{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4RQuEItXqjUN"
      },
      "source": [
        "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab 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": "STd3-4v9qjUO"
      },
      "source": [
        "### News"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5r82a21yqjUP"
      },
      "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": "2pK4zPKsqjUP"
      },
      "source": [
        "### Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "csoGx5FzqjUQ"
      },
      "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": "hhrWHOOFqjUQ"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install --no-deps --upgrade timm # For Gemma 4 vision/audio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TGMWlrRdzwgf"
      },
      "source": [
        "### Unsloth\n",
        "\n",
        "`FastModel` supports loading nearly any model now! This includes Vision and Text models!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
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        "execution": {
          "iopub.execute_input": "2025-07-20T12:16:21.155888Z",
          "iopub.status.busy": "2025-07-20T12:16:21.155077Z",
          "iopub.status.idle": "2025-07-20T12:17:36.514669Z",
          "shell.execute_reply": "2025-07-20T12:17:36.513831Z",
          "shell.execute_reply.started": "2025-07-20T12:16:21.155861Z"
        },
        "id": "-Xbb0cuLzwgf",
        "outputId": "73a29692-4e1d-4de7-e822-f49a5e5fa7f8",
        "trusted": true
      },
      "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": "f410af63026a45c99add96178f79d89f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading weights:   0%|          | 0/2130 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9673ddb6186a481bad6e2f7dd5be0e38"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/208 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "processor_config.json: 0.00B [00:00, ?B/s]"
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              "version_minor": 0,
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          "metadata": {}
        },
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "chat_template.jinja: 0.00B [00:00, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json: 0.00B [00:00, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2678b1294fc44508bbc031e1f8ebaa84"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/32.2M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d500f1a39c7f40f4b2c9d963f504d213"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from unsloth import FastModel\n",
        "import torch\n",
        "from huggingface_hub import snapshot_download\n",
        "\n",
        "fourbit_models = [\n",
        "    # Gemma 4 models\n",
        "    \"unsloth/gemma-4-E2B-it\",\n",
        "    \"unsloth/gemma-4-E2B\",\n",
        "    \"unsloth/gemma-4-E2B-it\",\n",
        "    \"unsloth/gemma-4-E4B\",\n",
        "    \"unsloth/gemma-4-31B-it\",\n",
        "    \"unsloth/gemma-4-31B\",\n",
        "    \"unsloth/gemma-4-26B-A4B-it\",\n",
        "    \"unsloth/gemma-4-26B-A4B\",\n",
        "] # More models at https://huggingface.co/unsloth\n",
        "\n",
        "model, processor = FastModel.from_pretrained(\n",
        "    model_name = \"unsloth/gemma-4-E4B-it\",\n",
        "    dtype = None, # None for auto detection\n",
        "    max_seq_length = 8192, # Choose any for long context!\n",
        "    load_in_4bit = True,  # 4 bit quantization to reduce memory\n",
        "    full_finetuning = False, # [NEW!] We have full finetuning now!\n",
        "    # token = \"YOUR_HF_TOKEN\", # HF Token for gated models\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ixr4dyTHVIcI"
      },
      "source": [
        "# Gemma 4 can process Text, Vision and Audio!\n",
        "\n",
        "Let's first experience how Gemma 4 can handle multimodal inputs. We use Gemma 4's recommended settings of `temperature = 1.0, top_p = 0.95, top_k = 64` but for this example we use `do_sample=False` for ASR."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-07-20T12:46:24.659070Z",
          "iopub.status.busy": "2025-07-20T12:46:24.658306Z",
          "iopub.status.idle": "2025-07-20T12:46:24.664411Z",
          "shell.execute_reply": "2025-07-20T12:46:24.663791Z",
          "shell.execute_reply.started": "2025-07-20T12:46:24.659043Z"
        },
        "id": "UsfUPU-oVQYu",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "from transformers import TextStreamer\n",
        "# Helper function for inference\n",
