diff --git "a/finetuning.ipynb" "b/finetuning.ipynb"
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+++ "b/finetuning.ipynb"
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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IqM-T1RTzY6C"
+      },
+      "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://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
+        "  <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
+        "  <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></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 own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth?tab=readme-ov-file#-installation-instructions).\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](#Save) (eg for Llama.cpp).\n",
+        "\n",
+        "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**\n",
+        "\n",
+        "Features in the notebook:\n",
+        "1. Uses Maxime Labonne's [FineTome 100K](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset.\n",
+        "1. Convert ShareGPT to HuggingFace format via `standardize_sharegpt`\n",
+        "2. Train on Completions / Assistant only via `train_on_responses_only`\n",
+        "3. Unsloth now supports Torch 2.4, all TRL & Xformers versions & Python 3.12!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2eSvM9zX_2d3"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "!pip install unsloth\n",
+        "# Also get the latest nightly Unsloth!\n",
+        "!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r2v_X2fA0Df5"
+      },
+      "source": [
+        "* We support Llama, Mistral, Phi-3, Gemma, Yi, DeepSeek, Qwen, TinyLlama, Vicuna, Open Hermes etc\n",
+        "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n",
+        "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n",
+        "* [**NEW**] We make Gemma-2 9b / 27b **2x faster**! See our [Gemma-2 9b notebook](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)\n",
+        "* [**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
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+        "colab": {
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+          "height": 325,
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+        "outputId": "6075eae7-af7f-4617-c25f-b790054c63d1"
+      },
+      "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 2024.11.10: Fast Llama patching. Transformers:4.46.2.\n",
+            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform: Linux.\n",
+            "O^O/ \\_/ \\    Torch: 2.5.1+cu121. CUDA: 7.5. CUDA Toolkit: 12.1. Triton: 3.1.0\n",
+            "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.28.post3. FA2 = False]\n",
+            " \"-____-\"     Free Apache 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%|          | 0.00/1.03G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4c3deec2baf94a8ea4c80478bdff01d3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "generation_config.json:   0%|          | 0.00/184 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "31af95f1ac3544f7a37be67c4a034080"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/54.6k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "eee9bc3f569b43d39e6f63306984095e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/9.09M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f7d925b7a7bf4383a45446c860df582e"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/454 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "aa83df54525940f6b0bda6541f7239a7"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from unsloth import FastLanguageModel\n",
+        "import torch\n",
+        "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n",
+        "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
+        "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
+        "\n",
+        "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
+        "fourbit_models = [\n",
+        "    \"unsloth/Meta-Llama-3.1-8B-bnb-4bit\",      # Llama-3.1 2x faster\n",
+        "    \"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit\",\n",
+        "    \"unsloth/Meta-Llama-3.1-70B-bnb-4bit\",\n",
+        "    \"unsloth/Meta-Llama-3.1-405B-bnb-4bit\",    # 4bit for 405b!\n",
+        "    \"unsloth/Mistral-Small-Instruct-2409\",     # Mistral 22b 2x faster!\n",
+        "    \"unsloth/mistral-7b-instruct-v0.3-bnb-4bit\",\n",
+        "    \"unsloth/Phi-3.5-mini-instruct\",           # Phi-3.5 2x faster!\n",
+        "    \"unsloth/Phi-3-medium-4k-instruct\",\n",
+        "    \"unsloth/gemma-2-9b-bnb-4bit\",\n",
+        "    \"unsloth/gemma-2-27b-bnb-4bit\",            # Gemma 2x faster!\n",
+        "    \"unsloth/Llama-3.2-1B-bnb-4bit\",           # NEW! Llama 3.2 models\n",
+        "    \"unsloth/Llama-3.2-1B-Instruct-bnb-4bit\",\n",
+        "    \"unsloth/Llama-3.2-3B-bnb-4bit\",\n",
+        "    \"unsloth/Llama-3.2-3B-Instruct-bnb-4bit\",\n",
+        "] # More models at https://huggingface.co/unsloth\n",
+        "\n",
+        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
+        "    #model_name = \"unsloth/Llama-3.2-3B-Instruct\", # or choose \"unsloth/Llama-3.2-1B-Instruct\"\n",
+        "    model_name = \"unsloth/Llama-3.2-1B-Instruct\",\n",
+        "    max_seq_length = max_seq_length,\n",
+        "    dtype = dtype,\n",
+        "    load_in_4bit = load_in_4bit,\n",
+        "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SXd9bTZd1aaL"
+      },
+      "source": [
+        "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6bZsfBuZDeCL",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c86a92de-a7fe-4065-a713-000b33202b43"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Unsloth 2024.11.10 patched 16 layers with 16 QKV layers, 16 O layers and 16 MLP layers.\n"
+          ]
+        }
+      ],
+      "source": [
+        "model = FastLanguageModel.get_peft_model(\n",
+        "    model,\n",
+        "    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
+        "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
+        "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
+        "    lora_alpha = 16,\n",
+        "    lora_dropout = 0, # Supports any, but = 0 is optimized\n",
+        "    bias = \"none\",    # Supports any, but = \"none\" is optimized\n",
+        "    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
+        "    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
+        "    random_state = 3407,\n",
+        "    use_rslora = False,  # We support rank stabilized LoRA\n",
+        "    loftq_config = None, # And LoftQ\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vITh0KVJ10qX"
+      },
+      "source": [
+        "<a name=\"Data\"></a>\n",
+        "### Data Prep\n",
+        "We now use the `Llama-3.1` format for conversation style finetunes. We use [Maxime Labonne's FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset in ShareGPT style. But we convert it to HuggingFace's normal multiturn format `(\"role\", \"content\")` instead of `(\"from\", \"value\")`/ Llama-3 renders multi turn conversations like below:\n",
+        "\n",
+        "```\n",
+        "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n",
+        "\n",
+        "Hello!<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n",
+        "\n",
+        "Hey there! How are you?<|eot_id|><|start_header_id|>user<|end_header_id|>\n",
+        "\n",
+        "I'm great thanks!<|eot_id|>\n",
+        "```\n",
+        "\n",
+        "We use our `get_chat_template` function to get the correct chat template. We support `zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, phi3, llama3` and more."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LjY75GoYUCB8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 113,
+          "referenced_widgets": [
+            "4ef2e9ed70594558b4bd281c7d6ca5d2",
+            "ee91f15ce7a7447b9b799f536f1457a9",
+            "b48803411188408daf2a8fc02d53c8f4",
+            "632df15c299c45ca99e54a96e7fd2043",
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+            "2f4b5f095da44c8e9e5278c41c11ac37",
+            "ea3657d821634d37b112cc0e94ae9295",
+            "266f0114d58c4bbf9aa1f604df52d2a2",
+            "794ae2a3bf744fa5900c42fe628389b4",
+            "e69d7656f4c541fea7a6b9fe3c088462",
+            "a0db3c5db7664185b44e37d975fb917c",
+            "0b3000219b5349b28213a108f67e8517",
+            "e2f4b572569e4aedbd4a0efac9156d67",
+            "ed566d0f5c074fa698e882210a1fd4ea",
+            "4d146134b7304beb967ee3dea28d05ff",
+            "b679084ddb2e46a48658d76afd22cb5c",
+            "b157ac6dd5bf4c2eacf03921bd3ee318",
+            "56a48a0353c148e88796bc1e7a70440b",
+            "8ea199ab8df346ab863f928d3e9dbcdb",
+            "7858c79373064059bacc8275e3687bf8",
+            "3706f26b7fdb4b61b931e86aca398711",
+            "a1833e5ec6604e7395429d7ccd119fa6",
+            "13ec5d369fbd4f7e8fcca76f69073817"
+          ]
+        },
+        "outputId": "5e627a20-c480-4c79-b79d-d08061ed5e53"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "README.md:   0%|          | 0.00/982 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "4ef2e9ed70594558b4bd281c7d6ca5d2"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "train-00000-of-00001.parquet:   0%|          | 0.00/117M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "5396fefacb2347f38112242a0305dd6a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split:   0%|          | 0/100000 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e2f4b572569e4aedbd4a0efac9156d67"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "from unsloth.chat_templates import get_chat_template\n",
+        "\n",
+        "tokenizer = get_chat_template(\n",
+        "    tokenizer,\n",
+        "    chat_template = \"llama-3.1\",\n",
+        ")\n",
+        "\n",
+        "def formatting_prompts_func(examples):\n",
+        "    convos = examples[\"conversations\"]\n",
+        "    texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n",
+        "    return { \"text\" : texts, }\n",
+        "pass\n",
+        "\n",
+        "from datasets import load_dataset\n",
+        "dataset = load_dataset(\"mlabonne/FineTome-100k\", split = \"train\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We now use `standardize_sharegpt` to convert ShareGPT style datasets into HuggingFace's generic format. This changes the dataset from looking like:\n",
+        "```\n",
+        "{\"from\": \"system\", \"value\": \"You are an assistant\"}\n",
+        "{\"from\": \"human\", \"value\": \"What is 2+2?\"}\n",
+        "{\"from\": \"gpt\", \"value\": \"It's 4.\"}\n",
+        "```\n",
+        "to\n",
+        "```\n",
+        "{\"role\": \"system\", \"content\": \"You are an assistant\"}\n",
+        "{\"role\": \"user\", \"content\": \"What is 2+2?\"}\n",
+        "{\"role\": \"assistant\", \"content\": \"It's 4.\"}\n",
+        "```"
+      ],
+      "metadata": {
+        "id": "K9CBpiISFa6C"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from unsloth.chat_templates import standardize_sharegpt\n",
+        "dataset = standardize_sharegpt(dataset)\n",
+        "dataset = dataset.map(formatting_prompts_func, batched = True,)"
+      ],
+      "metadata": {
+        "id": "oPXzJZzHEgXe",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81,
+          "referenced_widgets": [
+            "812ebb70dc004c7e927f17e2fbccee32",
+            "1e9071a14a784d1f815083ee90a42607",
+            "532d7d7d9d0e4fc481afd1c67b22b36d",
+            "a1250e16080049f685b127b273d404a4",
+            "3bf12a7280c947f49c9b02fce942a010",
+            "c257db8e607040e4862916035607bf19",
+            "73ac6107e4184349b01db9bcd7b2702d",
+            "711f473e57984072b78c49e3bdefcfdb",
+            "0467ea1f1fbc436189e74d470b8734c8",
+            "e0634a92b5d04d66b94e846d1cbfbdeb",
