{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Lu6trOyZlsLl" }, "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 + ⭐ Star us on Github ⭐\n", "
\n", "\n", "To install Unsloth on your local device, follow [our guide](https://unsloth.ai/docs/get-started/install). This notebook is licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).\n", "\n", "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & how to save it" ] }, { "cell_type": "markdown", "metadata": { "id": "y1CStbv9lsLl" }, "source": [ "### News" ] }, { "cell_type": "markdown", "metadata": { "id": "-dDBYtlOlsLm" }, "source": [ "Introducing **Unsloth Studio** - a new open source, no-code web UI to train and run LLMs. [Blog](https://unsloth.ai/docs/new/studio) • [Notebook](https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb)\n", "\n", "\n", "\n", "\n", "
\"Unsloth
Train models — no code needed		\"Unsloth
Run GGUF models on Mac, Windows & Linux
\n", "\n", "Train MoEs - DeepSeek, GLM, Qwen and gpt-oss 12x faster with 35% less VRAM. [Blog](https://unsloth.ai/docs/new/faster-moe)\n", "\n", "Ultra Long-Context Reinforcement Learning is here with 7x more context windows! [Blog](https://unsloth.ai/docs/new/grpo-long-context)\n", "\n", "New in Reinforcement Learning: [FP8 RL](https://unsloth.ai/docs/new/fp8-reinforcement-learning) • [Vision RL](https://unsloth.ai/docs/new/vision-reinforcement-learning-vlm-rl) • [Standby](https://unsloth.ai/docs/basics/memory-efficient-rl) • [gpt-oss RL](https://unsloth.ai/docs/new/gpt-oss-reinforcement-learning)\n", "\n", "Visit our docs for all our [model uploads](https://unsloth.ai/docs/get-started/unsloth-model-catalog) and [notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks)." ] }, { "cell_type": "markdown", "metadata": { "id": "r8rhPwTKlsLm" }, "source": [ "### Installation" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "6FRKDTtwlsLm" }, "outputs": [], "source": "%%capture\nimport os, re\nif \"COLAB_\" not in \"\".join(os.environ.keys()):\n !pip install unsloth # Do this in local & cloud setups\nelse:\n import torch; v = re.match(r'[\\d]{1,}\\.[\\d]{1,}', str(torch.__version__)).group(0)\n xformers = 'xformers==' + {'2.10':'0.0.34','2.9':'0.0.33.post1','2.8':'0.0.32.post2'}.get(v, \"0.0.34\")\n !pip install sentencepiece protobuf \"datasets==4.3.0\" \"huggingface_hub>=0.34.0\" hf_transfer\n !pip install --no-deps unsloth_zoo bitsandbytes accelerate {xformers} peft trl triton unsloth\n!pip install --no-deps transformers==5.5.0\n!pip install torchcodec\nimport torch; torch._dynamo.config.recompile_limit = 64;" }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "lBN09c1tUlSV" }, "outputs": [], "source": [ "%%capture\n", "!pip install --no-deps --upgrade timm # For Gemma 4 vision/audio" ] }, { "cell_type": "markdown", "metadata": { "id": "TGMWlrRdzwgf" }, "source": [ "### Unsloth\n", "\n", "`FastModel` supports loading nearly any model now! This includes Vision and Text models!" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 397, "referenced_widgets": [ "3d2921a997f043c781203236278c6444", "33f7c35206e7496c8b68189c982f6c96", "feb2769cdd91429186c2cad2d519e5e8", "49109d6abdda4b5987afb03c8ca82133", "c73912ed1a48494895d8d8d5953ba656", "c9899707f4ed4df9afdf80b91a75b2fc", "cbc932cc9d654db7afaadced26c8ef7a", "2be52eb70e904219a3a2256a3d466dc1", "3a8f32dbbda14e33a997a0a361c5042f", "2f6b8dafccee4a7386efd09f13d3f30a", "41eb3536d2b04d6aae65a11b87a09d3c", "67c4746cc365404d80e4187c371b2503", "b21842e752724217971c9f16ec028704", "52985a9a27ca465da304a09d532165a8", "e33733c0fad5429e9b8f017a62860bc3", "0181be8f1f67481aa6d90a6d34b3d27b", "0762c9ebd93843dd8c5a0bc0e44f21e6", "5698ed0353e74431b3658499cbb6184c", "0d48488f4d5a427a84527c913213fc17", "518191a0c4994a398e788e31a1612e46", "007fc851909b46b08b199ac4540a8391", "8697d011c5814742b7e5872c26c3a840", "e4db4b7388a24022b4166fb8398d84f6", "1a99b2cc5a834557be169221d5e5e922", "9f3c0e0b175d4d029a27b4425e190fdf", "fab9219a5f4f4ba094c7e3b79eee978f", "6b0b45c64c2f42bcad42097be582b582", "479c805860804745ac0addaebca04c08", "2af827637bc14b99966ac252e71ca1d3", "e01