        "def do_gemma_4_inference(messages, max_new_tokens = 128):\n",
        "    _ = model.generate(\n",
        "        **processor.apply_chat_template(\n",
        "            messages,\n",
        "            add_generation_prompt = True, # Must add for generation\n",
        "            tokenize = True,\n",
        "            return_dict = True,\n",
        "            return_tensors = \"pt\",\n",
        "        ).to(\"cuda\"),\n",
        "        max_new_tokens = max_new_tokens,\n",
        "        do_sample = False,\n",
        "        streamer = TextStreamer(processor, skip_prompt = True),\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wZrmFRZpZtGf"
      },
      "source": [
        "<h3>Let's Evaluate Gemma 4 Baseline Performance on German Transcription</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-07-20T12:46:26.982493Z",
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          "iopub.status.idle": "2025-07-20T12:46:28.166576Z",
          "shell.execute_reply": "2025-07-20T12:46:28.165889Z",
          "shell.execute_reply.started": "2025-07-20T12:46:26.982472Z"
        },
        "id": "GHBGeJhYcorh",
        "trusted": true,
        "outputId": "11d0f8cc-50b4-40ae-b2b0-c2c0481d0e2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
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            "4e4a231bbf20450fac8d6775cc6d1a1a",
            "ca763ea2a041412280338cdf806dafc3",
            "a8be70e3ed014df9bddd8f72dec5702e",
            "97c34bf202644e82bed87aa2defa5ed7",
            "92b1ae8f54b246b790ddd66493ddf7cc",
            "5a90282515bf44aa88374f3a7f05df1e",
            "904619a3cc624a25925cb66f6ab86564",
            "8870940de0534e98b9189e7c168b80e1"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/540 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7ead793edcdc4542868bc35988087a41"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "data/train-00000-of-00002.parquet:   0%|          | 0.00/495M [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "59102102d47c4d5b941afe38b1e327c5"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "data/train-00001-of-00002.parquet:   0%|          | 0.00/503M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7f2edc965f6d4418bfeaf01911425606"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/12038 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "84d7352d2921443fa2b76d60bd2a51d1"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset,Audio,concatenate_datasets\n",
        "\n",
        "dataset = load_dataset(\"kadirnar/Emilia-DE-B000000\", split = \"train\")\n",
        "\n",
        "# Select a single audio sample to reserve for testing.\n",
        "# This index is chosen from the full dataset before we create the smaller training split.\n",
        "test_audio = dataset[7546]\n",
        "\n",
        "dataset = dataset.select(range(3000))\n",
        "\n",
        "dataset = dataset.cast_column(\"audio\", Audio(sampling_rate = 16000))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-07-20T12:46:38.870979Z",
          "iopub.status.busy": "2025-07-20T12:46:38.870740Z",
          "iopub.status.idle": "2025-07-20T12:46:38.890720Z",
          "shell.execute_reply": "2025-07-20T12:46:38.890034Z",
          "shell.execute_reply.started": "2025-07-20T12:46:38.870960Z"
        },
        "id": "7IF_zeqScorh",
        "outputId": "e98fbd9d-47ff-4482-ec87-09af5365dd68",
        "trusted": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Ich, ich rechne direkt mich an. Das ist natürlich klar, nur, dass, äh, es politische Interessen gibt im Handel, im Austausch mit Waren, dass es politische Einflüsse gibt. Die Frage ist, die Alternative soll es nicht sein.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ],
            "text/html": [
              "\n",
              "                <audio  controls=\"controls\" >\n",
              "                    <source src=\"data:audio/wav;base64,[encoded data, 994300 characters]\" type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "from IPython.display import Audio, display\n",
        "print(test_audio['text'])\n",
        "Audio(test_audio['audio']['array'],rate = test_audio['audio']['sampling_rate'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And the translation of the audio from German to English is:\n",
        "\n",
        "> I—I hold myself directly accountable. That much is, of course, clear: namely, that there are political interests involved in trade—in the exchange of goods—and that political influences are at play. The question is: that should not be the alternative."