+            "25a2d2313408464ab7c3975dac67f7ea",
+            "7f813ef411164c409b824048045629c4",
+            "cd5cb20e39bf4100a04d742367f6b471",
+            "8b8ac219964d46aaa95a26a6158ff105",
+            "a7879fa411a44c1c8659c1cdbb4af836",
+            "7a744664d9cd47a3940b8cdcec843931",
+            "ae0a7038c08440859b9b196e808ec061",
+            "5d52bb8d317e47e9a3116870a1966c6e",
+            "7939f216c0f04b77b3d40045faa39722",
+            "e530f33144544ebbbb82474834d20684",
+            "7771f6f8459948d88c381122cc04e1c7",
+            "42ed2466e0dd46a98d49f4afa3f06a69"
+          ]
+        },
+        "outputId": "5fe7d12e-b4a8-494e-b8c6-0e8a800355bc"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Standardizing format:   0%|          | 0/100000 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "812ebb70dc004c7e927f17e2fbccee32"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/100000 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "7f813ef411164c409b824048045629c4"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We look at how the conversations are structured for item 5:"
+      ],
+      "metadata": {
+        "id": "ndDUB23CGAC5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dataset[5][\"conversations\"]"
+      ],
+      "metadata": {
+        "id": "gGFzmplrEy9I",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "484e2792-1c9d-492b-845a-aa095429fa38"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[{'content': 'How do astronomers determine the original wavelength of light emitted by a celestial body at rest, which is necessary for measuring its speed using the Doppler effect?',\n",
+              "  'role': 'user'},\n",
+              " {'content': 'Astronomers make use of the unique spectral fingerprints of elements found in stars. These elements emit and absorb light at specific, known wavelengths, forming an absorption spectrum. By analyzing the light received from distant stars and comparing it to the laboratory-measured spectra of these elements, astronomers can identify the shifts in these wavelengths due to the Doppler effect. The observed shift tells them the extent to which the light has been redshifted or blueshifted, thereby allowing them to calculate the speed of the star along the line of sight relative to Earth.',\n",
+              "  'role': 'assistant'}]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "And we see how the chat template transformed these conversations.\n",
+        "\n",
+        "**[Notice]** Llama 3.1 Instruct's default chat template default adds `\"Cutting Knowledge Date: December 2023\\nToday Date: 26 July 2024\"`, so do not be alarmed!"
+      ],
+      "metadata": {
+        "id": "GfzTdMtvGE6w"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dataset[5][\"text\"]"
+      ],
+      "metadata": {
+        "id": "vhXv0xFMGNKE",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 110
+        },
+        "outputId": "be28bd87-4697-42bc-b4df-27ab1f649aaf"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'<|begin_of_text|><|start_header_id|>system<|end_header_id|>\\n\\nCutting Knowledge Date: December 2023\\nToday Date: 26 July 2024\\n\\n<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nHow do astronomers determine the original wavelength of light emitted by a celestial body at rest, which is necessary for measuring its speed using the Doppler effect?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\nAstronomers make use of the unique spectral fingerprints of elements found in stars. These elements emit and absorb light at specific, known wavelengths, forming an absorption spectrum. By analyzing the light received from distant stars and comparing it to the laboratory-measured spectra of these elements, astronomers can identify the shifts in these wavelengths due to the Doppler effect. The observed shift tells them the extent to which the light has been redshifted or blueshifted, thereby allowing them to calculate the speed of the star along the line of sight relative to Earth.<|eot_id|>'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## WandDB for tracking"
+      ],
+      "metadata": {
+        "id": "bBQwzxFTIybF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install wandb"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5ySmoSUCIxho",
+        "outputId": "ee0c9d01-7aa1-4d4d-b264-6237a844df6d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: wandb in /usr/local/lib/python3.10/dist-packages (0.18.7)\n",
+            "Requirement already satisfied: click!=8.0.0,>=7.1 in /usr/local/lib/python3.10/dist-packages (from wandb) (8.1.7)\n",
+            "Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (0.4.0)\n",
+            "Requirement already satisfied: gitpython!=3.1.29,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (3.1.43)\n",
+            "Requirement already satisfied: platformdirs in /usr/local/lib/python3.10/dist-packages (from wandb) (4.3.6)\n",
+            "Requirement already satisfied: protobuf!=4.21.0,!=5.28.0,<6,>=3.19.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (3.20.3)\n",
+            "Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (5.9.5)\n",
+            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from wandb) (6.0.2)\n",
+            "Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (2.32.3)\n",
+            "Requirement already satisfied: sentry-sdk>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (2.18.0)\n",
+            "Requirement already satisfied: setproctitle in /usr/local/lib/python3.10/dist-packages (from wandb) (1.3.4)\n",
+            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from wandb) (75.1.0)\n",
+            "Requirement already satisfied: typing-extensions<5,>=4.4 in /usr/local/lib/python3.10/dist-packages (from wandb) (4.12.2)\n",
+            "Requirement already satisfied: six>=1.4.0 in /usr/local/lib/python3.10/dist-packages (from docker-pycreds>=0.4.0->wandb) (1.16.0)\n",
+            "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.10/dist-packages (from gitpython!=3.1.29,>=1.0.0->wandb) (4.0.11)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (3.4.0)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (3.10)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2.2.3)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2024.8.30)\n",
+            "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from gitdb<5,>=4.0.1->gitpython!=3.1.29,>=1.0.0->wandb) (5.0.1)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import wandb\n",
+        "wandb.login()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 145
+        },
+        "id": "TiZZLfXZI8wj",
+        "outputId": "5d917b98-db6c-43a8-fefc-78f6b2f87d6d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
+              "            function loadScript(url) {\n",
+              "            return new Promise(function(resolve, reject) {\n",
+              "                let newScript = document.createElement(\"script\");\n",
+              "                newScript.onerror = reject;\n",
+              "                newScript.onload = resolve;\n",
+              "                document.body.appendChild(newScript);\n",
+              "                newScript.src = url;\n",
+              "            });\n",
+              "            }\n",
+              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
+              "            const iframe = document.createElement('iframe')\n",
+              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
+              "            document.body.appendChild(iframe)\n",
+              "            const handshake = new Postmate({\n",
+              "                container: iframe,\n",
+              "                url: 'https://wandb.ai/authorize'\n",
+              "            });\n",
+              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
+              "            handshake.then(function(child) {\n",
+              "                child.on('authorize', data => {\n",
+              "                    clearTimeout(timeout)\n",
+              "                    resolve(data)\n",
+              "                });\n",
+              "            });\n",
+              "            })\n",
+              "        });\n",
+              "    "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
+            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
+            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            " ··········\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "os.environ[\"WANDB_PROJECT\"]=\"id2223\"\n",
+        "os.environ[\"WANDB_LOG_MODEL\"] = \"checkpoint\""
+      ],
+      "metadata": {
+        "id": "XfauH3pSMAQo"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "idAEIeSQ3xdS"
+      },
+      "source": [
+        "<a name=\"Train\"></a>\n",
+        "### Train the model\n",
+        "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). 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 TRL's `DPOTrainer`!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "95_Nn-89DhsL",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 67,
+          "referenced_widgets": [
+            "5861c997f06a4956ba05647bad0ca368",
+            "52e2667aa16c4b52aa9c602d4b8ee398",
+            "e90e270c6fd948f281f43205d18cc4db",
+            "a3f9a1468ac84a5a91f0114900d7d4d5",
+            "1d02b96ef30249f38b9dadd9159ea936",
+            "dc32c3f442d84e9dbda13be6a6ce3352",
+            "1b6a38357e584a7a824f29f0868a8575",
+            "a1267f96544242659062f9311f9bf9a2",
+            "7929f417cde840fc909e6fecb2a65ca1",
+            "6c3fcbd4e51740118fe65dd5626aab40",
+            "f6b3e230eb2b446dbf2eb5737c0128da"
+          ]
+        },
+        "outputId": "f0482359-fab1-492b-c60c-448264b1f8fd"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map (num_proc=4):   0%|          | 0/100000 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "5861c997f06a4956ba05647bad0ca368"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "max_steps is given, it will override any value given in num_train_epochs\n"
+          ]
+        }
+      ],
+      "source": [
+        "from trl import SFTTrainer\n",
+        "from transformers import TrainingArguments, DataCollatorForSeq2Seq\n",
+        "from unsloth import is_bfloat16_supported\n",
+        "\n",
+        "trainer = SFTTrainer(\n",
+        "    model = model,\n",
+        "    tokenizer = tokenizer,\n",
+        "    train_dataset = dataset,\n",
+        "    dataset_text_field = \"text\",\n",
+        "    max_seq_length = max_seq_length,\n",
+        "    data_collator = DataCollatorForSeq2Seq(tokenizer = tokenizer),\n",
+        "    dataset_num_proc = 4,\n",
+        "    packing = False, # Can make training 5x faster for short sequences.\n",
+        "    args = TrainingArguments(\n",
+        "        per_device_train_batch_size = 2,\n",
+        "        gradient_accumulation_steps = 4,\n",
+        "        warmup_steps = 5,\n",
+        "        # num_train_epochs = 1, # Set this for 1 full training run.\n",
+        "        save_steps=10, # saves every 10 steps\n",
+        "        max_steps = 20,\n",
+        "        learning_rate = 2e-4,\n",
+        "        fp16 = not is_bfloat16_supported(),\n",
+        "        bf16 = is_bfloat16_supported(),\n",
+        "        logging_steps = 1,\n",
+        "        optim = \"adamw_8bit\",\n",
+        "        weight_decay = 0.01,\n",
+        "        lr_scheduler_type = \"linear\",\n",
+        "        seed = 3407,\n",
+        "        output_dir = \"outputs\",\n",
+        "        report_to = \"wandb\", # Use this for WandB etc\n",
+        "    ),\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We also use Unsloth's `train_on_completions` method to only train on the assistant outputs and ignore the loss on the user's inputs."
