{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "2vQXvnUUsTzI" }, "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": "7j01DfVgsTzJ" }, "source": [ "### News" ] }, { "cell_type": "markdown", "metadata": { "id": "6dT42nHksTzJ" }, "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": "K7fgQkATsTzK" }, "source": [ "### Installation" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "vA7IKFdUsTzK" }, "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": "Mp4i13PHsTzK" }, "outputs": [], "source": [ "%%capture\n", "!pip install --no-deps --upgrade timm # For Gemma 4 vision/audio" ] }, { "cell_type": "markdown", "metadata": { "id": "GFOEZbP7ONMs" }, "source": [ "### Unsloth" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "QmUBVEnvCDJv", "colab": { "base_uri": "https://localhost:8080/", "height": 461, "referenced_widgets": [ "cca48201ab524349b98bd7dc3ec0a371", "a74922ad802c4cfe96d3f7c9d0215877", "30e429603fbb48469d80e5ca48305216", "2bc496ecfb4e410ba3ba46451dd38cac", "ddb467daf231485199b6820c1c87be59", "6c30ba7b3fc84a63b7b8975e8b17991c", "3b10e74e5c6b421092b314f71f7e9217", "d2bc2f8ade924beca4c2db847efa6f5d", "eed52dc4a67d4253983ee7b20918152d", "642d63ab9f7c451fa96a0633c1a7f23c", "72cbdfb8889a4da0bcc0a4e5d5a67ac2", "26121d7e6555452ab0f5ac2a6681d864", "e239143f8ec0425db6135a5d749c2c53", "b86cb0f30f674656aff10b0701f9f26f", "a7cf44db72fc47b8b8f7a084b7585656", "acbd99a66aa846d78396dcf40ebc8bd7", "35ae415e878e486fa0caad09b8408067", "0b4dda8099954b0d8851c05e24ed0e35", "cbd50ad058134a36bfcd386deac87fb8", "1d572d34d67b4d6b832203a27f44f93c", "b988bc006f284214883cba413dfb5fc3", "1b43696ca662419ea0c970b05d89441d", "69394dfeff2a4286bb7088f599bf4c0b", "a4e7a791f92449978900effc00e5100a", "49e5b9f4cea744fc8f38a8adabc0bd42", "08785606a9034297aa16181720c51142", "e6e357b71bb8474d82d98ce813aa2183", "a36232c6617141a1b0fdfe0108550ae0", "7563a57897d846f6ba41007b0c92135e", "da65718025e64f83a198f99f8fa48c3b", "96e509dc48aa456da20260357e60677b", "63f6774afc314b52b1ba82f5335d67a3", "b4357ad98d3b4113ae9dab45333b8ee4", "b7eddc272b2f4b0db9d910b1fa53bae1", "c25094678b464bd0b693dcfe333da14f", "5332bd2cc65145c49a843bc835d51373", "903d6cf82ea44919a1739b2fbae8f16a", "5382cb936c2b46d59e1819622ee8ef39", "bc3b830bad5c4d78b64406b4f19cc370", "09a61bb08189489687754346b906b875", "91bb3728a52042aeb9947caf54dda34e", "1aadf052247c44d8bb0081cd68234fa8", "1e5f5a2acd9b47ed972c98ef4428f03d", "31cbc371336f456795651381a7e3ad0a", "0dbb262cf30643659120f8cdb5b3171c", "abd6e0b4b3f24f1cb818aab793d61477", "b30a2b86573844d5bf4a6f43e5bd8823", "6c98eca1409f4e1ba2e7865b2992f51e", "bceaf428e3fc4bcea2318512a7c6ef4b", "5cdef7c8d58c441daaaddb9f34bc1a7d", "5c4901ca1dd44ee69bb5bb76b1cb3fb4", "25768d586f0f4d5cb9c86d01b13e7fa1", "f962bb7a63094308b1ec98cd84c90637", "9a305de1de4a4fb797d14e996e802ca0", "29a723a6b8b7436faa14620beff25597", "87fa7a1186c04503994d3ca6781f2fa2", "70dd1d3746a14a5cac5fb65edb8ac231", "6144783cd98542e3ac0488d1f1c92c35", "d605282333054e82a1d8507be830f02f", "dfbb2f74208243238c1dc88dcc4d45e7", "555c3a2ab47c4cb4b872fd067c15c600", "e60657aaf58d4373a3e777352ab720c3", "7c9085eed1be41b8b6d61f5eef968825", "0357ffa5f3224994a2917f7f09affff3", "062b5afb6d9f492aa62fd5547fc6d5eb", "6e53b93ebdf045e9b94eedd