      ],
      "metadata": {
        "id": "3XGomsRxl5d_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2025-07-20T12:18:05.249062Z",
          "iopub.status.busy": "2025-07-20T12:18:05.248355Z",
          "iopub.status.idle": "2025-07-20T12:18:37.319606Z",
          "shell.execute_reply": "2025-07-20T12:18:37.318802Z",
          "shell.execute_reply.started": "2025-07-20T12:18:05.249040Z"
        },
        "id": "BJr_D4O9Z2Zh",
        "outputId": "ab2cb578-03a5-4322-ea56-c9caa785c5a8",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sie reden direkt mich an, und ist mir völlig klar, dass es politische Interessen gibt im Handel, im Austausch mit Waren, dass es politische Einflüsse gibt. Wie qual ist die Alternative soll es nicht sein?<turn|>\n"
          ]
        }
      ],
      "source": [
        "messages = [\n",
        "    {\n",
        "        \"role\": \"system\",\n",
        "        \"content\": [\n",
        "            {\n",
        "                \"type\": \"text\",\n",
        "                \"text\": \"You are an assistant that transcribes speech accurately.\",\n",
        "            }\n",
        "        ],\n",
        "    },\n",
        "    {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": [\n",
        "            {\"type\": \"audio\", \"audio\": test_audio['audio']['array']},\n",
        "            {\"type\": \"text\", \"text\": \"Please transcribe this audio.\"}\n",
        "        ]\n",
        "    }\n",
        "]\n",
        "\n",
        "do_gemma_4_inference(messages, max_new_tokens = 256)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yc0nI6Gzcori"
      },
      "source": [
        "<h3>Baseline Model Performance: 32.43% Word Error Rate (WER) for this sample !</h3>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bw5XPyYFajyM"
      },
      "source": [
        "# Let's finetune Gemma 4!\n",
        "\n",
        "You can finetune the vision and text and audio parts"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXd9bTZd1aaL"
      },
      "source": [
        "We now add LoRA adapters so we only need to update a small amount of parameters!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-07-20T12:46:48.481871Z",
          "iopub.status.busy": "2025-07-20T12:46:48.481594Z",
          "iopub.status.idle": "2025-07-20T12:46:55.013627Z",
          "shell.execute_reply": "2025-07-20T12:46:55.012955Z",
          "shell.execute_reply.started": "2025-07-20T12:46:48.481854Z"
        },
        "id": "6bZsfBuZDeCL",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "model = FastModel.get_peft_model(\n",
        "    model,\n",
        "    finetune_vision_layers     = False, # 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 = 8,                              # The larger, the higher the accuracy, but might overfit\n",
        "    lora_alpha = 16,                    # 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 = [\n",
        "        \"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
        "        \"gate_proj\", \"up_proj\", \"down_proj\",\n",
        "\n",
        "        # Audio layers\n",
        "        \"post\", \"linear_start\", \"linear_end\",\n",
        "        \"embedding_projection\",\n",
        "        \"ffw_layer_1\", \"ffw_layer_2\",\n",
        "        \"output_proj\",\n",
        "    ]\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vITh0KVJ10qX"
      },
      "source": [
        "<a name=\"Data\"></a>\n",
        "### Data Prep\n",
        "We adapt the `kadirnar/Emilia-DE-B000000` dataset for our German ASR task using Gemma 4 multi-modal chat format. Each audio-text pair is structured into a conversation with `system`, `user`, and `assistant` roles. The processor then converts this into the final training format:\n",
        "\n",
        "```\n",
        "<bos><|turn>system\n",
        "You are an assistant that transcribes speech accurately.<turn|>\n",
        "<|turn>user\n",
        "<|audio|>Please transcribe this audio.<turn|>\n",
        "<|turn>model\n",
        "Ich, ich rechne direkt mich an.<turn|>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-07-20T12:47:03.723745Z",
          "iopub.status.busy": "2025-07-20T12:47:03.723405Z",
          "iopub.status.idle": "2025-07-20T12:47:03.729197Z",
          "shell.execute_reply": "2025-07-20T12:47:03.728434Z",
          "shell.execute_reply.started": "2025-07-20T12:47:03.723714Z"
        },
        "id": "o8caH7vlcorj",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "def format_intersection_data(samples: dict) -> dict[str, list]:\n",
        "    \"\"\"Format intersection dataset to match expected message format\"\"\"\n",
        "    formatted_samples = {\"messages\": []}\n",
        "    for idx in range(len(samples[\"audio\"])):\n",
        "        audio = samples[\"audio\"][idx][\"array\"]\n",
        "        label = str(samples[\"text\"][idx])\n",
        "\n",
        "        message = [\n",
        "            {\n",
        "                \"role\": \"system\",\n",
        "                \"content\": [\n",
        "                    {\n",
        "                        \"type\": \"text\",\n",
        "                        \"text\": \"You are an assistant that transcribes speech accurately.\",\n",
        "                    }\n",
        "                ],\n",
        "            },\n",
        "            {\n",
        "                \"role\": \"user\",\n",
        "                \"content\": [\n",
        "                    {\"type\": \"audio\", \"audio\": audio},\n",
        "                    {\"type\": \"text\", \"text\": \"Please transcribe this audio.\"}\n",
        "                ]\n",
        "            },\n",