+      ],
+      "metadata": {
+        "id": "C_sGp5XlG6dq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from unsloth.chat_templates import train_on_responses_only\n",
+        "trainer = train_on_responses_only(\n",
+        "    trainer,\n",
+        "    instruction_part = \"<|start_header_id|>user<|end_header_id|>\\n\\n\",\n",
+        "    response_part = \"<|start_header_id|>assistant<|end_header_id|>\\n\\n\",\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "juQiExuBG5Bt",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "c59cd7a65f8f4036b96276b2cf2bd00f",
+            "bfeb81731e1b477a9bf69caba3feadd2",
+            "47c96b8cae35465fb3899d3987476766",
+            "f09f0425e65b43148b634adad9edc3c2",
+            "fc25fc3c647a4d858d6ab01c0d2bffa5",
+            "40dfc0ad2e854bea859ad18142d506de",
+            "b407473976fb4bab9933b9d7e89b584a",
+            "93be6e157d56441f83089795021133ea",
+            "04b8c446b98e48e79a7654002dd8fa79",
+            "582293c1ec6f440885275491144463ab",
+            "5dc55a3d72e14e2f964560b37282a788"
+          ]
+        },
+        "outputId": "55d189a2-e1da-4daf-fce8-d71598d63c4f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Map:   0%|          | 0/100000 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c59cd7a65f8f4036b96276b2cf2bd00f"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We verify masking is actually done:"
+      ],
+      "metadata": {
+        "id": "Dv1NBUozV78l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tokenizer.decode(trainer.train_dataset[5][\"input_ids\"])"
+      ],
+      "metadata": {
+        "id": "LtsMVtlkUhja",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 110
+        },
+        "outputId": "8283780d-8f67-4cfb-cca1-2027f01e422e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'<|begin_of_text|><|start_header_id|>system<|end_header_id|>\\n\\nCutting Knowledge Date: December 2023\\nToday Date: 26 July 2024\\n\\n<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nHow do astronomers determine the original wavelength of light emitted by a celestial body at rest, which is necessary for measuring its speed using the Doppler effect?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\nAstronomers make use of the unique spectral fingerprints of elements found in stars. These elements emit and absorb light at specific, known wavelengths, forming an absorption spectrum. By analyzing the light received from distant stars and comparing it to the laboratory-measured spectra of these elements, astronomers can identify the shifts in these wavelengths due to the Doppler effect. The observed shift tells them the extent to which the light has been redshifted or blueshifted, thereby allowing them to calculate the speed of the star along the line of sight relative to Earth.<|eot_id|>'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "space = tokenizer(\" \", add_special_tokens = False).input_ids[0]\n",
+        "tokenizer.decode([space if x == -100 else x for x in trainer.train_dataset[5][\"labels\"]])"
+      ],
+      "metadata": {
+        "id": "_rD6fl8EUxnG",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 91
+        },
+        "outputId": "7bdb624d-4afb-479b-c935-d5a925f11da2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'                                                                \\n\\nAstronomers make use of the unique spectral fingerprints of elements found in stars. These elements emit and absorb light at specific, known wavelengths, forming an absorption spectrum. By analyzing the light received from distant stars and comparing it to the laboratory-measured spectra of these elements, astronomers can identify the shifts in these wavelengths due to the Doppler effect. The observed shift tells them the extent to which the light has been redshifted or blueshifted, thereby allowing them to calculate the speed of the star along the line of sight relative to Earth.<|eot_id|>'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We can see the System and Instruction prompts are successfully masked!"
+      ],
+      "metadata": {
+        "id": "3enWUM0jV-jV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2ejIt2xSNKKp",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4905a0e5-19fb-4948-e5ad-9ffa75619a4a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "GPU = Tesla T4. Max memory = 14.748 GB.\n",
+            "1.148 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": null,
+      "metadata": {
+        "id": "yqxqAZ7KJ4oL",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "9ab87013-9baf-45dc-c062-f02fa40ea758"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
+            "   \\\\   /|    Num examples = 100,000 | Num Epochs = 1\n",
+            "O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 4\n",
+            "\\        /    Total batch size = 8 | Total steps = 20\n",
+            " \"-____-\"     Number of trainable parameters = 11,272,192\n",
+            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Changes to your `wandb` environment variables will be ignored because your `wandb` session has already started. For more information on how to modify your settings with `wandb.init()` arguments, please refer to <a href='https://wandb.me/wandb-init' target=\"_blank\">the W&B docs</a>."
