{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2vQXvnUUsTzI"
      },
      "source": [
        "To run this, press \"*Runtime*\" and press \"*Run all*\" on a Google Colab L4 instance!\n",
        "<div class=\"align-center\">\n",
        "<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
        "<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
        "<a href=\"https://unsloth.ai/docs/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a> Join Discord if you need help + ⭐ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐\n",
        "</div>\n",
        "\n",
        "To install Unsloth on your local device, follow [our guide](https://unsloth.ai/docs/get-started/install). This notebook is licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).\n",
        "\n",
        "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & how to save it"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7j01DfVgsTzJ"
      },
      "source": [
        "### News"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6dT42nHksTzJ"
      },
      "source": [
        "Introducing **Unsloth Studio** - a new open source, no-code web UI to train and run LLMs. [Blog](https://unsloth.ai/docs/new/studio) • [Notebook](https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb)\n",
        "\n",
        "<table><tr>\n",
        "<td align=\"center\"><a href=\"https://unsloth.ai/docs/new/studio\"><img src=\"https://unsloth.ai/docs/~gitbook/image?url=https%3A%2F%2F3215535692-files.gitbook.io%2F~%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FxhOjnexMCB3dmuQFQ2Zq%252Fuploads%252FxV1PO5DbF3ksB51nE2Tw%252Fmore%2520cropped%2520ui%2520for%2520homepage.png%3Falt%3Dmedia%26token%3Df75942c9-3d8d-4b59-8ba2-1a4a38de1b86&width=376&dpr=3&quality=100&sign=a663c397&sv=2\" width=\"200\" height=\"120\" alt=\"Unsloth Studio Training UI\"></a><br><sub><b>Train models</b> — no code needed</sub></td>\n",
        "<td align=\"center\"><a href=\"https://unsloth.ai/docs/new/studio\"><img src=\"https://unsloth.ai/docs/~gitbook/image?url=https%3A%2F%2F3215535692-files.gitbook.io%2F~%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FxhOjnexMCB3dmuQFQ2Zq%252Fuploads%252FRCnTAZ6Uh88DIlU3g0Ij%252Fmainpage%2520unsloth.png%3Falt%3Dmedia%26token%3D837c96b6-bd09-4e81-bc76-fa50421e9bfb&width=376&dpr=3&quality=100&sign=c1a39da1&sv=2\" width=\"200\" height=\"120\" alt=\"Unsloth Studio Chat UI\"></a><br><sub><b>Run GGUF models</b> on Mac, Windows & Linux</sub></td>\n",
        "</tr></table>\n",
        "\n",
        "Train MoEs - DeepSeek, GLM, Qwen and gpt-oss 12x faster with 35% less VRAM. [Blog](https://unsloth.ai/docs/new/faster-moe)\n",
        "\n",
        "Ultra Long-Context Reinforcement Learning is here with 7x more context windows! [Blog](https://unsloth.ai/docs/new/grpo-long-context)\n",
        "\n",
        "New in Reinforcement Learning: [FP8 RL](https://unsloth.ai/docs/new/fp8-reinforcement-learning) • [Vision RL](https://unsloth.ai/docs/new/vision-reinforcement-learning-vlm-rl) • [Standby](https://unsloth.ai/docs/basics/memory-efficient-rl) • [gpt-oss RL](https://unsloth.ai/docs/new/gpt-oss-reinforcement-learning)\n",
        "\n",
        "Visit our docs for all our [model uploads](https://unsloth.ai/docs/get-started/unsloth-model-catalog) and [notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K7fgQkATsTzK"
      },
      "source": [
        "### Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "vA7IKFdUsTzK"
      },
      "outputs": [],
      "source": "%%capture\nimport os, re\nif \"COLAB_\" not in \"\".join(os.environ.keys()):\n    !pip install unsloth  # Do this in local & cloud setups\nelse:\n    import torch; v = re.match(r'[\\d]{1,}\\.[\\d]{1,}', str(torch.__version__)).group(0)\n    xformers = 'xformers==' + {'2.10':'0.0.34','2.9':'0.0.33.post1','2.8':'0.0.32.post2'}.get(v, \"0.0.34\")\n    !pip install sentencepiece protobuf \"datasets==4.3.0\" \"huggingface_hub>=0.34.0\" hf_transfer\n    !pip install --no-deps unsloth_zoo bitsandbytes accelerate {xformers} peft trl triton unsloth\n!pip install --no-deps transformers==5.5.0\n!pip install torchcodec\nimport torch; torch._dynamo.config.recompile_limit = 64;"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "Mp4i13PHsTzK"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install --no-deps --upgrade timm # For Gemma 4 vision/audio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GFOEZbP7ONMs"
      },
      "source": [
        "### Unsloth"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "QmUBVEnvCDJv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380,
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        },
        "outputId": "dbb6527a-1f29-4b7d-b338-7568d7a7ae00"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
            "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
            "==((====))==  Unsloth 2026.4.4: Fast Gemma4 patching. Transformers: 5.5.0.\n",
            "   \\\\   /|    Tesla T4. Num GPUs = 1. Max memory: 14.563 GB. Platform: Linux.\n",