        "            {\n",
        "                \"role\": \"assistant\",\n",
        "                \"content\":[{\"type\": \"text\", \"text\": label}]\n",
        "            }\n",
        "        ]\n",
        "        formatted_samples[\"messages\"].append(message)\n",
        "    return formatted_samples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-07-20T12:47:08.489955Z",
          "iopub.status.busy": "2025-07-20T12:47:08.489357Z",
          "iopub.status.idle": "2025-07-20T12:47:09.221727Z",
          "shell.execute_reply": "2025-07-20T12:47:09.221018Z",
          "shell.execute_reply.started": "2025-07-20T12:47:08.489932Z"
        },
        "id": "k7CQ3jvDcorj",
        "trusted": true,
        "outputId": "8aa74b98-32ea-4f20-a084-c8bb5b2d63e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "75e57ae8d4f94b3d9702a24a85b75f6e",
            "a452f87440d34e2285dee4941cf558e2",
            "d6d64f507469463ba1d678e966b78c28",
            "9269fd899b71476faeffe5ca24ff3c23",
            "d574cfbfa9f94ef7b1c037bc7884b8b5",
            "cc44984cc78442ba9b73df356ad6e6b3",
            "45cfbdff1db94eed80cbd6cf4ae19b3d",
            "68f32e20479e4d61bda9adff2532ba70",
            "c264561cd45b41109bcf3cacab8aa387",
            "63351416559f4bfdb6c21adceaee17e8",
            "5efb687bf68b45ca91797d17fbf88be4"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map (num_proc=4):   0%|          | 0/3000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "75e57ae8d4f94b3d9702a24a85b75f6e"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "dataset = dataset.map(format_intersection_data, batched = True, batch_size = 4, num_proc = 4)"
      ]
    },
    {
      "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`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2025-07-20T12:48:17.004874Z",
          "iopub.status.busy": "2025-07-20T12:48:17.004079Z",
          "iopub.status.idle": "2025-07-20T12:48:17.279559Z",
          "shell.execute_reply": "2025-07-20T12:48:17.278695Z",
          "shell.execute_reply.started": "2025-07-20T12:48:17.004848Z"
        },
        "id": "95_Nn-89DhsL",
        "trusted": true,
        "outputId": "4ab7d37a-efa4-43ab-b22d-e05c5ae7447e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "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": [
        "# Use UnslothVisionDataCollator which handles audio token alignment correctly\n",
        "from unsloth.trainer import UnslothVisionDataCollator\n",
        "from trl import SFTTrainer, SFTConfig\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model = model,\n",
        "    train_dataset = dataset,\n",
        "    processing_class = processor.tokenizer,\n",
        "    data_collator = UnslothVisionDataCollator(model, processor),\n",
        "    args = SFTConfig(\n",
        "        per_device_train_batch_size = 8,\n",
        "        gradient_accumulation_steps = 1,\n",
        "        warmup_ratio = 0.03,\n",
        "        # num_train_epochs = 1, # Use for full training runs\n",
        "        max_steps = 60,\n",
        "        learning_rate = 5e-5,\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\",\n",
        "        remove_unused_columns = False,\n",
        "\n",
        "        # The below are a must for audio finetuning:\n",
        "        dataset_text_field = \"\",\n",
        "        dataset_kwargs = {\"skip_prepare_dataset\": True},\n",
        "        max_length = 8192,\n",
        "    )\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "cellView": "form",
        "id": "2ejIt2xSNKKp",
        "trusted": true,
        "outputId": "1b81b661-b347-4ef8-bfa2-0e9211d96124",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPU = Tesla T4. Max memory = 14.563 GB.\n",
            "9.518 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": "markdown",
      "metadata": {
        "id": "CNP1Uidk9mrz"
      },
      "source": [
        "# Let's train the model!\n",
        "\n",
        "To resume a training run, set `trainer.train(resume_from_checkpoint = True)`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "execution": {
          "iopub.execute_input": "2025-07-20T12:48:20.209164Z",
          "iopub.status.busy": "2025-07-20T12:48:20.208832Z",
          "iopub.status.idle": "2025-07-20T13:42:42.607026Z",
          "shell.execute_reply": "2025-07-20T13:42:42.606099Z",
          "shell.execute_reply.started": "2025-07-20T12:48:20.209142Z"
        },
        "id": "yqxqAZ7KJ4oL",
        "outputId": "9e4aa7ed-a720-48e9-e35c-42f458023208",
        "trusted": true
      },
      "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 = 3,000 | Num Epochs = 1 | Total steps = 60\n",
            "O^O/ \\_/ \\    Batch size per device = 8 | Gradient accumulation steps = 1\n",
            "\\        /    Data Parallel GPUs = 1 | Total batch size (8 x 1 x 1) = 8\n",
            " \"-____-\"     Trainable parameters = 23,934,976 of 8,020,091,424 (0.30% trained)\n",