ca3fe18bd4f0faef2bf4df4faa892", "a37f76bac8d34ac982c7d6642c82d0ef", "bd43efb495044372b169c927f8d0584e", "58f143afe4ed43efa145bacb1d9bf542", "a5be5bfba3ec4648aa6ccd34f2eed259", "1b1b4f5f31a745ee98072cd87c99df89", "dab210ff7ce2430caf1e2d4302f8565a", "e2e043fe9da2485e9afd571d06bc27c7", "fe08286d945446788aa1a44dac8f7263", "0c82571976a040d3a0f6596cdca32b6e", "d5d3bf30ac524d00b0cc3982fb66386f", "84bbfab05a234b12ba5a2d38015c9f1a", "fea38f649a9e47b4a6185d85a62669a0", "0ef55274df7042b5ae47880fb72d82cc", "68b984644256400f9024e9d37998bd2f", "3a0184a9cfef443385dd49f20f12cfc1", "6535c0b518144e108ac22d4c523aef32", "83f222f05c974fa191d0f4fd6186b1b5", "70f3613a5f1f4661b8b9aff8295bf7bb", "2c80fe8defd7468da10b89d8de1f96b1", "af02af0771fe4f6e9d5f8123b6db676d", "6fe995128adb4c08b07cee2a7de09454", "653651f81f3349bdbee628cd37aafa91", "172bc6be7ccb4efe80b6bf255f426e0a", "dc4c8288184c4a69b1f19cdb5167e581", "51884ea25fd94727889e54da2fe4b311", "04f2d1fee6cf4450854a90afcca7352a", "204b778594564833814db99c06ca4fbb", "c914b297689a4b69b9d02b01fb0f4d78", "843a0d013b794eaa8927086df2a38ae7", "46c5ab9910614cb599b2694bb322658a", "bb7b78efc82c43f78b8579e0027b5ff4", "f0e4f49d60694bc5a603b3a37c7b6a0d", "db61162e59c94b4fa10ff4a4c0c97343", "0485e800a81f43b1943423f901bd1a4f", "b55db2c45b4048cda35766d236e4e84c", "26b5672854ef4849a6d3d6ec7429d87b", "0e51754fe18440349b35bcc438b1bf02", "be57bb04bde04a58bb37d91bfbf3b51c", "05b6d19db7c447a9ae7d380f9d7765d8", "aeb32719f54e42a4b08a53cd5363a0d5", "fde969cdd088420e972b911db636ae2d", "27af09daa2374b7f9ce779aa7192b178", "d22ddd345a85405c90fbceb0c623522d", "3f54b22697044f4cb7c4555d1e443eaf", "3169b2afaf5c4bc8bfd94d87aaca04b0", "5d6cb7d0eec540e4873f290ad0c51856", "111000cfd1af4e2db1f2c2557cd7206a" ] }, "id": "-Xbb0cuLzwgf", "outputId": "9c607d3f-f9ab-4716-d225-f3e351d135a1" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "🦥 Unsloth Zoo will now patch everything to make training faster!\n", "==((====))== Unsloth 2026.4.4: Fast Gemma4 patching. Transformers: 5.5.0.\n", " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.563 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.10.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.6.0\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.34. FA2 = False]\n", " \"-____-\" Free license: http://github.com/unslothai/unsloth\n", "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n", "Unsloth: QLoRA and full finetuning all not selected. Switching to 16bit LoRA.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/10.2G [00:00" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9jGeSb9bWe0k", "outputId": "fea060ef-c599-475e-b3fe-d6bf33d15a7c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The animal in the image is a **sloth**.\n", "\n", "Since you haven't provided any specific context (like a movie poster, a scene, or a specific source), I cannot tell you *which* films this specific sloth features in.\n", "\n", "**To help me answer your question, please provide more context, such as:**\n", "\n", "1. **A picture or a clip from a film.**\n", "2. **The name of a movie you are thinking of.**\n", "3. **Any other information you have about where you saw this animal.**\n" ] } ], "source": [ "sloth_link = \"https://files.worldwildlife.org/wwfcmsprod/images/Sloth_Sitting_iStock_3_12_2014/story_full_width/8l7pbjmj29_iStock_000011145477Large_mini__1_.jpg\"\n", "\n", "messages = [{\n", " \"role\" : \"user\",\n", " \"content\": [\n", " { \"type\": \"image\", \"image\" : sloth_link },\n", " { \"type\": \"text\", \"text\" : \"Which films does this animal feature in?\" }\n", " ]\n", "}]\n", "# You might have to wait 1 minute for Unsloth's auto compiler\n", "do_gemma_4_inference(messages, max_new_tokens = 256)" ] }, { "cell_type": "markdown", "metadata": { "id": "eh0BzbZPWtRD" }, "source": [ "Let's make a poem about sloths!" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "R3ExuK8cWuT3", "outputId": "f35f34d0-67fa-4693-ced3-1b5c3694db29" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "In emerald canopy, where sunlight streams,\n", "And moss-draped branches weave their silent dreams,\n", "There hangs a creature, slow and low,\n", "A sloth, in nature's gentle flow.