            "O^O/ \\_/ \\    Torch: 2.10.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.6.0\n",
            "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.34. FA2 = False]\n",
            " \"-____-\"     Free license: http://github.com/unslothai/unsloth\n",
            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/16.0G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "99bff77887c742b188878c8375844c00"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading weights:   0%|          | 0/2130 [00:00<?, ?it/s]"
            ],
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              "version_minor": 0,
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            }
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/208 [00:00<?, ?B/s]"
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          "metadata": {}
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              "processor_config.json: 0.00B [00:00, ?B/s]"
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          "data": {
            "text/plain": [
              "chat_template.jinja: 0.00B [00:00, ?B/s]"
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "13540698510845829fd4792cce58ae6e"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json: 0.00B [00:00, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ee5cfb4e1026483e8af39bce3177d2bb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/32.2M [00:00<?, ?B/s]"
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            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from unsloth import FastVisionModel # FastLanguageModel for LLMs\n",
        "import torch\n",
        "\n",
        "gemma4_models = [\n",
        "    # Gemma-4 instruct models:\n",
        "    \"unsloth/gemma-4-E2B-it\",\n",
        "    \"unsloth/gemma-4-E4B-it\",\n",
        "    \"unsloth/gemma-4-31B-it\",\n",
        "    \"unsloth/gemma-4-26B-A4B-it\",\n",
        "    # Gemma-4 base models:\n",
        "    \"unsloth/gemma-4-E2B\",\n",
        "    \"unsloth/gemma-4-E4B\",\n",
        "    \"unsloth/gemma-4-31B\",\n",
        "    \"unsloth/gemma-4-26B-A4B\",\n",
        "] # More models at https://huggingface.co/unsloth\n",
        "\n",
        "model, processor = FastVisionModel.from_pretrained(\n",
        "    \"unsloth/gemma-4-E4B-it\",\n",
        "    load_in_4bit = True, # Use 4bit to reduce memory use. False for 16bit LoRA.\n",
        "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for long context\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXd9bTZd1aaL"
      },
      "source": [
        "We now add LoRA adapters for parameter efficient fine-tuning, allowing us to train only 1% of all model parameters efficiently.\n",
        "\n",
        "**[NEW]** We also support fine-tuning only the vision component, only the language component, or both. Additionally, you can choose to fine-tune the attention modules, the MLP layers, or both!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "6bZsfBuZDeCL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0a30a82a-ec41-4b32-8a07-0a6bfe1f84fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unsloth: Making `model.base_model.model.model.language_model` require gradients\n"
          ]
        }
      ],
      "source": [
        "model = FastVisionModel.get_peft_model(\n",
        "    model,\n",
        "    finetune_vision_layers     = True, # False if not finetuning vision layers\n",
        "    finetune_language_layers   = True, # False if not finetuning language layers\n",
        "    finetune_attention_modules = True, # False if not finetuning attention layers\n",
        "    finetune_mlp_modules       = True, # False if not finetuning MLP layers\n",
        "\n",
        "    r = 32,                           # The larger, the higher the accuracy, but might overfit\n",
        "    lora_alpha = 32,                  # Recommended alpha == r at least\n",
        "    lora_dropout = 0,\n",
        "    bias = \"none\",\n",
        "    random_state = 3407,\n",
        "    use_rslora = False,               # We support rank stabilized LoRA\n",
        "    loftq_config = None,               # And LoftQ\n",
        "    target_modules = \"all-linear\",    # Optional now! Can specify a list if needed\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vITh0KVJ10qX"
      },
      "source": [
        "<a name=\"Data\"></a>\n",
        "### Data Prep\n",
        "We'll use a sampled dataset of handwritten math formulas. The objective is to convert these images into a computer-readable format—specifically LaTeX—so they can be rendered. This is particularly useful for complex expressions.\n",
        "\n",
        "You can access the dataset [here](https://huggingface.co/datasets/unsloth/LaTeX_OCR). The full dataset is [here](https://huggingface.co/datasets/linxy/LaTeX_OCR)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "LjY75GoYUCB8",
        "colab": {
