{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "n5NrROnmsJP4" }, "source": [ "To run this, press \"*Runtime*\" and press \"*Run all*\" on a Google Colab A100 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": "jjzmSqm7sJP4" }, "source": [ "### News" ] }, { "cell_type": "markdown", "metadata": { "id": "WDatJHaBsJP5" }, "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": "9TnXzZYMsJP5" }, "source": [ "### Installation" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "B2T3Z8F2sJP5" }, "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": { "id": "-Xbb0cuLzwgf", "outputId": "3d9968a4-cb68-4112-aa41-29a78c0c8ae0", "colab": { "base_uri": "https://localhost:8080/", "height": 461, "referenced_widgets": [ "ddfff68b86604e0daa29ef5bc5b097c3", "ad049bffb6ab409eab6268cb8b46e1d7", "1a7eb21cd8f14d0b98517dddd5a691c3", "8035803031e048be9cda8d78aaf83f1b", "2c3ad97c23f840f5997f3e5554ef7b02", "c731d59a7ea449948fa2de671225ddc2", "bff47b44ee1a49fbb0c69a24a7d1985b", "3753574c0ed14e6583ef6228f921efc3", "b897d2670a8e42ac89d526b7daf7d591", "e262ca340c0a4269a5f8571cfa85f1ce", "6b98b989f90842e79b20bbd8007ed266", "03d61f85075f4ccab14b1d7cb41139f0", "76bb1448130f41bd8d36591733b2bc13", "ac0b4b138d3b4702880b6eff4ace812e", "7d8ce2a07d0d4b0d8c0ce2dd9bce1d8f", "7deed7b9ad7e4d11b2e55e060295876f", "54d6d94660dc4131acb117491bc67fe4", "63a2d2156b0d4c9e9b62638d7b98a31a", "e49a0432d7ff4865937f127bb42d5694", "abf451cd86ee4736a2973e17957cb65a", "61b85a135758473ca21478a99b294662", "5b9428103bc0447b84830baba8cd0792", "3af787f578db4dde81a8515af76e2d4a", "cfb0f9b35dad41ff89f6d4c9294fb141", "12741070e1b344c5aef74e1f4e149ef0", "e53e734a00254b589f8bf2c46d99a157", "9515391e235540438acccbe364707b84", "6331c3b6d264458fa34668a0e5c29d6e", "05727779b5eb4dcaaea43a91e7b173cd", "7ff031eac8a2489493c94585a02af25c", "2d505dcd97d049d6ba2ff96c835c0f56", "e7d9e35834254739ba9c263eceec4ae3", "b92921f4b5e44191a70aa7f297d3b26d", "18e1bcd9e38b4de98b60ad764b20f9b4", "c364867b67644ee7b4480907f43a6333", "4abe4c12f83242db9b5f48ffc732c967", "463e555166254ca295339a5b2377aa74", "ac13c14fc8254d62b4bf2effba318efb", "98c1b6ab11854572a5472858a08db06d", "6ff843b22e7f48b6bb65be7de51d78a8", "acf6196472d1429ea88f6a960f90cfcd", "97742cb20ea3454fa324a3da91f7a23a", "2100a1ea915747d9bdec9368a15e3efb", "9ede245da88b4c288bf621cdba54872a", "90186299fc68489fa74be2f6b344fd1e", "1b5095988f394b059850d8596cc07a5e", "5ee00d5de3554137a3833eaf6a6555c4", "c6426c66c4f043f2871092cebc0b9a5c", "493a6fe8bb124802bed69d23de0c508e", "336f065bc8ea4f2795b98604beda5c56", "03635e8c58e44e609d578b894f41ab6c", "8f5360f02f2743ab833120c040d0da58", "96bfe52406b44c5987543008a918c8b5", "35c6063752e44868bf959336503a204b", "2ee9305b680d40429a206b409dc427d8", "15365e96b88b4592aa5a97edaf203faa", "01731ab574d7417982c3252be468048d", "09da0999a8a74a2daab528795edc9dfb", "fa232e3b8272477d8ad8afa420f0b966", "ba2ebae221444c57a7d147298a8aaaa5", "3eb88bb5f85d4176b63e462b8387d9d1", "c75d7c8d4f5e4f8c81a516f417f6e356", "10a2e0f49f934d649879f1379643b5c2", "93d00755b1c74145a989942550cb1256", "31414eee7ac24fb08c74df53771a46d1", "c93d71f1f5e74beeb3650aaf22518a7d", "f391afbe2b534ac4b046f3391d030b55", "a87b5ffeda5a4f63b9f167eb060aa960", "899a1113f94c4a12a97dd4a246ca34f5", "e80e0571b6a44a5ab5d8a308159d6599", "0b611f6c3f0a4dad9a4541f53d5a1018", "90523f78a4bb48a2a9be260a85c7d4fc", "fd1664aa2b75488dab5f33decb6e403b", "298cc95bd32e44399756388b2e8d6738", "43679efd9c104bbb828ac996d6c57d21", "5ae95fe9d7e74f10b46d784039063c14", "abe24447de574e13b6c4b0446d2e4c49", "e99b1841328642c496534c2d85ab2197", "df55839ea8174c3db3b208847b6a2edf", "37df8776bba04771bdf1b59ade53d7db", "86ed31f6ef22482aa68f56afa4f01855", "5d93b70b84864b638595700334b4806b", "381310ad93e34b9ca5a5970b329288bc", "0fb592680bd84a2b977d373c517ef948", "169b5a0ec13a4463bf31de75863ae124", "2cfbfed9370949bba912b19f725d8dcd", "83389fcc5d6043da807ae3b2bca6d278", "f018320c97c346689b9a2cee3e129bda", "9566c043613f4065a8acd0934a4fb91a", "2f3aad401e4a40f7a86a70fbaaa237b3", "dd6bbe22e8b94ffa9e22e6723ee31164", "1fb5fe9069d94a85bf295c9f43cd6a2e", "09853f9563db40dd8f991c0f11c343e2", "08361c6620a7421a92a237b88f74392b", "8ff05374922c463fa0cafc3166936916", "481b213c55944148af3be4289be99b3c", "39d2154a00014e7287a42d48959a39cd", "34602f08ab064669bdb72fd1a4699e14", "289443a77521422bbbb3fb454c763b7d" ] } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "Unsloth: Your Flash Attention 2 installation seems to be broken. Using Xformers instead. No performance changes will be seen.