResolvedQuestions` | ~400 | Real resolved Q&A \u2014 physics, ECS, URP/HDRP, UI Toolkit, editor tooling, platform issues, animation rigging |\n| `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 |\n| ML-Agents example agents | ~29 | RL agent patterns (Crawler, Walker, FoodCollector, Soccer, Pyramids, WallJump, Worm, Hallway, 3DBall, PushBlock, GridWorld, Sorter) |\n| ML-Agents core + sensors | ~5 | Agent.cs lifecycle, ray perception, camera sensor, vector sensor, grid sensor |\n| ECS/DOTS samples | ~10 | Jobs, SystemBase, ISystem, IComponentData, IAspect, Baker patterns |\n| uGUI | ~10 | Graphic, Image, Text, Button, Slider, Toggle, ScrollRect, Dropdown, InputField, LayoutGroup |\n| Netcode for GameObjects | ~5 | NetworkBehaviour, NetworkVariable, RPC, NetworkObject patterns |\n| Unity MCP tool calls (custom) | ~128 | Agentic Unity tool use: scene setup, scripting, prefabs, debugging, profiling, testing, shaders, reflection |\n| Design patterns (custom) | ~75 | ScriptableObject architecture, Object Pooling, Singleton, State Machine, Observer, Strategy, Command, Factory, Flyweight, Save/Load, ServiceLocator, EventBus |\n| **Total (deduped)** | **~45k** | |\n\n### Datasets considered and rejected\n\n| Source | Why skipped |\n|---|---|\n| `ibranze/codellama_unity3d_v1/v2` | 535 + 2k rows, wrong format, overlaps with vishnuOI |\n| `ZorraZabb/code25pile75_shuffle_filtered_unity3d_dedup` | 1M rows of noise \u2014 substring \"unity3d\" filter catches Python, JS, news articles, medical papers |\n| `supergoose/buzz_sources_244_unity3d-asset` | 101 rows of git diff YAML for .meta files |\n| `robertzeng/unity3ddoc` | 113k rows but shallow/trivial responses (5-15 word generic definitions) |\n| `DLMveloper/DLM_DataSet` | 3.6M rows but only ~3,570 \"Unity\"-tagged, all trivial (variable declarations, basic array ops) |\n| `Saibalaji25/unity-csharp-scripts` | Requires login + T&C agreement, unknown quality, 11 downloads/mo |\n\n## Training Configuration (M5 Pro 64GB)\n\n```\nModel: Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)\nBatch size: 1\nLayers: 16/64 (25% LoRA)\nLR: 1e-4\nMax seq: 4096\nIters: 2000\nPeak mem: ~25GB\nDuration: ~45-60 min\n```\n\n## GGUF Output Quantization\n\n| Type | Size | Best For |\n|---|---|---|\n| Q4_K_M | ~20 GB | 24GB+ systems |\n| Q5_K_M | ~23 GB | 32GB+ systems |\n| **Q6_K** | **~27 GB** | **M5 Pro 64GB (recommended)** |\n| Q8_0 | ~34 GB | 64GB+ systems (max quality) |\n\n## Unity-MCP Bridge\n\nRun the bridge setup to configure connections to Claude Desktop, Cursor, VS Code, or the included Python client:\n\n```bash\npython3 04_setup_unity_mcp_bridge.py\n```\n\nThen connect any MCP-compatible AI client (Claude Desktop, Cursor, VS Code with Cline/Copilot, Codex) to the Unity Editor.\n\n## Project Structure\n\n```\nunity-agent-training/\n\u251c\u2500\u2500 01_build_unity_mcp_dataset.py # MCP tool-calling dataset\n\u251c\u2500\u2500 02_build_unity_design_patterns_dataset.py # Design patterns dataset\n\u251c\u2500\u2500 03_train_and_export_unity_agent.py # MLX training + GGUF export\n\u251c\u2500\u2500 04_setup_unity_mcp_bridge.py # Unity-MCP integration\n\u251c\u2500\u2500 05_download_and_merge_datasets.py # HF dataset download + merge\n\u251c\u2500\u2500 06_extract_unity_repos.py # Unity org repo C# extraction\n\u251c\u2500\u2500 07_integrate_new_datasets.py # Newly discovered datasets\n\u251c\u2500\u2500 Modelfile # Ollama model definition\n\u251c\u2500\u2500 QUICKSTART.md # Quick start guide\n\u251c\u2500\u2500 datasets/\n\u2502 \u251c\u2500\u2500 unity_mcp_tool_calls_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_design_patterns_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_api_instructions.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 unity_repos_{train,valid,test}.jsonl # (from repo extraction)\n\u2502 \u251c\u2500\u2500 function_calling_full.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 train.jsonl # Merged MLX-ready\n\u2502 \u251c\u2500\u2500 valid.jsonl # Merged MLX-ready\n\u2502 \u2514\u2500\u2500 test.jsonl # Merged MLX-ready\n\u251c\u2500\u2500 adapters/ # LoRA adapters (after training)\n\u251c\u2500\u2500 fused_model/ # Fused MLX model\n\u251c\u2500\u2500 gguf/ # GGUF quantized models\n\u2514\u2500\u2500 mcp_config/ # Bridge configuration\n \u251c\u2500\u2500 claude_desktop_config.json\n \u251c\u2500\u2500 vscode_mcp.json\n \u251c\u2500\u2500 unity_mcp_server_config.json\n \u251c\u2500\u2500 unity_mcp_client.py\n \u2514\u2500\u2500 UnityAgentWindow.cs\n```\n\n```<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nexecute this project plan\n\n--- Attached Context ---\n\n\ud83d\udcc4 @file:`QUICKSTART.md` (551 tokens)\n```markdown\n# Quick Start\n\n## 1. Run the Fine-Tuned Model\n\n### MLX (recommended for inference):\n```bash\nmlx_lm.generate --model ./fused_model --prompt \"Your Unity task...\"\n```\n\n### Ollama:\nCreate Modelfile in this directory:\n```dockerfile\nFROM ./gguf/unity-agent-Q6_K.gguf\nTEMPLATE \"{{ .Prompt }}\"\nPARAMETER temperature 0.3\nPARAMETER num_ctx 4096\n```\n\nThen:\n```bash\nollama create unity-agent -f Modelfile\nollama serve\n```\n\n### LM Studio:\n- Open LM Studio \u2192 \"My Models\" \u2192 \"Add Model\"\n- Select ./gguf/unity-agent-Q6_K.gguf\n- Start server on port 1234\n\n## 2. Connect to Unity Editor\n\n### Option A: Unity-MCP CLI (recommended)\n```bash\nnpx -y @aigamedeveloper/mcp-server\n# Connects Claude Desktop, Cursor, or VS Code to Unity Editor\n```\n\n### Option B: Custom Editor Window\n- Copy `mcp_config/UnityAgentWindow.cs` to your project's `Assets/Editor/`\n- Window \u2192 Unity Agent \u2192 Chat\n\n### Option C: Python client\n```bash\npython3 mcp_config/unity_mcp_client.py --list\npython3 mcp_config/unity_mcp_client.py --tool gameobject-create \\\n --args '{\"name\":\"Cube\",\"primitiveType\":\"Cube\"}'\n```\n\n## 3. Example Workflows\n\n**Create a scene from scratch:**\n> \"Create a medieval village with 5 houses, a central fountain, and a player character\"\n\n**Fix bugs interactively:**\n> \"Read the console logs and fix the NullReferenceException in PlayerHealth.cs\"\n\n**Profile and optimize:**\n> \"Profile the game, find the bottleneck, and suggest optimizations\"\n\n**Design a system:**\n> \"Create a ScriptableObject-based inventory system with item pickup, UI display, and save/load\"\n\n## 4. Dataset Pipeline (full run)\n\n```bash\n# Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# Download external datasets\npython3 05_download_and_merge_datasets.py\npython3 06_extract_unity_repos.py\npython3 07_integrate_new_datasets.py\n\n# Train + export\npython3 03_train_and_export_unity_agent.py\n```\n\n## 5. Fine-Tuning Stages\n\n```bash\n# Prepare only (merge datasets)\npython3 03_train_and_export_unity_agent.py --stage prepare\n\n# Train only (QLoRA)\npython3 03_train_and_export_unity_agent.py --stage train\n\n# Fuse adapters and export GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n```\n\n```\n\n\ud83d\udcc4 @file:`README.md` (1740 tokens)\n```markdown\n# Unity Agent Developer \u2014 Model Training Pipeline\n\nBuild and deploy a Unity game development agent model on Apple Silicon (M5 Pro).\n\n## Quick Start\n\n```bash\n# 1. Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# 2. Download + merge everything (internet needed)\npython3 05_download_and_merge_datasets.py # vishnuOI + function-calling\npython3 06_extract_unity_repos.py # ML-Agents, ECS, uGUI, netcode\npython3 07_integrate_new_datasets.py # Erocal Q&A + Mix & Jam mechanics\n\n# 3. Fine-tune (MLX QLoRA on Apple Silicon)\npython3 03_train_and_export_unity_agent.py\n\n# 4. Export to GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n\n# 5. Run\nollama create unity-agent -f Modelfile\nollama run unity-agent\n```\n\n## Components\n\n| # | Script | What It Does | Output |\n|---|---|---|---|\n| 01 | `01_build_unity_mcp_dataset.py` | Generates ~128 MCP tool-calling examples from real game-dev tasks | `datasets/unity_mcp_tool_calls_*.jsonl` |\n| 02 | `02_build_unity_design_patterns_dataset.py` | Generates ~75 design pattern + bug-fix examples | `datasets/unity_design_patterns_*.jsonl` |\n| 03 | `03_train_and_export_unity_agent.py` | MLX QLoRA train \u2192 fuse \u2192 GGUF export \u2192 eval | `adapters/`, `fused_model/`, `gguf/` |\n| 04 | `04_setup_unity_mcp_bridge.py` | Sets up Unity-MCP bridge, configs for Claude/Cursor/VS Code | `mcp_config/` |\n| 05 | `05_download_and_merge_datasets.py` | Downloads `vishnuOI/unity-dev-instructions` (46k) + `hermes-function-calling-v1` (2k), merges everything | `datasets/train.jsonl` |\n| 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` |\n| 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` |\n\n## Dataset Composition (after all merges)\n\n| Source | Rows | Coverage |\n|---|---|---|\n| `vishnuOI/unity-dev-instructions` | ~42k (after dedup) | Unity API docs, Stack Overflow, GitHub code \u2014 scripting, rendering, physics, editor, XR, UI, animation, networking, math, performance, input |\n| `NousResearch/hermes-function-calling-v1` | ~1.8k | Generic tool-calling / JSON-mode structured outputs |\n| `Erocal/Unity_ResolvedQuestions` | ~400 | Real resolved Q&A \u2014 physics, ECS, URP/HDRP, UI Toolkit, editor tooling, platform issues, animation rigging |\n| `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 |\n| ML-Agents example agents | ~29 | RL agent patterns (Crawler, Walker, FoodCollector, Soccer, Pyramids, WallJump, Worm, Hallway, 3DBall, PushBlock, GridWorld, Sorter) |\n| ML-Agents core + sensors | ~5 | Agent.cs lifecycle, ray perception, camera sensor, vector sensor, grid sensor |\n| ECS/DOTS samples | ~10 | Jobs, SystemBase, ISystem, IComponentData, IAspect, Baker patterns |\n| uGUI | ~10 | Graphic, Image, Text, Button, Slider, Toggle, ScrollRect, Dropdown, InputField, LayoutGroup |\n| Netcode for GameObjects | ~5 | NetworkBehaviour, NetworkVariable, RPC, NetworkObject patterns |\n| Unity MCP tool calls (custom) | ~128 | Agentic Unity tool use: scene setup, scripting, prefabs, debugging, profiling, testing, shaders, reflection |\n| Design patterns (custom) | ~75 | ScriptableObject architecture, Object Pooling, Singleton, State Machine, Observer, Strategy, Command, Factory, Flyweight, Save/Load, ServiceLocator, EventBus |\n| **Total (deduped)** | **~45k** | |\n\n### Datasets considered and rejected\n\n| Source | Why skipped |\n|---|---|\n| `ibranze/codellama_unity3d_v1/v2` | 535 + 2k rows, wrong format, overlaps with vishnuOI |\n| `ZorraZabb/code25pile75_shuffle_filtered_unity3d_dedup` | 1M rows of noise \u2014 substring \"unity3d\" filter catches Python, JS, news articles, medical papers |\n| `supergoose/buzz_sources_244_unity3d-asset` | 101 rows of git diff YAML for .meta files |\n| `robertzeng/unity3ddoc` | 113k rows but shallow/trivial responses (5-15 word generic definitions) |\n| `DLMveloper/DLM_DataSet` | 3.6M rows but only ~3,570 \"Unity\"-tagged, all trivial (variable declarations, basic array ops) |\n| `Saibalaji25/unity-csharp-scripts` | Requires login + T&C agreement, unknown quality, 11 downloads/mo |\n\n## Training Configuration (M5 Pro 64GB)\n\n```\nModel: Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)\nBatch size: 1\nLayers: 16/64 (25% LoRA)\nLR: 1e-4\nMax seq: 4096\nIters: 2000\nPeak mem: ~25GB\nDuration: ~45-60 min\n```\n\n## GGUF Output Quantization\n\n| Type | Size | Best For |\n|---|---|---|\n| Q4_K_M | ~20 GB | 24GB+ systems |\n| Q5_K_M | ~23 GB | 32GB+ systems |\n| **Q6_K** | **~27 GB** | **M5 Pro 64GB (recommended)** |\n| Q8_0 | ~34 GB | 64GB+ systems (max quality) |\n\n## Unity-MCP Bridge\n\nRun the bridge setup to configure connections to Claude Desktop, Cursor, VS Code, or the included Python client:\n\n```bash\npython3 04_setup_unity_mcp_bridge.py\n```\n\nThen connect any MCP-compatible AI client (Claude Desktop, Cursor, VS Code with Cline/Copilot, Codex) to the Unity Editor.\n\n## Project Structure\n\n```\nunity-agent-training/\n\u251c\u2500\u2500 01_build_unity_mcp_dataset.py # MCP tool-calling dataset\n\u251c\u2500\u2500 02_build_unity_design_patterns_dataset.py # Design patterns dataset\n\u251c\u2500\u2500 03_train_and_export_unity_agent.py # MLX training + GGUF export\n\u251c\u2500\u2500 04_setup_unity_mcp_bridge.py # Unity-MCP integration\n\u251c\u2500\u2500 05_download_and_merge_datasets.py # HF dataset download + merge\n\u251c\u2500\u2500 06_extract_unity_repos.py # Unity org repo C# extraction\n\u251c\u2500\u2500 07_integrate_new_datasets.py # Newly discovered datasets\n\u251c\u2500\u2500 Modelfile # Ollama model definition\n\u251c\u2500\u2500 QUICKSTART.md # Quick start guide\n\u251c\u2500\u2500 datasets/\n\u2502 \u251c\u2500\u2500 unity_mcp_tool_calls_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_design_patterns_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_api_instructions.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 unity_repos_{train,valid,test}.jsonl # (from repo extraction)\n\u2502 \u251c\u2500\u2500 function_calling_full.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 train.jsonl # Merged MLX-ready\n\u2502 \u251c\u2500\u2500 valid.jsonl # Merged MLX-ready\n\u2502 \u2514\u2500\u2500 test.jsonl # Merged MLX-ready\n\u251c\u2500\u2500 adapters/ # LoRA adapters (after training)\n\u251c\u2500\u2500 fused_model/ # Fused MLX model\n\u251c\u2500\u2500 gguf/ # GGUF quantized models\n\u2514\u2500\u2500 mcp_config/ # Bridge configuration\n \u251c\u2500\u2500 claude_desktop_config.json\n \u251c\u2500\u2500 vscode_mcp.json\n \u251c\u2500\u2500 unity_mcp_server_config.json\n \u251c\u2500\u2500 unity_mcp_client.py\n \u2514\u2500\u2500 UnityAgentWindow.cs\n```\n\n```<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in Data Analyst profile. Prioritize data loading, Python analysis, and charting.\n<|im_end|>\n<|im_start|>user\nfind me best Llama 3.3 70B custom fine-tuned model for coding<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for an 'image-generator' profile focused on AI image generation, image editing, design workflows, and visual content creation. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant for an image generation agent. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nI need to process this. Please help.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nI need to process proc_0a497b8b5935 exited (exit code -15,. Please help.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\n[IMPORTANT: Background process proc_0a497b8b5935 exited (exit code -15, SIGTERM).