\n", "\n", "With fur the shade of bark and leaf,\n", "A quiet grace, beyond belief.\n", "He moves with purpose, soft and deep,\n", "While the hurried world is fast asleep.\n", "\n", "A master of the languid pace,\n", "A gentle smile upon his face.\n", "He clings to limbs with steady hold,\n", "A story in his stillness told.\n", "\n", "The world rushes by in streaks of light,\n", "But he moves through amber\n" ] } ], "source": [ "messages = [{\n", " \"role\": \"user\",\n", " \"content\": [{ \"type\" : \"text\",\n", " \"text\" : \"Write a poem about sloths.\" }]\n", "}]\n", "do_gemma_4_inference(messages)" ] }, { "cell_type": "markdown", "metadata": { "id": "wZrmFRZpZtGf" }, "source": [ "# Gemma 4 can also hear!" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 76 }, "id": "68crYajNZtw1", "outputId": "6983d839-522c-4d43-82f3-1f13adf867ff" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " " ] }, "metadata": {}, "execution_count": 7 } ], "source": [ "from IPython.display import Audio, display\n", "Audio(\"https://www.nasa.gov/wp-content/uploads/2015/01/591240main_JFKmoonspeech.mp3\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "k3vrdoa0Z01X" }, "outputs": [], "source": [ "!wget -qqq https://www.nasa.gov/wp-content/uploads/2015/01/591240main_JFKmoonspeech.mp3 -O audio.mp3" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BJr_D4O9Z2Zh", "outputId": "d3bd6949-cd9a-413a-ea31-03f8ae798f1b" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The audio is about the belief that a nation should commit itself to achieving the goal of landing a man on the moon and returning him safely to the Earth before this decade is out.\n", "\n" ] } ], "source": [ "audio_file = \"audio.mp3\"\n", "\n", "messages = [{\n", " \"role\" : \"user\",\n", " \"content\": [\n", " { \"type\": \"audio\", \"audio\" : audio_file },\n", " { \"type\": \"text\", \"text\" : \"What is this audio about?\" }\n", " ]\n", "}]\n", "do_gemma_4_inference(messages, max_new_tokens = 256)" ] }, { "cell_type": "markdown", "metadata": { "id": "L15JuAmmaOkB" }, "source": [ "# Let's combine all 3 modalities together!" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "is37bsDZaRwV", "outputId": "ac8a7bb9-aea5-44b0-f6a6-b276dac72236" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The image is of a koala, and the audio is a speech about a nation committing to landing a man on the moon and returning him safely within a decade. The two are unrelated.\n", "\n" ] } ], "source": [ "messages = [{\n", " \"role\" : \"user\",\n", " \"content\": [\n", " { \"type\": \"audio\", \"audio\" : audio_file },\n", " { \"type\": \"image\", \"image\" : sloth_link },\n", " { \"type\": \"text\", \"text\" : \"What is this audio and image about? \"\\\n", " \"How are they related?\" }\n", " ]\n", "}]\n", "do_gemma_4_inference(messages, max_new_tokens = 256)" ] }, { "cell_type": "markdown", "metadata": { "id": "Bw5XPyYFajyM" }, "source": [ "# Let's finetune Gemma 4!\n", "\n", "You can finetune the vision and text parts for now through selection - the audio part can also be finetuned - we're working to make it selectable as well!" ] }, { "cell_type": "markdown", "metadata": { "id": "SXd9bTZd1aaL" }, "source": [ "We now add LoRA adapters so we only need to update a small amount of parameters!" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6bZsfBuZDeCL", "outputId": "0dfe237b-52ce-4215-b38b-de73ff7fb01b" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Unsloth: Making `model.base_model.model.model.language_model` require gradients\n" ] } ], "source": [ "model = FastModel.get_peft_model(\n", " model,\n", " finetune_vision_layers = False, # Turn off for just text!\n", " finetune_language_layers = True, # Should leave on!\n", " finetune_attention_modules = True, # Attention good for GRPO\n", " finetune_mlp_modules = True, # Should leave on always!