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            "72e9d3ea09714d1b868d70a85cc1732d",
            "1ece85a4c59d4f52b737399c1f2751c7",
            "a2e6128a027049a88094639640518b73",
            "c9eab4588c46468f8a476936c7c69fa3",
            "dedda5c9231042ba929049b41fbf1810",
            "736b7e9afc8841ef81341d03934cb646",
            "eefb235872ed4a858432d2abbc78e228"
          ]
        },
        "outputId": "ef8a166a-89f8-4d96-c179-f11766183203"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/519 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5951a9e9c2e947c68add360a7dc5ce92"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "data/train-00000-of-00001.parquet:   0%|          | 0.00/344M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4be36a52100f4428bdde5c677f97da30"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "data/test-00000-of-00001.parquet:   0%|          | 0.00/38.2M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1a5cfa89e65b4d5eb4bad5b8384941d9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/68686 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3507926c364e4d2492f44547e8be6026"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/7632 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "49bae6aaa3fd4c2f9ae513c34a6224c4"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"unsloth/LaTeX_OCR\", split = \"train\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W1W2Qhsz6rUT"
      },
      "source": [
        "Let's take an overview of the dataset. We'll examine the second image and its corresponding caption."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "bfcSGwIb6p_R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6bece379-4ee3-45c7-efbf-3e00c5387936"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['image', 'text'],\n",
              "    num_rows: 68686\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "uOLWY2936t1n",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "ed0547da-90d6-49cf-d7e3-3fb4362dd1ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=320x50>"
            ],
            "image/png": "[encoded data, 8500 characters]\n",
            "image/jpeg": "[encoded data, 5008 characters]\n"
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "dataset[2][\"image\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "lXjfJr4W6z8P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "8cf867a2-2e4b-4d4c-9d4d-a790b17603df"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'H ^ { \\\\prime } = \\\\beta N \\\\int d \\\\lambda \\\\biggl \\\\{ \\\\frac { 1 } { 2 \\\\beta ^ { 2 } N ^ { 2 } } \\\\partial _ { \\\\lambda } \\\\zeta ^ { \\\\dagger } \\\\partial _ { \\\\lambda } \\\\zeta + V ( \\\\lambda ) \\\\zeta ^ { \\\\dagger } \\\\zeta \\\\biggr \\\\} \\\\ .'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "dataset[2][\"text\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rKHxfZua1CrS"
      },
      "source": [
        "We can also render LaTeX directly in the browser!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "nPopsxAC1CrS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "6a4ef07a-7a3f-4c40-8f94-f7c892a6c0fe"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Math object>"
            ],
            "text/latex": "$\\displaystyle \\sigma ^ { \\mu } \\frac { \\lambda ^ { a } } { 2 } A _ { \\mu } ^ { a } .$"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from IPython.display import display, Math, Latex\n",
        "\n",
        "latex = dataset[3][\"text\"]\n",
        "display(Math(latex))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K9CBpiISFa6C"
      },
      "source": [
        "To format the dataset, all vision fine-tuning tasks should follow this format:\n",
        "\n",
        "```python\n",
        "[\n",
        "    {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": [\n",
        "            {\"type\": \"text\", \"text\": instruction},\n",
        "            {\"type\": \"image\", \"image\": sample[\"image\"]},\n",
        "        ],\n",
        "    },\n",
        "    {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": [\n",
        "            {\"type\": \"text\", \"text\": instruction},\n",
        "            {\"type\": \"image\", \"image\": sample[\"image\"]},\n",
        "        ],\n",
        "    },\n",
        "]\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "oPXzJZzHEgXe"
      },
      "outputs": [],
      "source": [
        "instruction = \"Write the LaTeX representation for this image.\"\n",
        "\n",
        "def convert_to_conversation(sample):\n",
        "    conversation = [\n",
        "        {\n",
        "            \"role\": \"user\",\n",
        "            \"content\": [\n",
        "                {\"type\": \"text\", \"text\": instruction},\n",
        "                {\"type\": \"image\", \"image\": sample[\"image\"]},\n",
        "            ],\n",
        "        },\n",