\nCommand: unset PYTHONPATH; /Users/jacobeen/.hermes/hermes-agent/venv/bin/python3 03_train_and_export_unity_agent.py train 2>&1\nOutput:\n4<00:26, 1.25s/it]\rCalculating loss...: 20%|\u2588\u2588 | 5/25 [00:04<00:21, 1.08s/it]\rCalculating loss...: 24%|\u2588\u2588\u258d | 6/25 [00:05<00:16, 1.18it/s]\rCalculating loss...: 28%|\u2588\u2588\u258a | 7/25 [00:06<00:16, 1.07it/s]\rCalculating loss...: 32%|\u2588\u2588\u2588\u258f | 8/25 [00:07<00:15, 1.07it/s]\rCalculating loss...: 36%|\u2588\u2588\u2588\u258c | 9/25 [00:08<00:17, 1.10s/it]\rCalculating loss...: 40%|\u2588\u2588\u2588\u2588 | 10/25 [00:10<00:19, 1.27s/it]\rCalculating loss...: 44%|\u2588\u2588\u2588\u2588\u258d | 11/25 [00:11<00:17, 1.28s/it]\rCalculating loss...: 48%|\u2588\u2588\u2588\u2588\u258a | 12/25 [00:12<00:14, 1.13s/it]\rCalculating loss...: 52%|\u2588\u2588\u2588\u2588\u2588\u258f | 13/25 [00:14<00:14, 1.24s/it]\rCalculating loss...: 56%|\u2588\u2588\u2588\u2588\u2588\u258c | 14/25 [00:14<00:10, 1.02it/s]\rCalculating loss...: 60%|\u2588\u2588\u2588\u2588\u2588\u2588 | 15/25 [00:14<00:08, 1.24it/s]\rCalculating loss...: 64%|\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 16/25 [00:16<00:09, 1.00s/it]\rCalculating loss...: 68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 17/25 [00:16<00:07, 1.13it/s]\rCalculating loss...: 72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 18/25 [00:17<00:06, 1.11it/s]\rCalculating loss...: 76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 19/25 [00:18<00:04, 1.25it/s]\rCalculating loss...: 80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 20/25 [00:18<00:03, 1.46it/s]\rCalculating loss...: 84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 21/25 [00:19<00:03, 1.33it/s]\rCalculating loss...: 88%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 22/25 [00:20<00:02, 1.24it/s]\rCalculating loss...: 92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 23/25 [00:21<00:01, 1.26it/s]\rCalculating loss...: 96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 24/25 [00:22<00:00, 1.27it/s]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.10s/it]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.04it/s]\nIter 100: Val loss nan, Val took 24.072s\nIter 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\n/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\n warnings.warn('resource_tracker: There appear to be %d '\n]<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in General Assistant profile. Use the most efficient tool for each request.\n<|im_end|>\n<|im_start|>user\n[IMPORTANT: Background process proc_0a497b8b5935 exited (exit code -15, SIGTERM).\nCommand: unset PYTHONPATH; /Users/jacobeen/.hermes/hermes-agent/venv/bin/python3 03_train_and_export_unity_agent.py train 2>&1\nOutput:\n4<00:26, 1.25s/it]\rCalculating loss...: 20%|\u2588\u2588 | 5/25 [00:04<00:21, 1.08s/it]\rCalculating loss...: 24%|\u2588\u2588\u258d | 6/25 [00:05<00:16, 1.18it/s]\rCalculating loss...: 28%|\u2588\u2588\u258a | 7/25 [00:06<00:16, 1.07it/s]\rCalculating loss...: 32%|\u2588\u2588\u2588\u258f | 8/25 [00:07<00:15, 1.07it/s]\rCalculating loss...: 36%|\u2588\u2588\u2588\u258c | 9/25 [00:08<00:17, 1.10s/it]\rCalculating loss...: 40%|\u2588\u2588\u2588\u2588 | 10/25 [00:10<00:19, 1.27s/it]\rCalculating loss...: 44%|\u2588\u2588\u2588\u2588\u258d | 11/25 [00:11<00:17, 1.28s/it]\rCalculating loss...: 48%|\u2588\u2588\u2588\u2588\u258a | 12/25 [00:12<00:14, 1.13s/it]\rCalculating loss...: 52%|\u2588\u2588\u2588\u2588\u2588\u258f | 13/25 [00:14<00:14, 1.24s/it]\rCalculating loss...: 56%|\u2588\u2588\u2588\u2588\u2588\u258c | 14/25 [00:14<00:10, 1.02it/s]\rCalculating loss...: 60%|\u2588\u2588\u2588\u2588\u2588\u2588 | 15/25 [00:14<00:08, 1.24it/s]\rCalculating loss...: 64%|\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 16/25 [00:16<00:09, 1.00s/it]\rCalculating loss...: 68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 17/25 [00:16<00:07, 1.13it/s]\rCalculating loss...: 72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 18/25 [00:17<00:06, 1.11it/s]\rCalculating loss...: 76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 19/25 [00:18<00:04, 1.25it/s]\rCalculating loss...: 80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 20/25 [00:18<00:03, 1.46it/s]\rCalculating loss...: 84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 21/25 [00:19<00:03, 1.33it/s]\rCalculating loss...: 88%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 22/25 [00:20<00:02, 1.24it/s]\rCalculating loss...: 92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 23/25 [00:21<00:01, 1.26it/s]\rCalculating loss...: 96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 24/25 [00:22<00:00, 1.27it/s]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.10s/it]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.04it/s]\nIter 100: Val loss nan, Val took 24.072s\nIter 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\n/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\n warnings.warn('resource_tracker: There appear to be %d '\n]<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\ncan you make sure that every single profile is utilizing our Obsidian.md, Hindsight and othr plugins?\n\n[System: The active model for this chat has changed to Ornith-1.0-35B-MTPLX via provider omlx-mbp. From this point forward, use this runtime metadata when answering questions about what model/provider is active.]\n\ncan you make sure that every single profile is utilizing our Obsidian.md, Hindsight and othr plugins?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nCan you process this?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\ni need to know if you are making a GGUF model or MLX model?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for a 'game-dev' profile focused on Unity game development, 3D graphics, game logic, shaders, and game engine workflows. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant for a game developer. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\noh okey so we want to MAX out the quality at 2000<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for a 'cybersec' profile focused on security auditing, penetration testing, vulnerability research, and security analysis. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant for a security-focused agent. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in System Admin profile. Prioritize terminal commands, process management, and system tools.\n<|im_end|>\n<|im_start|>user\nprogress?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\n[IMPORTANT: Background process proc_0a497b8b5935 exited (exit code -15, SIGTERM).\nCommand: unset PYTHONPATH; /Users/jacobeen/.hermes/hermes-agent/venv/bin/python3 03_train_and_export_unity_agent.py train 2>&1\nOutput:\n4<00:26, 1.25s/it]\rCalculating loss...: 20%|\u2588\u2588 | 5/25 [00:04<00:21, 1.08s/it]\rCalculating loss...: 24%|\u2588\u2588\u258d | 6/25 [00:05<00:16, 1.18it/s]\rCalculating loss...: 28%|\u2588\u2588\u258a | 7/25 [00:06<00:16, 1.07it/s]\rCalculating loss...: 32%|\u2588\u2588\u2588\u258f | 8/25 [00:07<00:15, 1.07it/s]\rCalculating loss...: 36%|\u2588\u2588\u2588\u258c | 9/25 [00:08<00:17, 1.10s/it]\rCalculating loss...: 40%|\u2588\u2588\u2588\u2588 | 10/25 [00:10<00:19, 1.27s/it]\rCalculating loss...: 44%|\u2588\u2588\u2588\u2588\u258d | 11/25 [00:11<00:17, 1.28s/it]\rCalculating loss...: 48%|\u2588\u2588\u2588\u2588\u258a | 12/25 [00:12<00:14, 1.13s/it]\rCalculating loss...: 52%|\u2588\u2588\u2588\u2588\u2588\u258f | 13/25 [00:14<00:14, 1.24s/it]\rCalculating loss...: 56%|\u2588\u2588\u2588\u2588\u2588\u258c | 14/25 [00:14<00:10, 1.02it/s]\rCalculating loss...: 60%|\u2588\u2588\u2588\u2588\u2588\u2588 | 15/25 [00:14<00:08, 1.24it/s]\rCalculating loss...: 64%|\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 16/25 [00:16<00:09, 1.00s/it]\rCalculating loss...: 68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 17/25 [00:16<00:07, 1.13it/s]\rCalculating loss...: 72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 18/25 [00:17<00:06, 1.11it/s]\rCalculating loss...: 76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 19/25 [00:18<00:04, 1.25it/s]\rCalculating loss...: 80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 20/25 [00:18<00:03, 1.46it/s]\rCalculating loss...: 84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 21/25 [00:19<00:03, 1.33it/s]\rCalculating loss...: 88%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 22/25 [00:20<00:02, 1.24it/s]\rCalculating loss...: 92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 23/25 [00:21<00:01, 1.26it/s]\rCalculating loss...: 96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 24/25 [00:22<00:00, 1.27it/s]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.10s/it]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.04it/s]\nIter 100: Val loss nan, Val took 24.072s\nIter 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\n/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\n warnings.warn('resource_tracker: There appear to be %d '\n]<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\ni need to know if you are making a GGUF model or MLX model?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nCan you check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nexecute this project plan\n\n--- Attached Context ---\n\n\ud83d\udcc4 @file:`QUICKSTART.md` (551 tokens)\n```markdown\n# Quick Start\n\n## 1. Run the Fine-Tuned Model\n\n### MLX (recommended for inference):\n```bash\nmlx_lm.generate --model ./fused_model --prompt \"Your Unity task...\"\n```\n\n### Ollama:\nCreate Modelfile in this directory:\n```dockerfile\nFROM ./gguf/unity-agent-Q6_K.gguf\nTEMPLATE \"{{ .Prompt }}\"\nPARAMETER temperature 0.3\nPARAMETER num_ctx 4096\n```\n\nThen:\n```bash\nollama create unity-agent -f Modelfile\nollama serve\n```\n\n### LM Studio:\n- Open LM Studio \u2192 \"My Models\" \u2192 \"Add Model\"\n- Select ./gguf/unity-agent-Q6_K.gguf\n- Start server on port 1234\n\n## 2. Connect to Unity Editor\n\n### Option A: Unity-MCP CLI (recommended)\n```bash\nnpx -y @aigamedeveloper/mcp-server\n# Connects Claude Desktop, Cursor, or VS Code to Unity Editor\n```\n\n### Option B: Custom Editor Window\n- Copy `mcp_config/UnityAgentWindow.cs` to your project's `Assets/Editor/`\n- Window \u2192 Unity Agent \u2192 Chat\n\n### Option C: Python client\n```bash\npython3 mcp_config/unity_mcp_client.py --list\npython3 mcp_config/unity_mcp_client.py --tool gameobject-create \\\n --args '{\"name\":\"Cube\",\"primitiveType\":\"Cube\"}'\n```\n\n## 3. Example Workflows\n\n**Create a scene from scratch:**\n> \"Create a medieval village with 5 houses, a central fountain, and a player character\"\n\n**Fix bugs interactively:**\n> \"Read the console logs and fix the NullReferenceException in PlayerHealth.cs\"\n\n**Profile and optimize:**\n> \"Profile the game, find the bottleneck, and suggest optimizations\"\n\n**Design a system:**\n> \"Create a ScriptableObject-based inventory system with item pickup, UI display, and save/load\"\n\n## 4. Dataset Pipeline (full run)\n\n```bash\n# Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# Download external datasets\npython3 05_download_and_merge_datasets.py\npython3 06_extract_unity_repos.py\npython3 07_integrate_new_datasets.py\n\n# Train + export\npython3 03_train_and_export_unity_agent.py\n```\n\n## 5. Fine-Tuning Stages\n\n```bash\n# Prepare only (merge datasets)\npython3 03_train_and_export_unity_agent.py --stage prepare\n\n# Train only (QLoRA)\npython3 03_train_and_export_unity_agent.py --stage train\n\n# Fuse adapters and export GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n```\n\n```\n\n\ud83d\udcc4 @file:`README.md` (1740 tokens)\n```markdown\n# Unity Agent Developer \u2014 Model Training Pipeline\n\nBuild and deploy a Unity game development agent model on Apple Silicon (M5 Pro).\n\n## Quick Start\n\n```bash\n# 1. Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# 2. Download + merge everything (internet needed)\npython3 05_download_and_merge_datasets.py # vishnuOI + function-calling\npython3 06_extract_unity_repos.py # ML-Agents, ECS, uGUI, netcode\npython3 07_integrate_new_datasets.py # Erocal Q&A + Mix & Jam mechanics\n\n# 3. Fine-tune (MLX QLoRA on Apple Silicon)\npython3 03_train_and_export_unity_agent.py\n\n# 4. Export to GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n\n# 5. Run\nollama create unity-agent -f Modelfile\nollama run unity-agent\n```\n\n## Components\n\n| # | Script | What It Does | Output |\n|---|---|---|---|\n| 01 | `01_build_unity_mcp_dataset.py` | Generates ~128 MCP tool-calling examples from real game-dev tasks | `datasets/unity_mcp_tool_calls_*.jsonl` |\n| 02 | `02_build_unity_design_patterns_dataset.py` | Generates ~75 design pattern + bug-fix examples | `datasets/unity_design_patterns_*.jsonl` |\n| 03 | `03_train_and_export_unity_agent.py` | MLX QLoRA train \u2192 fuse \u2192 GGUF export \u2192 eval | `adapters/`, `fused_model/`, `gguf/` |\n| 04 | `04_setup_unity_mcp_bridge.py` | Sets up Unity-MCP bridge, configs for Claude/Cursor/VS Code | `mcp_config/` |\n| 05 | `05_download_and_merge_datasets.py` | Downloads `vishnuOI/unity-dev-instructions` (46k) + `hermes-function-calling-v1` (2k), merges everything | `datasets/train.jsonl` |\n| 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` |\n| 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` |\n\n## Dataset Composition (after all merges)\n\n| Source | Rows | Coverage |\n|---|---|---|\n| `vishnuOI/unity-dev-instructions` | ~42k (after dedup) | Unity API docs, Stack Overflow, GitHub code \u2014 scripting, rendering, physics, editor, XR, UI, animation, networking, math, performance, input |\n| `NousResearch/hermes-function-calling-v1` | ~1.8k | Generic tool-calling / JSON-mode structured outputs |\n| `Erocal/Unity_ResolvedQuestions` | ~400 | Real resolved Q&A \u2014 physics, ECS, URP/HDRP, UI Toolkit, editor tooling, platform issues, animation rigging |\n| `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 |\n| ML-Agents example agents | ~29 | RL agent patterns (Crawler, Walker, FoodCollector, Soccer, Pyramids, WallJump, Worm, Hallway, 3DBall, PushBlock, GridWorld, Sorter) |\n| ML-Agents core + sensors | ~5 | Agent.cs lifecycle, ray perception, camera sensor, vector sensor, grid sensor |\n| ECS/DOTS samples | ~10 | Jobs, SystemBase, ISystem, IComponentData, IAspect, Baker patterns |\n| uGUI | ~10 | Graphic, Image, Text, Button, Slider, Toggle, ScrollRect, Dropdown, InputField, LayoutGroup |\n| Netcode for GameObjects | ~5 | NetworkBehaviour, NetworkVariable, RPC, NetworkObject patterns |\n| Unity MCP tool calls (custom) | ~128 | Agentic Unity tool use: scene setup, scripting, prefabs, debugging, profiling, testing, shaders, reflection |\n| Design patterns (custom) | ~75 | ScriptableObject architecture, Object Pooling, Singleton, State Machine, Observer, Strategy, Command, Factory, Flyweight, Save/Load, ServiceLocator, EventBus |\n| **Total (deduped)** | **~45k** | |\n\n### Datasets considered and rejected\n\n| Source | Why skipped |\n|---|---|\n| `ibranze/codellama_unity3d_v1/v2` | 535 + 2k rows, wrong format, overlaps with vishnuOI |\n| `ZorraZabb/code25pile75_shuffle_filtered_unity3d_dedup` | 1M rows of noise \u2014 substring \"unity3d\" filter catches Python, JS, news articles, medical papers |\n| `supergoose/buzz_sources_244_unity3d-asset` | 101 rows of git diff YAML for .meta files |\n| `robertzeng/unity3ddoc` | 113k rows but shallow/trivial responses (5-15 word generic definitions) |\n| `DLMveloper/DLM_DataSet` | 3.6M rows but only ~3,570 \"Unity\"-tagged, all trivial (variable declarations, basic array ops) |\n| `Saibalaji25/unity-csharp-scripts` | Requires login + T&C agreement, unknown quality, 11 downloads/mo |\n\n## Training Configuration (M5 Pro 64GB)\n\n```\nModel: Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)\nBatch size: 1\nLayers: 16/64 (25% LoRA)\nLR: 1e-4\nMax seq: 4096\nIters: 2000\nPeak mem: ~25GB\nDuration: ~45-60 min\n```\n\n## GGUF Output Quantization\n\n| Type | Size | Best For |\n|---|---|---|\n| Q4_K_M | ~20 GB | 24GB+ systems |\n| Q5_K_M | ~23 GB | 32GB+ systems |\n| **Q6_K** | **~27 GB** | **M5 Pro 64GB (recommended)** |\n| Q8_0 | ~34 GB | 64GB+ systems (max quality) |\n\n## Unity-MCP Bridge\n\nRun the bridge setup to configure connections to Claude Desktop, Cursor, VS Code, or the included Python client:\n\n```bash\npython3 04_setup_unity_mcp_bridge.py\n```\n\nThen connect any MCP-compatible AI client (Claude Desktop, Cursor, VS Code with Cline/Copilot, Codex) to the Unity Editor.