{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# SakThai Plus 1.5B v11 - Free T4 Training\n",
        "\n",
        "Runs the same QLoRA+rsLoRA recipe as the HF Job version, but on the **free Kaggle T4 GPU** (30h/week).\n",
        "Dataset: `sakthai-combined-v11` (2,965 rows, bench-aligned tool schemas).\n",
        "\n",
        "**Setup before running:**\n",
        "1. Upload `train-sakthai-1.5b-kaggle.py` to the notebook (Add Input > Upload as file, or paste into a cell with `%%writefile`)\n",
        "2. Add `HF_TOKEN` as a Kaggle Secret (Settings > Add Secret, name `HF_TOKEN`)\n",
        "3. Select Accelerator: `GPU T4 x2` (or T4 x1)\n",
        "4. Run all cells\n",
        "\n",
        "Output: adapter `Nanthasit/sakthai-plus-1.5b-lora` + merged `Nanthasit/sakthai-plus-1.5b`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from kaggle_secrets import UserSecretsClient\n",
        "import os\n",
        "os.environ['HF_TOKEN'] = UserSecretsClient().get_secret('HF_TOKEN')\n",
        "print('HF_TOKEN set:', bool(os.environ['HF_TOKEN']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "!pip install -q transformers trl peft datasets accelerate bitsandbytes huggingface_hub\n",
        "!pip show trl | head -1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "!python train-sakthai-1.5b-kaggle.py"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {"provenance": []},
    "kaggle": {
      "accelerator": "GPU T4 x2",
      "dataSources": [],
      "kernelType": "Notebook",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.11"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}