{
  "nbformat": 4,
  "nbformat_minor": 5,
  "cells": [
    {
      "cell_type": "markdown",
      "id": "post-00",
      "metadata": {},
      "source": [
        "# Makata Milestone4: checkpoint91 post-training evaluation\n",
        "\n",
        "One complete900-question FilBench Belebele evaluation. Unchanged FP32 base plus frozen LoRA; no quantization, training, prompt changes or checkpoint selection. Free T4 only; stop at any payment requirement. The32 instruction-following tasks remain separate and unscored.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "post-01",
      "metadata": {},
      "source": [
        "## 1. Verify source and install the pinned evaluation environment\n",
        "Select a free T4 first. No benchmark data loads in this section.\n"
      ]
    },
    {
      "cell_type": "code",
      "id": "post-02",
      "metadata": {},
      "source": [
        "import base64, hashlib, io, json, os, pathlib, subprocess, sys, zipfile\n",
        "data = base64.b64decode('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')\n",
        "assert hashlib.sha256(data).hexdigest() == 'd01c97b7d704371a87b1e6357aaf3f0ecfe1e3004c3171a2d7f22a7afb762924'\n",
        "ROOT = pathlib.Path('/content/makata-post-d01c97b7d704')\n",
        "ROOT.mkdir(parents=True, exist_ok=True)\n",
        "with zipfile.ZipFile(io.BytesIO(data)) as archive:\n",
        "    hashes = json.loads(archive.read('source-manifest.json'))\n",
        "    assert set(archive.namelist()) == set(hashes) | {'source-manifest.json'}\n",
        "    for name, expected in hashes.items():\n",
        "        content = archive.read(name)\n",
        "        assert hashlib.sha256(content).hexdigest() == expected\n",
        "        target = (ROOT / name).resolve()\n",
        "        assert target.is_relative_to(ROOT.resolve())\n",
        "        target.parent.mkdir(parents=True, exist_ok=True)\n",
        "        if target.exists():\n",
        "            assert target.read_bytes() == content\n",
        "        else:\n",
        "            target.write_bytes(content)\n",
        "os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
        "os.environ['HF_HUB_DISABLE_TELEMETRY'] = '1'\n",
        "subprocess.run(['nvidia-smi'], check=True)\n",
        "subprocess.run([sys.executable, '-m', 'pip', 'install', '-q', 'uv==0.11.28'], check=True)\n",
        "UV = [sys.executable, '-m', 'uv']\n",
        "ENV = ROOT / 'research/.venv'\n",
        "PY = ENV / 'bin/python'\n",
        "if not PY.exists():\n",
        "    subprocess.run(UV + ['venv', '--python', '3.12.13', str(ENV)], check=True)\n",
        "UPSTREAM = ROOT / 'research/.cache/filbench-lighteval'\n",
        "if not UPSTREAM.exists():\n",
        "    UPSTREAM.parent.mkdir(parents=True, exist_ok=True)\n",
        "    subprocess.run(['git', 'clone', 'https://github.com/filbench/lighteval.git', str(UPSTREAM)], check=True)\n",
        "subprocess.run(['git', '-C', str(UPSTREAM), 'checkout', '7ad52795d7d218d4679f93a019b58dde4b749c5b'], check=True)\n",
        "subprocess.run(UV + ['pip', 'sync', '--python', str(PY), '--torch-backend', 'cu126', 'requirements-post-training-colab.txt'], cwd=ROOT / 'research', check=True)\n",
        "subprocess.run(UV + ['pip', 'check', '--python', str(PY)], check=True)\n",
        "SESSION = ROOT / 'research/runs/post-training'\n",
        "INPUTS = ROOT / 'research/runs/adapter-inputs'\n",
        "RESULT_ZIP = ROOT / 'makata-post-training-results.zip'\n",
        "def run(command, *args):\n",
        "    subprocess.run([str(PY), '-X', 'utf8', str(ROOT / 'research/post_training.py'), command, *map(str, args)], cwd=ROOT, check=True)\n",
        "print('Frozen source SHA256:', 'd01c97b7d704371a87b1e6357aaf3f0ecfe1e3004c3171a2d7f22a7afb762924')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "post-03",
      "metadata": {},
      "source": [
        "## 2. Upload adapter, run offline checks and freeze\n",
        "Use Files → Upload to session storage for private-adapter.zip. No training data or retained benchmark records are uploaded.\n"
      ]
    },
    {
      "cell_type": "code",
      "id": "post-04",
      "metadata": {},