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mrobzy\u001b[0m (\u001b[33mid2223\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Tracking run with wandb version 0.18.7"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Run data is saved locally in <code>/content/wandb/run-20241130_071312-925e6em1</code>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Syncing run <strong><a href='https://wandb.ai/id2223/id2223/runs/925e6em1' target=\"_blank\">outputs</a></strong> to <a href='https://wandb.ai/id2223/id2223' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br/>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              " View project at <a href='https://wandb.ai/id2223/id2223' target=\"_blank\">https://wandb.ai/id2223/id2223</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              " View run at <a href='https://wandb.ai/id2223/id2223/runs/925e6em1' target=\"_blank\">https://wandb.ai/id2223/id2223/runs/925e6em1</a>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='20' max='20' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [20/20 01:17, 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>0.867000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>2</td>\n",
+              "      <td>0.962500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>3</td>\n",
+              "      <td>1.164500</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>4</td>\n",
+              "      <td>1.044200</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>5</td>\n",
+              "      <td>0.833600</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>6</td>\n",
+              "      <td>1.056700</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>7</td>\n",
+              "      <td>0.747000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>8</td>\n",
+              "      <td>1.111800</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>9</td>\n",
+              "      <td>1.005200</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>10</td>\n",
+              "      <td>0.881000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>11</td>\n",
+              "      <td>0.999900</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>12</td>\n",
+              "      <td>1.253400</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>13</td>\n",
+              "      <td>1.063600</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>14</td>\n",
+              "      <td>0.750100</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>15</td>\n",
+              "      <td>1.004400</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>16</td>\n",
+              "      <td>0.741900</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>17</td>\n",
+              "      <td>1.153800</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>18</td>\n",
+              "      <td>0.986800</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>19</td>\n",
+              "      <td>0.886300</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <td>20</td>\n",
+              "      <td>1.025200</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table><p>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./outputs/checkpoint-10)... Done. 0.5s\n",
+            "\u001b[34m\u001b[1mwandb\u001b[0m: Adding directory to artifact (./outputs/checkpoint-20)... Done. 0.4s\n",
+            "max_steps is given, it will override any value given in num_train_epochs\n"
+          ]
+        }
+      ],
+      "source": [
+        "trainer_stats = trainer.train()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "wandb.finish()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 388,
+          "referenced_widgets": [
+            "d6accc4038424f5fbfcc8c54762ec8ca",
+            "bfd14116621b448ea29e716b0dd41492",
+            "597b6317dd954285814f1d80c817a683",
+            "1689c268efe84d9c8fa8bae7871e5c09",
+            "566c9df6d64149cea2c47f04a631fac7",
+            "2c804df2de23445b8a8f746b86897e77",
+            "99bd1c24e6674d20b17372d090cbac05",
+            "bfa5e6218f91406483b601ff48a677e3"
+          ]
+        },
+        "id": "n7LkysP0N_-_",
+        "outputId": "ed35d58d-b030-43ef-de96-bd38691e440c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "VBox(children=(Label(value='83.362 MB of 146.758 MB uploaded\\r'), FloatProgress(value=0.5680246166833091, max=…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "d6accc4038424f5fbfcc8c54762ec8ca"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <style>\n",
+              "        .wandb-row {\n",
+              "            display: flex;\n",
+              "            flex-direction: row;\n",
+              "            flex-wrap: wrap;\n",
+              "            justify-content: flex-start;\n",
+              "            width: 100%;\n",
+              "        }\n",
+              "        .wandb-col {\n",
+              "            display: flex;\n",
+              "            flex-direction: column;\n",
+              "            flex-basis: 100%;\n",
+              "            flex: 1;\n",
+              "            padding: 10px;\n",
+              "        }\n",
+              "    </style>\n",
+              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>▁▁▂▂▂▃▃▄▄▄▅▅▅▆▆▇▇▇███</td></tr><tr><td>train/global_step</td><td>▁▁▂▂▂▃▃▄▄▄▅▅▅▆▆▇▇▇███</td></tr><tr><td>train/grad_norm</td><td>▅▇█▅▄▅▃▅▂▃▁▃▅▂▂▃▄▆▂▄</td></tr><tr><td>train/learning_rate</td><td>▂▄▅▇██▇▇▆▆▅▅▄▄▃▃▂▂▁▁</td></tr><tr><td>train/loss</td><td>▃▄▇▅▂▅▁▆▅▃▅█▅▁▅▁▇▄▃▅</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>total_flos</td><td>701492545757184.0</td></tr><tr><td>train/epoch</td><td>0.0016</td></tr><tr><td>train/global_step</td><td>20</td></tr><tr><td>train/grad_norm</td><td>0.30762</td></tr><tr><td>train/learning_rate</td><td>0</td></tr><tr><td>train/loss</td><td>1.0252</td></tr><tr><td>train_loss</td><td>0.97694</td></tr><tr><td>train_runtime</td><td>116.4671</td></tr><tr><td>train_samples_per_second</td><td>1.374</td></tr><tr><td>train_steps_per_second</td><td>0.172</td></tr></table><br/></div></div>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              " View run <strong style=\"color:#cdcd00\">outputs</strong> at: <a href='https://wandb.ai/id2223/id2223/runs/925e6em1' target=\"_blank\">https://wandb.ai/id2223/id2223/runs/925e6em1</a><br/> View project at: <a href='https://wandb.ai/id2223/id2223' target=\"_blank\">https://wandb.ai/id2223/id2223</a><br/>Synced 5 W&B file(s), 0 media file(s), 21 artifact file(s) and 0 other file(s)"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "Find logs at: <code>./wandb/run-20241130_071312-925e6em1/logs</code>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pCqnaKmlO1U9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2e8ef0ea-aed5-4f13-cfae-89756cda2747"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "116.4671 seconds used for training.\n",
+            "1.94 minutes used for training.\n",
+            "Peak reserved memory = 2.471 GB.\n",
+            "Peak reserved memory for training = 1.323 GB.\n",
+            "Peak reserved memory % of max memory = 16.755 %.\n",
+            "Peak reserved memory for training % of max memory = 8.971 %.\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(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\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",
+      "source": [],
+      "metadata": {
+        "id": "n7B8irCKyPqo"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "bNcR8PnfN-8a"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ekOmTR1hSNcr"
+      },
+      "source": [
+        "<a name=\"Inference\"></a>\n",
+        "### Inference\n",
+        "Let's run the model! You can change the instruction and input - leave the output blank!\n",
+        "\n",
+        "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**\n",
+        "\n",
+        "We use `min_p = 0.1` and `temperature = 1.5`. Read this [Tweet](https://x.com/menhguin/status/1826132708508213629) for more information on why."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kR3gIAX-SM2q",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "174cc3e4-4a78-4985-eb18-8f09c5a4ffc3"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['<|begin_of_text|><|start_header_id|>system<|end_header_id|>\\n\\nCutting Knowledge Date: December 2023\\nToday Date: 26 July 2024\\n\\n<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nContinue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\nThe sequence continues as:\\n\\n2, 5, 8,<|eot_id|>']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ],
+      "source": [
+        "from unsloth.chat_templates import get_chat_template\n",
+        "\n",
+        "tokenizer = get_chat_template(\n",
+        "    tokenizer,\n",
+        "    chat_template = \"llama-3.1\",\n",
+        ")\n",
+        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
+        "\n",
+        "messages = [\n",
+        "    {\"role\": \"user\", \"content\": \"Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,\"},\n",
+        "]\n",
+        "inputs = tokenizer.apply_chat_template(\n",
+        "    messages,\n",
+        "    tokenize = True,\n",
+        "    add_generation_prompt = True, # Must add for generation\n",
+        "    return_tensors = \"pt\",\n",
+        ").to(\"cuda\")\n",
+        "\n",
+        "outputs = model.generate(input_ids = inputs, max_new_tokens = 64, use_cache = True,\n",
+        "                         temperature = 1.5, min_p = 0.1)\n",
+        "tokenizer.batch_decode(outputs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CrSvZObor0lY"
+      },
+      "source": [
+        " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "e2pEuRb1r2Vg",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "cb3e42ee-ade6-4d03-d9f0-ce48c2cc1c64"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1 1 2 3 5 8<|eot_id|>\n"
+          ]
+        }
+      ],
+      "source": [
+        "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
+        "\n",
+        "messages = [\n",
+        "    {\"role\": \"user\", \"content\": \"Continue the fibonnaci sequence: 1, 1, 2, 3, 5, 8,\"},\n",
+        "]\n",
+        "inputs = tokenizer.apply_chat_template(\n",
+        "    messages,\n",
+        "    tokenize = True,\n",
+        "    add_generation_prompt = True, # Must add for generation\n",
+        "    return_tensors = \"pt\",\n",
+        ").to(\"cuda\")\n",
+        "\n",
+        "from transformers import TextStreamer\n",
+        "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
+        "_ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,\n",
+        "                   use_cache = True, temperature = 1.5, min_p = 0.1)"
+      ]
+    },
+    {
+      "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 Huggingface'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": null,
+      "metadata": {
+        "id": "upcOlWe7A1vc",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 200,
+          "referenced_widgets": [
+            "9ffdda8f9c5743d3ad68d7ac04836005",
+            "b546cf0dd67341d0ab800c24d0d64f5e",
+            "7753719d5b5e42cb9be5885022331912",
+            "f7dfa3fc06344c089688036d578b1885",
+            "24a33ba11104499d8dab96238ed6874c",
+            "eb7863aa333d404987033d2766d759ca",
+            "a787d6060cd34e6e8a8862857a4e6109",
+            "92a836db3d8f43d3830c799ae716b633",
+            "07dddca6c2024c3b883e794062e50991",
+            "3efbb924cbc94df6b4f40d0fc2579965",
+            "6939625444fc4441b0572bb97bb15937",
+            "561ecd47a1ff4767ad1e17d8e08f3520",
+            "c2c9eb9167e14896a46d948e59fde31e",
+            "b8136af993a94cfab591d4e803d19785",
+            "bd14eaf3d91641eea6f575f1a8bf92b1",
+            "d2d14b1077ec4f5e8aa8a4a7e4235141",
+            "f768e8ca2c2a42f888b5bd899612edfd",
+            "acc685df3e0e4cf4ad23e3ac47186e1c",
+            "f1cde7f63e794b02ad03c0dbd1e6de2d",
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+            "5f750c954a054f9495ba31e4c67fb11d",
+            "74d9c8a3f9dd46cb9463a69edb81e060",
+            "e7369f1637ba4ee29fd1b927fb4d7676",
+            "bd0cd3ea500c4a33aa883cf976da141a",
+            "1ce0936c2aa549ae902374ce31d47be0",
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+            "b1c220c35b26431ebb1772d7c64626e6",
+            "2dd1298568c24417911a1810c4e89122",
+            "9a3cb949f94c4ed091cab2a114c6e29e",
+            "cdcbb5332d96412db375e495742665ef",
+            "bc33c568986e4663aca6a11e9bd4de16",
+            "97c4468ff7384d019ba7f5acaf4cc5a9",
+            "e180aa9d9c394236ae61e32715a0e60f",