\n\n## Project Structure\n\n```\nunity-agent-training/\n\u251c\u2500\u2500 01_build_unity_mcp_dataset.py # MCP tool-calling dataset\n\u251c\u2500\u2500 02_build_unity_design_patterns_dataset.py # Design patterns dataset\n\u251c\u2500\u2500 03_train_and_export_unity_agent.py # MLX training + GGUF export\n\u251c\u2500\u2500 04_setup_unity_mcp_bridge.py # Unity-MCP integration\n\u251c\u2500\u2500 05_download_and_merge_datasets.py # HF dataset download + merge\n\u251c\u2500\u2500 06_extract_unity_repos.py # Unity org repo C# extraction\n\u251c\u2500\u2500 07_integrate_new_datasets.py # Newly discovered datasets\n\u251c\u2500\u2500 Modelfile # Ollama model definition\n\u251c\u2500\u2500 QUICKSTART.md # Quick start guide\n\u251c\u2500\u2500 datasets/\n\u2502 \u251c\u2500\u2500 unity_mcp_tool_calls_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_design_patterns_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_api_instructions.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 unity_repos_{train,valid,test}.jsonl # (from repo extraction)\n\u2502 \u251c\u2500\u2500 function_calling_full.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 train.jsonl # Merged MLX-ready\n\u2502 \u251c\u2500\u2500 valid.jsonl # Merged MLX-ready\n\u2502 \u2514\u2500\u2500 test.jsonl # Merged MLX-ready\n\u251c\u2500\u2500 adapters/ # LoRA adapters (after training)\n\u251c\u2500\u2500 fused_model/ # Fused MLX model\n\u251c\u2500\u2500 gguf/ # GGUF quantized models\n\u2514\u2500\u2500 mcp_config/ # Bridge configuration\n \u251c\u2500\u2500 claude_desktop_config.json\n \u251c\u2500\u2500 vscode_mcp.json\n \u251c\u2500\u2500 unity_mcp_server_config.json\n \u251c\u2500\u2500 unity_mcp_client.py\n \u2514\u2500\u2500 UnityAgentWindow.cs\n```\n\n```<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nmake a research on internet what will be best setup for each profile i have<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nCan you process this?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for a 'game-dev' profile focused on Unity game development, 3D graphics, game logic, shaders, and game engine workflows. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant for a game developer. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in General Assistant profile. Use the most efficient tool for each request.\n<|im_end|>\n<|im_start|>user\nmake a research on internet what will be best setup for each profile i have<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nI need to process proc_0a497b8b5935 exited (exit code -15,. Please help.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in Data Analyst profile. Prioritize data loading, Python analysis, and charting.\n<|im_end|>\n<|im_start|>user\n[ASYNC DELEGATION BATCH COMPLETE \u2014 deleg_2d07ecd6]\nA 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 \u2014 act on these or re-dispatch if things have changed.\n\nDispatched: 2026-07-13 23:59:18 (11m48s ago)\nContext 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.\nRole: leaf Model: ? Total duration: 708.72s\n\n--- \u2713 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) ---\nContext length exceeded (379 tokens). Cannot compress further.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome: 10/10 repos cloned successfully, 0 failures.**\n\nAll 10 OpenClaw repositories are now present at `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | clawdinators | \u2705 |\n| 2 | openclaw-ansible | \u2705 |\n| 3 | casa | \u2705 |\n| 4 | spogo | \u2705 |\n| 5 | goplaces | \u2705 |\n| 6 | voice-community | \u2705 |\n| 7 | acpx | \u2705 |\n| 8 | community | \u2705 |\n| 9 | caclawphony | \u2705 |\n| 10 | discrawl | \u2705 |\n\nNo issues encountered \u2014 all clones completed cleanly with exit code 0.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Result: 10/10 repos cloned with zero failures.**\n\nCloned to `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | `slacrawl` | \u2705 Cloned |\n| 2 | `crabline` | \u2705 Cloned |\n| 3 | `releases` | \u2705 Cloned (15,075 files) |\n| 4 | `docs` | \u2705 Cloned |\n| 5 | `esp-openclaw-node` | \u2705 Cloned |\n| 6 | `shellbench` | \u2705 Cloned (23,628 files) |\n| 7 | `notcrawl` | \u2705 Cloned |\n| 8 | `clawsweeper` | \u2705 Cloned |\n| 9 | `clownfish` | \u2705 Cloned |\n| 10 | `crabpot` | \u2705 Cloned |\n\nNo issues encountered. All 10 repositories are present and fully cloned.\n\n--- \u2713 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) ---\nContext length exceeded: max compression attempts (3) reached.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome:** All 10 OpenClaw repositories cloned successfully into `/Volumes/this_and_that/projects/multi-repo_0/`.\n\n**Cloned repos:**\n1. \u2705 crawlbar\n2. \u2705 clawgrit-reports (previously cloned)\n3. \u2705 fs-safe\n4. \u2705 ask-molty\n5. \u2705 graincrawl\n6. \u2705 clickclack\n7. \u2705 clawdex\n8. \u2705 proxyline\n9. \u2705 telecrawl\n10. \u2705 clawpatch\n\n**Issues:** None. All 10 clones completed with exit code 0.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome: 10/10 repos cloned successfully, 0 failures.**\n\nCloned to `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | crabfleet | \u2705 |\n| 2 | agent-skills | \u2705 |\n| 3 | rfcs | \u2705 |\n| 4 | rastermill | \u2705 |\n| 5 | libopus-wasm | \u2705 |\n| 6 | octopool | \u2705 |\n| 7 | clawpdf | \u2705 |\n| 8 | photoscrawl | \u2705 |\n| 9 | clawrouter | \u2705 |\n| 10 | imsgcrawl | \u2705 |\n\nNo issues encountered \u2014 all clones completed cleanly with no errors.\n\n--- \u2713 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) ---\nContext length exceeded (435 tokens). Cannot compress further.\n\n--- \u2713 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) ---\nContext length exceeded (1,277 tokens). Cannot compress further.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nI need to check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.. Please help.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for an 'office-manager' profile focused on office productivity, document management, spreadsheets, presentations, email management, scheduling, and administrative tasks. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in Creative profile. Prioritize content generation and design-oriented tools.\n<|im_end|>\n<|im_start|>user\nmy laptop crashed. Continue where we left or in case something went corrupt then lets start over<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in Creative profile. Prioritize content generation and design-oriented tools.\n<|im_end|>\n<|im_start|>user\nprogress?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nI need to process this. Please help.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nlower iter so we won't hit OOM<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for a 'game-dev' profile focused on Unity game development, 3D graphics, game logic, shaders, and game engine workflows. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant for a game developer. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nI need to process this. Please help.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\ni need to know if you are making a GGUF model or MLX model?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in Developer profile. Prioritize code execution, file editing, and terminal access.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for an 'image-generator' profile focused on AI image generation, image editing, design workflows, and visual content creation. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant for an image generation agent. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nCan you process this?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for an 'office-manager' profile focused on office productivity, document management, spreadsheets, presentations, email management, scheduling, and administrative tasks. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\ni need to know if you are making a GGUF model or MLX model?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nlower iter so we won't hit OOM<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in System Admin profile. Prioritize terminal commands, process management, and system tools.\n<|im_end|>\n<|im_start|>user\nprogress?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\n[IMPORTANT: Background process proc_0a497b8b5935 exited (exit code -15, SIGTERM).\nCommand: unset PYTHONPATH; /Users/jacobeen/.hermes/hermes-agent/venv/bin/python3 03_train_and_export_unity_agent.py train 2>&1\nOutput:\n4<00:26, 1.25s/it]\rCalculating loss...: 20%|\u2588\u2588 | 5/25 [00:04<00:21, 1.08s/it]\rCalculating loss...: 24%|\u2588\u2588\u258d | 6/25 [00:05<00:16, 1.18it/s]\rCalculating loss...: 28%|\u2588\u2588\u258a | 7/25 [00:06<00:16, 1.07it/s]\rCalculating loss...: 32%|\u2588\u2588\u2588\u258f | 8/25 [00:07<00:15, 1.07it/s]\rCalculating loss...: 36%|\u2588\u2588\u2588\u258c | 9/25 [00:08<00:17, 1.10s/it]\rCalculating loss...: 40%|\u2588\u2588\u2588\u2588 | 10/25 [00:10<00:19, 1.27s/it]\rCalculating loss...: 44%|\u2588\u2588\u2588\u2588\u258d | 11/25 [00:11<00:17, 1.28s/it]\rCalculating loss...: 48%|\u2588\u2588\u2588\u2588\u258a | 12/25 [00:12<00:14, 1.13s/it]\rCalculating loss...: 52%|\u2588\u2588\u2588\u2588\u2588\u258f | 13/25 [00:14<00:14, 1.24s/it]\rCalculating loss...: 56%|\u2588\u2588\u2588\u2588\u2588\u258c | 14/25 [00:14<00:10, 1.02it/s]\rCalculating loss...: 60%|\u2588\u2588\u2588\u2588\u2588\u2588 | 15/25 [00:14<00:08, 1.24it/s]\rCalculating loss...: 64%|\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 16/25 [00:16<00:09, 1.00s/it]\rCalculating loss...: 68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 17/25 [00:16<00:07, 1.13it/s]\rCalculating loss...: 72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 18/25 [00:17<00:06, 1.11it/s]\rCalculating loss...: 76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 19/25 [00:18<00:04, 1.25it/s]\rCalculating loss...: 80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 20/25 [00:18<00:03, 1.46it/s]\rCalculating loss...: 84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 21/25 [00:19<00:03, 1.33it/s]\rCalculating loss...: 88%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 22/25 [00:20<00:02, 1.24it/s]\rCalculating loss...: 92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 23/25 [00:21<00:01, 1.26it/s]\rCalculating loss...: 96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 24/25 [00:22<00:00, 1.27it/s]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.10s/it]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.04it/s]\nIter 100: Val loss nan, Val took 24.072s\nIter 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\n/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\n warnings.warn('resource_tracker: There appear to be %d '\n]<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\n[ASYNC DELEGATION BATCH COMPLETE \u2014 deleg_2d07ecd6]\nA 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 \u2014 act on these or re-dispatch if things have changed.\n\nDispatched: 2026-07-13 23:59:18 (11m48s ago)\nContext 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.\nRole: leaf Model: ? Total duration: 708.72s\n\n--- \u2713 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) ---\nContext length exceeded (379 tokens). Cannot compress further.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome: 10/10 repos cloned successfully, 0 failures.**\n\nAll 10 OpenClaw repositories are now present at `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | clawdinators | \u2705 |\n| 2 | openclaw-ansible | \u2705 |\n| 3 | casa | \u2705 |\n| 4 | spogo | \u2705 |\n| 5 | goplaces | \u2705 |\n| 6 | voice-community | \u2705 |\n| 7 | acpx | \u2705 |\n| 8 | community | \u2705 |\n| 9 | caclawphony | \u2705 |\n| 10 | discrawl | \u2705 |\n\nNo issues encountered \u2014 all clones completed cleanly with exit code 0.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Result: 10/10 repos cloned with zero failures.**\n\nCloned to `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | `slacrawl` | \u2705 Cloned |\n| 2 | `crabline` | \u2705 Cloned |\n| 3 | `releases` | \u2705 Cloned (15,075 files) |\n| 4 | `docs` | \u2705 Cloned |\n| 5 | `esp-openclaw-node` | \u2705 Cloned |\n| 6 | `shellbench` | \u2705 Cloned (23,628 files) |\n| 7 | `notcrawl` | \u2705 Cloned |\n| 8 | `clawsweeper` | \u2705 Cloned |\n| 9 | `clownfish` | \u2705 Cloned |\n| 10 | `crabpot` | \u2705 Cloned |\n\nNo issues encountered. All 10 repositories are present and fully cloned.\n\n--- \u2713 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) ---\nContext length exceeded: max compression attempts (3) reached.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome:** All 10 OpenClaw repositories cloned successfully into `/Volumes/this_and_that/projects/multi-repo_0/`.\n\n**Cloned repos:**\n1. \u2705 crawlbar\n2. \u2705 clawgrit-reports (previously cloned)\n3. \u2705 fs-safe\n4. \u2705 ask-molty\n5. \u2705 graincrawl\n6. \u2705 clickclack\n7. \u2705 clawdex\n8. \u2705 proxyline\n9. \u2705 telecrawl\n10. \u2705 clawpatch\n\n**Issues:** None. All 10 clones completed with exit code 0.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome: 10/10 repos cloned successfully, 0 failures.**\n\nCloned to `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | crabfleet | \u2705 |\n| 2 | agent-skills | \u2705 |\n| 3 | rfcs | \u2705 |\n| 4 | rastermill | \u2705 |\n| 5 | libopus-wasm | \u2705 |\n| 6 | octopool | \u2705 |\n| 7 | clawpdf | \u2705 |\n| 8 | photoscrawl | \u2705 |\n| 9 | clawrouter | \u2705 |\n| 10 | imsgcrawl | \u2705 |\n\nNo issues encountered \u2014 all clones completed cleanly with no errors.\n\n--- \u2713 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) ---\nContext length exceeded (435 tokens). Cannot compress further.\n\n--- \u2713 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) ---\nContext length exceeded (1,277 tokens). Cannot compress further.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\n[IMPORTANT: Background process proc_0a497b8b5935 exited (exit code -15, SIGTERM).\nCommand: unset PYTHONPATH; /Users/jacobeen/.hermes/hermes-agent/venv/bin/python3 03_train_and_export_unity_agent.py train 2>&1\nOutput:\n4<00:26, 1.25s/it]\rCalculating loss...: 20%|\u2588\u2588 | 5/25 [00:04<00:21, 1.08s/it]\rCalculating loss...: 24%|\u2588\u2588\u258d | 6/25 [00:05<00:16, 1.18it/s]\rCalculating loss...: 28%|\u2588\u2588\u258a | 7/25 [00:06<00:16, 1.07it/s]\rCalculating loss...: 32%|\u2588\u2588\u2588\u258f | 8/25 [00:07<00:15, 1.07it/s]\rCalculating loss...: 36%|\u2588\u2588\u2588\u258c | 9/25 [00:08<00:17, 1.10s/it]\rCalculating loss...: 40%|\u2588\u2588\u2588\u2588 | 10/25 [00:10<00:19, 1.27s/it]\rCalculating loss...: 44%|\u2588\u2588\u2588\u2588\u258d | 11/25 [00:11<00:17, 1.28s/it]\rCalculating loss...: 48%|\u2588\u2588\u2588\u2588\u258a | 12/25 [00:12<00:14, 1.13s/it]\rCalculating loss...: 52%|\u2588\u2588\u2588\u2588\u2588\u258f | 13/25 [00:14<00:14, 1.24s/it]\rCalculating loss...: 56%|\u2588\u2588\u2588\u2588\u2588\u258c | 14/25 [00:14<00:10, 1.02it/s]\rCalculating loss...: 60%|\u2588\u2588\u2588\u2588\u2588\u2588 | 15/25 [00:14<00:08, 1.24it/s]\rCalculating loss...: 64%|\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 16/25 [00:16<00:09, 1.00s/it]\rCalculating loss...: 68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 17/25 [00:16<00:07, 1.13it/s]\rCalculating loss...: 72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 18/25 [00:17<00:06, 1.11it/s]\rCalculating loss...: 76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 19/25 [00:18<00:04, 1.25it/s]\rCalculating loss...: 80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 20/25 [00:18<00:03, 1.46it/s]\rCalculating loss...: 84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 21/25 [00:19<00:03, 1.33it/s]\rCalculating loss...: 88%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 22/25 [00:20<00:02, 1.24it/s]\rCalculating loss...: 92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 23/25 [00:21<00:01, 1.26it/s]\rCalculating loss...: 96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 24/25 [00:22<00:00, 1.27it/s]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.10s/it]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.04it/s]\nIter 100: Val loss nan, Val took 24.072s\nIter 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\n/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\n warnings.warn('resource_tracker: There appear to be %d '\n]<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\ni need to know if you are making a GGUF model or MLX model?