\n", "\n", " r = 8, # Larger = higher accuracy, but might overfit\n", " lora_alpha = 8, # Recommended alpha == r at least\n", " lora_dropout = 0,\n", " bias = \"none\",\n", " random_state = 3407,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "vITh0KVJ10qX" }, "source": [ "\n", "### Data Prep\n", "We now use the `Gemma-4` format for conversation style finetunes. We use [Maxime Labonne's FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset in ShareGPT style. Gemma-4 renders multi turn conversations like below:\n", "\n", "```\n", "<|turn>user\n", "Hello\n", "<|turn>model\n", "Hey there!\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, phi4, qwen2.5, gemma3, gemma-4` and more." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "LjY75GoYUCB8" }, "outputs": [], "source": [ "from unsloth.chat_templates import get_chat_template\n", "tokenizer = get_chat_template(\n", " tokenizer,\n", " chat_template = \"gemma-4\",\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "ZQkXuGYxbJ-e" }, "source": [ "We get the first 3000 rows of the dataset" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 113, "referenced_widgets": [ "870cc718caea4b09a6ac7d43bcc34b4c", "0dce530f23a24cddab63d607f3b94965", "bfa5e47f4ec74dfc83cf4c9f72b7953f", "c7b4dc08d4ae40af9a2ab2b78a1dad0f", "c7e64de97ac6421499e2ad9f67bc419a", "2807725d165b4877929693661171736c", "d7d6863aea5a4db2bfdda6c708291dcc", "74d4f2959b4047c89182c1ec63df08e2", "184954dceb45488483a30405f1cf550c", "6e9a6f1ec4e142558c91ed9f80398f00", "4c07e50fbb6c42d7ba20b7f084d52b71", "80cc55a66a5f4305bcf1eccd67b7693b", "ddda884ec6c54903b9bd741e5ab0cd8d", "eac214a702d14084965a8be6e1e0db6d", "f1f42573c8564030b75d7af02a9f7f7e", "c9f8619f01b04e5996e62f7842ba499b", "621afa2e11564f148fe1b41d33b306fe", "1fbb8fbc099f43ff85df3adb39b7f5c4", "3db6e53661084022ac038dd2ca442cee", "90c6842f23544dd4bb04f1e4680cedc5", "7e853b0ed3b943e4aa31fa0d483d3013", "23b50cb43c144d5fb129571c793b1059", "59296292f4e2444589b75c2839758a6d", "d8bb24a7e9db4b1a9087f6cc54e9d819", "0911afbaf58c47cabfb0699552aaf1bb", "48a152c98a374fdbbb661bd90e82c46e", "e38f05f599024a47bf3dd607a39e05ad", "ba075479987849dd888b0b1bc9f4add4", "511d5c2a1d634ab1839d803830cc014e", "b171c249f897489487353a4169875212", "b47e4c66ac1a41bda658d59ec27920a9", "cf60f97b2e7f4061a19b2106308c1595", "ed59a95a95c743c6bd169a0c5da95f67" ] }, "id": "Mkq4RvEq7FQr", "outputId": "b60787f8-5ff1-4ac5-c4ca-725f3f5abf73" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0%| | 0.00/982 [00:00` token using removeprefix(`''`) since we're finetuning. The Processor will add this token before training and the model expects only one." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "30d566f7f7cd4f26a57fcd9df0310bdc", "59291b76f1fb44adbdf509d43359bf71", "6b5887f069914368ae613bb2be3c8d33", "b246910de0dc431089632dd353ebeb29", "c1229f0b7b6e49daaa72dc04d6c03df9", "3a28dde28ae84bfd993354af8402df68", "b812bec84a814eb79442ec2028d7ad3d", "fb523ac744564bc4961e51b91a46b0a8", "9795836b1c24448bacf121ecdcff86f6", "6bc70efc138f4c0c8d619b32623966bb", "f78de814eb3a445993231ab3855a3801" ] }, "id": "1ahE8Ys37JDJ", "outputId": "a8c460ca-41ba-4bb5-a6f7-139ddd663ee5" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/3000 [00:00') for convo in convos]\n", " return { \"text\" : texts, }\n", "\n", "dataset = dataset.map(formatting_prompts_func, batched = True)" ] }, { "cell_type": "markdown", "metadata": { "id": "ndDUB23CGAC5" }, "source": [ "Let's see how the chat template did! Notice there is no `` token as the processor tokenizer will be adding one." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 105 }, "id": "gGFzmplrEy9I", "outputId": "eecc6464-06a8-4b9a-8694-05b5c5201abf" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'<|turn>user\\nWhat is the modulus operator in programming and how can I use it to calculate the modulus of two given numbers?