diff --git "a/notebook.ipynb" "b/notebook.ipynb" new file mode 100644--- /dev/null +++ "b/notebook.ipynb" @@ -0,0 +1,8838 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "wh4ZpnndMvaO" + }, + "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 own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\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)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qBia8GVKMvaS" + }, + "source": [ + "### News" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yb-D9qhRMvaU" + }, + "source": [ + "Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning).\n", + "\n", + "Read our **[Gemma 3N Guide](https://docs.unsloth.ai/basics/gemma-3n-how-to-run-and-fine-tune)** and check out our new **[Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs)** quants which outperforms other quantization methods!\n", + "\n", + "Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "N3z2yeVsMvaV" + }, + "source": [ + "### Installation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pS-P_shPMvaX" + }, + "outputs": [], + "source": [ + "%%capture\n", + "import os\n", + "if \"COLAB_\" not in \"\".join(os.environ.keys()):\n", + " !pip install unsloth\n", + "else:\n", + " # Do this only in Colab notebooks! Otherwise use pip install unsloth\n", + " !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl triton cut_cross_entropy unsloth_zoo\n", + " !pip install sentencepiece protobuf \"datasets>=3.4.1\" huggingface_hub hf_transfer\n", + " !pip install --no-deps unsloth" + ] + }, + { + "cell_type": "code", + "source": [ + "%%capture\n", + "# Install latest transformers for Gemma 3N\n", + "!pip install --no-deps git+https://github.com/huggingface/transformers.git # Only for Gemma 3N\n", + "!pip install --no-deps --upgrade timm # Only for Gemma 3N" + ], + "metadata": { + "id": "lBN09c1tUlSV" + }, + "execution_count": null, + "outputs": [] + }, + { + "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": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 675, + "referenced_widgets": [ + "40ed46c9e0624662827489732729b513", + "1348559ff1864cd8b9cd95c9802916f7", + "a845170c1d2f44a6b60a6e28d3b99afd", + "d0d728ebd0d540e8973180cf9a048b93", + "13b7efc84b2c43418866422966afee79", + "d2af29bc58994b0d8c930e4e1c718c64", + "f0aa2b81446a41f4b387753f5361aec2", + "203f0a1dca8347fa8095b9e656965d4a", + "d2ec7f8181f44b61a2f943417a4b6791", + "93de22d41af74a3cba1b32c4f3eec264", + "62e353c496784d3eafff5153085e7c35", + "c8e48086a51b43949f57d59246fb630a", + "03f67ae7b5824ed5948c01e362f91358", + "c26d1c9d3242491189f4918a1a5cc279", + "7c2e1d4d81c848b38726861b01b1b2ef", + "284d6739ad4d4e6a93b5a745c601d7a3", + "0c044a85662245c4bf9d8fe25c4c36a2", + "dd72206d2bb34a3c9dea585473bae3e8", + "107fbb1daf5446d4ad547cc119adbbf6", + "fed14d4c08e045fe860b3c4e1d576426", + 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everything to make training faster!\n", + "==((====))== Unsloth 2025.6.12: Fast Gemma3N patching. Transformers: 4.54.0.dev0.\n", + " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n", + "O^O/ \\_/ \\ Torch: 2.6.0+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.2.0\n", + "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.29.post3. 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: Gemma3N does not support SDPA - switching to eager!\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "model.safetensors.index.json: 0.00B [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "40ed46c9e0624662827489732729b513" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "model-00001-of-00003.safetensors: 0%| | 0.00/3.72G [00:00" + ], + "metadata": { + "id": "q2-ddk0CWeTA" + } + }, + { + "cell_type": "code", + "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_3n_inference(messages, max_new_tokens = 256)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9jGeSb9bWe0k", + "outputId": "f070d726-1436-49ea-c7f5-0715bc96c71d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "This adorable animal is a **sloth**! \n", + "\n", + "Sloths have appeared in a few films, most notably:\n", + "\n", + "* **Zootopia (2016):** The sloth Flash is a memorable character in this Disney movie.