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nCan you process this?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\ni need to know if you are making a GGUF model or MLX model?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nwhich custom made model on HuggingFace is extraordinarly smart?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nI need to process this. Please help.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nexecute this project plan\n\n--- Attached Context ---\n\n\ud83d\udcc4 @file:`QUICKSTART.md` (551 tokens)\n```markdown\n# Quick Start\n\n## 1. Run the Fine-Tuned Model\n\n### MLX (recommended for inference):\n```bash\nmlx_lm.generate --model ./fused_model --prompt \"Your Unity task...\"\n```\n\n### Ollama:\nCreate Modelfile in this directory:\n```dockerfile\nFROM ./gguf/unity-agent-Q6_K.gguf\nTEMPLATE \"{{ .Prompt }}\"\nPARAMETER temperature 0.3\nPARAMETER num_ctx 4096\n```\n\nThen:\n```bash\nollama create unity-agent -f Modelfile\nollama serve\n```\n\n### LM Studio:\n- Open LM Studio \u2192 \"My Models\" \u2192 \"Add Model\"\n- Select ./gguf/unity-agent-Q6_K.gguf\n- Start server on port 1234\n\n## 2. Connect to Unity Editor\n\n### Option A: Unity-MCP CLI (recommended)\n```bash\nnpx -y @aigamedeveloper/mcp-server\n# Connects Claude Desktop, Cursor, or VS Code to Unity Editor\n```\n\n### Option B: Custom Editor Window\n- Copy `mcp_config/UnityAgentWindow.cs` to your project's `Assets/Editor/`\n- Window \u2192 Unity Agent \u2192 Chat\n\n### Option C: Python client\n```bash\npython3 mcp_config/unity_mcp_client.py --list\npython3 mcp_config/unity_mcp_client.py --tool gameobject-create \\\n --args '{\"name\":\"Cube\",\"primitiveType\":\"Cube\"}'\n```\n\n## 3. Example Workflows\n\n**Create a scene from scratch:**\n> \"Create a medieval village with 5 houses, a central fountain, and a player character\"\n\n**Fix bugs interactively:**\n> \"Read the console logs and fix the NullReferenceException in PlayerHealth.cs\"\n\n**Profile and optimize:**\n> \"Profile the game, find the bottleneck, and suggest optimizations\"\n\n**Design a system:**\n> \"Create a ScriptableObject-based inventory system with item pickup, UI display, and save/load\"\n\n## 4. Dataset Pipeline (full run)\n\n```bash\n# Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# Download external datasets\npython3 05_download_and_merge_datasets.py\npython3 06_extract_unity_repos.py\npython3 07_integrate_new_datasets.py\n\n# Train + export\npython3 03_train_and_export_unity_agent.py\n```\n\n## 5. Fine-Tuning Stages\n\n```bash\n# Prepare only (merge datasets)\npython3 03_train_and_export_unity_agent.py --stage prepare\n\n# Train only (QLoRA)\npython3 03_train_and_export_unity_agent.py --stage train\n\n# Fuse adapters and export GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n```\n\n```\n\n\ud83d\udcc4 @file:`README.md` (1740 tokens)\n```markdown\n# Unity Agent Developer \u2014 Model Training Pipeline\n\nBuild and deploy a Unity game development agent model on Apple Silicon (M5 Pro).\n\n## Quick Start\n\n```bash\n# 1. Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# 2. Download + merge everything (internet needed)\npython3 05_download_and_merge_datasets.py # vishnuOI + function-calling\npython3 06_extract_unity_repos.py # ML-Agents, ECS, uGUI, netcode\npython3 07_integrate_new_datasets.py # Erocal Q&A + Mix & Jam mechanics\n\n# 3. Fine-tune (MLX QLoRA on Apple Silicon)\npython3 03_train_and_export_unity_agent.py\n\n# 4. Export to GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n\n# 5. Run\nollama create unity-agent -f Modelfile\nollama run unity-agent\n```\n\n## Components\n\n| # | Script | What It Does | Output |\n|---|---|---|---|\n| 01 | `01_build_unity_mcp_dataset.py` | Generates ~128 MCP tool-calling examples from real game-dev tasks | `datasets/unity_mcp_tool_calls_*.jsonl` |\n| 02 | `02_build_unity_design_patterns_dataset.py` | Generates ~75 design pattern + bug-fix examples | `datasets/unity_design_patterns_*.jsonl` |\n| 03 | `03_train_and_export_unity_agent.py` | MLX QLoRA train \u2192 fuse \u2192 GGUF export \u2192 eval | `adapters/`, `fused_model/`, `gguf/` |\n| 04 | `04_setup_unity_mcp_bridge.py` | Sets up Unity-MCP bridge, configs for Claude/Cursor/VS Code | `mcp_config/` |\n| 05 | `05_download_and_merge_datasets.py` | Downloads `vishnuOI/unity-dev-instructions` (46k) + `hermes-function-calling-v1` (2k), merges everything | `datasets/train.jsonl` |\n| 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` |\n| 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` |\n\n## Dataset Composition (after all merges)\n\n| Source | Rows | Coverage |\n|---|---|---|\n| `vishnuOI/unity-dev-instructions` | ~42k (after dedup) | Unity API docs, Stack Overflow, GitHub code \u2014 scripting, rendering, physics, editor, XR, UI, animation, networking, math, performance, input |\n| `NousResearch/hermes-function-calling-v1` | ~1.8k | Generic tool-calling / JSON-mode structured outputs |\n| `Erocal/Unity_ResolvedQuestions` | ~400 | Real resolved Q&A \u2014 physics, ECS, URP/HDRP, UI Toolkit, editor tooling, platform issues, animation rigging |\n| `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 |\n| ML-Agents example agents | ~29 | RL agent patterns (Crawler, Walker, FoodCollector, Soccer, Pyramids, WallJump, Worm, Hallway, 3DBall, PushBlock, GridWorld, Sorter) |\n| ML-Agents core + sensors | ~5 | Agent.cs lifecycle, ray perception, camera sensor, vector sensor, grid sensor |\n| ECS/DOTS samples | ~10 | Jobs, SystemBase, ISystem, IComponentData, IAspect, Baker patterns |\n| uGUI | ~10 | Graphic, Image, Text, Button, Slider, Toggle, ScrollRect, Dropdown, InputField, LayoutGroup |\n| Netcode for GameObjects | ~5 | NetworkBehaviour, NetworkVariable, RPC, NetworkObject patterns |\n| Unity MCP tool calls (custom) | ~128 | Agentic Unity tool use: scene setup, scripting, prefabs, debugging, profiling, testing, shaders, reflection |\n| Design patterns (custom) | ~75 | ScriptableObject architecture, Object Pooling, Singleton, State Machine, Observer, Strategy, Command, Factory, Flyweight, Save/Load, ServiceLocator, EventBus |\n| **Total (deduped)** | **~45k** | |\n\n### Datasets considered and rejected\n\n| Source | Why skipped |\n|---|---|\n| `ibranze/codellama_unity3d_v1/v2` | 535 + 2k rows, wrong format, overlaps with vishnuOI |\n| `ZorraZabb/code25pile75_shuffle_filtered_unity3d_dedup` | 1M rows of noise \u2014 substring \"unity3d\" filter catches Python, JS, news articles, medical papers |\n| `supergoose/buzz_sources_244_unity3d-asset` | 101 rows of git diff YAML for .meta files |\n| `robertzeng/unity3ddoc` | 113k rows but shallow/trivial responses (5-15 word generic definitions) |\n| `DLMveloper/DLM_DataSet` | 3.6M rows but only ~3,570 \"Unity\"-tagged, all trivial (variable declarations, basic array ops) |\n| `Saibalaji25/unity-csharp-scripts` | Requires login + T&C agreement, unknown quality, 11 downloads/mo |\n\n## Training Configuration (M5 Pro 64GB)\n\n```\nModel: Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)\nBatch size: 1\nLayers: 16/64 (25% LoRA)\nLR: 1e-4\nMax seq: 4096\nIters: 2000\nPeak mem: ~25GB\nDuration: ~45-60 min\n```\n\n## GGUF Output Quantization\n\n| Type | Size | Best For |\n|---|---|---|\n| Q4_K_M | ~20 GB | 24GB+ systems |\n| Q5_K_M | ~23 GB | 32GB+ systems |\n| **Q6_K** | **~27 GB** | **M5 Pro 64GB (recommended)** |\n| Q8_0 | ~34 GB | 64GB+ systems (max quality) |\n\n## Unity-MCP Bridge\n\nRun the bridge setup to configure connections to Claude Desktop, Cursor, VS Code, or the included Python client:\n\n```bash\npython3 04_setup_unity_mcp_bridge.py\n```\n\nThen connect any MCP-compatible AI client (Claude Desktop, Cursor, VS Code with Cline/Copilot, Codex) to the Unity Editor.\n\n## Project Structure\n\n```\nunity-agent-training/\n\u251c\u2500\u2500 01_build_unity_mcp_dataset.py # MCP tool-calling dataset\n\u251c\u2500\u2500 02_build_unity_design_patterns_dataset.py # Design patterns dataset\n\u251c\u2500\u2500 03_train_and_export_unity_agent.py # MLX training + GGUF export\n\u251c\u2500\u2500 04_setup_unity_mcp_bridge.py # Unity-MCP integration\n\u251c\u2500\u2500 05_download_and_merge_datasets.py # HF dataset download + merge\n\u251c\u2500\u2500 06_extract_unity_repos.py # Unity org repo C# extraction\n\u251c\u2500\u2500 07_integrate_new_datasets.py # Newly discovered datasets\n\u251c\u2500\u2500 Modelfile # Ollama model definition\n\u251c\u2500\u2500 QUICKSTART.md # Quick start guide\n\u251c\u2500\u2500 datasets/\n\u2502 \u251c\u2500\u2500 unity_mcp_tool_calls_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_design_patterns_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_api_instructions.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 unity_repos_{train,valid,test}.jsonl # (from repo extraction)\n\u2502 \u251c\u2500\u2500 function_calling_full.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 train.jsonl # Merged MLX-ready\n\u2502 \u251c\u2500\u2500 valid.jsonl # Merged MLX-ready\n\u2502 \u2514\u2500\u2500 test.jsonl # Merged MLX-ready\n\u251c\u2500\u2500 adapters/ # LoRA adapters (after training)\n\u251c\u2500\u2500 fused_model/ # Fused MLX model\n\u251c\u2500\u2500 gguf/ # GGUF quantized models\n\u2514\u2500\u2500 mcp_config/ # Bridge configuration\n \u251c\u2500\u2500 claude_desktop_config.json\n \u251c\u2500\u2500 vscode_mcp.json\n \u251c\u2500\u2500 unity_mcp_server_config.json\n \u251c\u2500\u2500 unity_mcp_client.py\n \u2514\u2500\u2500 UnityAgentWindow.cs\n```\n\n```<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nI need to check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.. Please help.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nwhich custom made model on HuggingFace is extraordinarly smart?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\n[IMPORTANT: Background process proc_0a497b8b5935 exited (exit code -15, SIGTERM).\nCommand: unset PYTHONPATH; /Users/jacobeen/.hermes/hermes-agent/venv/bin/python3 03_train_and_export_unity_agent.py train 2>&1\nOutput:\n4<00:26, 1.25s/it]\rCalculating loss...: 20%|\u2588\u2588 | 5/25 [00:04<00:21, 1.08s/it]\rCalculating loss...: 24%|\u2588\u2588\u258d | 6/25 [00:05<00:16, 1.18it/s]\rCalculating loss...: 28%|\u2588\u2588\u258a | 7/25 [00:06<00:16, 1.07it/s]\rCalculating loss...: 32%|\u2588\u2588\u2588\u258f | 8/25 [00:07<00:15, 1.07it/s]\rCalculating loss...: 36%|\u2588\u2588\u2588\u258c | 9/25 [00:08<00:17, 1.10s/it]\rCalculating loss...: 40%|\u2588\u2588\u2588\u2588 | 10/25 [00:10<00:19, 1.27s/it]\rCalculating loss...: 44%|\u2588\u2588\u2588\u2588\u258d | 11/25 [00:11<00:17, 1.28s/it]\rCalculating loss...: 48%|\u2588\u2588\u2588\u2588\u258a | 12/25 [00:12<00:14, 1.13s/it]\rCalculating loss...: 52%|\u2588\u2588\u2588\u2588\u2588\u258f | 13/25 [00:14<00:14, 1.24s/it]\rCalculating loss...: 56%|\u2588\u2588\u2588\u2588\u2588\u258c | 14/25 [00:14<00:10, 1.02it/s]\rCalculating loss...: 60%|\u2588\u2588\u2588\u2588\u2588\u2588 | 15/25 [00:14<00:08, 1.24it/s]\rCalculating loss...: 64%|\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 16/25 [00:16<00:09, 1.00s/it]\rCalculating loss...: 68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 17/25 [00:16<00:07, 1.13it/s]\rCalculating loss...: 72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 18/25 [00:17<00:06, 1.11it/s]\rCalculating loss...: 76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 19/25 [00:18<00:04, 1.25it/s]\rCalculating loss...: 80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 20/25 [00:18<00:03, 1.46it/s]\rCalculating loss...: 84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 21/25 [00:19<00:03, 1.33it/s]\rCalculating loss...: 88%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 22/25 [00:20<00:02, 1.24it/s]\rCalculating loss...: 92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 23/25 [00:21<00:01, 1.26it/s]\rCalculating loss...: 96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 24/25 [00:22<00:00, 1.27it/s]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.10s/it]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.04it/s]\nIter 100: Val loss nan, Val took 24.072s\nIter 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\n/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\n warnings.warn('resource_tracker: There appear to be %d '\n]<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in Developer profile. Prioritize code execution, file editing, and terminal access.\n<|im_end|>\n<|im_start|>user\nexecute this project plan\n\n--- Attached Context ---\n\n\ud83d\udcc4 @file:`QUICKSTART.md` (551 tokens)\n```markdown\n# Quick Start\n\n## 1. Run the Fine-Tuned Model\n\n### MLX (recommended for inference):\n```bash\nmlx_lm.generate --model ./fused_model --prompt \"Your Unity task...\"\n```\n\n### Ollama:\nCreate Modelfile in this directory:\n```dockerfile\nFROM ./gguf/unity-agent-Q6_K.gguf\nTEMPLATE \"{{ .Prompt }}\"\nPARAMETER temperature 0.3\nPARAMETER num_ctx 4096\n```\n\nThen:\n```bash\nollama create unity-agent -f Modelfile\nollama serve\n```\n\n### LM Studio:\n- Open LM Studio \u2192 \"My Models\" \u2192 \"Add Model\"\n- Select ./gguf/unity-agent-Q6_K.gguf\n- Start server on port 1234\n\n## 2. Connect to Unity Editor\n\n### Option A: Unity-MCP CLI (recommended)\n```bash\nnpx -y @aigamedeveloper/mcp-server\n# Connects Claude Desktop, Cursor, or VS Code to Unity Editor\n```\n\n### Option B: Custom Editor Window\n- Copy `mcp_config/UnityAgentWindow.cs` to your project's `Assets/Editor/`\n- Window \u2192 Unity Agent \u2192 Chat\n\n### Option C: Python client\n```bash\npython3 mcp_config/unity_mcp_client.py --list\npython3 mcp_config/unity_mcp_client.py --tool gameobject-create \\\n --args '{\"name\":\"Cube\",\"primitiveType\":\"Cube\"}'\n```\n\n## 3. Example Workflows\n\n**Create a scene from scratch:**\n> \"Create a medieval village with 5 houses, a central fountain, and a player character\"\n\n**Fix bugs interactively:**\n> \"Read the console logs and fix the NullReferenceException in PlayerHealth.cs\"\n\n**Profile and optimize:**\n> \"Profile the game, find the bottleneck, and suggest optimizations\"\n\n**Design a system:**\n> \"Create a ScriptableObject-based inventory system with item pickup, UI display, and save/load\"\n\n## 4. Dataset Pipeline (full run)\n\n```bash\n# Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# Download external datasets\npython3 05_download_and_merge_datasets.py\npython3 06_extract_unity_repos.py\npython3 07_integrate_new_datasets.py\n\n# Train + export\npython3 03_train_and_export_unity_agent.py\n```\n\n## 5. Fine-Tuning Stages\n\n```bash\n# Prepare only (merge datasets)\npython3 03_train_and_export_unity_agent.py --stage prepare\n\n# Train only (QLoRA)\npython3 03_train_and_export_unity_agent.py --stage train\n\n# Fuse adapters and export GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n```\n\n```\n\n\ud83d\udcc4 @file:`README.md` (1740 tokens)\n```markdown\n# Unity Agent Developer \u2014 Model Training Pipeline\n\nBuild and deploy a Unity game development agent model on Apple Silicon (M5 Pro).\n\n## Quick Start\n\n```bash\n# 1. Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# 2. Download + merge everything (internet needed)\npython3 05_download_and_merge_datasets.py # vishnuOI + function-calling\npython3 06_extract_unity_repos.py # ML-Agents, ECS, uGUI, netcode\npython3 07_integrate_new_datasets.py # Erocal Q&A + Mix & Jam mechanics\n\n# 3. Fine-tune (MLX QLoRA on Apple Silicon)\npython3 03_train_and_export_unity_agent.py\n\n# 4. Export to GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n\n# 5. Run\nollama create unity-agent -f Modelfile\nollama run unity-agent\n```\n\n## Components\n\n| # | Script | What It Does | Output |\n|---|---|---|---|\n| 01 | `01_build_unity_mcp_dataset.py` | Generates ~128 MCP tool-calling examples from real game-dev tasks | `datasets/unity_mcp_tool_calls_*.jsonl` |\n| 02 | `02_build_unity_design_patterns_dataset.py` | Generates ~75 design pattern + bug-fix examples | `datasets/unity_design_patterns_*.jsonl` |\n| 03 | `03_train_and_export_unity_agent.py` | MLX QLoRA train \u2192 fuse \u2192 GGUF export \u2192 eval | `adapters/`, `fused_model/`, `gguf/` |\n| 04 | `04_setup_unity_mcp_bridge.py` | Sets up Unity-MCP bridge, configs for Claude/Cursor/VS Code | `mcp_config/` |\n| 05 | `05_download_and_merge_datasets.py` | Downloads `vishnuOI/unity-dev-instructions` (46k) + `hermes-function-calling-v1` (2k), merges everything | `datasets/train.jsonl` |\n| 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` |\n| 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` |\n\n## Dataset Composition (after all merges)\n\n| Source | Rows | Coverage |\n|---|---|---|\n| `vishnuOI/unity-dev-instructions` | ~42k (after dedup) | Unity API docs, Stack Overflow, GitHub code \u2014 scripting, rendering, physics, editor, XR, UI, animation, networking, math, performance, input |\n| `NousResearch/hermes-function-calling-v1` | ~1.8k | Generic tool-calling / JSON-mode structured outputs |\n| `Erocal/Unity_ResolvedQuestions` | ~400 | Real resolved Q&A \u2014 physics, ECS, URP/HDRP, UI Toolkit, editor tooling, platform issues, animation rigging |\n| `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 |\n| ML-Agents example agents | ~29 | RL agent patterns (Crawler, Walker, FoodCollector, Soccer, Pyramids, WallJump, Worm, Hallway, 3DBall, PushBlock, GridWorld, Sorter) |\n| ML-Agents core + sensors | ~5 | Agent.cs lifecycle, ray perception, camera sensor, vector sensor, grid sensor |\n| ECS/DOTS samples | ~10 | Jobs, SystemBase, ISystem, IComponentData, IAspect, Baker patterns |\n| uGUI | ~10 | Graphic, Image, Text, Button, Slider, Toggle, ScrollRect, Dropdown, InputField, LayoutGroup |\n| Netcode for GameObjects | ~5 | NetworkBehaviour, NetworkVariable, RPC, NetworkObject patterns |\n| Unity MCP tool calls (custom) | ~128 | Agentic Unity tool use: scene setup, scripting, prefabs, debugging, profiling, testing, shaders, reflection |\n| Design patterns (custom) | ~75 | ScriptableObject architecture, Object Pooling, Singleton, State Machine, Observer, Strategy, Command, Factory, Flyweight, Save/Load, ServiceLocator, EventBus |\n| **Total (deduped)** | **~45k** | |\n\n### Datasets considered and rejected\n\n| Source | Why skipped |\n|---|---|\n| `ibranze/codellama_unity3d_v1/v2` | 535 + 2k rows, wrong format, overlaps with vishnuOI |\n| `ZorraZabb/code25pile75_shuffle_filtered_unity3d_dedup` | 1M rows of noise \u2014 substring \"unity3d\" filter catches Python, JS, news articles, medical papers |\n| `supergoose/buzz_sources_244_unity3d-asset` | 101 rows of git diff YAML for .meta files |\n| `robertzeng/unity3ddoc` | 113k rows but shallow/trivial responses (5-15 word generic definitions) |\n| `DLMveloper/DLM_DataSet` | 3.6M rows but only ~3,570 \"Unity\"-tagged, all trivial (variable declarations, basic array ops) |\n| `Saibalaji25/unity-csharp-scripts` | Requires login + T&C agreement, unknown quality, 11 downloads/mo |\n\n## Training Configuration (M5 Pro 64GB)\n\n```\nModel: Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)\nBatch size: 1\nLayers: 16/64 (25% LoRA)\nLR: 1e-4\nMax seq: 4096\nIters: 2000\nPeak mem: ~25GB\nDuration: ~45-60 min\n```\n\n## GGUF Output Quantization\n\n| Type | Size | Best For |\n|---|---|---|\n| Q4_K_M | ~20 GB | 24GB+ systems |\n| Q5_K_M | ~23 GB | 32GB+ systems |\n| **Q6_K** | **~27 GB** | **M5 Pro 64GB (recommended)** |\n| Q8_0 | ~34 GB | 64GB+ systems (max quality) |\n\n## Unity-MCP Bridge\n\nRun the bridge setup to configure connections to Claude Desktop, Cursor, VS Code, or the included Python client:\n\n```bash\npython3 04_setup_unity_mcp_bridge.py\n```\n\nThen connect any MCP-compatible AI client (Claude Desktop, Cursor, VS Code with Cline/Copilot, Codex) to the Unity Editor.\n\n## Project Structure\n\n```\nunity-agent-training/\n\u251c\u2500\u2500 01_build_unity_mcp_dataset.py # MCP tool-calling dataset\n\u251c\u2500\u2500 02_build_unity_design_patterns_dataset.py # Design patterns dataset\n\u251c\u2500\u2500 03_train_and_export_unity_agent.py # MLX training + GGUF export\n\u251c\u2500\u2500 04_setup_unity_mcp_bridge.py # Unity-MCP integration\n\u251c\u2500\u2500 05_download_and_merge_datasets.py # HF dataset download + merge\n\u251c\u2500\u2500 06_extract_unity_repos.py # Unity org repo C# extraction\n\u251c\u2500\u2500 07_integrate_new_datasets.py # Newly discovered datasets\n\u251c\u2500\u2500 Modelfile # Ollama model definition\n\u251c\u2500\u2500 QUICKSTART.md # Quick start guide\n\u251c\u2500\u2500 datasets/\n\u2502 \u251c\u2500\u2500 unity_mcp_tool_calls_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_design_patterns_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_api_instructions.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 unity_repos_{train,valid,test}.jsonl # (from repo extraction)\n\u2502 \u251c\u2500\u2500 function_calling_full.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 train.jsonl # Merged MLX-ready\n\u2502 \u251c\u2500\u2500 valid.jsonl # Merged MLX-ready\n\u2502 \u2514\u2500\u2500 test.jsonl # Merged MLX-ready\n\u251c\u2500\u2500 adapters/ # LoRA adapters (after training)\n\u251c\u2500\u2500 fused_model/ # Fused MLX model\n\u251c\u2500\u2500 gguf/ # GGUF quantized models\n\u2514\u2500\u2500 mcp_config/ # Bridge configuration\n \u251c\u2500\u2500 claude_desktop_config.json\n \u251c\u2500\u2500 vscode_mcp.json\n \u251c\u2500\u2500 unity_mcp_server_config.json\n \u251c\u2500\u2500 unity_mcp_client.py\n \u2514\u2500\u2500 UnityAgentWindow.cs\n```\n\n```<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nCan you process this?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\ncan you make sure that every single profile is utilizing our Obsidian.md, Hindsight and othr plugins?\n\n[System: The active model for this chat has changed to Ornith-1.0-35B-MTPLX via provider omlx-mbp. From this point forward, use this runtime metadata when answering questions about what model/provider is active.]\n\ncan you make sure that every single profile is utilizing our Obsidian.md, Hindsight and othr plugins?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for an 'image-generator' profile focused on AI image generation, image editing, design workflows, and visual content creation. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant for an image generation agent. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for an 'image-generator' profile focused on AI image generation, image editing, design workflows, and visual content creation. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant for an image generation agent. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\n[ASYNC DELEGATION BATCH COMPLETE \u2014 deleg_2d07ecd6]\nA 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 \u2014 act on these or re-dispatch if things have changed.\n\nDispatched: 2026-07-13 23:59:18 (11m48s ago)\nContext 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.\nRole: leaf Model: ? Total duration: 708.72s\n\n--- \u2713 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) ---\nContext length exceeded (379 tokens). Cannot compress further.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome: 10/10 repos cloned successfully, 0 failures.**\n\nAll 10 OpenClaw repositories are now present at `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | clawdinators | \u2705 |\n| 2 | openclaw-ansible | \u2705 |\n| 3 | casa | \u2705 |\n| 4 | spogo | \u2705 |\n| 5 | goplaces | \u2705 |\n| 6 | voice-community | \u2705 |\n| 7 | acpx | \u2705 |\n| 8 | community | \u2705 |\n| 9 | caclawphony | \u2705 |\n| 10 | discrawl | \u2705 |\n\nNo issues encountered \u2014 all clones completed cleanly with exit code 0.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Result: 10/10 repos cloned with zero failures.**\n\nCloned to `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | `slacrawl` | \u2705 Cloned |\n| 2 | `crabline` | \u2705 Cloned |\n| 3 | `releases` | \u2705 Cloned (15,075 files) |\n| 4 | `docs` | \u2705 Cloned |\n| 5 | `esp-openclaw-node` | \u2705 Cloned |\n| 6 | `shellbench` | \u2705 Cloned (23,628 files) |\n| 7 | `notcrawl` | \u2705 Cloned |\n| 8 | `clawsweeper` | \u2705 Cloned |\n| 9 | `clownfish` | \u2705 Cloned |\n| 10 | `crabpot` | \u2705 Cloned |\n\nNo issues encountered. All 10 repositories are present and fully cloned.\n\n--- \u2713 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) ---\nContext length exceeded: max compression attempts (3) reached.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome:** All 10 OpenClaw repositories cloned successfully into `/Volumes/this_and_that/projects/multi-repo_0/`.\n\n**Cloned repos:**\n1. \u2705 crawlbar\n2. \u2705 clawgrit-reports (previously cloned)\n3. \u2705 fs-safe\n4. \u2705 ask-molty\n5. \u2705 graincrawl\n6. \u2705 clickclack\n7. \u2705 clawdex\n8. \u2705 proxyline\n9. \u2705 telecrawl\n10. \u2705 clawpatch\n\n**Issues:** None. All 10 clones completed with exit code 0.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome: 10/10 repos cloned successfully, 0 failures.**\n\nCloned to `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | crabfleet | \u2705 |\n| 2 | agent-skills | \u2705 |\n| 3 | rfcs | \u2705 |\n| 4 | rastermill | \u2705 |\n| 5 | libopus-wasm | \u2705 |\n| 6 | octopool | \u2705 |\n| 7 | clawpdf | \u2705 |\n| 8 | photoscrawl | \u2705 |\n| 9 | clawrouter | \u2705 |\n| 10 | imsgcrawl | \u2705 |\n\nNo issues encountered \u2014 all clones completed cleanly with no errors.\n\n--- \u2713 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) ---\nContext length exceeded (435 tokens). Cannot compress further.\n\n--- \u2713 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) ---\nContext length exceeded (1,277 tokens). Cannot compress further.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for an 'image-generator' profile focused on AI image generation, image editing, design workflows, and visual content creation. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant for an image generation agent. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nI need to process this. Please help.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nCan you find me best Llama 3.3 70B?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nCan you execute this project plan --- Attached?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nexecute this project plan\n\n--- Attached Context ---\n\n\ud83d\udcc4 @file:`QUICKSTART.md` (551 tokens)\n```markdown\n# Quick Start\n\n## 1. Run the Fine-Tuned Model\n\n### MLX (recommended for inference):\n```bash\nmlx_lm.generate --model ./fused_model --prompt \"Your Unity task...\"\n```\n\n### Ollama:\nCreate Modelfile in this directory:\n```dockerfile\nFROM ./gguf/unity-agent-Q6_K.gguf\nTEMPLATE \"{{ .Prompt }}\"\nPARAMETER temperature 0.3\nPARAMETER num_ctx 4096\n```\n\nThen:\n```bash\nollama create unity-agent -f Modelfile\nollama serve\n```\n\n### LM Studio:\n- Open LM Studio \u2192 \"My Models\" \u2192 \"Add Model\"\n- Select ./gguf/unity-agent-Q6_K.gguf\n- Start server on port 1234\n\n## 2. Connect to Unity Editor\n\n### Option A: Unity-MCP CLI (recommended)\n```bash\nnpx -y @aigamedeveloper/mcp-server\n# Connects Claude Desktop, Cursor, or VS Code to Unity Editor\n```\n\n### Option B: Custom Editor Window\n- Copy `mcp_config/UnityAgentWindow.cs` to your project's `Assets/Editor/`\n- Window \u2192 Unity Agent \u2192 Chat\n\n### Option C: Python client\n```bash\npython3 mcp_config/unity_mcp_client.py --list\npython3 mcp_config/unity_mcp_client.py --tool gameobject-create \\\n --args '{\"name\":\"Cube\",\"primitiveType\":\"Cube\"}'\n```\n\n## 3. Example Workflows\n\n**Create a scene from scratch:**\n> \"Create a medieval village with 5 houses, a central fountain, and a player character\"\n\n**Fix bugs interactively:**\n> \"Read the console logs and fix the NullReferenceException in PlayerHealth.cs\"\n\n**Profile and optimize:**\n> \"Profile the game, find the bottleneck, and suggest optimizations\"\n\n**Design a system:**\n> \"Create a ScriptableObject-based inventory system with item pickup, UI display, and save/load\"\n\n## 4. Dataset Pipeline (full run)\n\n```bash\n# Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# Download external datasets\npython3 05_download_and_merge_datasets.py\npython3 06_extract_unity_repos.py\npython3 07_integrate_new_datasets.py\n\n# Train + export\npython3 03_train_and_export_unity_agent.py\n```\n\n## 5. Fine-Tuning Stages\n\n```bash\n# Prepare only (merge datasets)\npython3 03_train_and_export_unity_agent.py --stage prepare\n\n# Train only (QLoRA)\npython3 03_train_and_export_unity_agent.py --stage train\n\n# Fuse adapters and export GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n```\n\n```\n\n\ud83d\udcc4 @file:`README.md` (1740 tokens)\n```markdown\n# Unity Agent Developer \u2014 Model Training Pipeline\n\nBuild and deploy a Unity game development agent model on Apple Silicon (M5 Pro).\n\n## Quick Start\n\n```bash\n# 1. Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# 2. Download + merge everything (internet needed)\npython3 05_download_and_merge_datasets.py # vishnuOI + function-calling\npython3 06_extract_unity_repos.py # ML-Agents, ECS, uGUI, netcode\npython3 07_integrate_new_datasets.py # Erocal Q&A + Mix & Jam mechanics\n\n# 3. Fine-tune (MLX QLoRA on Apple Silicon)\npython3 03_train_and_export_unity_agent.py\n\n# 4. Export to GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n\n# 5. Run\nollama create unity-agent -f Modelfile\nollama run unity-agent\n```\n\n## Components\n\n| # | Script | What It Does | Output |\n|---|---|---|---|\n| 01 | `01_build_unity_mcp_dataset.py` | Generates ~128 MCP tool-calling examples from real game-dev tasks | `datasets/unity_mcp_tool_calls_*.jsonl` |\n| 02 | `02_build_unity_design_patterns_dataset.py` | Generates ~75 design pattern + bug-fix examples | `datasets/unity_design_patterns_*.jsonl` |\n| 03 | `03_train_and_export_unity_agent.py` | MLX QLoRA train \u2192 fuse \u2192 GGUF export \u2192 eval | `adapters/`, `fused_model/`, `gguf/` |\n| 04 | `04_setup_unity_mcp_bridge.py` | Sets up Unity-MCP bridge, configs for Claude/Cursor/VS Code | `mcp_config/` |\n| 05 | `05_download_and_merge_datasets.py` | Downloads `vishnuOI/unity-dev-instructions` (46k) + `hermes-function-calling-v1` (2k), merges everything | `datasets/train.jsonl` |\n| 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` |\n| 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` |\n\n## Dataset Composition (after all merges)\n\n| Source | Rows | Coverage |\n|---|---|---|\n| `vishnuOI/unity-dev-instructions` | ~42k (after dedup) | Unity API docs, Stack Overflow, GitHub code \u2014 scripting, rendering, physics, editor, XR, UI, animation, networking, math, performance, input |\n| `NousResearch/hermes-function-calling-v1` | ~1.8k | Generic tool-calling / JSON-mode structured outputs |\n| `Erocal/Unity_ResolvedQuestions` | ~400 | Real resolved Q&A \u2014 physics, ECS, URP/HDRP, UI Toolkit, editor tooling, platform issues, animation rigging |\n| `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 |\n| ML-Agents example agents | ~29 | RL agent patterns (Crawler, Walker, FoodCollector, Soccer, Pyramids, WallJump, Worm, Hallway, 3DBall, PushBlock, GridWorld, Sorter) |\n| ML-Agents core + sensors | ~5 | Agent.cs lifecycle, ray perception, camera sensor, vector sensor, grid sensor |\n| ECS/DOTS samples | ~10 | Jobs, SystemBase, ISystem, IComponentData, IAspect, Baker patterns |\n| uGUI | ~10 | Graphic, Image, Text, Button, Slider, Toggle, ScrollRect, Dropdown, InputField, LayoutGroup |\n| Netcode for GameObjects | ~5 | NetworkBehaviour, NetworkVariable, RPC, NetworkObject patterns |\n| Unity MCP tool calls (custom) | ~128 | Agentic Unity tool use: scene setup, scripting, prefabs, debugging, profiling, testing, shaders, reflection |\n| Design patterns (custom) | ~75 | ScriptableObject architecture, Object Pooling, Singleton, State Machine, Observer, Strategy, Command, Factory, Flyweight, Save/Load, ServiceLocator, EventBus |\n| **Total (deduped)** | **~45k** | |\n\n### Datasets considered and rejected\n\n| Source | Why skipped |\n|---|---|\n| `ibranze/codellama_unity3d_v1/v2` | 535 + 2k rows, wrong format, overlaps with vishnuOI |\n| `ZorraZabb/code25pile75_shuffle_filtered_unity3d_dedup` | 1M rows of noise \u2014 substring \"unity3d\" filter catches Python, JS, news articles, medical papers |\n| `supergoose/buzz_sources_244_unity3d-asset` | 101 rows of git diff YAML for .meta files |\n| `robertzeng/unity3ddoc` | 113k rows but shallow/trivial responses (5-15 word generic definitions) |\n| `DLMveloper/DLM_DataSet` | 3.6M rows but only ~3,570 \"Unity\"-tagged, all trivial (variable declarations, basic array ops) |\n| `Saibalaji25/unity-csharp-scripts` | Requires login + T&C agreement, unknown quality, 11 downloads/mo |\n\n## Training Configuration (M5 Pro 64GB)\n\n```\nModel: Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)\nBatch size: 1\nLayers: 16/64 (25% LoRA)\nLR: 1e-4\nMax seq: 4096\nIters: 2000\nPeak mem: ~25GB\nDuration: ~45-60 min\n```\n\n## GGUF Output Quantization\n\n| Type | Size | Best For |\n|---|---|---|\n| Q4_K_M | ~20 GB | 24GB+ systems |\n| Q5_K_M | ~23 GB | 32GB+ systems |\n| **Q6_K** | **~27 GB** | **M5 Pro 64GB (recommended)** |\n| Q8_0 | ~34 GB | 64GB+ systems (max quality) |\n\n## Unity-MCP Bridge\n\nRun the bridge setup to configure connections to Claude Desktop, Cursor, VS Code, or the included Python client:\n\n```bash\npython3 04_setup_unity_mcp_bridge.py\n```\n\nThen connect any MCP-compatible AI client (Claude Desktop, Cursor, VS Code with Cline/Copilot, Codex) to the Unity Editor.\n\n## Project Structure\n\n```\nunity-agent-training/\n\u251c\u2500\u2500 01_build_unity_mcp_dataset.py # MCP tool-calling dataset\n\u251c\u2500\u2500 02_build_unity_design_patterns_dataset.py # Design patterns dataset\n\u251c\u2500\u2500 03_train_and_export_unity_agent.py # MLX training + GGUF export\n\u251c\u2500\u2500 04_setup_unity_mcp_bridge.py # Unity-MCP integration\n\u251c\u2500\u2500 05_download_and_merge_datasets.py # HF dataset download + merge\n\u251c\u2500\u2500 06_extract_unity_repos.py # Unity org repo C# extraction\n\u251c\u2500\u2500 07_integrate_new_datasets.py # Newly discovered datasets\n\u251c\u2500\u2500 Modelfile # Ollama model definition\n\u251c\u2500\u2500 QUICKSTART.md # Quick start guide\n\u251c\u2500\u2500 datasets/\n\u2502 \u251c\u2500\u2500 unity_mcp_tool_calls_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_design_patterns_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_api_instructions.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 unity_repos_{train,valid,test}.jsonl # (from repo extraction)\n\u2502 \u251c\u2500\u2500 function_calling_full.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 train.jsonl # Merged MLX-ready\n\u2502 \u251c\u2500\u2500 valid.jsonl # Merged MLX-ready\n\u2502 \u2514\u2500\u2500 test.jsonl # Merged MLX-ready\n\u251c\u2500\u2500 adapters/ # LoRA adapters (after training)\n\u251c\u2500\u2500 fused_model/ # Fused MLX model\n\u251c\u2500\u2500 gguf/ # GGUF quantized models\n\u2514\u2500\u2500 mcp_config/ # Bridge configuration\n \u251c\u2500\u2500 claude_desktop_config.json\n \u251c\u2500\u2500 vscode_mcp.json\n \u251c\u2500\u2500 unity_mcp_server_config.json\n \u251c\u2500\u2500 unity_mcp_client.py\n \u2514\u2500\u2500 UnityAgentWindow.cs\n```\n\n```<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\ni need to know if you are making a GGUF model or MLX model?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\n[ASYNC DELEGATION BATCH COMPLETE \u2014 deleg_2d07ecd6]\nA 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 \u2014 act on these or re-dispatch if things have changed.\n\nDispatched: 2026-07-13 23:59:18 (11m48s ago)\nContext 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.\nRole: leaf Model: ? Total duration: 708.72s\n\n--- \u2713 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) ---\nContext length exceeded (379 tokens). Cannot compress further.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome: 10/10 repos cloned successfully, 0 failures.**\n\nAll 10 OpenClaw repositories are now present at `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | clawdinators | \u2705 |\n| 2 | openclaw-ansible | \u2705 |\n| 3 | casa | \u2705 |\n| 4 | spogo | \u2705 |\n| 5 | goplaces | \u2705 |\n| 6 | voice-community | \u2705 |\n| 7 | acpx | \u2705 |\n| 8 | community | \u2705 |\n| 9 | caclawphony | \u2705 |\n| 10 | discrawl | \u2705 |\n\nNo issues encountered \u2014 all clones completed cleanly with exit code 0.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Result: 10/10 repos cloned with zero failures.**\n\nCloned to `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | `slacrawl` | \u2705 Cloned |\n| 2 | `crabline` | \u2705 Cloned |\n| 3 | `releases` | \u2705 Cloned (15,075 files) |\n| 4 | `docs` | \u2705 Cloned |\n| 5 | `esp-openclaw-node` | \u2705 Cloned |\n| 6 | `shellbench` | \u2705 Cloned (23,628 files) |\n| 7 | `notcrawl` | \u2705 Cloned |\n| 8 | `clawsweeper` | \u2705 Cloned |\n| 9 | `clownfish` | \u2705 Cloned |\n| 10 | `crabpot` | \u2705 Cloned |\n\nNo issues encountered. All 10 repositories are present and fully cloned.\n\n--- \u2713 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) ---\nContext length exceeded: max compression attempts (3) reached.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome:** All 10 OpenClaw repositories cloned successfully into `/Volumes/this_and_that/projects/multi-repo_0/`.\n\n**Cloned repos:**\n1. \u2705 crawlbar\n2. \u2705 clawgrit-reports (previously cloned)\n3. \u2705 fs-safe\n4. \u2705 ask-molty\n5. \u2705 graincrawl\n6. \u2705 clickclack\n7. \u2705 clawdex\n8. \u2705 proxyline\n9. \u2705 telecrawl\n10. \u2705 clawpatch\n\n**Issues:** None. All 10 clones completed with exit code 0.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome: 10/10 repos cloned successfully, 0 failures.**\n\nCloned to `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | crabfleet | \u2705 |\n| 2 | agent-skills | \u2705 |\n| 3 | rfcs | \u2705 |\n| 4 | rastermill | \u2705 |\n| 5 | libopus-wasm | \u2705 |\n| 6 | octopool | \u2705 |\n| 7 | clawpdf | \u2705 |\n| 8 | photoscrawl | \u2705 |\n| 9 | clawrouter | \u2705 |\n| 10 | imsgcrawl | \u2705 |\n\nNo issues encountered \u2014 all clones completed cleanly with no errors.\n\n--- \u2713 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) ---\nContext length exceeded (435 tokens). Cannot compress further.\n\n--- \u2713 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) ---\nContext length exceeded (1,277 tokens). Cannot compress further.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in General Assistant profile. Use the most efficient tool for each request.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for a 'cybersec' profile focused on security auditing, penetration testing, vulnerability research, and security analysis. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant for a security-focused agent. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nI need to process proc_0a497b8b5935 exited (exit code -15,. Please help.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in Developer profile. Prioritize code execution, file editing, and terminal access.\n<|im_end|>\n<|im_start|>user\nResearch the best Hermes Agent plugin configuration for a 'cybersec' profile focused on security auditing, penetration testing, vulnerability research, and security analysis. Consider which plugins (model providers, web search, browser, memory, tools) are most relevant for a security-focused agent. Focus on practical recommendations: which plugins to enable, which to skip, and why. Check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nCan you check https://hermes-agent.nousresearch.com/docs for any profile-specific guidance.?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nCan you process this?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\noh okey so we want to MAX out the quality at 2000<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nmy laptop crashed. Continue where we left or in case something went corrupt then lets start over<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in General Assistant profile. Use the most efficient tool for each request.\n<|im_end|>\n<|im_start|>user\n[IMPORTANT: Background process proc_0a497b8b5935 exited (exit code -15, SIGTERM).\nCommand: unset PYTHONPATH; /Users/jacobeen/.hermes/hermes-agent/venv/bin/python3 03_train_and_export_unity_agent.py train 2>&1\nOutput:\n4<00:26, 1.25s/it]\rCalculating loss...: 20%|\u2588\u2588 | 5/25 [00:04<00:21, 1.08s/it]\rCalculating loss...: 24%|\u2588\u2588\u258d | 6/25 [00:05<00:16, 1.18it/s]\rCalculating loss...: 28%|\u2588\u2588\u258a | 7/25 [00:06<00:16, 1.07it/s]\rCalculating loss...: 32%|\u2588\u2588\u2588\u258f | 8/25 [00:07<00:15, 1.07it/s]\rCalculating loss...: 36%|\u2588\u2588\u2588\u258c | 9/25 [00:08<00:17, 1.10s/it]\rCalculating loss...: 40%|\u2588\u2588\u2588\u2588 | 10/25 [00:10<00:19, 1.27s/it]\rCalculating loss...: 44%|\u2588\u2588\u2588\u2588\u258d | 11/25 [00:11<00:17, 1.28s/it]\rCalculating loss...: 48%|\u2588\u2588\u2588\u2588\u258a | 12/25 [00:12<00:14, 1.13s/it]\rCalculating loss...: 52%|\u2588\u2588\u2588\u2588\u2588\u258f | 13/25 [00:14<00:14, 1.24s/it]\rCalculating loss...: 56%|\u2588\u2588\u2588\u2588\u2588\u258c | 14/25 [00:14<00:10, 1.02it/s]\rCalculating loss...: 60%|\u2588\u2588\u2588\u2588\u2588\u2588 | 15/25 [00:14<00:08, 1.24it/s]\rCalculating loss...: 64%|\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 16/25 [00:16<00:09, 1.00s/it]\rCalculating loss...: 68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 17/25 [00:16<00:07, 1.13it/s]\rCalculating loss...: 72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 18/25 [00:17<00:06, 1.11it/s]\rCalculating loss...: 76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 19/25 [00:18<00:04, 1.25it/s]\rCalculating loss...: 80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 20/25 [00:18<00:03, 1.46it/s]\rCalculating loss...: 84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 21/25 [00:19<00:03, 1.33it/s]\rCalculating loss...: 88%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 22/25 [00:20<00:02, 1.24it/s]\rCalculating loss...: 92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 23/25 [00:21<00:01, 1.26it/s]\rCalculating loss...: 96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 24/25 [00:22<00:00, 1.27it/s]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.10s/it]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.04it/s]\nIter 100: Val loss nan, Val took 24.072s\nIter 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\n/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\n warnings.warn('resource_tracker: There appear to be %d '\n]<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nexecute this project plan\n\n--- Attached Context ---\n\n\ud83d\udcc4 @file:`QUICKSTART.md` (551 tokens)\n```markdown\n# Quick Start\n\n## 1. Run the Fine-Tuned Model\n\n### MLX (recommended for inference):\n```bash\nmlx_lm.generate --model ./fused_model --prompt \"Your Unity task...\"\n```\n\n### Ollama:\nCreate Modelfile in this directory:\n```dockerfile\nFROM ./gguf/unity-agent-Q6_K.gguf\nTEMPLATE \"{{ .Prompt }}\"\nPARAMETER temperature 0.3\nPARAMETER num_ctx 4096\n```\n\nThen:\n```bash\nollama create unity-agent -f Modelfile\nollama serve\n```\n\n### LM Studio:\n- Open LM Studio \u2192 \"My Models\" \u2192 \"Add Model\"\n- Select ./gguf/unity-agent-Q6_K.gguf\n- Start server on port 1234\n\n## 2. Connect to Unity Editor\n\n### Option A: Unity-MCP CLI (recommended)\n```bash\nnpx -y @aigamedeveloper/mcp-server\n# Connects Claude Desktop, Cursor, or VS Code to Unity Editor\n```\n\n### Option B: Custom Editor Window\n- Copy `mcp_config/UnityAgentWindow.cs` to your project's `Assets/Editor/`\n- Window \u2192 Unity Agent \u2192 Chat\n\n### Option C: Python client\n```bash\npython3 mcp_config/unity_mcp_client.py --list\npython3 mcp_config/unity_mcp_client.py --tool gameobject-create \\\n --args '{\"name\":\"Cube\",\"primitiveType\":\"Cube\"}'\n```\n\n## 3. Example Workflows\n\n**Create a scene from scratch:**\n> \"Create a medieval village with 5 houses, a central fountain, and a player character\"\n\n**Fix bugs interactively:**\n> \"Read the console logs and fix the NullReferenceException in PlayerHealth.cs\"\n\n**Profile and optimize:**\n> \"Profile the game, find the bottleneck, and suggest optimizations\"\n\n**Design a system:**\n> \"Create a ScriptableObject-based inventory system with item pickup, UI display, and save/load\"\n\n## 4. Dataset Pipeline (full run)\n\n```bash\n# Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# Download external datasets\npython3 05_download_and_merge_datasets.py\npython3 06_extract_unity_repos.py\npython3 07_integrate_new_datasets.py\n\n# Train + export\npython3 03_train_and_export_unity_agent.py\n```\n\n## 5. Fine-Tuning Stages\n\n```bash\n# Prepare only (merge datasets)\npython3 03_train_and_export_unity_agent.py --stage prepare\n\n# Train only (QLoRA)\npython3 03_train_and_export_unity_agent.py --stage train\n\n# Fuse adapters and export GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n```\n\n```\n\n\ud83d\udcc4 @file:`README.md` (1740 tokens)\n```markdown\n# Unity Agent Developer \u2014 Model Training Pipeline\n\nBuild and deploy a Unity game development agent model on Apple Silicon (M5 Pro).\n\n## Quick Start\n\n```bash\n# 1. Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# 2. Download + merge everything (internet needed)\npython3 05_download_and_merge_datasets.py # vishnuOI + function-calling\npython3 06_extract_unity_repos.py # ML-Agents, ECS, uGUI, netcode\npython3 07_integrate_new_datasets.py # Erocal Q&A + Mix & Jam mechanics\n\n# 3. Fine-tune (MLX QLoRA on Apple Silicon)\npython3 03_train_and_export_unity_agent.py\n\n# 4. Export to GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n\n# 5. Run\nollama create unity-agent -f Modelfile\nollama run unity-agent\n```\n\n## Components\n\n| # | Script | What It Does | Output |\n|---|---|---|---|\n| 01 | `01_build_unity_mcp_dataset.py` | Generates ~128 MCP tool-calling examples from real game-dev tasks | `datasets/unity_mcp_tool_calls_*.jsonl` |\n| 02 | `02_build_unity_design_patterns_dataset.py` | Generates ~75 design pattern + bug-fix examples | `datasets/unity_design_patterns_*.jsonl` |\n| 03 | `03_train_and_export_unity_agent.py` | MLX QLoRA train \u2192 fuse \u2192 GGUF export \u2192 eval | `adapters/`, `fused_model/`, `gguf/` |\n| 04 | `04_setup_unity_mcp_bridge.py` | Sets up Unity-MCP bridge, configs for Claude/Cursor/VS Code | `mcp_config/` |\n| 05 | `05_download_and_merge_datasets.py` | Downloads `vishnuOI/unity-dev-instructions` (46k) + `hermes-function-calling-v1` (2k), merges everything | `datasets/train.jsonl` |\n| 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` |\n| 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` |\n\n## Dataset Composition (after all merges)\n\n| Source | Rows | Coverage |\n|---|---|---|\n| `vishnuOI/unity-dev-instructions` | ~42k (after dedup) | Unity API docs, Stack Overflow, GitHub code \u2014 scripting, rendering, physics, editor, XR, UI, animation, networking, math, performance, input |\n| `NousResearch/hermes-function-calling-v1` | ~1.8k | Generic tool-calling / JSON-mode structured outputs |\n| `Erocal/Unity_ResolvedQuestions` | ~400 | Real resolved Q&A \u2014 physics, ECS, URP/HDRP, UI Toolkit, editor tooling, platform issues, animation rigging |\n| `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 |\n| ML-Agents example agents | ~29 | RL agent patterns (Crawler, Walker, FoodCollector, Soccer, Pyramids, WallJump, Worm, Hallway, 3DBall, PushBlock, GridWorld, Sorter) |\n| ML-Agents core + sensors | ~5 | Agent.cs lifecycle, ray perception, camera sensor, vector sensor, grid sensor |\n| ECS/DOTS samples | ~10 | Jobs, SystemBase, ISystem, IComponentData, IAspect, Baker patterns |\n| uGUI | ~10 | Graphic, Image, Text, Button, Slider, Toggle, ScrollRect, Dropdown, InputField, LayoutGroup |\n| Netcode for GameObjects | ~5 | NetworkBehaviour, NetworkVariable, RPC, NetworkObject patterns |\n| Unity MCP tool calls (custom) | ~128 | Agentic Unity tool use: scene setup, scripting, prefabs, debugging, profiling, testing, shaders, reflection |\n| Design patterns (custom) | ~75 | ScriptableObject architecture, Object Pooling, Singleton, State Machine, Observer, Strategy, Command, Factory, Flyweight, Save/Load, ServiceLocator, EventBus |\n| **Total (deduped)** | **~45k** | |\n\n### Datasets considered and rejected\n\n| Source | Why skipped |\n|---|---|\n| `ibranze/codellama_unity3d_v1/v2` | 535 + 2k rows, wrong format, overlaps with vishnuOI |\n| `ZorraZabb/code25pile75_shuffle_filtered_unity3d_dedup` | 1M rows of noise \u2014 substring \"unity3d\" filter catches Python, JS, news articles, medical papers |\n| `supergoose/buzz_sources_244_unity3d-asset` | 101 rows of git diff YAML for .meta files |\n| `robertzeng/unity3ddoc` | 113k rows but shallow/trivial responses (5-15 word generic definitions) |\n| `DLMveloper/DLM_DataSet` | 3.6M rows but only ~3,570 \"Unity\"-tagged, all trivial (variable declarations, basic array ops) |\n| `Saibalaji25/unity-csharp-scripts` | Requires login + T&C agreement, unknown quality, 11 downloads/mo |\n\n## Training Configuration (M5 Pro 64GB)\n\n```\nModel: Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)\nBatch size: 1\nLayers: 16/64 (25% LoRA)\nLR: 1e-4\nMax seq: 4096\nIters: 2000\nPeak mem: ~25GB\nDuration: ~45-60 min\n```\n\n## GGUF Output Quantization\n\n| Type | Size | Best For |\n|---|---|---|\n| Q4_K_M | ~20 GB | 24GB+ systems |\n| Q5_K_M | ~23 GB | 32GB+ systems |\n| **Q6_K** | **~27 GB** | **M5 Pro 64GB (recommended)** |\n| Q8_0 | ~34 GB | 64GB+ systems (max quality) |\n\n## Unity-MCP Bridge\n\nRun the bridge setup to configure connections to Claude Desktop, Cursor, VS Code, or the included Python client:\n\n```bash\npython3 04_setup_unity_mcp_bridge.py\n```\n\nThen connect any MCP-compatible AI client (Claude Desktop, Cursor, VS Code with Cline/Copilot, Codex) to the Unity Editor.\n\n## Project Structure\n\n```\nunity-agent-training/\n\u251c\u2500\u2500 01_build_unity_mcp_dataset.py # MCP tool-calling dataset\n\u251c\u2500\u2500 02_build_unity_design_patterns_dataset.py # Design patterns dataset\n\u251c\u2500\u2500 03_train_and_export_unity_agent.py # MLX training + GGUF export\n\u251c\u2500\u2500 04_setup_unity_mcp_bridge.py # Unity-MCP integration\n\u251c\u2500\u2500 05_download_and_merge_datasets.py # HF dataset download + merge\n\u251c\u2500\u2500 06_extract_unity_repos.py # Unity org repo C# extraction\n\u251c\u2500\u2500 07_integrate_new_datasets.py # Newly discovered datasets\n\u251c\u2500\u2500 Modelfile # Ollama model definition\n\u251c\u2500\u2500 QUICKSTART.md # Quick start guide\n\u251c\u2500\u2500 datasets/\n\u2502 \u251c\u2500\u2500 unity_mcp_tool_calls_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_design_patterns_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_api_instructions.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 unity_repos_{train,valid,test}.jsonl # (from repo extraction)\n\u2502 \u251c\u2500\u2500 function_calling_full.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 train.jsonl # Merged MLX-ready\n\u2502 \u251c\u2500\u2500 valid.jsonl # Merged MLX-ready\n\u2502 \u2514\u2500\u2500 test.jsonl # Merged MLX-ready\n\u251c\u2500\u2500 adapters/ # LoRA adapters (after training)\n\u251c\u2500\u2500 fused_model/ # Fused MLX model\n\u251c\u2500\u2500 gguf/ # GGUF quantized models\n\u2514\u2500\u2500 mcp_config/ # Bridge configuration\n \u251c\u2500\u2500 claude_desktop_config.json\n \u251c\u2500\u2500 vscode_mcp.json\n \u251c\u2500\u2500 unity_mcp_server_config.json\n \u251c\u2500\u2500 unity_mcp_client.py\n \u2514\u2500\u2500 UnityAgentWindow.cs\n```\n\n```<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nexecute this project plan\n\n--- Attached Context ---\n\n\ud83d\udcc4 @file:`QUICKSTART.md` (551 tokens)\n```markdown\n# Quick Start\n\n## 1. Run the Fine-Tuned Model\n\n### MLX (recommended for inference):\n```bash\nmlx_lm.generate --model ./fused_model --prompt \"Your Unity task...\"\n```\n\n### Ollama:\nCreate Modelfile in this directory:\n```dockerfile\nFROM ./gguf/unity-agent-Q6_K.gguf\nTEMPLATE \"{{ .Prompt }}\"\nPARAMETER temperature 0.3\nPARAMETER num_ctx 4096\n```\n\nThen:\n```bash\nollama create unity-agent -f Modelfile\nollama serve\n```\n\n### LM Studio:\n- Open LM Studio \u2192 \"My Models\" \u2192 \"Add Model\"\n- Select ./gguf/unity-agent-Q6_K.gguf\n- Start server on port 1234\n\n## 2. Connect to Unity Editor\n\n### Option A: Unity-MCP CLI (recommended)\n```bash\nnpx -y @aigamedeveloper/mcp-server\n# Connects Claude Desktop, Cursor, or VS Code to Unity Editor\n```\n\n### Option B: Custom Editor Window\n- Copy `mcp_config/UnityAgentWindow.cs` to your project's `Assets/Editor/`\n- Window \u2192 Unity Agent \u2192 Chat\n\n### Option C: Python client\n```bash\npython3 mcp_config/unity_mcp_client.py --list\npython3 mcp_config/unity_mcp_client.py --tool gameobject-create \\\n --args '{\"name\":\"Cube\",\"primitiveType\":\"Cube\"}'\n```\n\n## 3. Example Workflows\n\n**Create a scene from scratch:**\n> \"Create a medieval village with 5 houses, a central fountain, and a player character\"\n\n**Fix bugs interactively:**\n> \"Read the console logs and fix the NullReferenceException in PlayerHealth.cs\"\n\n**Profile and optimize:**\n> \"Profile the game, find the bottleneck, and suggest optimizations\"\n\n**Design a system:**\n> \"Create a ScriptableObject-based inventory system with item pickup, UI display, and save/load\"\n\n## 4. Dataset Pipeline (full run)\n\n```bash\n# Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# Download external datasets\npython3 05_download_and_merge_datasets.py\npython3 06_extract_unity_repos.py\npython3 07_integrate_new_datasets.py\n\n# Train + export\npython3 03_train_and_export_unity_agent.py\n```\n\n## 5. Fine-Tuning Stages\n\n```bash\n# Prepare only (merge datasets)\npython3 03_train_and_export_unity_agent.py --stage prepare\n\n# Train only (QLoRA)\npython3 03_train_and_export_unity_agent.py --stage train\n\n# Fuse adapters and export GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n```\n\n```\n\n\ud83d\udcc4 @file:`README.md` (1740 tokens)\n```markdown\n# Unity Agent Developer \u2014 Model Training Pipeline\n\nBuild and deploy a Unity game development agent model on Apple Silicon (M5 Pro).\n\n## Quick Start\n\n```bash\n# 1. Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# 2. Download + merge everything (internet needed)\npython3 05_download_and_merge_datasets.py # vishnuOI + function-calling\npython3 06_extract_unity_repos.py # ML-Agents, ECS, uGUI, netcode\npython3 07_integrate_new_datasets.py # Erocal Q&A + Mix & Jam mechanics\n\n# 3. Fine-tune (MLX QLoRA on Apple Silicon)\npython3 03_train_and_export_unity_agent.py\n\n# 4. Export to GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n\n# 5. Run\nollama create unity-agent -f Modelfile\nollama run unity-agent\n```\n\n## Components\n\n| # | Script | What It Does | Output |\n|---|---|---|---|\n| 01 | `01_build_unity_mcp_dataset.py` | Generates ~128 MCP tool-calling examples from real game-dev tasks | `datasets/unity_mcp_tool_calls_*.jsonl` |\n| 02 | `02_build_unity_design_patterns_dataset.py` | Generates ~75 design pattern + bug-fix examples | `datasets/unity_design_patterns_*.jsonl` |\n| 03 | `03_train_and_export_unity_agent.py` | MLX QLoRA train \u2192 fuse \u2192 GGUF export \u2192 eval | `adapters/`, `fused_model/`, `gguf/` |\n| 04 | `04_setup_unity_mcp_bridge.py` | Sets up Unity-MCP bridge, configs for Claude/Cursor/VS Code | `mcp_config/` |\n| 05 | `05_download_and_merge_datasets.py` | Downloads `vishnuOI/unity-dev-instructions` (46k) + `hermes-function-calling-v1` (2k), merges everything | `datasets/train.jsonl` |\n| 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` |\n| 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` |\n\n## Dataset Composition (after all merges)\n\n| Source | Rows | Coverage |\n|---|---|---|\n| `vishnuOI/unity-dev-instructions` | ~42k (after dedup) | Unity API docs, Stack Overflow, GitHub code \u2014 scripting, rendering, physics, editor, XR, UI, animation, networking, math, performance, input |\n| `NousResearch/hermes-function-calling-v1` | ~1.8k | Generic tool-calling / JSON-mode structured outputs |\n| `Erocal/Unity_ResolvedQuestions` | ~400 | Real resolved Q&A \u2014 physics, ECS, URP/HDRP, UI Toolkit, editor tooling, platform issues, animation rigging |\n| `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 |\n| ML-Agents example agents | ~29 | RL agent patterns (Crawler, Walker, FoodCollector, Soccer, Pyramids, WallJump, Worm, Hallway, 3DBall, PushBlock, GridWorld, Sorter) |\n| ML-Agents core + sensors | ~5 | Agent.cs lifecycle, ray perception, camera sensor, vector sensor, grid sensor |\n| ECS/DOTS samples | ~10 | Jobs, SystemBase, ISystem, IComponentData, IAspect, Baker patterns |\n| uGUI | ~10 | Graphic, Image, Text, Button, Slider, Toggle, ScrollRect, Dropdown, InputField, LayoutGroup |\n| Netcode for GameObjects | ~5 | NetworkBehaviour, NetworkVariable, RPC, NetworkObject patterns |\n| Unity MCP tool calls (custom) | ~128 | Agentic Unity tool use: scene setup, scripting, prefabs, debugging, profiling, testing, shaders, reflection |\n| Design patterns (custom) | ~75 | ScriptableObject architecture, Object Pooling, Singleton, State Machine, Observer, Strategy, Command, Factory, Flyweight, Save/Load, ServiceLocator, EventBus |\n| **Total (deduped)** | **~45k** | |\n\n### Datasets considered and rejected\n\n| Source | Why skipped |\n|---|---|\n| `ibranze/codellama_unity3d_v1/v2` | 535 + 2k rows, wrong format, overlaps with vishnuOI |\n| `ZorraZabb/code25pile75_shuffle_filtered_unity3d_dedup` | 1M rows of noise \u2014 substring \"unity3d\" filter catches Python, JS, news articles, medical papers |\n| `supergoose/buzz_sources_244_unity3d-asset` | 101 rows of git diff YAML for .meta files |\n| `robertzeng/unity3ddoc` | 113k rows but shallow/trivial responses (5-15 word generic definitions) |\n| `DLMveloper/DLM_DataSet` | 3.6M rows but only ~3,570 \"Unity\"-tagged, all trivial (variable declarations, basic array ops) |\n| `Saibalaji25/unity-csharp-scripts` | Requires login + T&C agreement, unknown quality, 11 downloads/mo |\n\n## Training Configuration (M5 Pro 64GB)\n\n```\nModel: Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)\nBatch size: 1\nLayers: 16/64 (25% LoRA)\nLR: 1e-4\nMax seq: 4096\nIters: 2000\nPeak mem: ~25GB\nDuration: ~45-60 min\n```\n\n## GGUF Output Quantization\n\n| Type | Size | Best For |\n|---|---|---|\n| Q4_K_M | ~20 GB | 24GB+ systems |\n| Q5_K_M | ~23 GB | 32GB+ systems |\n| **Q6_K** | **~27 GB** | **M5 Pro 64GB (recommended)** |\n| Q8_0 | ~34 GB | 64GB+ systems (max quality) |\n\n## Unity-MCP Bridge\n\nRun the bridge setup to configure connections to Claude Desktop, Cursor, VS Code, or the included Python client:\n\n```bash\npython3 04_setup_unity_mcp_bridge.py\n```\n\nThen connect any MCP-compatible AI client (Claude Desktop, Cursor, VS Code with Cline/Copilot, Codex) to the Unity Editor.\n\n## Project Structure\n\n```\nunity-agent-training/\n\u251c\u2500\u2500 01_build_unity_mcp_dataset.py # MCP tool-calling dataset\n\u251c\u2500\u2500 02_build_unity_design_patterns_dataset.py # Design patterns dataset\n\u251c\u2500\u2500 03_train_and_export_unity_agent.py # MLX training + GGUF export\n\u251c\u2500\u2500 04_setup_unity_mcp_bridge.py # Unity-MCP integration\n\u251c\u2500\u2500 05_download_and_merge_datasets.py # HF dataset download + merge\n\u251c\u2500\u2500 06_extract_unity_repos.py # Unity org repo C# extraction\n\u251c\u2500\u2500 07_integrate_new_datasets.py # Newly discovered datasets\n\u251c\u2500\u2500 Modelfile # Ollama model definition\n\u251c\u2500\u2500 QUICKSTART.md # Quick start guide\n\u251c\u2500\u2500 datasets/\n\u2502 \u251c\u2500\u2500 unity_mcp_tool_calls_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_design_patterns_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_api_instructions.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 unity_repos_{train,valid,test}.jsonl # (from repo extraction)\n\u2502 \u251c\u2500\u2500 function_calling_full.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 train.jsonl # Merged MLX-ready\n\u2502 \u251c\u2500\u2500 valid.jsonl # Merged MLX-ready\n\u2502 \u2514\u2500\u2500 test.jsonl # Merged MLX-ready\n\u251c\u2500\u2500 adapters/ # LoRA adapters (after training)\n\u251c\u2500\u2500 fused_model/ # Fused MLX model\n\u251c\u2500\u2500 gguf/ # GGUF quantized models\n\u2514\u2500\u2500 mcp_config/ # Bridge configuration\n \u251c\u2500\u2500 claude_desktop_config.json\n \u251c\u2500\u2500 vscode_mcp.json\n \u251c\u2500\u2500 unity_mcp_server_config.json\n \u251c\u2500\u2500 unity_mcp_client.py\n \u2514\u2500\u2500 UnityAgentWindow.cs\n```\n\n```<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\n[ASYNC DELEGATION BATCH COMPLETE \u2014 deleg_2d07ecd6]\nA 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 \u2014 act on these or re-dispatch if things have changed.\n\nDispatched: 2026-07-13 23:59:18 (11m48s ago)\nContext 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.\nRole: leaf Model: ? Total duration: 708.72s\n\n--- \u2713 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) ---\nContext length exceeded (379 tokens). Cannot compress further.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome: 10/10 repos cloned successfully, 0 failures.**\n\nAll 10 OpenClaw repositories are now present at `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | clawdinators | \u2705 |\n| 2 | openclaw-ansible | \u2705 |\n| 3 | casa | \u2705 |\n| 4 | spogo | \u2705 |\n| 5 | goplaces | \u2705 |\n| 6 | voice-community | \u2705 |\n| 7 | acpx | \u2705 |\n| 8 | community | \u2705 |\n| 9 | caclawphony | \u2705 |\n| 10 | discrawl | \u2705 |\n\nNo issues encountered \u2014 all clones completed cleanly with exit code 0.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Result: 10/10 repos cloned with zero failures.