        "        {\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": sample[\"text\"]}]},\n",
        "    ]\n",
        "    return {\"messages\": conversation}\n",
        "pass"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FY-9u-OD6_gE"
      },
      "source": [
        "Let's convert the dataset into the \"correct\" format for finetuning:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "gFW2qXIr7Ezy"
      },
      "outputs": [],
      "source": [
        "converted_dataset = [convert_to_conversation(sample) for sample in dataset]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ndDUB23CGAC5"
      },
      "source": [
        "The first example is now structured like below:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "gGFzmplrEy9I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0033ce5a-b6a2-4031-e333-febdc8ee37ff"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'messages': [{'role': 'user',\n",
              "   'content': [{'type': 'text',\n",
              "     'text': 'Write the LaTeX representation for this image.'},\n",
              "    {'type': 'image',\n",
              "     'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=160x40>}]},\n",
              "  {'role': 'assistant',\n",
              "   'content': [{'type': 'text',\n",
              "     'text': '{ \\\\frac { N } { M } } \\\\in { \\\\bf Z } , { \\\\frac { M } { P } } \\\\in { \\\\bf Z } , { \\\\frac { P } { Q } } \\\\in { \\\\bf Z }'}]}]}"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "converted_dataset[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MsRPBIb0JJ6c"
      },
      "source": [
        "Lets take the Gemma 4 instruction chat template and use it in our base model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "exoDVEvmJN-6"
      },
      "outputs": [],
      "source": [
        "from unsloth import get_chat_template\n",
        "\n",
        "processor = get_chat_template(\n",
        "    processor,\n",
        "    \"gemma-4\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FecKS-dA82f5"
      },
      "source": [
        "Before fine-tuning, let us evaluate the base model's performance. We do not expect strong results, as it has not encountered this chat template before."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "vcat4UxA81vr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1184ad38-de99-49bd-d623-48b0892e8daf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "```latex\n",
            "H' = \\beta N \\int d\\lambda \\left\\{ \\frac{1}{2\\beta^2 N^2} \\partial_\\lambda \\zeta^\\dagger \\partial_\\lambda \\zeta + V(\\lambda) \\zeta^\\dagger \\zeta \\right\\}\n",
            "```<turn|>\n"
          ]
        }
      ],
      "source": [
        "image = dataset[2][\"image\"]\n",
        "instruction = \"Write the LaTeX representation for this image.\"\n",
        "\n",
        "messages = [\n",
        "    {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": instruction}],\n",
        "    }\n",
        "]\n",
        "input_text = processor.apply_chat_template(messages, add_generation_prompt = True)\n",
        "inputs = processor(\n",
        "    image,\n",
        "    input_text,\n",
        "    add_special_tokens = False,\n",
        "    return_tensors = \"pt\",\n",
        ").to(\"cuda\")\n",
        "\n",
        "from transformers import TextStreamer\n",
        "\n",
        "text_streamer = TextStreamer(processor, skip_prompt = True)\n",
        "result = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n",
        "                        use_cache = True, temperature = 1.0, top_p = 0.95, top_k = 64)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FeAiMlQ71CrS"
      },
      "source": [
        "You can see it's absolutely terrible! It doesn't follow instructions at all"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idAEIeSQ3xdS"
      },
      "source": [
        "<a name=\"Train\"></a>\n",
        "### Train the model\n",
        "Now let's train our model. We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support `DPOTrainer` and `GRPOTrainer` for reinforcement learning!\n",
        "\n",
        "We use our new `UnslothVisionDataCollator` which will help in our vision finetuning setup."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "95_Nn-89DhsL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ecdc9c7a-7177-45a5-cdee-77f06ec9842f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unsloth: Model does not have a default image size - using 512\n"
          ]
        }
      ],
      "source": [
        "from unsloth.trainer import UnslothVisionDataCollator\n",
        "from trl import SFTTrainer, SFTConfig\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model = model,\n",
        "    train_dataset = converted_dataset,\n",
        "    processing_class = processor.tokenizer,\n",
        "    data_collator = UnslothVisionDataCollator(model, processor),\n",
        "    args = SFTConfig(\n",
        "        per_device_train_batch_size = 1,\n",