            "Caching is incompatible with gradient checkpointing in Gemma4TextDecoderLayer. Setting `past_key_values=None`.\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 02:44, 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>17.025774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>16.762154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>16.299833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>16.981674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>16.810152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>16.151384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>14.198338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>13.161330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>11.982812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>10.303127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>9.856644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>9.201151</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>8.912704</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>7.209361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>6.705412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>6.741477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>6.189445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>5.548326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>4.765179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>4.548351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>4.192586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>4.059164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>4.241319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>3.192038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>3.979971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>3.580244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>3.562336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>3.121466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>3.284021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>2.820877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>2.781770</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>3.181130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>3.296061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>2.908674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>3.456495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>3.174899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>2.976336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>3.142226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>2.514387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>2.838711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>3.000938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>2.302069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>3.237894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>2.881329</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>3.214046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>3.095760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>2.509252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>2.897425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>2.668692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>2.657610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>2.777081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>2.495918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>2.955339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>2.717725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>3.013832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>2.617149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>2.862764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>2.879027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>3.030071</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>2.632949</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "trainer_stats = trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "cellView": "form",
        "id": "pCqnaKmlO1U9",
        "trusted": true,
        "outputId": "20983c94-9f93-4053-e1f2-271c51939b65",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "193.5248 seconds used for training.\n",
            "3.23 minutes used for training.\n",
            "Peak reserved memory = 11.139 GB.\n",
            "Peak reserved memory for training = 1.621 GB.\n",
            "Peak reserved memory % of max memory = 76.488 %.\n",
            "Peak reserved memory for training % of max memory = 11.131 %.\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 via Unsloth native inference! According to the `Gemma-4` team, the recommended settings for inference are `temperature = 1.0, top_p = 0.95, top_k = 64` but for this example we use `do_sample=False` for ASR."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "execution": {
          "iopub.execute_input": "2025-07-20T13:57:34.004664Z",
          "iopub.status.busy": "2025-07-20T13:57:34.004306Z",
          "iopub.status.idle": "2025-07-20T13:57:59.332316Z",
          "shell.execute_reply": "2025-07-20T13:57:59.331671Z",
          "shell.execute_reply.started": "2025-07-20T13:57:34.004639Z"
        },
        "id": "kR3gIAX-SM2q",
        "outputId": "ab8ef181-270a-42c5-ea08-25b9d092ca51",
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sie reden direkt mich an, und ist mir völlig klar, dass äh es politische Interessen gibt im Handel, im Austausch mit Waren, dass es politische Einflüsse gibt. Wie qual ist die Alternative soll es nicht sein?<turn|>\n"
          ]
        }
      ],
      "source": [
        "messages = [\n",
        "    {\n",
        "        \"role\": \"system\",\n",
        "        \"content\": [\n",
        "            {\n",
        "                \"type\": \"text\",\n",
        "                \"text\": \"You are an assistant that transcribes speech accurately.\",\n",
        "            }\n",
        "        ],\n",
        "    },\n",
        "    {\n",
        "        \"role\": \"user\",\n",
        "        \"content\": [\n",
        "            {\"type\": \"audio\", \"audio\": test_audio['audio']['array']},\n",
        "            {\"type\": \"text\", \"text\": \"Please transcribe this audio.\"}\n",
        "        ]\n",
        "    }\n",
        "]\n",
        "\n",
        "do_gemma_4_inference(messages, max_new_tokens = 256)"
      ]
    },
    {