\n", + "* **Over the Hedge (2006):** A sloth named Stella makes a cameo appearance. \n", + "\n", + "They're often used for comedic effect due to their slow movements! \n", + "\n", + "\n", + "\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Let's make a poem about sloths!" + ], + "metadata": { + "id": "eh0BzbZPWtRD" + } + }, + { + "cell_type": "code", + "source": [ + "messages = [{\n", + " \"role\": \"user\",\n", + " \"content\": [{ \"type\" : \"text\",\n", + " \"text\" : \"Write a poem about sloths.\" }]\n", + "}]\n", + "do_gemma_3n_inference(messages)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "R3ExuK8cWuT3", + "outputId": "fa0849db-b267-4d30-f556-ff22a12747f0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "In emerald canopy, a gentle sway,\n", + "The sloth hangs suspended, come what may.\n", + "A master of stillness, a creature serene,\n", + "Living life at a pace rarely seen.\n", + "\n", + "With fur of soft moss and a smile so slow,\n", + "He watches the world in a tranquil flow.\n", + "No rush, no hurry, no frantic chase,\n", + "Just peaceful existence in this leafy place.\n", + "\n", + "He munches on leaves, a verdant delight,\n", + "And dreams in the sun, bathed in golden light.\n", + "A slow-moving marvel, a quirky design,\n", + "A symbol of patience, uniquely divine.\n", + "\n", + "He blends with the\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Gemma 3N can also hear!" + ], + "metadata": { + "id": "wZrmFRZpZtGf" + } + }, + { + "cell_type": "code", + "source": [ + "from IPython.display import Audio, display\n", + "Audio(\"https://upload.wikimedia.org/wikipedia/en/6/61/I_Have_A_Dream_sample.ogg\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 61 + }, + "id": "68crYajNZtw1", + "outputId": "68dde433-5e01-4b34-a6f1-ef8c40786943" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " " + ] + }, + "metadata": {}, + "execution_count": 7 + } + ] + }, + { + "cell_type": "code", + "source": [ + "!wget -qqq https://upload.wikimedia.org/wikipedia/en/6/61/I_Have_A_Dream_sample.ogg" + ], + "metadata": { + "id": "k3vrdoa0Z01X" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "audio_file = \"I_Have_A_Dream_sample.ogg\"\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_3n_inference(messages, max_new_tokens = 256)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BJr_D4O9Z2Zh", + "outputId": "4a9b561f-8bac-4d9f-9a73-c67d5af8b97f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The audio is a recording of a portion of Martin Luther King Jr.'s famous \"I Have a Dream\" speech. \n", + "\n", + "The speech expresses his dream for a future where his children will be judged not by the color of their skin but by the content of their character. It's a powerful message of equality and hope.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Let's combine all 3 modalities together!" + ], + "metadata": { + "id": "L15JuAmmaOkB" + } + }, + { + "cell_type": "code", + "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_3n_inference(messages, max_new_tokens = 256)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "is37bsDZaRwV", + "outputId": "d8a0cef5-5dd8-4545-cb73-5fe6bb984790" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The audio is a recording of **Martin Luther King Jr.'s famous \"I Have a Dream\" speech**. \n", + "\n", + "The image shows a **sloth hanging upside down from a tree branch**. \n", + "\n", + "The connection between the audio and the image is **symbolic and metaphorical**. The \"I Have a Dream\" speech is about a future where people are judged by their character, not their skin color. The sloth, known for its slow and peaceful nature, can be seen as a symbol of patience and hope for this future. \n", + "\n", + "The image might be used to evoke a sense of calm and perseverance, mirroring the enduring hope and struggle for equality expressed in King's speech. It could also be a way to visually represent the slow but steady progress towards a more just and equitable society. \n", + "\n", + "It's important to note that this connection is not literal. There's no direct relationship between a sloth and the content of King's speech. However, the image serves as a powerful visual metaphor for the themes of hope, patience, and the long journey towards achieving a dream of equality.