        "        gradient_accumulation_steps = 4,\n",
        "        max_grad_norm = 0.3,\n",
        "        warmup_ratio = 0.03,\n",
        "        max_steps = 60,\n",
        "        # num_train_epochs = 2, # Set this instead of max_steps for full training runs\n",
        "        learning_rate = 2e-4,\n",
        "        logging_steps = 1,\n",
        "        save_strategy = \"steps\",\n",
        "        optim = \"adamw_8bit\",\n",
        "        weight_decay = 0.001,\n",
        "        lr_scheduler_type = \"cosine\",\n",
        "        seed = 3407,\n",
        "        output_dir = \"outputs\",\n",
        "        report_to = \"none\", # For Weights and Biases or others\n",
        "\n",
        "        # You MUST put the below items for vision finetuning:\n",
        "        remove_unused_columns = False,\n",
        "        dataset_text_field = \"\",\n",
        "        dataset_kwargs = {\"skip_prepare_dataset\": True},\n",
        "        max_length = 2048,\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "cellView": "form",
        "id": "2ejIt2xSNKKp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e46dd33-1258-4978-abce-4a0cd70c28ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU = Tesla T4. Max memory = 14.563 GB.\n",
            "10.182 GB of memory reserved.\n"
          ]
        }
      ],
      "source": [
        "# @title Show current memory stats\n",
        "gpu_stats = torch.cuda.get_device_properties(0)\n",
        "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
        "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
        "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
        "print(f\"{start_gpu_memory} GB of memory reserved.\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "yqxqAZ7KJ4oL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1611d77d-130b-4509-f49d-da4422a7a0e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': 2}.\n",
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
            "   \\\\   /|    Num examples = 68,686 | Num Epochs = 1 | Total steps = 60\n",
            "O^O/ \\_/ \\    Batch size per device = 1 | Gradient accumulation steps = 4\n",
            "\\        /    Data Parallel GPUs = 1 | Total batch size (1 x 4 x 1) = 4\n",
            " \"-____-\"     Trainable parameters = 82,444,288 of 8,078,600,736 (1.02% trained)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unsloth: Will smartly offload gradients to save VRAM!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [60/60 05:25, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>14.862482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>15.983265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>15.897610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>16.153982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>16.364323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>12.335323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>6.774734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>5.239524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>4.966103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>3.271874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>3.070101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>2.938359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>2.469718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>2.040107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>1.912474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>2.391622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>2.170793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>1.760992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>1.899228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>1.493368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>1.680966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>1.567590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>1.590823</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>1.522763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>1.543306</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>1.648688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>1.278674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>1.528238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>1.577930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>1.366876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>1.197964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>1.834775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>1.473532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>1.572434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>1.176874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>1.155894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>1.070827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>1.249489</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>1.477776