\n", "🦥 Unsloth Zoo will now patch everything to make training faster!\n", "==((====))== Unsloth 2026.4.4: Fast Gemma4 patching. Transformers: 5.5.0.\n", " \\\\ /| NVIDIA A100-SXM4-40GB. Num GPUs = 1. Max memory: 39.494 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.10.0+cu128. CUDA: 8.0. CUDA Toolkit: 12.8. Triton: 3.6.0\n", "\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.34. FA2 = False]\n", " \"-____-\" Free license: http://github.com/unslothai/unsloth\n", "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors.index.json: 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "ddfff68b86604e0daa29ef5bc5b097c3" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading (incomplete total...): 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "03d61f85075f4ccab14b1d7cb41139f0" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Fetching 2 files: 0%| | 0/2 [00:00" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "9jGeSb9bWe0k", "outputId": "e95da582-2668-4936-9aec-3ffdde2bb771", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The animal in the image is a sloth. Sloths have appeared in several popular films, most notably:\n", "\n", "* **Zootopia (2016):** Featuring the character Flash, a slow-moving sloth who works at the Department of Mammal Vehicles.\n", "* **Ice Age (2002):** Featuring Sid, a ground sloth who is one of the main protagonists of the series.\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": { "id": "R3ExuK8cWuT3", "outputId": "3a0a6496-79ef-4921-8dd5-96ba5ba77fc9", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "In the emerald hush of the canopy high,\n", "Where the orchids bloom and the parrots fly,\n", "Dwells a gentle soul in a velvet coat,\n", "A slow-motion dream, a drifting boat.\n", "\n", "With curved, steady claws and a sleepy gaze,\n", "He wanders the wild in a languid haze.\n", "No rush for the fruit, no race for the prize,\n", "Just the soft, golden light of the tropical skies.\n", "\n", "He breathes with the wind, he sways with the breeze,\n", "A living extension of moss-covered trees.\n", "In the folds of his fur, a miniature world,\n", "Where algae and\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": "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": 7, "metadata": { "id": "6bZsfBuZDeCL" }, "outputs": [], "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": 8, "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-thinking\",\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "ZQkXuGYxbJ-e" }, "source": [ "We get the first 3000 rows of the dataset" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "Mkq4RvEq7FQr", "outputId": "43449058-686e-430f-c009-c3e795af23ac", "colab": { "base_uri": "https://localhost:8080/", "height": 113, "referenced_widgets": [ "91d72aaeb61042e4947b071d3938d141", "a63c2648c298475dbbd524a7981cc2ad", "29adf88558b64e93b179cd1087032ae2", "807ca6b59c494877acd1d7672e2d0e42", "4627ade20aea4966a15122cdd52f6a25", "73068a350b9a481d9363f24053235c4b", "db04d1ca966a43b0be0bbd3ad4ddb515", "024c0c5a1c5542babf6cf130f0648b27", "14a012520c1340ee9264630bb456ce95", "2be7b0a20ea843af879c32c6d8707138", "d60a95c9eb6d4ca5be81eb67ac34b87d", "68995941f8e649a1a5265b073c86f84c", "f4b859550a47428ba4988a7c582443eb", "66402b5fde234614b92ea5786b108f5c", "e6e4248babc74ac9ae46c47fb3d2a371", "4caa19e33d8b4be7a1d3b083b0c1948d", "f62d9af82c9146beab3ed27b85077af5", "88f1deb88087491eaf91cc902772ab79", "3c3b82e37f1249c29dcbc663dd95d4f8", "d3bf74e15c984629aafd83ad8f5e0d92", "9371b6a9e05847cba1b74278e68955ad", "502e2c08bc2941448e984a1f50401c72", "59cbb4c555d848079284ffa4f63cdb04", "e0b731ea60c24ff98c0803c6072568b0", "a01e4ad102e74c869953e5303fb5da79", "956c0ca869d04bd084abff9e9ff8afa7", "095b148b6c8d4201a4c631fd0903c11d", "1527b6baf1e24722aecbe33f6ffd1e52", "ba42d40812c1453ab8208bce2693e79d", "1fd1672a2bb041e8be285cd8fb1a413e", "5f1175f9e50e4183b99fc7106a9b39c7", "95800a9caeed4806a66b25ead152aed2", "476c623468944e438825f5269b5acf04" ] } }, "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": 