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Let's finetune Gemma 3N!\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!" + ], + "metadata": { + "id": "Bw5XPyYFajyM" + } + }, + { + "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": null, + "metadata": { + "id": "6bZsfBuZDeCL", + "outputId": "52472f8e-cd55-47e7-82ed-b03264ad1b3d", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "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-3` format for conversation style finetunes. We use [Maxime Labonne's FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset in ShareGPT style. Gemma-3 renders multi turn conversations like below:\n", + "\n", + "```\n", + "user\n", + "Hello!\n", + "model\n", + "Hey there!\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, phi4, qwen2.5, gemma3` and more." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LjY75GoYUCB8" + }, + "outputs": [], + "source": [ + "from unsloth.chat_templates import get_chat_template\n", + "tokenizer = get_chat_template(\n", + " tokenizer,\n", + " chat_template = \"gemma-3\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "source": [ + "We get the first 3000 rows of the dataset" + ], + "metadata": { + "id": "ZQkXuGYxbJ-e" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Mkq4RvEq7FQr", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 169, + "referenced_widgets": [ + "4f9d977850ea479488a18af2fbd2c606", + "a91cba78c2a741d8bed7520261b5fc87", + "3b66a241808a43d293fc22ce2d2c050b", + "6eae87e4d07a4f37bceae0307ac9d483", + "0c2621b6599c4b8c94b93bc0cf2472cb", + "a2b2e5a6a0c949cfb5f35bb28c4ef9a6", + "5c058455293647d3a7b0e9490eefb96f", + "6766c08e64a84176a19895ba1a2e2988", + "c27cb86cd4f2400696d40a6823f80adc", + "bb88da6977e94694882057c1ec9ace1f", + "ab545986a8ed4ac2b49c161761201f6e", + "1bf90be3e82a4b4b8fdd81ca719f9d94", + "a5ad0bad814d4eef9ea8236699e6e071", + "0fd9c71795604fceaeac592dfb7152f5", + "9d8cf731a8054b41a97f00504fb66152", + "fc3a42d6dfda47a7b9cd261e0e9ba3b3", + "0ef0d5f9cf774d78b080e6549c01a9dc", + "0da5249afae34862b707dd2d9f08ce7f", + "6f2e9e1d6a984e049bb070d67602b649", + "f5ff2981d51149318b2bbef9197c64dd", + "6ae77f61172a47a7892dc40683939dad", + "e77cea13dc0645bbbe7b548690945f73", + "21a737636bd34890b63f748e2ad6aea7", + "007d58e515b34d80a303e8493fb6e726", + "eef95ebc8cf243a4a1f65dc9878f6305", + "8419bfdccb384abba5cc926e24325bfe", + "7341640e892e4df5bd39cb0c0429cdc5", + "033e3a93c052418487e32e2cabc12990", + "b0cf479ea0f24cbd820e4c09b4c44d97", + "dbd8322d1e574c289524cc82017f61cc", + "457b79de10214f51b5f178daff77bda0", + "42bac23a3f8e4be49e56aaccbc49d655", + "6f69db3b37fa401dbd3cf659bc83c6f5" + ] + }, + "outputId": "c8e19834-144d-4a3a-8b0f-a6fc6434df89" + }, + "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": null, + "metadata": { + "id": "1ahE8Ys37JDJ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "61414149c23e4cb6ba732efcdbeee408", + "af0213a2f1974b19948b169fa3abec7e", + "41d65ff1b5b84534b92a81a0a44e0610", + "e523826f7ede43958876fcb0d9c300c1", + "c835f882ec4c45d0bf95d3f6d17dd6d1", + "841fdfca229b413b90774789b8c90686", + "d3166e5eccdc43048f4e4b09725c1592", + "477c62ae6efb4c1fb2fa0e8b6ae70642", + "78bd3f2909d446caa30681de1bcb8793", + "0f63130623f548c7a30efaede52ee68d", + "47979d0b42dd40a8acc67f2b3fdac756" + ] + }, + "outputId": "0998981a-5404-4a13-a30d-4d11765df229" + }, + "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": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 210 + }, + "id": "gGFzmplrEy9I", + "outputId": "6fbf9365-198d-4346-8281-465f4840f207" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'user\\nWhat is the modulus operator in programming and how can I use it to calculate the modulus of two given numbers?