{ "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", "
\n", " \n", " \n", " Join Discord if you need help + โญ Star us on Github โญ\n", "
\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": { "id": "QmUBVEnvCDJv", "colab": { "base_uri": "https://localhost:8080/", "height": 325, "referenced_widgets": [ "4c3deec2baf94a8ea4c80478bdff01d3", "bb975f0ad3d74a6395d1923c5a53dc39", "2a5dd426aafb4b3f9d793797abf7d9de", "2d6df02166fc4642b4bfdcc0cfd865a6", "9505c5ed83394db6a5b343b0276d6901", "666f9103ec5c41bc891b05a901c616cf", "e16037bafd8b454d8c434e85efed28dd", "236a2e7c3a454f0c8a2ca24a0907255e", "7a132f9d1e3848f994f98609236a1b6f", "a06ec7523d9744e297ff6338512360f9", "1a1d139b772d441797ae128be73db0dc", "31af95f1ac3544f7a37be67c4a034080", "4df2859b402941fc97d55070577fb97d", "e1adc042d1fb462ba6ff0aa78b4682a5", "8df3feb0d379485cb67359b331e8eadc", "96d6f856c69a4345b91a882f28a1f4d6", "69cacfb40879486281c496c495b41a8b", "31d2c055316e4437a7bd01c06cb305fb", "0577e447b0c94726bc21517298ac2cb8", "4d8ddbfb9856401cb197b932c8eb3c9c", "d3684e6bf5f84cee9d54db03d89e0e05", "29c14add20b64cf9acc2615fe8059df2", "eee9bc3f569b43d39e6f63306984095e", "4e53974041184decb4264bd0106b290e", "9abb96ca74da49fa840c561590ada0d2", "1fce6848db9e4c85b70b2398015f91df", "aac974f4fe8d418bb417ce72de56cc76", "62d8bcccd42d4d2cb01f882aa77e2e93", "f4e68d5f7ae846b78277ef7048b2d9fe", "339ac2f6f46e478f92d976e66d0279e3", "4cb1776ddaab40a18f5429048986219b", "cd64808256fd49c4a487e7f9024655ee", "9e36beaa7f344c2c9ce4f2267048445d", "f7d925b7a7bf4383a45446c860df582e", "343b5acebc4f40df8435009d864547af", "94b3f061f56e46d8855ed8b22a36723a", "8eae1393abeb4271b898031efafaa73b", "b08feca372624beea25a04d51de239e6", "7d31e8d7836a419cb1e86a770c23e156", "31757a48490c472685daee4728207ae5", "fa27d942953849569801a255609aabd8", "893496c19cbb48bc88faec4f564244a5", "43ba4aee4eba41f98711c0dd4885c3c9", "6533fd4ecd1b4b1e8a234dee190bb1a7", "aa83df54525940f6b0bda6541f7239a7", "930e448a64f445c9a68c7b9c50b94115", "429bdc8caf4f45bbbffaef73c77429e1", "49e32833321e41499050e07ff5a11f79", "cf41ce1014eb4dceabe51497c7680167", "8fb8af69d4a342859cce7135cdfad95a", "2292a009cfd04819b2baeeeb69aca17c", "7810fd97276e4192a0af08b5485f809c", "1bd5bf129f304ad9a051eac47a6e33d4", "5dc2fbc9d37f4a04a720a5b9ed3c6062", "547dbb4d9a8144aea87ec148dad75989" ] }, "outputId": "6075eae7-af7f-4617-c25f-b790054c63d1" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "๐Ÿฆฅ Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "๐Ÿฆฅ 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 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": [ "\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", "e04f3dc11da4496c87f2551780a1eb75", "d619b61774b0481eae38e76e8d67c814", "541820d63c434329904588c4dddb75f5", "8b936eaf90804b16a87aaf107c592279", "38d3238f9ee143eb87137aa4fc91cf40", "ce31f2dd6b9044ae9d076cc8a8396286", "31222533a8284ec7ac2e446141dba9ff", "5396fefacb2347f38112242a0305dd6a", "bfa4248cf9d841f0911419d114e60e24", "7aa5fee3a54f491bb7c9f252c56bf05e", "75c5837c4c83417f930424649dae7a3e", "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<|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": [ "" ], "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": [ " ยทยทยทยทยทยทยทยทยทยท\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": [ "\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 [00:00user<|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<|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": [ "" ], "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 the W&B docs." ] }, "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": [ "" ], "text/html": [ "Tracking run with wandb version 0.18.7" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "Run data is saved locally in /content/wandb/run-20241130_071312-925e6em1" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "Syncing run outputs to Weights & Biases (docs)
" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ " View project at https://wandb.ai/id2223/id2223" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ " View run at https://wandb.ai/id2223/id2223/runs/925e6em1" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
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StepTraining Loss
10.867000
20.962500
31.164500
41.044200
50.833600
61.056700
70.747000
81.111800
91.005200
100.881000
110.999900
121.253400
131.063600
140.750100
151.004400
160.741900
171.153800
180.986800
190.886300
201.025200