+            "dfdc111bbf64426bb3cc3f1743dd30f9",
+            "c7c0301758244604baadf96b487ed369",
+            "b5f8e89742104bf88e07281d796402d3",
+            "e8e4073ade4549f1a8c02f75f26a6a24",
+            "7843923bd0a24c9a831c57be9c00a86c",
+            "f934af809a40455b8dce4b87a6fc3dba",
+            "6a89fc221533445bb36c3e6bda1f0c5d",
+            "438a3692d4aa4f109add67c89482f880",
+            "e3d5b65a9db04bbc9035f0afb86e0d7e",
+            "0d309915b27244f1b55c849e168af607",
+            "dd44275d92be413a8aa9f74a8c282d5c"
+          ]
+        },
+        "outputId": "b092e22f-e95a-44fc-b376-256932ff0f01"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "README.md:   0%|          | 0.00/594 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "9ffdda8f9c5743d3ad68d7ac04836005"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/1 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "561ecd47a1ff4767ad1e17d8e08f3520"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "adapter_model.safetensors:   0%|          | 0.00/45.1M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e7369f1637ba4ee29fd1b927fb4d7676"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saved model to https://huggingface.co/Robzy/lora_model\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "README.md:   0%|          | 0.00/5.18k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "dfdc111bbf64426bb3cc3f1743dd30f9"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "No files have been modified since last commit. Skipping to prevent empty commit.\n",
+            "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n"
+          ]
+        }
+      ],
+      "source": [
+        "token  = ''\n",
+        "model.save_pretrained(\"lora_model\") # Local saving\n",
+        "tokenizer.save_pretrained(\"lora_model\")\n",
+        "model.push_to_hub(\"Robzy/lora_model\", token = token) # Online saving\n",
+        "tokenizer.push_to_hub(\"Robzy-lora_model\", token = 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": null,
+      "metadata": {
+        "id": "MKX_XKs_BNZR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bb1d9fed-44b4-4c7f-bdd1-d88e0ea5f82b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "==((====))==  Unsloth 2024.11.10: Fast Llama patching. Transformers:4.46.2.\n",
+            "   \\\\   /|    GPU: Tesla T4. Max memory: 14.748 GB. Platform: Linux.\n",
+            "O^O/ \\_/ \\    Torch: 2.5.1+cu121. CUDA: 7.5. CUDA Toolkit: 12.1. Triton: 3.1.0\n",
+            "\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.28.post3. FA2 = False]\n",
+            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
+            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Exception ignored in: <function _xla_gc_callback at 0x7d95afa5d6c0>\n",
+            "Traceback (most recent call last):\n",
+            "  File \"/usr/local/lib/python3.10/dist-packages/jax/_src/lib/__init__.py\", line 96, in _xla_gc_callback\n",
+            "    def _xla_gc_callback(*args):\n",
+            "KeyboardInterrupt: \n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The French capital's towering edifice stands at a height of 60 meters. This is a high-rise, modern skyscraper with a sleek and aerodynamic design that captures the beauty of the Parisian skyline. The tower is made of a lightweight material with a series of clear glass panels at its top, allowing a flood of light to enter the interior. The outside is clad in dark-colored glass panels, which provide the structural framework and aesthetic appeal of the tower.<|eot_id|>\n"
+          ]
+        }
+      ],
+      "source": [
+        "if True:\n",
+        "    from unsloth import FastLanguageModel\n",
+        "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
+        "        model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
+        "        max_seq_length = max_seq_length,\n",
+        "        dtype = dtype,\n",
+        "        load_in_4bit = load_in_4bit,\n",
+        "    )\n",
+        "    FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
+        "\n",
+        "messages = [\n",
+        "    {\"role\": \"user\", \"content\": \"Describe a tall tower in the capital of France.\"},\n",
+        "]\n",
+        "inputs = tokenizer.apply_chat_template(\n",
+        "    messages,\n",
+        "    tokenize = True,\n",
+        "    add_generation_prompt = True, # Must add for generation\n",
+        "    return_tensors = \"pt\",\n",
+        ").to(\"cuda\")\n",
+        "\n",
+        "from transformers import TextStreamer\n",
+        "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
+        "_ = model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128,\n",
+        "                   use_cache = True, temperature = 1.5, min_p = 0.1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QQMjaNrjsU5_"
+      },
+      "source": [
+        "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yFfaXG0WsQuE"
+      },
+      "outputs": [],
+      "source": [
+        "if False:\n",
+        "    # I highly do NOT suggest - use Unsloth if possible\n",
+        "    from peft import AutoPeftModelForCausalLM\n",
+        "    from transformers import AutoTokenizer\n",
+        "    model = AutoPeftModelForCausalLM.from_pretrained(\n",
+        "        \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
+        "        load_in_4bit = load_in_4bit,\n",
+        "    )\n",
+        "    tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")"
+      ]
+    },
+    {
+      "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 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. 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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iHjt_SMYsd3P"
+      },
+      "outputs": [],
+      "source": [
+        "# Merge to 16bit\n",
+        "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n",
+        "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n",
+        "\n",
+        "# Merge to 4bit\n",
+        "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n",
+        "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n",
+        "\n",
+        "# Just LoRA adapters\n",
+        "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n",
+        "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TCv4vXHd61i7"
+      },
+      "source": [
+        "### GGUF / llama.cpp Conversion\n",
+        "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n",
+        "\n",
+        "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n",
+        "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n",
+        "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n",
+        "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n",
+        "\n",
+        "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "FqfebeAdT073",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000,
+          "referenced_widgets": [
+            "ade4b4c1ee22436c9b85356243a50485",
+            "32da6c3892244f43b63e6367a5d46786",
+            "23c56d5c74a647f1b7a7e27914b4536a",
+            "cd82a504a6ad4a5190c8e386436019cf",
+            "379785fe7af442dcb697d68cac807a86",
+            "8c719f03a3ed42a6b5e74251d2a88cf1",
+            "5bcb82d8e1694e218582449d041557dc",
+            "74b7ec150d1446b28fe11790dea287c7",
+            "33184c9b6a4a44ff836da7203256c816",
+            "e11365da21304c02a45a5c9adf2a44da",
+            "2552d10b8fba4ea3a004e24adf612dcb",
+            "6ce199a077814319a6de805eda52df2b",
+            "faf4e352836e44cda599df406d941b20",
+            "216f8da7d8194fa0894169a55e770657",
+            "2ab662f6640349d6b829472c6cdc8f53",
+            "ebb9c1711d334cdfb9e89ed227519e94",
+            "ab9cd76dbbc34df4a148cd3593af7d4f",
+            "f3e45682c0564513a9184619ead4c771",
+            "1d2300dba31d4753805eb3b9db8c6910",
+            "15bd7445a49d45cb95fc704662d3cd04",
+            "be543450da1340d0b775184f5bb99dd3",
+            "28b4ae40aee14e599925e248ff1b68e7"
+          ]
+        },
+        "outputId": "726bb767-cd5d-4c05-b717-5a05f574d60c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
+            "Unsloth: Will use up to 5.61 out of 12.67 RAM for saving.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 16/16 [00:00<00:00, 39.52it/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Unsloth: Saving tokenizer... Done.\n",
+            "Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n",
+            "Unsloth: Saving Robzy/Llama-3.2-1B-Instruct-Finetuned-q4_k_m/pytorch_model.bin...\n",
+            "Done.\n",
+            "==((====))==  Unsloth: Conversion from QLoRA to GGUF information\n",
+            "   \\\\   /|    [0] Installing llama.cpp will take 3 minutes.\n",
+            "O^O/ \\_/ \\    [1] Converting HF to GGUF 16bits will take 3 minutes.\n",
+            "\\        /    [2] Converting GGUF 16bits to ['q4_k_m'] will take 10 minutes each.\n",
+            " \"-____-\"     In total, you will have to wait at least 16 minutes.\n",
+            "\n",
+            "Unsloth: [0] Installing llama.cpp. This will take 3 minutes...\n",
+            "Unsloth: [1] Converting model at Robzy/Llama-3.2-1B-Instruct-Finetuned-q4_k_m into f16 GGUF format.\n",
+            "The output location will be /content/Robzy/Llama-3.2-1B-Instruct-Finetuned-q4_k_m/unsloth.F16.gguf\n",
+            "This will take 3 minutes...\n",
+            "INFO:hf-to-gguf:Loading model: Llama-3.2-1B-Instruct-Finetuned-q4_k_m\n",
+            "INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only\n",
+            "INFO:hf-to-gguf:Exporting model...\n",
+            "INFO:hf-to-gguf:rope_freqs.weight,           torch.float32 --> F32, shape = {32}\n",
+            "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model.bin'\n",
+            "INFO:hf-to-gguf:token_embd.weight,           torch.float16 --> F16, shape = {2048, 128256}\n",
+            "INFO:hf-to-gguf:blk.0.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.0.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.0.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.0.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.0.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.0.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.0.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
+            "INFO:hf-to-gguf:blk.0.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.0.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.1.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.1.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.1.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.1.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.1.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.1.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.1.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
+            "INFO:hf-to-gguf:blk.1.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.1.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.2.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.2.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.2.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.2.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.2.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.2.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.2.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
+            "INFO:hf-to-gguf:blk.2.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.2.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.3.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.3.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.3.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.3.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.3.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.3.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.3.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
+            "INFO:hf-to-gguf:blk.3.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.3.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.4.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.4.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.4.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.4.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.4.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.4.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.4.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