\\nmodel\\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 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`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 77, + "referenced_widgets": [ + "47419a17294948ceaa267ef9527aa837", + "bd0fdbe7f0604573ad8c3bc37a9dc198", + "ab03e3818d5748a4896575fd185340d0", + "9c30a95489304187968dc32e9d13f54d", + "9ec7beff192f4fb0ac423db202d290e3", + "212893e31d954120ba2ce19da79ab668", + "65da1d31689644ecb6d39ad3cadb180e", + "5cf72421beee4f409d0e4fc9bb3aac48", + "45ac61fb76df43f9aa4ea0b3691a9575", + "6bc32a460ccc4c468a5a92c026296c82", + "0e41ec04e37b45fba9cf6ecd03f4f708" + ] + }, + "id": "95_Nn-89DhsL", + "outputId": "5b9ee0a6-5856-4407-bcf6-d5f0def28572" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Unsloth: Tokenizing [\"text\"] (num_proc=2): 0%| | 0/3000 [00:00user\\n\",\n", + " response_part = \"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": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 210 + }, + "id": "LtsMVtlkUhja", + "outputId": "f3ff4268-9a5e-4298-f757-9a124697c149" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'user\\nWhat is the modulus operator in programming and how can I use it to calculate the modulus of two given numbers?\\nmodel\\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": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 210 + }, + "id": "_rD6fl8EUxnG", + "outputId": "94c75714-f5db-4854-f4cc-d832768ac5e2" + }, + "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": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2ejIt2xSNKKp", + "outputId": "b2007315-61b6-4c50-f44f-128182fca757" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "GPU = Tesla T4. Max memory = 14.741 GB.\n", + "12.592 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": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "yqxqAZ7KJ4oL", + "outputId": "dd9488e9-eaba-4628-b89f-4b90a8663ad4" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n", + " \\\\ /| Num examples = 3,000 | Num Epochs = 1 | Total steps = 30\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 = 19,210,240 of 4,000,000,000 (0.48% trained)\n", + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\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", + " [30/30 03:57, Epoch 0/1]\n", + "
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StepTraining Loss
17.838200
27.321400
35.612000
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56.055700
65.708900
75.883100
86.726200
96.340900
106.254000
118.979900
126.578000
138.251400
149.181700
156.717700
168.310600
177.068600
187.544400
194.389900
203.215800
213.547600
224.041100
233.491700
243.100900
253.017400
262.235200
272.572800
282.524600
292.193600
302.651600

" + ] + }, + "metadata": {} + } + ], + "source": [ + "trainer_stats = trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pCqnaKmlO1U9", + "outputId": "bf1534bd-8217-47ac-aefe-f655c1d0941b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "338.048 seconds used for training.\n", + "5.63 minutes used for training.\n", + "Peak reserved memory = 12.592 GB.\n", + "Peak reserved memory for training = 0.0 GB.\n", + "Peak reserved memory % of max memory = 85.422 %.\n", + "Peak reserved memory for training % of max memory = 0.0 %.\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-3` team, the recommended settings for inference are `temperature = 1.0, top_p = 0.95, top_k = 64`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kR3gIAX-SM2q", + "outputId": "098ca4bf-5590-4e46-a098-c1f04bbd7b1c" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['user\\nContinue the sequence: 1, 1, 2, 3, 5, 8,\\nmodel\\nThe sequence is the Fibonacci sequence. Each number is the sum of the two preceding numbers.\\n\\n1, 1, 2, 3, 5, 8, **13, 21, 34...**\\n\\nSo the next number is 13.\\n']" + ] + }, + "metadata": {}, + "execution_count": 25 + } + ], + "source": [ + "from unsloth.chat_templates import get_chat_template\n", + "tokenizer = get_chat_template(\n", + " tokenizer,\n", + " chat_template = \"gemma-3\",\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-3 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": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "e2pEuRb1r2Vg", + "outputId": "1a8234b0-ac1e-4d75-bf57-65da5a4f7f67" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The sky is blue because of a phenomenon called **Rayleigh scattering**. Here's a breakdown of what happens:\n", + "\n", + "* **Sunlight is made of all colors:** White sunlight is actually composed of all the colors of the rainbow (red, orange, yellow, green, blue, indigo, violet).