" ] }, "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=โ€ฆ" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "d6accc4038424f5fbfcc8c54762ec8ca" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", "

Run history:


train/epochโ–โ–โ–‚โ–‚โ–‚โ–ƒโ–ƒโ–„โ–„โ–„โ–…โ–…โ–…โ–†โ–†โ–‡โ–‡โ–‡โ–ˆโ–ˆโ–ˆ
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train/grad_normโ–…โ–‡โ–ˆโ–…โ–„โ–…โ–ƒโ–…โ–‚โ–ƒโ–โ–ƒโ–…โ–‚โ–‚โ–ƒโ–„โ–†โ–‚โ–„
train/learning_rateโ–‚โ–„โ–…โ–‡โ–ˆโ–ˆโ–‡โ–‡โ–†โ–†โ–…โ–…โ–„โ–„โ–ƒโ–ƒโ–‚โ–‚โ–โ–
train/lossโ–ƒโ–„โ–‡โ–…โ–‚โ–…โ–โ–†โ–…โ–ƒโ–…โ–ˆโ–…โ–โ–…โ–โ–‡โ–„โ–ƒโ–…

Run summary:


total_flos701492545757184.0
train/epoch0.0016
train/global_step20
train/grad_norm0.30762
train/learning_rate0
train/loss1.0252
train_loss0.97694
train_runtime116.4671
train_samples_per_second1.374
train_steps_per_second0.172

" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ " View run outputs at: https://wandb.ai/id2223/id2223/runs/925e6em1
View project at: https://wandb.ai/id2223/id2223
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": [ "" ], "text/html": [ "Find logs at: ./wandb/run-20241130_071312-925e6em1/logs" ] }, "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": [ "\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": [ "\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", "7e2cb29d01954118b1e1f96f77195723", "5f750c954a054f9495ba31e4c67fb11d", "74d9c8a3f9dd46cb9463a69edb81e060", "e7369f1637ba4ee29fd1b927fb4d7676", "bd0cd3ea500c4a33aa883cf976da141a", "1ce0936c2aa549ae902374ce31d47be0", "c1e66d4831b74997afc9b199464cdff7", "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\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%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 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%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 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] = [\"ฤ  ฤ \", \"ฤ  ฤ ฤ ฤ \", \"ฤ ฤ  ฤ ฤ \", \"...\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\n", " \n", " \n", " Support our work if you can! Thanks!\n", "" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "4c3deec2baf94a8ea4c80478bdff01d3": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_bb975f0ad3d74a6395d1923c5a53dc39", "IPY_MODEL_2a5dd426aafb4b3f9d793797abf7d9de", "IPY_MODEL_2d6df02166fc4642b4bfdcc0cfd865a6" ], "layout": "IPY_MODEL_9505c5ed83394db6a5b343b0276d6901" } }, "bb975f0ad3d74a6395d1923c5a53dc39": { "model_module": "@jupyter-widgets/controls", 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