12, "metadata": { "id": "1ahE8Ys37JDJ", "outputId": "11a09b9e-9fab-45cc-aef4-81b027ebc64b", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "e74375a06bf84083bf0198a59d0a8204", "d097e15ca15c4d2eb74520873c028809", "09205f37e867471cba0c6d9bc93d1fa6", "8ff9730f06e44b4f835a79f8aa948815", "1984c66eaca44ac487b177b939d31122", "42355bb88a2246d4bb25e05c6d1e6aca", "7369d3e3e2bf4530ab7f0999b393a2ea", "3caf02c6f3524f889a4a22e2c09ea2ec", "3f97ac1599e5474f818d83971acc18e9", "04e9b0514e284cf3aba462aca7906064", "0bea037f647346fc9feff2f63659422d" ] } }, "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": 13, "metadata": { "id": "gGFzmplrEy9I", "outputId": "7d50b4dc-d3ff-49fd-dba6-7511ee9c7c07", "colab": { "base_uri": "https://localhost:8080/", "height": 140 } }, "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\\n<|channel>thought\\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": 13 } ], "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": 14, "metadata": { "id": "95_Nn-89DhsL", "outputId": "369cc7d6-dfd2-4e4a-c117-495ffd50d06d", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "872c4aa8a612431aaf11581cf8b7e966", "cc0fdf131fe944ae8686ab413b48b40b", "ccf366fe1da44dad97716da52be0fa95", "559f5ce577684e7d8fbb3eeca3df7481", "9ab84bc270d5426ea6503200962c0008", "cdeb781f1d16460c8155bd7616790995", "86b55c60b19b4f60b632c94ffdb3597d", "2025f98694a14209a17310de5e27f29f", "22e6e92bdff246b5b4f8aa2240bcba77", "0d0d95b0a65643fdb1aac22b0fc22f25", "bee9f63027394da28a6faa2cda06d291" ] } }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Unsloth: Tokenizing [\"text\"] (num_proc=16): 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": 16, "metadata": { "id": "LtsMVtlkUhja", "outputId": "68552bcc-1c20-48e9-8a78-505dced229c9", "colab": { "base_uri": "https://localhost:8080/", "height": 140 } }, "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\\n<|channel>thought\\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": 16 } ], "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": 17, "metadata": { "id": "_rD6fl8EUxnG", "outputId": "b44c5e2f-068f-4a31-a31e-34ed10678a6d", "colab": { "base_uri": "https://localhost:8080/", "height": 140 } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "' <|channel>thought\\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": [ "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": 18, "metadata": { "cellView": "form", "id": "2ejIt2xSNKKp", "outputId": "7a278f07-915e-44b9-d018-c342eb3d9256", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPU = NVIDIA A100-SXM4-40GB. Max memory = 39.494 GB.\n", "18.57 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": 19, "metadata": { "id": "yqxqAZ7KJ4oL", "outputId": "bed52b39-9792-4141-e4aa-3a7f8e3e6d49", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': 2}.\n", "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n", " \\\\ /| Num examples = 3,000 | Num Epochs = 1 | Total steps = 60\n", "O^O/ \\_/ \\ Batch size per device = 1 | Gradient accumulation steps = 4\n", "\\ / Data Parallel GPUs = 1 | Total batch size (1 x 4 x 1) = 4\n", " \"-____-\" Trainable parameters = 61,214,720 of 31,334,301,232 (0.20% 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 05:57, Epoch 0/1]\n", "
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StepTraining Loss
12.130297
20.630456
30.910843
41.590092
51.243832
60.751323
70.740214
80.595984
90.828650
101.148587
111.111826
120.612088
130.677837
140.893246