\n", + "* **\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-3 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 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": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "upcOlWe7A1vc", + "outputId": "2c2aa03d-6116-4bb5-fdfa-0527718f8ffd" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['gemma-3n/processor_config.json']" + ] + }, + "metadata": {}, + "execution_count": 27 + } + ], + "source": [ + "model.save_pretrained(\"gemma-3n\") # Local saving\n", + "tokenizer.save_pretrained(\"gemma-3n\")\n", + "# model.push_to_hub(\"HF_ACCOUNT/gemma-3\", token = \"...\") # Online saving\n", + "# tokenizer.push_to_hub(\"HF_ACCOUNT/gemma-3\", token = \"...\") # Online saving" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AEEcJ4qfC7Lp" + }, + "source": [ + "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MKX_XKs_BNZR", + "outputId": "3c3453e6-24ce-4080-a9de-e4936872eb20" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Gemma-3N is a family of open-weights large language models (LLMs) created by the Gemma team at Google DeepMind. Here's a breakdown of what that means:\n", + "\n", + "* **Open-weights:** This is a key aspect. \"Open-weights\" means the model's parameters (the learned values within the model) are publicly available. This allows researchers, developers, and enthusiasts to download, use, and fine-tune the model for their own purposes, fostering innovation and transparency in the field of AI.\n", + "* **Large Language Model (LLM):** Gemma-3N is a\n" + ] + } + ], + "source": [ + "if False:\n", + " from unsloth import FastModel\n", + " model, tokenizer = FastModel.from_pretrained(\n", + " model_name = \"lora_model\", # 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-3N?\",}]\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-3 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-3N-finetune`. Set `if False` to `if True` to let it run!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iHjt_SMYsd3P" + }, + "outputs": [], + "source": [ + "if False: # Change to True to save finetune!\n", + " model.save_pretrained_merged(\"gemma-3N-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": null, + "metadata": { + "id": "ZV-CiKPrIFG0" + }, + "outputs": [], + "source": [ + "if False: # Change to True to upload finetune\n", + " model.push_to_hub_merged(\n", + " \"HF_ACCOUNT/gemma-3N-finetune\", tokenizer,\n", + " token = \"hf_...\"\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": null, + "metadata": { + "id": "FqfebeAdT073" + }, + "outputs": [], + "source": [ + "if False: # Change to True to save to GGUF\n", + " model.save_pretrained_gguf(\n", + " \"gemma-3N-finetune\",\n", + " quantization_type = \"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": null, + "metadata": { + "id": "ZgcJIhJ0I_es" + }, + "outputs": [], + "source": [ + "if False: # Change to True to upload GGUF\n", + " model.push_to_hub_gguf(\n", + " \"gemma-3N-finetune\",\n", + " quantization_type = \"Q8_0\", # Only Q8_0, BF16, F16 supported\n", + " repo_id = \"HF_ACCOUNT/gemma-3N-finetune-gguf\",\n", + " token = \"hf_...\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pnz9QOYTMvbH" + }, + "source": [ + "Now, use the `gemma-3N-finetune.gguf` file or `gemma-3N-finetune-Q4_K_M.gguf` file in llama.cpp or a UI based system like Jan or Open WebUI. You can install Jan [here](https://github.com/janhq/jan) and Open WebUI [here](https://github.com/open-webui/open-webui)\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 links:\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", + "6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n", + "\n", + "

\n", + " \n", + " \n", + " \n", + "\n", + " Join Discord if you need help + ⭐️ Star us on Github ⭐️\n", + "
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