**\n\nCloned to `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | `slacrawl` | \u2705 Cloned |\n| 2 | `crabline` | \u2705 Cloned |\n| 3 | `releases` | \u2705 Cloned (15,075 files) |\n| 4 | `docs` | \u2705 Cloned |\n| 5 | `esp-openclaw-node` | \u2705 Cloned |\n| 6 | `shellbench` | \u2705 Cloned (23,628 files) |\n| 7 | `notcrawl` | \u2705 Cloned |\n| 8 | `clawsweeper` | \u2705 Cloned |\n| 9 | `clownfish` | \u2705 Cloned |\n| 10 | `crabpot` | \u2705 Cloned |\n\nNo issues encountered. All 10 repositories are present and fully cloned.\n\n--- \u2713 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) ---\nContext length exceeded: max compression attempts (3) reached.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome:** All 10 OpenClaw repositories cloned successfully into `/Volumes/this_and_that/projects/multi-repo_0/`.\n\n**Cloned repos:**\n1. \u2705 crawlbar\n2. \u2705 clawgrit-reports (previously cloned)\n3. \u2705 fs-safe\n4. \u2705 ask-molty\n5. \u2705 graincrawl\n6. \u2705 clickclack\n7. \u2705 clawdex\n8. \u2705 proxyline\n9. \u2705 telecrawl\n10. \u2705 clawpatch\n\n**Issues:** None. All 10 clones completed with exit code 0.\n\n--- \u2713 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) ---\nAll 10 repos cloned successfully. Here's the summary:\n\n**Outcome: 10/10 repos cloned successfully, 0 failures.**\n\nCloned to `/Volumes/this_and_that/projects/multi-repo_0/`:\n\n| # | Repo | Status |\n|---|------|--------|\n| 1 | crabfleet | \u2705 |\n| 2 | agent-skills | \u2705 |\n| 3 | rfcs | \u2705 |\n| 4 | rastermill | \u2705 |\n| 5 | libopus-wasm | \u2705 |\n| 6 | octopool | \u2705 |\n| 7 | clawpdf | \u2705 |\n| 8 | photoscrawl | \u2705 |\n| 9 | clawrouter | \u2705 |\n| 10 | imsgcrawl | \u2705 |\n\nNo issues encountered \u2014 all clones completed cleanly with no errors.\n\n--- \u2713 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) ---\nContext length exceeded (435 tokens). Cannot compress further.\n\n--- \u2713 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) ---\nContext length exceeded (1,277 tokens). Cannot compress further.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in General Assistant profile. Use the most efficient tool for each request.\n<|im_end|>\n<|im_start|>user\ncan unsloth fine tune and train MLX?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nwhich custom made model on HuggingFace is extraordinarly smart?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\ni need to know if you are making a GGUF model or MLX model?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nmy laptop crashed. Continue where we left or in case something went corrupt then lets start over<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nfind me best Llama 3.3 70B custom fine-tuned model for coding<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nprogress?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nI need to process this. Please help.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\n[IMPORTANT: Background process proc_0a497b8b5935 exited (exit code -15, SIGTERM).\nCommand: unset PYTHONPATH; /Users/jacobeen/.hermes/hermes-agent/venv/bin/python3 03_train_and_export_unity_agent.py train 2>&1\nOutput:\n4<00:26, 1.25s/it]\rCalculating loss...: 20%|\u2588\u2588 | 5/25 [00:04<00:21, 1.08s/it]\rCalculating loss...: 24%|\u2588\u2588\u258d | 6/25 [00:05<00:16, 1.18it/s]\rCalculating loss...: 28%|\u2588\u2588\u258a | 7/25 [00:06<00:16, 1.07it/s]\rCalculating loss...: 32%|\u2588\u2588\u2588\u258f | 8/25 [00:07<00:15, 1.07it/s]\rCalculating loss...: 36%|\u2588\u2588\u2588\u258c | 9/25 [00:08<00:17, 1.10s/it]\rCalculating loss...: 40%|\u2588\u2588\u2588\u2588 | 10/25 [00:10<00:19, 1.27s/it]\rCalculating loss...: 44%|\u2588\u2588\u2588\u2588\u258d | 11/25 [00:11<00:17, 1.28s/it]\rCalculating loss...: 48%|\u2588\u2588\u2588\u2588\u258a | 12/25 [00:12<00:14, 1.13s/it]\rCalculating loss...: 52%|\u2588\u2588\u2588\u2588\u2588\u258f | 13/25 [00:14<00:14, 1.24s/it]\rCalculating loss...: 56%|\u2588\u2588\u2588\u2588\u2588\u258c | 14/25 [00:14<00:10, 1.02it/s]\rCalculating loss...: 60%|\u2588\u2588\u2588\u2588\u2588\u2588 | 15/25 [00:14<00:08, 1.24it/s]\rCalculating loss...: 64%|\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 16/25 [00:16<00:09, 1.00s/it]\rCalculating loss...: 68%|\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 17/25 [00:16<00:07, 1.13it/s]\rCalculating loss...: 72%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f | 18/25 [00:17<00:06, 1.11it/s]\rCalculating loss...: 76%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 19/25 [00:18<00:04, 1.25it/s]\rCalculating loss...: 80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588 | 20/25 [00:18<00:03, 1.46it/s]\rCalculating loss...: 84%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 21/25 [00:19<00:03, 1.33it/s]\rCalculating loss...: 88%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258a | 22/25 [00:20<00:02, 1.24it/s]\rCalculating loss...: 92%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258f| 23/25 [00:21<00:01, 1.26it/s]\rCalculating loss...: 96%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c| 24/25 [00:22<00:00, 1.27it/s]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.10s/it]\rCalculating loss...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 25/25 [00:24<00:00, 1.04it/s]\nIter 100: Val loss nan, Val took 24.072s\nIter 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\n/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\n warnings.warn('resource_tracker: There appear to be %d '\n]<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nI need to process this. Please help.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nI need to process this. Please help.<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are in Data Analyst profile. Prioritize data loading, Python analysis, and charting.\n<|im_end|>\n<|im_start|>user\nwhich custom made model on HuggingFace is extraordinarly smart?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nlower iter so we won't hit OOM<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nCan you process proc_0a497b8b5935 exited (exit code -15,?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nprogress?<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nexecute this project plan\n\n--- Attached Context ---\n\n\ud83d\udcc4 @file:`QUICKSTART.md` (551 tokens)\n```markdown\n# Quick Start\n\n## 1. Run the Fine-Tuned Model\n\n### MLX (recommended for inference):\n```bash\nmlx_lm.generate --model ./fused_model --prompt \"Your Unity task...\"\n```\n\n### Ollama:\nCreate Modelfile in this directory:\n```dockerfile\nFROM ./gguf/unity-agent-Q6_K.gguf\nTEMPLATE \"{{ .Prompt }}\"\nPARAMETER temperature 0.3\nPARAMETER num_ctx 4096\n```\n\nThen:\n```bash\nollama create unity-agent -f Modelfile\nollama serve\n```\n\n### LM Studio:\n- Open LM Studio \u2192 \"My Models\" \u2192 \"Add Model\"\n- Select ./gguf/unity-agent-Q6_K.gguf\n- Start server on port 1234\n\n## 2. Connect to Unity Editor\n\n### Option A: Unity-MCP CLI (recommended)\n```bash\nnpx -y @aigamedeveloper/mcp-server\n# Connects Claude Desktop, Cursor, or VS Code to Unity Editor\n```\n\n### Option B: Custom Editor Window\n- Copy `mcp_config/UnityAgentWindow.cs` to your project's `Assets/Editor/`\n- Window \u2192 Unity Agent \u2192 Chat\n\n### Option C: Python client\n```bash\npython3 mcp_config/unity_mcp_client.py --list\npython3 mcp_config/unity_mcp_client.py --tool gameobject-create \\\n --args '{\"name\":\"Cube\",\"primitiveType\":\"Cube\"}'\n```\n\n## 3. Example Workflows\n\n**Create a scene from scratch:**\n> \"Create a medieval village with 5 houses, a central fountain, and a player character\"\n\n**Fix bugs interactively:**\n> \"Read the console logs and fix the NullReferenceException in PlayerHealth.cs\"\n\n**Profile and optimize:**\n> \"Profile the game, find the bottleneck, and suggest optimizations\"\n\n**Design a system:**\n> \"Create a ScriptableObject-based inventory system with item pickup, UI display, and save/load\"\n\n## 4. Dataset Pipeline (full run)\n\n```bash\n# Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# Download external datasets\npython3 05_download_and_merge_datasets.py\npython3 06_extract_unity_repos.py\npython3 07_integrate_new_datasets.py\n\n# Train + export\npython3 03_train_and_export_unity_agent.py\n```\n\n## 5. Fine-Tuning Stages\n\n```bash\n# Prepare only (merge datasets)\npython3 03_train_and_export_unity_agent.py --stage prepare\n\n# Train only (QLoRA)\npython3 03_train_and_export_unity_agent.py --stage train\n\n# Fuse adapters and export GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n```\n\n```\n\n\ud83d\udcc4 @file:`README.md` (1740 tokens)\n```markdown\n# Unity Agent Developer \u2014 Model Training Pipeline\n\nBuild and deploy a Unity game development agent model on Apple Silicon (M5 Pro).\n\n## Quick Start\n\n```bash\n# 1. Generate custom datasets\npython3 01_build_unity_mcp_dataset.py\npython3 02_build_unity_design_patterns_dataset.py\n\n# 2. Download + merge everything (internet needed)\npython3 05_download_and_merge_datasets.py # vishnuOI + function-calling\npython3 06_extract_unity_repos.py # ML-Agents, ECS, uGUI, netcode\npython3 07_integrate_new_datasets.py # Erocal Q&A + Mix & Jam mechanics\n\n# 3. Fine-tune (MLX QLoRA on Apple Silicon)\npython3 03_train_and_export_unity_agent.py\n\n# 4. Export to GGUF\npython3 03_train_and_export_unity_agent.py --stage export\n\n# 5. Run\nollama create unity-agent -f Modelfile\nollama run unity-agent\n```\n\n## Components\n\n| # | Script | What It Does | Output |\n|---|---|---|---|\n| 01 | `01_build_unity_mcp_dataset.py` | Generates ~128 MCP tool-calling examples from real game-dev tasks | `datasets/unity_mcp_tool_calls_*.jsonl` |\n| 02 | `02_build_unity_design_patterns_dataset.py` | Generates ~75 design pattern + bug-fix examples | `datasets/unity_design_patterns_*.jsonl` |\n| 03 | `03_train_and_export_unity_agent.py` | MLX QLoRA train \u2192 fuse \u2192 GGUF export \u2192 eval | `adapters/`, `fused_model/`, `gguf/` |\n| 04 | `04_setup_unity_mcp_bridge.py` | Sets up Unity-MCP bridge, configs for Claude/Cursor/VS Code | `mcp_config/` |\n| 05 | `05_download_and_merge_datasets.py` | Downloads `vishnuOI/unity-dev-instructions` (46k) + `hermes-function-calling-v1` (2k), merges everything | `datasets/train.jsonl` |\n| 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` |\n| 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` |\n\n## Dataset Composition (after all merges)\n\n| Source | Rows | Coverage |\n|---|---|---|\n| `vishnuOI/unity-dev-instructions` | ~42k (after dedup) | Unity API docs, Stack Overflow, GitHub code \u2014 scripting, rendering, physics, editor, XR, UI, animation, networking, math, performance, input |\n| `NousResearch/hermes-function-calling-v1` | ~1.8k | Generic tool-calling / JSON-mode structured outputs |\n| `Erocal/Unity_ResolvedQuestions` | ~400 | Real resolved Q&A \u2014 physics, ECS, URP/HDRP, UI Toolkit, editor tooling, platform issues, animation rigging |\n| `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 |\n| ML-Agents example agents | ~29 | RL agent patterns (Crawler, Walker, FoodCollector, Soccer, Pyramids, WallJump, Worm, Hallway, 3DBall, PushBlock, GridWorld, Sorter) |\n| ML-Agents core + sensors | ~5 | Agent.cs lifecycle, ray perception, camera sensor, vector sensor, grid sensor |\n| ECS/DOTS samples | ~10 | Jobs, SystemBase, ISystem, IComponentData, IAspect, Baker patterns |\n| uGUI | ~10 | Graphic, Image, Text, Button, Slider, Toggle, ScrollRect, Dropdown, InputField, LayoutGroup |\n| Netcode for GameObjects | ~5 | NetworkBehaviour, NetworkVariable, RPC, NetworkObject patterns |\n| Unity MCP tool calls (custom) | ~128 | Agentic Unity tool use: scene setup, scripting, prefabs, debugging, profiling, testing, shaders, reflection |\n| Design patterns (custom) | ~75 | ScriptableObject architecture, Object Pooling, Singleton, State Machine, Observer, Strategy, Command, Factory, Flyweight, Save/Load, ServiceLocator, EventBus |\n| **Total (deduped)** | **~45k** | |\n\n### Datasets considered and rejected\n\n| Source | Why skipped |\n|---|---|\n| `ibranze/codellama_unity3d_v1/v2` | 535 + 2k rows, wrong format, overlaps with vishnuOI |\n| `ZorraZabb/code25pile75_shuffle_filtered_unity3d_dedup` | 1M rows of noise \u2014 substring \"unity3d\" filter catches Python, JS, news articles, medical papers |\n| `supergoose/buzz_sources_244_unity3d-asset` | 101 rows of git diff YAML for .meta files |\n| `robertzeng/unity3ddoc` | 113k rows but shallow/trivial responses (5-15 word generic definitions) |\n| `DLMveloper/DLM_DataSet` | 3.6M rows but only ~3,570 \"Unity\"-tagged, all trivial (variable declarations, basic array ops) |\n| `Saibalaji25/unity-csharp-scripts` | Requires login + T&C agreement, unknown quality, 11 downloads/mo |\n\n## Training Configuration (M5 Pro 64GB)\n\n```\nModel: Qwen2.5-Coder-32B-Instruct (4-bit QLoRA)\nBatch size: 1\nLayers: 16/64 (25% LoRA)\nLR: 1e-4\nMax seq: 4096\nIters: 2000\nPeak mem: ~25GB\nDuration: ~45-60 min\n```\n\n## GGUF Output Quantization\n\n| Type | Size | Best For |\n|---|---|---|\n| Q4_K_M | ~20 GB | 24GB+ systems |\n| Q5_K_M | ~23 GB | 32GB+ systems |\n| **Q6_K** | **~27 GB** | **M5 Pro 64GB (recommended)** |\n| Q8_0 | ~34 GB | 64GB+ systems (max quality) |\n\n## Unity-MCP Bridge\n\nRun the bridge setup to configure connections to Claude Desktop, Cursor, VS Code, or the included Python client:\n\n```bash\npython3 04_setup_unity_mcp_bridge.py\n```\n\nThen connect any MCP-compatible AI client (Claude Desktop, Cursor, VS Code with Cline/Copilot, Codex) to the Unity Editor.\n\n## Project Structure\n\n```\nunity-agent-training/\n\u251c\u2500\u2500 01_build_unity_mcp_dataset.py # MCP tool-calling dataset\n\u251c\u2500\u2500 02_build_unity_design_patterns_dataset.py # Design patterns dataset\n\u251c\u2500\u2500 03_train_and_export_unity_agent.py # MLX training + GGUF export\n\u251c\u2500\u2500 04_setup_unity_mcp_bridge.py # Unity-MCP integration\n\u251c\u2500\u2500 05_download_and_merge_datasets.py # HF dataset download + merge\n\u251c\u2500\u2500 06_extract_unity_repos.py # Unity org repo C# extraction\n\u251c\u2500\u2500 07_integrate_new_datasets.py # Newly discovered datasets\n\u251c\u2500\u2500 Modelfile # Ollama model definition\n\u251c\u2500\u2500 QUICKSTART.md # Quick start guide\n\u251c\u2500\u2500 datasets/\n\u2502 \u251c\u2500\u2500 unity_mcp_tool_calls_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_design_patterns_{train,valid,test}.jsonl\n\u2502 \u251c\u2500\u2500 unity_api_instructions.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 unity_repos_{train,valid,test}.jsonl # (from repo extraction)\n\u2502 \u251c\u2500\u2500 function_calling_full.jsonl # (downloaded)\n\u2502 \u251c\u2500\u2500 train.jsonl # Merged MLX-ready\n\u2502 \u251c\u2500\u2500 valid.jsonl # Merged MLX-ready\n\u2502 \u2514\u2500\u2500 test.jsonl # Merged MLX-ready\n\u251c\u2500\u2500 adapters/ # LoRA adapters (after training)\n\u251c\u2500\u2500 fused_model/ # Fused MLX model\n\u251c\u2500\u2500 gguf/ # GGUF quantized models\n\u2514\u2500\u2500 mcp_config/ # Bridge configuration\n \u251c\u2500\u2500 claude_desktop_config.json\n \u251c\u2500\u2500 vscode_mcp.json\n \u251c\u2500\u2500 unity_mcp_server_config.json\n \u251c\u2500\u2500 unity_mcp_client.py\n \u2514\u2500\u2500 UnityAgentWindow.cs\n```\n\n```<|im_end|>", "source": "session_extraction"}
{"prompt": "<|im_start|>system\nYou are an AI assistant inside Hermes Desktop. Use tools when appropriate. Think before acting, verify after each tool call.\n<|im_end|>\n<|im_start|>user\nCan you process this?<|im_end|>", "source": "session_extraction"}