\\n<|turn>model\\nIn programming, the modulus operator is represented by the \\'%\\' symbol. It calculates the remainder when one number is divided by another. To calculate the modulus of two given numbers, you can use the modulus operator in the following way:\\n\\n```python\\n# Calculate the modulus\\nModulus = a % b\\n\\nprint(\"Modulus of the given numbers is: \", Modulus)\\n```\\n\\nIn this code snippet, the variables \\'a\\' and \\'b\\' represent the two given numbers for which you want to calculate the modulus. By using the modulus operator \\'%\\', we calculate the remainder when \\'a\\' is divided by \\'b\\'. The result is then stored in the variable \\'Modulus\\'. Finally, the modulus value is printed using the \\'print\\' statement.\\n\\nFor example, if \\'a\\' is 10 and \\'b\\' is 4, the modulus calculation would be 10 % 4, which equals 2. Therefore, the output of the above code would be:\\n\\n```\\nModulus of the given numbers is: 2\\n```\\n\\nThis means that the modulus of 10 and 4 is 2.\\n'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 17 } ], "source": [ "dataset[100][\"text\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "idAEIeSQ3xdS" }, "source": [ "\n", "### Train the model\n", "Now let's train our model. We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "96c5653090984af8843866b1ee7c35b1", "7f32dee0a2ab4ed8bb99042ff5daf2da", "d990a3318024413983e1e38369e6dfb1", "97372097bc6442cd8e2cd518edffc2a1", "c36c6d5aab3b49ebb00885dc6ef6efba", "cf45f9592f0140449cec334a3607c4dc", "96118ea74ed049b1877344e3fa1e1bb5", "09ab14ae65364f67b19aa6e0f0421e5e", "fc6de80b1d174253bf1c6419cd32ddae", "4b347b13474545c4be6eb5992112ca5d", "39574ff310c54895b267b9bf1dcddb2a" ] }, "id": "95_Nn-89DhsL", "outputId": "13e4e38f-cc4c-4456-86c0-211cb101542b" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Unsloth: Tokenizing [\"text\"] (num_proc=6): 0%| | 0/3000 [00:00user\\n\",\n", " response_part = \"<|turn>model\\n\",\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "Dv1NBUozV78l" }, "source": [ "Let's verify masking the instruction part is done! Let's print the 100th row again. Notice how the sample only has a single `` as expected!" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 105 }, "id": "LtsMVtlkUhja", "outputId": "61386897-8d1f-4bd1-8c37-54cf5e26409f" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'<|turn>user\\nWhat is the modulus operator in programming and how can I use it to calculate the modulus of two given numbers?\\n<|turn>model\\nIn programming, the modulus operator is represented by the \\'%\\' symbol. It calculates the remainder when one number is divided by another. To calculate the modulus of two given numbers, you can use the modulus operator in the following way:\\n\\n```python\\n# Calculate the modulus\\nModulus = a % b\\n\\nprint(\"Modulus of the given numbers is: \", Modulus)\\n```\\n\\nIn this code snippet, the variables \\'a\\' and \\'b\\' represent the two given numbers for which you want to calculate the modulus. By using the modulus operator \\'%\\', we calculate the remainder when \\'a\\' is divided by \\'b\\'. The result is then stored in the variable \\'Modulus\\'. Finally, the modulus value is printed using the \\'print\\' statement.\\n\\nFor example, if \\'a\\' is 10 and \\'b\\' is 4, the modulus calculation would be 10 % 4, which equals 2. Therefore, the output of the above code would be:\\n\\n```\\nModulus of the given numbers is: 2\\n```\\n\\nThis means that the modulus of 10 and 4 is 2.\\n'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 20 } ], "source": [ "tokenizer.decode(trainer.train_dataset[100][\"input_ids\"])" ] }, { "cell_type": "markdown", "metadata": { "id": "4Kyjy__m9KY3" }, "source": [ "Now let's print the masked out example - you should see only the answer is present:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 105 }, "id": "_rD6fl8EUxnG", "outputId": "cc23b442-0129-4226-b7b8-56c39ef80d11" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "' In programming, the modulus operator is represented by the \\'%\\' symbol. It calculates the remainder when one number is divided by another. To calculate the modulus of two given numbers, you can use the modulus operator in the following way:\\n\\n```python\\n# Calculate the modulus\\nModulus = a % b\\n\\nprint(\"Modulus of the given numbers is: \", Modulus)\\n```\\n\\nIn this code snippet, the variables \\'a\\' and \\'b\\' represent the two given numbers for which you want to calculate the modulus. By using the modulus operator \\'%\\', we calculate the remainder when \\'a\\' is divided by \\'b\\'. The result is then stored in the variable \\'Modulus\\'. Finally, the modulus value is printed using the \\'print\\' statement.