      "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, either use Hugging Face's `push_to_hub` for an online save or `save_pretrained` for a local save.\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": 16,
      "metadata": {
        "id": "upcOlWe7A1vc",
        "trusted": true,
        "outputId": "049b82a5-2741-4f7b-824b-cd36a7b445e0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['gemma_4_lora/processor_config.json']"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "model.save_pretrained(\"gemma_4_lora\")  # Local saving\n",
        "processor.save_pretrained(\"gemma_4_lora\")\n",
        "# model.push_to_hub(\"HF_ACCOUNT/gemma_4_lora\", token = \"YOUR_HF_TOKEN\") # Online saving\n",
        "# processor.push_to_hub(\"HF_ACCOUNT/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": 17,
      "metadata": {
        "id": "MKX_XKs_BNZR",
        "trusted": true,
        "outputId": "0df68115-9684-45ca-ef64-fb04f3db1ec7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "I am Gemma 4, a Large Language Model developed by Google DeepMind. I am an open weights model.<turn|>\n"
          ]
        }
      ],
      "source": [
        "if False:\n",
        "    from unsloth import FastModel\n",
        "    model, processor = FastModel.from_pretrained(\n",
        "        model_name = \"gemma_4_lora\", # YOUR MODEL YOU USED FOR TRAINING\n",
        "        max_seq_length = 2048,\n",
        "        load_in_4bit = True,\n",
        "    )\n",
        "\n",
        "messages = [{\n",
        "    \"role\": \"user\",\n",
        "    \"content\": [{\"type\" : \"text\", \"text\" : \"What is Gemma-4?\",}]\n",
        "}]\n",
        "inputs = processor.apply_chat_template(\n",
        "    messages,\n",
        "    add_generation_prompt = True, # Must add for generation\n",
        "    return_tensors = \"pt\",\n",
        "    tokenize = True,\n",
        "    return_dict = True,\n",
        ").to(\"cuda\")\n",
        "\n",
        "from transformers import TextStreamer\n",
        "_ = model.generate(\n",
        "    **inputs,\n",
        "    max_new_tokens = 128, # Increase for longer outputs!\n",
        "    # Recommended Gemma-4 settings!\n",
        "    temperature = 1.0, top_p = 0.95, top_k = 64,\n",
        "    streamer = TextStreamer(processor, skip_prompt = True),\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f422JgM9sdVT"
      },
      "source": [
        "### Saving to float16 for VLLM\n",
        "\n",
        "We also support saving to `float16` directly for deployment! We save it in the folder `gemma-4-finetune`. Set `if False` to `if True` to let it run!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "iHjt_SMYsd3P",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "if False: # Change to True to save finetune!\n",
        "    model.save_pretrained_merged(\"gemma-4\", processor)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z6O48DbNIAr0"
      },
      "source": [
        "If you want to upload / push to your Hugging Face account, set `if False` to `if True` and add your Hugging Face token and upload location!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "ZV-CiKPrIFG0",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "if False: # Change to True to upload finetune\n",
        "    model.push_to_hub_merged(\n",
        "        \"HF_ACCOUNT/gemma-4-finetune\", processor,\n",
        "        token = \"YOUR_HF_TOKEN\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TCv4vXHd61i7"
      },
      "source": [
        "### GGUF / llama.cpp Conversion\n",
        "To save to `GGUF` / `llama.cpp`, we support it natively now for all models! For now, you can convert easily to `Q8_0, F16 or BF16` precision. `Q4_K_M` for 4bit will come later!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "FqfebeAdT073",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "if False: # Change to True to save to GGUF\n",
        "    model.save_pretrained_gguf(\n",
        "        \"gemma_4_finetune\",\n",
        "        processor,\n",
        "        quantization_method = \"Q8_0\", # For now only Q8_0, BF16, F16 supported\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q974YEVPI7JS"
      },
      "source": [
        "Likewise, if you want to instead push to GGUF to your Hugging Face account, set `if False` to `if True` and add your Hugging Face token and upload location!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "ZgcJIhJ0I_es",
        "trusted": true
      },
      "outputs": [],
      "source": [
        "if False: # Change to True to upload GGUF\n",
        "    model.push_to_hub_gguf(\n",
        "        \"HF_ACCOUNT/gemma_4_finetune\",\n",
        "        processor,\n",
        "        quantization_method = \"Q8_0\", # Only Q8_0, BF16, F16 supported\n",
        "        token = \"YOUR_HF_TOKEN\",\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pnz9QOYTMvbH"
      },
      "source": [
        "Now, use the `gemma-4-finetune.gguf` file or `gemma-4-finetune-Q4_K_M.gguf` file in llama.cpp.\n",
        "\n",
        "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)."
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
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      "gpuType": "T4",
      "provenance": [],
      "machine_shape": "hm"
    },
    "kaggle": {
      "accelerator": "nvidiaTeslaT4",
      "dataSources": [],
      "dockerImageVersionId": 31040,
      "isGpuEnabled": true,
      "isInternetEnabled": true,
      "language": "python",
      "sourceType": "notebook"
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    "kernelspec": {
      "display_name": "Python 3",
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      "file_extension": ".py",
      "mimetype": "text/x-python",
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