ops/kaggle_kernel: ready-to-push submission bundle

ops/kaggle_kernel/kernel-metadata.json

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+{
+  "id": "tchbcb/qwenjev-submission",
+  "title": "qwenjev-submission",
+  "code_file": "submission.ipynb",
+  "language": "python",
+  "kernel_type": "notebook",
+  "is_private": "false",
+  "enable_gpu": "true",
+  "enable_internet": "false",
+  "dataset_sources": [],
+  "competition_sources": [
+    "arc-prize-2026-arc-agi-3"
+  ],
+  "kernel_sources": []
+}

ops/kaggle_kernel/submission.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": "# QwenJev hybrid agent - ARC Prize 2026 (ARC-AGI-3)\n\nSystem-1: Qwen3-VL-2B + QLoRA adapters (vision tower intact, T4-trained)  \nSystem-2: Qwen3.5-9B multimodal controller (frozen, selective wake-up)\n\nArtifacts (attach as Kaggle Datasets):\n  * qwenjev-code      -> this repo (qwenjev/ package tree)\n  * qwenjev-weights   -> t4_assets_v2/checkpoints/* (qwenjev_s1.pt,\n                         hf_lora/, stage_a_lora/) + Qwen3-VL-2B base\n  * qwenjev-wheels    -> t4_assets/wheels/*.whl (57 pinned wheels)\n"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": "# 1. offline deps: our pinned wheels first, then the competition bundle\nimport glob, subprocess, sys\nours = sorted(glob.glob('/kaggle/input/qwenjev-wheels/*.whl'))\nif ours:\n    subprocess.run([sys.executable, '-m', 'pip', 'install', '--no-index',\n                    '--find-links', ours[0].rsplit('/', 1)[0],\n                    'transformers==5.16.1', 'peft==0.20.0',\n                    'accelerate==1.14.0', 'bitsandbytes==0.50.2'],\n                   check=False)\ncomp = sorted(glob.glob('/kaggle/input/*/arc_agi_3_wheels/*.whl'))\nif comp:\n    subprocess.run([sys.executable, '-m', 'pip', 'install', '--no-index',\n                    '--find-links', comp[0].rsplit('/', 1)[0], 'arc-agi'],\n                   check=False)\n"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": "# 2. offline model paths (everything pre-attached, no internet)\nimport os\nW = '/kaggle/input/qwenjev-weights'\nos.environ['HF_HUB_OFFLINE'] = '1'\nos.environ.setdefault('QWEN3VL2B_BASE', W + '/Qwen3-VL-2B-Instruct')  # base\nos.environ.setdefault('S1_CHECKPOINT', W + '/checkpoints')  # qwenjev_s1.pt + hf_lora/\nos.environ.setdefault('S2_ENDPOINT', '')   # in-process transformers path\n"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": "# 3. build System-1 exactly as the T4 eval did (merged adapters)\nimport sys, torch\nsys.path.insert(0, '/kaggle/input/qwenjev-code')   # this repo as a Dataset\nfrom qwenjev.system1 import System1, System1Config\nfrom qwenjev.hf_compat import resume_adapters\n\nckpt = os.environ['S1_CHECKPOINT']\ncfg = System1Config(backend='hf', hf_model=os.environ['QWEN3VL2B_BASE'],\n                    hf_quant4=False, max_leaf_tokens=512, score_levels=10,\n                    leaf_batch_size=8)                 # inference chunk\ns1 = System1(cfg)                          # device auto-resolved\ns1.load_heads(ckpt + '/qwenjev_s1.pt')\nresume_adapters(s1._backbone, ckpt + '/hf_lora', merge=True)  # fold LoRA\n\n# 4. run the hybrid agent over the hidden games; the official\n#    sample-submission driver (competition Code tab) owns the game\n#    loop - plug this System-1 into QwenJevAgent as shown there.\n# from agent.qwenjev_agent import QwenJevAgent\n# AGENT = QwenJevAgent(system1=s1)\n"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}