\\n\\nFor example, if \\'a\\' is 10 and \\'b\\' is 4, the modulus calculation would be 10 % 4, which equals 2. Therefore, the output of the above code would be:\\n\\n```\\nModulus of the given numbers is: 2\\n```\\n\\nThis means that the modulus of 10 and 4 is 2.\\n'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 21 } ], "source": [ "tokenizer.decode([tokenizer.pad_token_id if x == -100 else x for x in trainer.train_dataset[100][\"labels\"]]).replace(tokenizer.pad_token, \" \")" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "2ejIt2xSNKKp", "outputId": "945b9e2e-c2bd-4ffd-906d-d7d7450437be" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPU = Tesla T4. Max memory = 14.563 GB.\n", "10.098 GB of memory reserved.\n" ] } ], "source": [ "# @title Show current memory stats\n", "gpu_stats = torch.cuda.get_device_properties(0)\n", "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n", "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n", "print(f\"{start_gpu_memory} GB of memory reserved.\")" ] }, { "cell_type": "markdown", "metadata": { "id": "CNP1Uidk9mrz" }, "source": [ "# Let's train the model!\n", "\n", "To resume a training run, set `trainer.train(resume_from_checkpoint = True)`" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "yqxqAZ7KJ4oL", "outputId": "c4804f43-ad0d-48be-fcfc-a89558cc74b9" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': 2}.\n", "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n", " \\\\ /| Num examples = 2,991 | Num Epochs = 1 | Total steps = 60\n", "O^O/ \\_/ \\ Batch size per device = 1 | Gradient accumulation steps = 4\n", "\\ / Data Parallel GPUs = 1 | Total batch size (1 x 4 x 1) = 4\n", " \"-____-\" Trainable parameters = 12,668,928 of 5,135,846,944 (0.25% trained)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Unsloth: Will smartly offload gradients to save VRAM!\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [60/60 03:11, Epoch 0/1]\n", "
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StepTraining Loss
111.235785
212.824577
312.463327
412.219341
512.442764
611.932236
712.602695
812.851998
910.863073
109.147686
119.397802
128.469434
138.135841
146.840250
157.372794
166.193974
176.062288
186.291557
196.233539
205.865170
215.440270
225.247680
235.856437
245.654868
255.209495
265.182337
275.069244
284.234691
294.857911
304.938684
314.718597
324.803062
334.649268
344.894489
354.491835
364.749773
374.720836
384.491292
394.462323
403.844747
414.891779
424.629851
434.111910
443.651132
454.298693
464.591220
474.157773
484.565719
493.915479
503.632185
514.593100
523.698324
534.600374
544.302113
553.800784
564.000387
574.510599
584.181003
593.902064
604.236151

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "pCqnaKmlO1U9", "outputId": "f19a554f-1476-4256-a1ad-211c9a800ede" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "222.9893 seconds used for training.\n", "3.72 minutes used for training.\n", "Peak reserved memory = 10.836 GB.\n", "Peak reserved memory for training = 0.738 GB.\n", "Peak reserved memory % of max memory = 74.408 %.\n", "Peak reserved memory for training % of max memory = 5.068 %.\n" ] } ], "source": [ "# @title Show final memory and time stats\n", "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", "used_percentage = round(used_memory / max_memory * 100, 3)\n", "lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n", "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", "print(\n", " f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n", ")\n", "print(f\"Peak reserved memory = {used_memory} GB.\")\n", "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" ] }, { "cell_type": "markdown", "metadata": { "id": "ekOmTR1hSNcr" }, "source": [ "\n", "### Inference\n", "Let's run the model via Unsloth native inference! According to the `Gemma-4` team, the recommended settings for inference are `temperature = 1.0, top_p = 0.95, top_k = 64`" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kR3gIAX-SM2q", "outputId": "c8379be3-ca9a-4791-920c-9b6cdd46511a" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['<|turn>user\\nContinue the sequence: 1, 1, 2, 3, 5, 8,\\n<|turn>model\\nThe next number in the sequence is **13**.\\n\\nThis is the **Fibonacci sequence**, where each number is the sum of the two preceding ones:\\n\\n* $1 + 1 = 2$\\n* $1 + 2 = 3$\\n* $2 + 3 = 5']" ], "text/html": [ "