+            "INFO:hf-to-gguf:blk.4.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.4.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.5.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.5.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.5.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.5.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.5.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.5.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.5.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
+            "INFO:hf-to-gguf:blk.5.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.5.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.6.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.6.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.6.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.6.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.6.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.6.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.6.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
+            "INFO:hf-to-gguf:blk.6.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.6.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.7.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.7.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.7.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.7.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.7.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.7.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.7.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
+            "INFO:hf-to-gguf:blk.7.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.7.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.8.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.8.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.8.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.8.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.8.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.8.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.8.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
+            "INFO:hf-to-gguf:blk.8.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.8.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.9.attn_q.weight,         torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.9.attn_k.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.9.attn_v.weight,         torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.9.attn_output.weight,    torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.9.ffn_gate.weight,       torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.9.ffn_up.weight,         torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.9.ffn_down.weight,       torch.float16 --> F16, shape = {8192, 2048}\n",
+            "INFO:hf-to-gguf:blk.9.attn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.9.ffn_norm.weight,       torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.10.attn_q.weight,        torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.10.attn_k.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.10.attn_v.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.10.attn_output.weight,   torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.10.ffn_gate.weight,      torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.10.ffn_up.weight,        torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.10.ffn_down.weight,      torch.float16 --> F16, shape = {8192, 2048}\n",
+            "INFO:hf-to-gguf:blk.10.attn_norm.weight,     torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.10.ffn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.11.attn_q.weight,        torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.11.attn_k.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.11.attn_v.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.11.attn_output.weight,   torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.11.ffn_gate.weight,      torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.11.ffn_up.weight,        torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.11.ffn_down.weight,      torch.float16 --> F16, shape = {8192, 2048}\n",
+            "INFO:hf-to-gguf:blk.11.attn_norm.weight,     torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.11.ffn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.12.attn_q.weight,        torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.12.attn_k.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.12.attn_v.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.12.attn_output.weight,   torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.12.ffn_gate.weight,      torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.12.ffn_up.weight,        torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.12.ffn_down.weight,      torch.float16 --> F16, shape = {8192, 2048}\n",
+            "INFO:hf-to-gguf:blk.12.attn_norm.weight,     torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.12.ffn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.13.attn_q.weight,        torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.13.attn_k.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.13.attn_v.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.13.attn_output.weight,   torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.13.ffn_gate.weight,      torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.13.ffn_up.weight,        torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.13.ffn_down.weight,      torch.float16 --> F16, shape = {8192, 2048}\n",
+            "INFO:hf-to-gguf:blk.13.attn_norm.weight,     torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.13.ffn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.14.attn_q.weight,        torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.14.attn_k.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.14.attn_v.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.14.attn_output.weight,   torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.14.ffn_gate.weight,      torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.14.ffn_up.weight,        torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.14.ffn_down.weight,      torch.float16 --> F16, shape = {8192, 2048}\n",
+            "INFO:hf-to-gguf:blk.14.attn_norm.weight,     torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.14.ffn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.15.attn_q.weight,        torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.15.attn_k.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.15.attn_v.weight,        torch.float16 --> F16, shape = {2048, 512}\n",
+            "INFO:hf-to-gguf:blk.15.attn_output.weight,   torch.float16 --> F16, shape = {2048, 2048}\n",
+            "INFO:hf-to-gguf:blk.15.ffn_gate.weight,      torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.15.ffn_up.weight,        torch.float16 --> F16, shape = {2048, 8192}\n",
+            "INFO:hf-to-gguf:blk.15.ffn_down.weight,      torch.float16 --> F16, shape = {8192, 2048}\n",
+            "INFO:hf-to-gguf:blk.15.attn_norm.weight,     torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:blk.15.ffn_norm.weight,      torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:output_norm.weight,          torch.float16 --> F32, shape = {2048}\n",
+            "INFO:hf-to-gguf:Set meta model\n",
+            "INFO:hf-to-gguf:Set model parameters\n",
+            "INFO:hf-to-gguf:gguf: context length = 131072\n",
+            "INFO:hf-to-gguf:gguf: embedding length = 2048\n",
+            "INFO:hf-to-gguf:gguf: feed forward length = 8192\n",
+            "INFO:hf-to-gguf:gguf: head count = 32\n",
+            "INFO:hf-to-gguf:gguf: key-value head count = 8\n",
+            "INFO:hf-to-gguf:gguf: rope theta = 500000.0\n",
+            "INFO:hf-to-gguf:gguf: rms norm epsilon = 1e-05\n",
+            "INFO:hf-to-gguf:gguf: file type = 1\n",
+            "INFO:hf-to-gguf:Set model tokenizer\n",
+            "INFO:gguf.vocab:Adding 280147 merge(s).\n",
+            "INFO:gguf.vocab:Setting special token type bos to 128000\n",
+            "INFO:gguf.vocab:Setting special token type eos to 128009\n",
+            "INFO:gguf.vocab:Setting special token type pad to 128004\n",
+            "INFO:gguf.vocab:Setting chat_template to {{- bos_token }}\n",
+            "{%- if custom_tools is defined %}\n",
+            "    {%- set tools = custom_tools %}\n",
+            "{%- endif %}\n",
+            "{%- if not tools_in_user_message is defined %}\n",
+            "    {%- set tools_in_user_message = true %}\n",
+            "{%- endif %}\n",
+            "{%- if not date_string is defined %}\n",
+            "    {%- set date_string = \"26 July 2024\" %}\n",
+            "{%- endif %}\n",
+            "{%- if not tools is defined %}\n",
+            "    {%- set tools = none %}\n",
+            "{%- endif %}\n",
+            "\n",
+            "{#- This block extracts the system message, so we can slot it into the right place. #}\n",
+            "{%- if messages[0]['role'] == 'system' %}\n",
+            "    {%- set system_message = messages[0]['content'] %}\n",
+            "    {%- set messages = messages[1:] %}\n",
+            "{%- else %}\n",
+            "    {%- set system_message = \"\" %}\n",
+            "{%- endif %}\n",
+            "\n",
+            "{#- System message + builtin tools #}\n",
+            "{{- \"<|start_header_id|>system<|end_header_id|>\n",
+            "\n",
+            "\" }}\n",
+            "{%- if builtin_tools is defined or tools is not none %}\n",
+            "    {{- \"Environment: ipython\n",
+            "\" }}\n",
+            "{%- endif %}\n",
+            "{%- if builtin_tools is defined %}\n",
+            "    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\n",
+            "\n",
+            "\"}}\n",
+            "{%- endif %}\n",
+            "{{- \"Cutting Knowledge Date: December 2023\n",
+            "\" }}\n",
+            "{{- \"Today Date: \" + date_string + \"\n",
+            "\n",
+            "\" }}\n",
+            "{%- if tools is not none and not tools_in_user_message %}\n",
+            "    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n",
+            "    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n",
+            "    {{- \"Do not use variables.\n",
+            "\n",
+            "\" }}\n",
+            "    {%- for t in tools %}\n",
+            "        {{- t | tojson(indent=4) }}\n",
+            "        {{- \"\n",
+            "\n",
+            "\" }}\n",
+            "    {%- endfor %}\n",
+            "{%- endif %}\n",
+            "{{- system_message }}\n",
+            "{{- \"<|eot_id|>\" }}\n",
+            "\n",
+            "{#- Custom tools are passed in a user message with some extra guidance #}\n",
+            "{%- if tools_in_user_message and not tools is none %}\n",
+            "    {#- Extract the first user message so we can plug it in here #}\n",
+            "    {%- if messages | length != 0 %}\n",
+            "        {%- set first_user_message = messages[0]['content'] %}\n",
+            "        {%- set messages = messages[1:] %}\n",
+            "    {%- else %}\n",
+            "        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n",
+            "{%- endif %}\n",
+            "    {{- '<|start_header_id|>user<|end_header_id|>\n",
+            "\n",
+            "' -}}\n",
+            "    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n",
+            "    {{- \"with its proper arguments that best answers the given prompt.\n",
+            "\n",
+            "\" }}\n",
+            "    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n",
+            "    {{- \"Do not use variables.\n",
+            "\n",
+            "\" }}\n",
+            "    {%- for t in tools %}\n",
+            "        {{- t | tojson(indent=4) }}\n",
+            "        {{- \"\n",
+            "\n",
+            "\" }}\n",
+            "    {%- endfor %}\n",
+            "    {{- first_user_message + \"<|eot_id|>\"}}\n",
+            "{%- endif %}\n",
+            "\n",
+            "{%- for message in messages %}\n",
+            "    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n",
+            "        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n",
+            "\n",
+            "'+ message['content'] + '<|eot_id|>' }}\n",
+            "    {%- elif 'tool_calls' in message %}\n",
+            "        {%- if not message.tool_calls|length == 1 %}\n",
+            "            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n",
+            "        {%- endif %}\n",