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>1.618961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>1.207805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>0.868922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>1.415766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>1.279130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>1.078420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>1.076866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>1.207423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>1.021715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>1.184945</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.114685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>1.116125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>1.270778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>0.977663</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>1.013628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>1.137954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>1.791641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>0.879617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>1.045508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>1.497848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>1.182628</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "trainer_stats = trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "cellView": "form",
        "id": "pCqnaKmlO1U9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "308b4484-426c-4d82-b8b2-4f40918425d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "353.8392 seconds used for training.\n",
            "5.9 minutes used for training.\n",
            "Peak reserved memory = 10.777 GB.\n",
            "Peak reserved memory for training = 0.595 GB.\n",
            "Peak reserved memory % of max memory = 74.003 %.\n",
            "Peak reserved memory for training % of max memory = 4.086 %.\n"
          ]
        }
      ],
      "source": [
        "# @title Show final memory and time stats\n",
        "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
        "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
        "used_percentage = round(used_memory / max_memory * 100, 3)\n",
        "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n",
        "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
        "print(\n",
        "    f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n",
        ")\n",
        "print(f\"Peak reserved memory = {used_memory} GB.\")\n",
        "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
        "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
        "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ekOmTR1hSNcr"
      },
      "source": [
        "<a name=\"Inference\"></a>\n",
        "### Inference\n",
        "Let's run the model! You can modify the instruction and input—just leave the output blank.\n",
        "\n",
        "We'll use the best hyperparameters for inference on Gemma: `top_p=0.95`, `top_k=64`, and `temperature=1.0`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "kR3gIAX-SM2q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79616e26-3a56-41d0-8306-06889f63b9fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\\[\\left[[B_n^{\\pm}, b_2^{\\mp}], b_2^{\\pm}\\right] = nB_n^{\\pm}, \\quad \\left[[B_n^{\\mp}, b_2^{\\pm}], b_2^{\\mp}\\right] = nB_n^{\\mp}.\\]<turn|>\n"
          ]
        }
      ],
      "source": [
        "image = dataset[10][\"image\"]\n",
        "instruction = \"Write the LaTeX representation for this image.\"\n",
        "\n",
        "messages = [\n",
        "    {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": instruction}],\n",
        "    }\n",
        "]\n",
        "\n",
        "input_text = processor.apply_chat_template(messages, add_generation_prompt = True)\n",
        "\n",
        "inputs = processor(\n",
        "    image,\n",
        "    input_text,\n",
        "    add_special_tokens = False,\n",
        "    return_tensors = \"pt\",\n",
        ").to(\"cuda\")\n",
        "\n",
        "from transformers import TextStreamer\n",
        "\n",
        "text_streamer = TextStreamer(processor, skip_prompt = True)\n",
        "result = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n",
        "                        use_cache = True, temperature = 1.0, top_p = 0.95, top_k = 64)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMuVrWbjAzhc"
      },
      "source": [
        "<a name=\"Save\"></a>\n",
        "### Saving, loading finetuned models\n",
        "To save the final model as LoRA adapters, use Hugging Face’s `push_to_hub` for online saving, or `save_pretrained` for local storage.\n",
        "\n",
        "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "upcOlWe7A1vc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "808a9a7d-0e26-4635-c918-41f0525310eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['gemma_4_lora/processor_config.json']"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "model.save_pretrained(\"gemma_4_lora\")  # Local saving\n",