['<bos><|turn>user\\nContinue the sequence: 1, 1, 2, 3, 5, 8,<turn|>\\n<|turn>model\\nThe next number in the sequence is **13**.\\n\\nThis is the **Fibonacci sequence**, where each number is the sum of the two preceding ones:\\n\\n* $1 + 1 = 2$\\n* $1 + 2 = 3$\\n* $2 + 3 = 5']
" ] }, "metadata": {}, "execution_count": 25 } ], "source": [ "from unsloth.chat_templates import get_chat_template\n", "tokenizer = get_chat_template(\n", " tokenizer,\n", " chat_template = \"gemma-4\",\n", ")\n", "messages = [{\n", " \"role\": \"user\",\n", " \"content\": [{\n", " \"type\" : \"text\",\n", " \"text\" : \"Continue the sequence: 1, 1, 2, 3, 5, 8,\",\n", " }]\n", "}]\n", "inputs = tokenizer.apply_chat_template(\n", " messages,\n", " add_generation_prompt = True, # Must add for generation\n", " return_tensors = \"pt\",\n", " tokenize = True,\n", " return_dict = True,\n", ").to(\"cuda\")\n", "outputs = model.generate(\n", " **inputs,\n", " max_new_tokens = 64, # Increase for longer outputs!\n", " # Recommended Gemma-4 settings!\n", " temperature = 1.0, top_p = 0.95, top_k = 64,\n", ")\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": 26, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e2pEuRb1r2Vg", "outputId": "b7611dba-3cdc-455b-c201-cce7b36b2fb9" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The sky appears blue due to a phenomenon called **Rayleigh scattering**. Here's a breakdown of why this happens:\n", "\n", "### 1. Sunlight is Made of Different Colors\n", "\n", "Sunlight, which appears white to us, is actually composed of a spectrum of different colors (the colors of the rainbow: red, orange\n" ] } ], "source": [ "messages = [{\n", " \"role\": \"user\",\n", " \"content\": [{\"type\" : \"text\", \"text\" : \"Why is the sky blue?\",}]\n", "}]\n", "inputs = tokenizer.apply_chat_template(\n", " messages,\n", " add_generation_prompt = True, # Must add for generation\n", " return_tensors = \"pt\",\n", " tokenize = True,\n", " return_dict = True,\n", ").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "_ = model.generate(\n", " **inputs,\n", " max_new_tokens = 64, # Increase for longer outputs!\n", " # Recommended Gemma-4 settings!\n", " temperature = 1.0, top_p = 0.95, top_k = 64,\n", " streamer = TextStreamer(tokenizer, skip_prompt = True),\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "uMuVrWbjAzhc" }, "source": [ "\n", "### Saving, loading finetuned models\n", "To save the final model as LoRA adapters, either use Hugging Face's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", "\n", "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "upcOlWe7A1vc", "outputId": "3844523c-69ab-4501-dbe7-80fd84852cdf" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['gemma_4_lora/processor_config.json']" ] }, "metadata": {}, "execution_count": 27 } ], "source": [ "model.save_pretrained(\"gemma_4_lora\") # Local saving\n", "tokenizer.save_pretrained(\"gemma_4_lora\")\n", "# model.push_to_hub(\"HF_ACCOUNT/gemma_4_lora\", token = \"YOUR_HF_TOKEN\") # Online saving\n", "# tokenizer.push_to_hub(\"HF_ACCOUNT/gemma_4_lora\", token = \"YOUR_HF_TOKEN\") # Online saving" ] }, { "cell_type": "markdown", "metadata": { "id": "AEEcJ4qfC7Lp" }, "source": [ "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MKX_XKs_BNZR", "outputId": "db79fc3b-4187-48fc-b26c-8e7725425121" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "I am Gemma-4, a Large Language Model developed by Google DeepMind. I am an open weights model.