+            "        {%- set tool_call = message.tool_calls[0].function %}\n",
+            "        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n",
+            "            {{- '<|start_header_id|>assistant<|end_header_id|>\n",
+            "\n",
+            "' -}}\n",
+            "            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n",
+            "            {%- for arg_name, arg_val in tool_call.arguments | items %}\n",
+            "                {{- arg_name + '=\"' + arg_val + '\"' }}\n",
+            "                {%- if not loop.last %}\n",
+            "                    {{- \", \" }}\n",
+            "                {%- endif %}\n",
+            "                {%- endfor %}\n",
+            "            {{- \")\" }}\n",
+            "        {%- else  %}\n",
+            "            {{- '<|start_header_id|>assistant<|end_header_id|>\n",
+            "\n",
+            "' -}}\n",
+            "            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n",
+            "            {{- '\"parameters\": ' }}\n",
+            "            {{- tool_call.arguments | tojson }}\n",
+            "            {{- \"}\" }}\n",
+            "        {%- endif %}\n",
+            "        {%- if builtin_tools is defined %}\n",
+            "            {#- This means we're in ipython mode #}\n",
+            "            {{- \"<|eom_id|>\" }}\n",
+            "        {%- else %}\n",
+            "            {{- \"<|eot_id|>\" }}\n",
+            "        {%- endif %}\n",
+            "    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n",
+            "        {{- \"<|start_header_id|>ipython<|end_header_id|>\n",
+            "\n",
+            "\" }}\n",
+            "        {%- if message.content is mapping or message.content is iterable %}\n",
+            "            {{- message.content | tojson }}\n",
+            "        {%- else %}\n",
+            "            {{- message.content }}\n",
+            "        {%- endif %}\n",
+            "        {{- \"<|eot_id|>\" }}\n",
+            "    {%- endif %}\n",
+            "{%- endfor %}\n",
+            "{%- if add_generation_prompt %}\n",
+            "    {{- '<|start_header_id|>assistant<|end_header_id|>\n",
+            "\n",
+            "' }}\n",
+            "{%- endif %}\n",
+            "\n",
+            "INFO:hf-to-gguf:Set model quantization version\n",
+            "INFO:gguf.gguf_writer:Writing the following files:\n",
+            "INFO:gguf.gguf_writer:/content/Robzy/Llama-3.2-1B-Instruct-Finetuned-q4_k_m/unsloth.F16.gguf: n_tensors = 147, total_size = 2.5G\n",
+            "Writing: 100%|██████████| 2.47G/2.47G [00:36<00:00, 67.2Mbyte/s]\n",
+            "INFO:hf-to-gguf:Model successfully exported to /content/Robzy/Llama-3.2-1B-Instruct-Finetuned-q4_k_m/unsloth.F16.gguf\n",
+            "Unsloth: Conversion completed! Output location: /content/Robzy/Llama-3.2-1B-Instruct-Finetuned-q4_k_m/unsloth.F16.gguf\n",
+            "Unsloth: [2] Converting GGUF 16bit into q4_k_m. This will take 20 minutes...\n",
+            "main: build = 4227 (0533e7fb)\n",
+            "main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu\n",
+            "main: quantizing '/content/Robzy/Llama-3.2-1B-Instruct-Finetuned-q4_k_m/unsloth.F16.gguf' to '/content/Robzy/Llama-3.2-1B-Instruct-Finetuned-q4_k_m/unsloth.Q4_K_M.gguf' as Q4_K_M using 4 threads\n",
+            "llama_model_loader: loaded meta data with 30 key-value pairs and 147 tensors from /content/Robzy/Llama-3.2-1B-Instruct-Finetuned-q4_k_m/unsloth.F16.gguf (version GGUF V3 (latest))\n",
+            "llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n",
+            "llama_model_loader: - kv   0:                       general.architecture str              = llama\n",
+            "llama_model_loader: - kv   1:                               general.type str              = model\n",
+            "llama_model_loader: - kv   2:                               general.name str              = Llama 3.2 1b Instruct Bnb 4bit\n",
+            "llama_model_loader: - kv   3:                       general.organization str              = Unsloth\n",
+            "llama_model_loader: - kv   4:                           general.finetune str              = instruct-bnb-4bit\n",
+            "llama_model_loader: - kv   5:                           general.basename str              = llama-3.2\n",
+            "llama_model_loader: - kv   6:                         general.size_label str              = 1B\n",
+            "llama_model_loader: - kv   7:                          llama.block_count u32              = 16\n",
+            "llama_model_loader: - kv   8:                       llama.context_length u32              = 131072\n",
+            "llama_model_loader: - kv   9:                     llama.embedding_length u32              = 2048\n",
+            "llama_model_loader: - kv  10:                  llama.feed_forward_length u32              = 8192\n",
+            "llama_model_loader: - kv  11:                 llama.attention.head_count u32              = 32\n",
+            "llama_model_loader: - kv  12:              llama.attention.head_count_kv u32              = 8\n",
+            "llama_model_loader: - kv  13:                       llama.rope.freq_base f32              = 500000.000000\n",
+            "llama_model_loader: - kv  14:     llama.attention.layer_norm_rms_epsilon f32              = 0.000010\n",
+            "llama_model_loader: - kv  15:                 llama.attention.key_length u32              = 64\n",
+            "llama_model_loader: - kv  16:               llama.attention.value_length u32              = 64\n",
+            "llama_model_loader: - kv  17:                          general.file_type u32              = 1\n",
+            "llama_model_loader: - kv  18:                           llama.vocab_size u32              = 128256\n",
+            "llama_model_loader: - kv  19:                 llama.rope.dimension_count u32              = 64\n",
+            "llama_model_loader: - kv  20:                       tokenizer.ggml.model str              = gpt2\n",
+            "llama_model_loader: - kv  21:                         tokenizer.ggml.pre str              = llama-bpe\n",
+            "llama_model_loader: - kv  22:                      tokenizer.ggml.tokens arr[str,128256]  = [\"!\", \"\\\"\", \"#\", \"$\", \"%\", \"&\", \"'\", ...\n",
+            "llama_model_loader: - kv  23:                  tokenizer.ggml.token_type arr[i32,128256]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...\n",
+            "llama_model_loader: - kv  24:                      tokenizer.ggml.merges arr[str,280147]  = [\"Ġ Ġ\", \"Ġ ĠĠĠ\", \"ĠĠ ĠĠ\", \"...\n",
+            "llama_model_loader: - kv  25:                tokenizer.ggml.bos_token_id u32              = 128000\n",
+            "llama_model_loader: - kv  26:                tokenizer.ggml.eos_token_id u32              = 128009\n",
+            "llama_model_loader: - kv  27:            tokenizer.ggml.padding_token_id u32              = 128004\n",
+            "llama_model_loader: - kv  28:                    tokenizer.chat_template str              = {{- bos_token }}\\n{%- if custom_tools ...\n",
+            "llama_model_loader: - kv  29:               general.quantization_version u32              = 2\n",
+            "llama_model_loader: - type  f32:   34 tensors\n",
+            "llama_model_loader: - type  f16:  113 tensors\n",
+            "[   1/ 147]                   output_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[   2/ 147]                    rope_freqs.weight - [   32,     1,     1,     1], type =    f32, size =    0.000 MB\n",
+            "[   3/ 147]                    token_embd.weight - [ 2048, 128256,     1,     1], type =    f16, converting to q6_K .. size =   501.00 MiB ->   205.49 MiB\n",
+            "[   4/ 147]                  blk.0.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[   5/ 147]               blk.0.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[   6/ 147]             blk.0.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[   7/ 147]                  blk.0.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[   8/ 147]                  blk.0.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
+            "[   9/ 147]                blk.0.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
+            "[  10/ 147]                blk.0.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  11/ 147]                blk.0.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  12/ 147]                  blk.0.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  13/ 147]                  blk.1.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[  14/ 147]               blk.1.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  15/ 147]             blk.1.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  16/ 147]                  blk.1.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  17/ 147]                  blk.1.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
+            "[  18/ 147]                blk.1.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
+            "[  19/ 147]                blk.1.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  20/ 147]                blk.1.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  21/ 147]                  blk.1.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  22/ 147]                  blk.2.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[  23/ 147]               blk.2.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  24/ 147]             blk.2.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  25/ 147]                  blk.2.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  26/ 147]                  blk.2.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[  27/ 147]                blk.2.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  28/ 147]                blk.2.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  29/ 147]                blk.2.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  30/ 147]                  blk.2.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  31/ 147]                  blk.3.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[  32/ 147]               blk.3.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  33/ 147]             blk.3.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  34/ 147]                  blk.3.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  35/ 147]                  blk.3.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[  36/ 147]                blk.3.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  37/ 147]                blk.3.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  38/ 147]                blk.3.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  39/ 147]                  blk.3.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  40/ 147]                  blk.4.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[  41/ 147]               blk.4.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  42/ 147]             blk.4.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  43/ 147]                  blk.4.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  44/ 147]                  blk.4.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
+            "[  45/ 147]                blk.4.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