        "processor.save_pretrained(\"gemma_4_lora\")\n",
        "# model.push_to_hub(\"your_name/gemma_4_lora\", token = \"YOUR_HF_TOKEN\") # Online saving\n",
        "# processor.push_to_hub(\"your_name/gemma_4_lora\", token = \"YOUR_HF_TOKEN\") # Online saving"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AEEcJ4qfC7Lp"
      },
      "source": [
        "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "MKX_XKs_BNZR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b7d5dfd-34b8-45e0-ef9e-1378d260fd7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This equation appears to be from a field of physics, likely **theoretical physics**, specifically **electrodynamics**, **gauge theories**, or **relativistic field theory**, given the use of Greek letters ($\\mu, \\alpha, \\beta$), tensor notation ($D^\\alpha_\\mu \\tilde{A}^\\alpha_\\mu$), and the structure of the equation.\n",
            "\n",
            "Here is a breakdown of the components and the likely physical meaning:\n",
            "\n",
            "### Breakdown of the Equation\n",
            "\n",
            "$$D^\\alpha_\\mu \\tilde{A}^\\alpha_\\mu = 0$$\n",
            "\n",
            "1.  **$D^\\alpha_\\mu$ (Covariant Derivative or Differential\n"
          ]
        }
      ],
      "source": [
        "if False:\n",
        "    from unsloth import FastVisionModel\n",
        "\n",
        "    model, processor = FastVisionModel.from_pretrained(\n",
        "        model_name = \"gemma_4_lora\",  # YOUR MODEL YOU USED FOR TRAINING\n",
        "        load_in_4bit = True,  # Set to False for 16bit LoRA\n",
        "    )\n",
        "\n",
        "sample = dataset[1]\n",
        "image = sample[\"image\"].convert(\"RGB\")\n",
        "messages = [\n",
        "    {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": [\n",
        "            {\n",
        "                \"type\": \"text\",\n",
        "                \"text\": sample[\"text\"],\n",
        "            },\n",
        "            {\n",
        "                \"type\": \"image\",\n",
        "            },\n",
        "        ],\n",
        "    },\n",
        "]\n",
        "input_text = processor.apply_chat_template(messages, add_generation_prompt = True)\n",
        "inputs = processor(\n",
        "    image,\n",
        "    input_text,\n",
        "    add_special_tokens = False,\n",
        "    return_tensors = \"pt\",\n",
        ").to(\"cuda\")\n",
        "\n",
        "from transformers import TextStreamer\n",
        "\n",
        "text_streamer = TextStreamer(processor.tokenizer, skip_prompt = True)\n",
        "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n",
        "                   use_cache = True, temperature = 1.0, top_p = 0.95, top_k = 64)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f422JgM9sdVT"
      },
      "source": [
        "### Saving to float16 for VLLM\n",
        "\n",
        "We also support saving to `float16` directly. Select `merged_16bit` for float16. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens. See [our docs](https://unsloth.ai/docs/basics/inference-and-deployment) for more deployment options."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "iHjt_SMYsd3P"
      },
      "outputs": [],
      "source": [
        "# Select ONLY 1 to save! (Both not needed!)\n",
        "\n",
        "# Save locally to 16bit\n",
        "if False: model.save_pretrained_merged(\"unsloth_finetune\", processor,)\n",
        "\n",
        "# To export and save to your Hugging Face account\n",
        "if False: model.push_to_hub_merged(\"YOUR_USERNAME/unsloth_finetune\", processor, token = \"YOUR_HF_TOKEN\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TSjNVDCYv-yr"
      },
      "source": [
        "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
        "\n",
        "Some other resources:\n",
        "1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n",
        "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n",
        "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n",
        "4. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://unsloth.ai/docs/get-started/unsloth-notebooks)!\n",
        "\n",
        "<div class=\"align-center\">\n",
        "  <a href=\"https://unsloth.ai\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
        "  <a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
        "  <a href=\"https://unsloth.ai/docs/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a>\n",
        "\n",
        "  Join Discord if you need help + ⭐️ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐️\n",
        "</div>\n",
        "\n",
        "  This notebook and all Unsloth notebooks are licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme)."
      ]
    }
  ],
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