\n" ] } ], "source": [ "if False:\n", " from unsloth import FastModel\n", " model, tokenizer = FastModel.from_pretrained(\n", " model_name = \"gemma_4_lora\", # YOUR MODEL YOU USED FOR TRAINING\n", " max_seq_length = 2048,\n", " load_in_4bit = True,\n", " )\n", "\n", "messages = [{\n", " \"role\": \"user\",\n", " \"content\": [{\"type\" : \"text\", \"text\" : \"What is Gemma-4?\",}]\n", "}]\n", "inputs = tokenizer.apply_chat_template(\n", " messages,\n", " add_generation_prompt = True, # Must add for generation\n", " return_tensors = \"pt\",\n", " tokenize = True,\n", " return_dict = True,\n", ").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "_ = model.generate(\n", " **inputs,\n", " max_new_tokens = 128, # Increase for longer outputs!\n", " # Recommended Gemma-4 settings!\n", " temperature = 1.0, top_p = 0.95, top_k = 64,\n", " streamer = TextStreamer(tokenizer, skip_prompt = True),\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "f422JgM9sdVT" }, "source": [ "### Saving to float16 for VLLM\n", "\n", "We also support saving to `float16` directly for deployment! We save it in the folder `gemma-4-finetune`. Set `if False` to `if True` to let it run!" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "id": "iHjt_SMYsd3P" }, "outputs": [], "source": [ "if False: # Change to True to save finetune!\n", " model.save_pretrained_merged(\"gemma-4-finetune\", tokenizer)" ] }, { "cell_type": "markdown", "metadata": { "id": "z6O48DbNIAr0" }, "source": [ "If you want to upload / push to your Hugging Face account, set `if False` to `if True` and add your Hugging Face token and upload location!" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "id": "ZV-CiKPrIFG0" }, "outputs": [], "source": [ "if False: # Change to True to upload finetune\n", " model.push_to_hub_merged(\n", " \"HF_ACCOUNT/gemma-4-finetune\", tokenizer,\n", " token = \"YOUR_HF_TOKEN\"\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "TCv4vXHd61i7" }, "source": [ "### GGUF / llama.cpp Conversion\n", "To save to `GGUF` / `llama.cpp`, we support it natively now for all models! For now, you can convert easily to `Q8_0, F16 or BF16` precision. `Q4_K_M` for 4bit will come later!" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "id": "FqfebeAdT073" }, "outputs": [], "source": [ "if False: # Change to True to save to GGUF\n", " model.save_pretrained_gguf(\n", " \"gemma_4_finetune\",\n", " tokenizer,\n", " quantization_method = \"Q8_0\", # For now only Q8_0, BF16, F16 supported\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "Q974YEVPI7JS" }, "source": [ "Likewise, if you want to instead push to GGUF to your Hugging Face account, set `if False` to `if True` and add your Hugging Face token and upload location!" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "id": "ZgcJIhJ0I_es" }, "outputs": [], "source": [ "if False: # Change to True to upload GGUF\n", " model.push_to_hub_gguf(\n", " \"HF_ACCOUNT/gemma_4_finetune\",\n", " tokenizer,\n", " quantization_method = \"Q8_0\", # Only Q8_0, BF16, F16 supported\n", " token = \"YOUR_HF_TOKEN\",\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "pnz9QOYTMvbH" }, "source": [ "Now, use the `gemma-4-finetune.gguf` file or `gemma-4-finetune-Q4_K_M.gguf` file in llama.cpp.\n", "\n", "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", "\n", "Some other resources:\n", "1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n", "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n", "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n", "4. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://unsloth.ai/docs/get-started/unsloth-notebooks)!\n", "\n", "
\n", " \n", " \n", " \n", "\n", " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", "
\n", "\n", " This notebook and all Unsloth notebooks are licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme)." ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.11" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "3d2921a997f043c781203236278c6444": { "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", 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