+            "[  46/ 147]                blk.4.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  47/ 147]                blk.4.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  48/ 147]                  blk.4.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  49/ 147]                  blk.5.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[  50/ 147]               blk.5.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  51/ 147]             blk.5.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  52/ 147]                  blk.5.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  53/ 147]                  blk.5.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[  54/ 147]                blk.5.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  55/ 147]                blk.5.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  56/ 147]                blk.5.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  57/ 147]                  blk.5.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  58/ 147]                  blk.6.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[  59/ 147]               blk.6.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  60/ 147]             blk.6.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  61/ 147]                  blk.6.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  62/ 147]                  blk.6.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[  63/ 147]                blk.6.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  64/ 147]                blk.6.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  65/ 147]                blk.6.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  66/ 147]                  blk.6.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  67/ 147]                  blk.7.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[  68/ 147]               blk.7.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  69/ 147]             blk.7.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  70/ 147]                  blk.7.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  71/ 147]                  blk.7.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
+            "[  72/ 147]                blk.7.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
+            "[  73/ 147]                blk.7.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  74/ 147]                blk.7.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  75/ 147]                  blk.7.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  76/ 147]                  blk.8.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[  77/ 147]               blk.8.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  78/ 147]             blk.8.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  79/ 147]                  blk.8.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  80/ 147]                  blk.8.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[  81/ 147]                blk.8.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  82/ 147]                blk.8.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  83/ 147]                blk.8.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  84/ 147]                  blk.8.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  85/ 147]                  blk.9.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[  86/ 147]               blk.9.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  87/ 147]             blk.9.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  88/ 147]                  blk.9.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  89/ 147]                  blk.9.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[  90/ 147]                blk.9.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  91/ 147]                blk.9.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  92/ 147]                blk.9.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  93/ 147]                  blk.9.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[  94/ 147]                 blk.10.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[  95/ 147]              blk.10.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[  96/ 147]            blk.10.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  97/ 147]                 blk.10.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[  98/ 147]                 blk.10.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
+            "[  99/ 147]               blk.10.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
+            "[ 100/ 147]               blk.10.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[ 101/ 147]               blk.10.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[ 102/ 147]                 blk.10.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[ 103/ 147]                 blk.11.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[ 104/ 147]              blk.11.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[ 105/ 147]            blk.11.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[ 106/ 147]                 blk.11.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[ 107/ 147]                 blk.11.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[ 108/ 147]               blk.11.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[ 109/ 147]               blk.11.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[ 110/ 147]               blk.11.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[ 111/ 147]                 blk.11.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[ 112/ 147]                 blk.12.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[ 113/ 147]              blk.12.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[ 114/ 147]            blk.12.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[ 115/ 147]                 blk.12.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[ 116/ 147]                 blk.12.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[ 117/ 147]               blk.12.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[ 118/ 147]               blk.12.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[ 119/ 147]               blk.12.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[ 120/ 147]                 blk.12.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[ 121/ 147]                 blk.13.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[ 122/ 147]              blk.13.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[ 123/ 147]            blk.13.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[ 124/ 147]                 blk.13.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[ 125/ 147]                 blk.13.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
+            "[ 126/ 147]               blk.13.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
+            "[ 127/ 147]               blk.13.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[ 128/ 147]               blk.13.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[ 129/ 147]                 blk.13.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[ 130/ 147]                 blk.14.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[ 131/ 147]              blk.14.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[ 132/ 147]            blk.14.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[ 133/ 147]                 blk.14.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[ 134/ 147]                 blk.14.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
+            "[ 135/ 147]               blk.14.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
+            "[ 136/ 147]               blk.14.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[ 137/ 147]               blk.14.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[ 138/ 147]                 blk.14.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[ 139/ 147]                 blk.15.attn_k.weight - [ 2048,   512,     1,     1], type =    f16, converting to q4_K .. size =     2.00 MiB ->     0.56 MiB\n",
+            "[ 140/ 147]              blk.15.attn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[ 141/ 147]            blk.15.attn_output.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[ 142/ 147]                 blk.15.attn_q.weight - [ 2048,  2048,     1,     1], type =    f16, converting to q4_K .. size =     8.00 MiB ->     2.25 MiB\n",
+            "[ 143/ 147]                 blk.15.attn_v.weight - [ 2048,   512,     1,     1], type =    f16, converting to q6_K .. size =     2.00 MiB ->     0.82 MiB\n",
+            "[ 144/ 147]               blk.15.ffn_down.weight - [ 8192,  2048,     1,     1], type =    f16, converting to q6_K .. size =    32.00 MiB ->    13.12 MiB\n",
+            "[ 145/ 147]               blk.15.ffn_gate.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "[ 146/ 147]               blk.15.ffn_norm.weight - [ 2048,     1,     1,     1], type =    f32, size =    0.008 MB\n",
+            "[ 147/ 147]                 blk.15.ffn_up.weight - [ 2048,  8192,     1,     1], type =    f16, converting to q4_K .. size =    32.00 MiB ->     9.00 MiB\n",
+            "llama_model_quantize_internal: model size  =  2357.26 MB\n",
+            "llama_model_quantize_internal: quant size  =   762.81 MB\n",
+            "\n",
+            "main: quantize time = 167649.07 ms\n",
+            "main:    total time = 167649.07 ms\n",
+            "Unsloth: Conversion completed! Output location: /content/Robzy/Llama-3.2-1B-Instruct-Finetuned-q4_k_m/unsloth.Q4_K_M.gguf\n",
+            "Unsloth: Uploading GGUF to Huggingface Hub...\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/1 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ade4b4c1ee22436c9b85356243a50485"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "unsloth.Q4_K_M.gguf:   0%|          | 0.00/808M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6ce199a077814319a6de805eda52df2b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saved GGUF to https://huggingface.co/Robzy/Llama-3.2-1B-Instruct-Finetuned-q4_k_m\n"
+          ]
+        }
+      ],
+      "source": [
+        "hf_model_name = 'Robzy/Llama-3.2-1B-Instruct-Finetuned-q4_k_m'\n",
+        "token  = ''\n",
+        "\n",
+        "# Save to 8bit Q8_0\n",
+        "if False: model.save_pretrained_gguf(\"model\", tokenizer,token)\n",
+        "# Remember to go to https://huggingface.co/settings/tokens for a token!\n",
+        "# And change hf to your username!\n",
+        "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n",
+        "\n",
+        "# Save to 16bit GGUF\n",
+        "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n",
+        "if False: model.push_to_hub_gguf(hf_model_name, tokenizer, quantization_method = \"f16\", token = token)\n",
+        "\n",
+        "# Save to q4_k_m GGUF\n",
+        "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n",
+        "if True: model.push_to_hub_gguf(hf_model_name, tokenizer, quantization_method = \"q4_k_m\", token = token)\n",
+        "\n",
+        "# Save to multiple GGUF options - much faster if you want multiple!\n",
+        "if False:\n",
+        "    model.push_to_hub_gguf(\n",
+        "        \"hf/model\", # Change hf to your username!\n",
+        "        tokenizer,\n",
+        "        quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n",
+        "        token = \"\", # Get a token at https://huggingface.co/settings/tokens\n",
+        "    )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bDp0zNpwe6U_"
+      },
+      "source": [
+        "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html).\n",
+        "\n",
+        "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Zt9CHJqO6p30"
+      },
+      "source": [
+        "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) 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 links:\n",
+        "1. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n",
+        "2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n",
+        "3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n",
+        "4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n",
+        "5. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)\n",
+        "6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n",
+        "7. `ChatML` for ShareGPT datasets, [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing)\n",
+        "8. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n",
+        "9. [**NEW**] We make Phi-3 Medium / Mini **2x faster**! See our [Phi-3 Medium notebook](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing)\n",
+        "10. [**NEW**] We make Gemma-2 9b / 27b **2x faster**! See our [Gemma-2 9b notebook](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)\n",
+        "11. [**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)\n",
+        "12. [**NEW**] We make Mistral NeMo 12B 2x faster and fit in under 12GB of VRAM! [Mistral NeMo notebook](https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing)\n",
+        "\n",
+        "<div class=\"align-center\">\n",
+        "  <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
+        "  <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
+        "  <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Support our work if you can! Thanks!\n",
+        "</div>"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "widgets": {
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+}
\ No newline at end of file