150.733738
160.908046
171.022293
180.960453
191.008064
200.916117
210.572186
221.075967
230.941994
240.896985
250.553296
260.467511
270.577387
280.588091
290.524273
300.754770
310.678074
320.652198
331.039165
340.474116
350.921311
360.881754
370.611182
380.940461
390.909078
400.630252
410.829833
420.835820
430.623156
440.523088
450.542758
461.208341
470.628660
480.642084
490.914339
501.025399
510.500276
520.597512
530.595572
540.555974
550.767994
560.910350
570.447900
580.765359
590.853433
600.745598

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "cellView": "form", "id": "pCqnaKmlO1U9", "outputId": "881756ba-9573-4a68-ec6e-14e4cf325d93", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "390.1241 seconds used for training.\n", "6.5 minutes used for training.\n", "Peak reserved memory = 21.43 GB.\n", "Peak reserved memory for training = 2.86 GB.\n", "Peak reserved memory % of max memory = 54.261 %.\n", "Peak reserved memory for training % of max memory = 7.242 %.\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": 21, "metadata": { "id": "kR3gIAX-SM2q", "outputId": "cdfa1bd8-62cd-4355-cd4a-1026a16807ea", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['<|turn>user\\nContinue the sequence: 1, 1, 2, 3, 5, 8,\\n<|turn>model\\n<|channel>thought\\n13']" ], "text/html": [ "

['<bos><|turn>user\\nContinue the sequence: 1, 1, 2, 3, 5, 8,<turn|>\\n<|turn>model\\n<|channel>thought\\n<channel|>13<turn|>']
" ] }, "metadata": {}, "execution_count": 21 } ], "source": [ "from unsloth.chat_templates import get_chat_template\n", "tokenizer = get_chat_template(\n", " tokenizer,\n", " chat_template = \"gemma-4-thinking\",\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", " use_cache = True,\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": 22, "metadata": { "id": "e2pEuRb1r2Vg", "outputId": "4c132d52-60a3-4024-8500-3a779f3dc9d3", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "<|channel>thought\n", "The shortest answer is: **Rayleigh scattering.**\n", "\n", "Here is the more detailed explanation of how it works:\n", "\n", "### 1. Sunlight is a spectrum of colors\n", "Although sunlight looks white, it is actually made up of all the colors of the rainbow (red, orange, yellow, green,\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", " use_cache = True,\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": 23, "metadata": { "id": "upcOlWe7A1vc", "outputId": "f2c735b5-ddc0-4664-a256-72143545e918", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['gemma_4_lora/processor_config.json']" ] }, "metadata": {}, "execution_count": 23 } ], "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": 24, "metadata": { "id": "MKX_XKs_BNZR", "outputId": "b12693a9-bca7-4e37-e9d4-1ceeed5b6907", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "<|channel>thought\n", "As of my current knowledge, there is no official model called **Gemma-4**.\n", "\n", "It is possible you are thinking of one of the following:\n", "\n", "1. **Gemma (by Google):** This is a family of lightweight, open-weights models built from the same technology used to create the Gemini models. The current versions are **Gemma** and **Gemma 2**.\n", "2. **GPT-4 (by OpenAI):** This is the powerful multimodal large language model that powers the paid version of ChatGPT.\n", "3. **Llama (by Meta):** Meta has\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", " use_cache = True,\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": 25, "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": 26, "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": 27, "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": 28, "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": "A100", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "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": { "ddfff68b86604e0daa29ef5bc5b097c3": { "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": 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