      "source": [
        "# Upload private-adapter.zip with Colab's Files pane -> Upload to session storage.\n",
        "UPLOAD = pathlib.Path('/content/private-adapter.zip')\n",
        "assert UPLOAD.is_file(), 'Upload the adapter bundle through the Files pane first.'\n",
        "assert hashlib.sha256(UPLOAD.read_bytes()).hexdigest() == 'a232f9ce61e11c99a548e016d1caedbdd3d4e1519e6d84cafaea1c9dbaed8039'\n",
        "run('unpack', '--archive', UPLOAD, '--output', INPUTS)\n",
        "subprocess.run([str(PY), '-X', 'utf8', '-m', 'unittest', 'discover', '-s', 'research/tests', '-p', 'test_post_training.py', '-v'], cwd=ROOT, check=True)\n",
        "run('integration', '--output', ROOT / 'research/runs/tiny-integration')\n",
        "run('freeze', '--inputs', INPUTS, '--output', SESSION)\n",
        "print('Ready: exact adapter91; float32; batch1; no benchmark accessed.')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "post-05",
      "metadata": {},
      "source": [
        "## 3. Start the single authorized benchmark run\n",
        "Expect roughly35–60 minutes on T4. The first synthetic-logit check proves disabled-adapter/base equivalence before test-set access.\n"
      ]
    },
    {
      "cell_type": "code",
      "id": "post-06",
      "metadata": {},
      "source": [
        "# This starts exactly one guarded subprocess. Do not rerun this cell.\n",
        "assert not (SESSION / 'attempt.json').exists(), 'An evaluation was already attempted.'\n",
        "assert 'PROCESS' not in globals(), 'Evaluation process already created.'\n",
        "LOG = (SESSION / 'evaluation.log').open('w')\n",
        "PROCESS = subprocess.Popen([str(PY), '-X', 'utf8', str(ROOT / 'research/post_training.py'), 'evaluate', '--inputs', str(INPUTS), '--output', str(SESSION)], cwd=ROOT, stdout=LOG, stderr=subprocess.STDOUT)\n",
        "print('Evaluation PID:', PROCESS.pid)\n",
        "print('One full900-question run started. Monitor with the next cell; never train or tune here.')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "post-07",
      "metadata": {},
      "source": [
        "## 4. Monitor safely\n",
        "This cell only reads process status/logs and may be rerun.\n"
      ]
    },
    {
      "cell_type": "code",
      "id": "post-08",
      "metadata": {},
      "source": [
        "code = PROCESS.poll()\n",
        "print('Running' if code is None else 'Process exit: ' + str(code))\n",
        "print((SESSION / 'evaluation.log').read_text(errors='replace')[-4500:])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "post-09",
      "metadata": {},
      "source": [
        "## 5. Verify and archive\n",
        "Run only after the process exits. Failed attempts remain incomplete.\n"
      ]
    },
    {
      "cell_type": "code",
      "id": "post-10",
      "metadata": {},
      "source": [
        "assert PROCESS.poll() is not None, 'Still running; use the monitor cell.'\n",
        "LOG.close()\n",
        "try:\n",
        "    assert PROCESS.returncode == 0, 'Incomplete run; preserve evidence, do not automatically retry.'\n",
        "    run('verify', '--output', SESSION)\n",
        "finally:\n",
        "    if not RESULT_ZIP.exists():\n",
        "        run('archive', '--output', SESSION, '--archive', RESULT_ZIP)\n",
        "print('Result archive:', RESULT_ZIP)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "post-11",
      "metadata": {},
      "source": [
        "## 6. Download evidence\n",
        "Import locally, then compare against the retained base predictions. Never publish the adapter or raw benchmark examples.\n"
      ]
    },
    {
      "cell_type": "code",
      "id": "post-12",
      "metadata": {},
      "source": [
        "from google.colab import files\n",
        "assert RESULT_ZIP.exists()\n",
        "files.download(str(RESULT_ZIP))\n",
        "print('Retain the runtime until local import and paired comparison pass.')\n"
      ],
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python3",
      "language": "python",
      "name": "python3"
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    "accelerator": "GPU",
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