1bd489b3d", "d2a7e731aa05474299243a698d8f8e76", "9c8ecc577ca6479aad38d1af6c783685", "5cd3447374284aa6a9d62e5a72f5ff3c", "b49a335de14944a5afb36a8ec263c6c8", "94e9455af5d94b089a5ceb122446b782", "3aae33b4c0ef45e597513265bc4b0681", "2efb2666390a482b913a30109a7e2472", "0ce0b6f052db48c28f7788e95df7c1f3", "01917cd89aef4db0aa18675636f4e981", "58ee20c84f774dceaa69985bd3c63afa", "9d8eb917864e4be1b788f9cfcd64e182", "a955958aff0444a4839aafca8566065c", "867f014d40bd409fa6c845436b050fd3", "dd08aebf4c9b44b692534426ea948cdb", "6e0c925129ea45b6bce91769e084e9f3", "e3a3e391d3114a94985fd3db6cc5ee1a", "c224a759bea64ffbbf3b645a59cd79c5", "e696b055c6934f1186078767877ff2a2", "319292819e264a19a27685601c70f9b6", "bb1795785a6a44ec9b17b31b1075cbb3", "5536ee38467e4557bbb1209cb7b4e424", "b9c49626bcbb4e8bb6d4a01a08fab4dd", "4cfc78a661dd4837b26a78d7dba376b0", "ee345697fcfc4468a50627bcff074655", "3ed7a054b0104bd09132227f1ed413ba", "59667e10734c42218476077f7741da15", "e1d2d9a63d3f43dca2a8e097470d2c76", "a743692b1eae456cad70ecf0d3752493", "d6ad093be12e4a3e9b7c75160aeb2fb9", "746d3b1ccba940e69fc9bf50eb1986c8", "580577e9f9a5425f9ca10479f42b3d16", "27b4eafed4584bcda6f88f1fb82f9212", "d6caa192c711497b856f18df2f5d9a62" ] }, "outputId": "a348cecd-dff0-4511-80fb-c1c3e1079230" }, "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": "cca48201ab524349b98bd7dc3ec0a371" } }, "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": "26121d7e6555452ab0f5ac2a6681d864" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Fetching 2 files: 0%| | 0/2 [00:00\n", "### Data Prep\n", "We'll use a sampled dataset of handwritten math formulas. The objective is to convert these images into a computer-readable format—specifically LaTeX—so they can be rendered. This is particularly useful for complex expressions.\n", "\n", "You can access the dataset [here](https://huggingface.co/datasets/unsloth/LaTeX_OCR). The full dataset is [here](https://huggingface.co/datasets/linxy/LaTeX_OCR)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "LjY75GoYUCB8", "colab": { "base_uri": "https://localhost:8080/", "height": 177, "referenced_widgets": [ "19f9e04dafa744d0bbafe658a8cd4479", "f261579b58c94894a6c186255415e54b", "d36d30757ab34602ab6411c282171802", "0ac1ef374ce34494a9ade7b68c1fcb7a", "588e76027ce9499e8e7683f00c8bd999", "23bb3393ad0342aaa993c5109af01cbb", "1b9c16155dec43db992c6140ea535912", "5b938c6422c64286aed0276899c260b3", "74fce0116e3b4cf4abde0af7563ebccb", "a67007d24e9b4a11a35f955db150d486", "f91df33daa9348e7abede2d855dc0ecb", "717b21eed2084ebe9ddcf9a0da8c1c7f", "1e690555c06540359b89b87c6ae62a9c", "bddd2d47d2bd40fb9d68be94ebacc0d7", "b875ef0b812d44cd8e1fd102b597149b", "91c2f197fcf94b02bbbb780cced6c0de", "8cd8dc2aafd04685b8cb3fb746f08005", "f30f8e430cad427d9086bc99f6f146be", "c048a0b91a5a4bf7803fb3d5ba994acd", "6de4762ccf1a42e6b51f18880b37cd50", 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5hge0zPtqHOaTdiUiN9XMa5Qnj6xCt5YV9sBNdug2NlxgVLCkVFt6Vw+LnuTxFJDQLo3xyYTMptmPMICBidCWWLXCSbQQGwyFAjYMjMPqhQuFSkbXjCuoRFAbV/kUljJ711FjYPI1z+DEm2mYwTCo2tGqwqDoFYcRLSdhVtRSqB3XnHVfccUVYEgKxpxKPRRzTiNva6mh1TLcIvbbtAs/w+rb2DGTQPOO4IkaZZ6wLSzyIoJEY056yJXNqhrdjEtNRWC06CKNq8PmmWSlAieX81WYYNxa2hfTKKUpTNgz+riFsPYPGPLV/FlSZWMw4FZFPgDcOGQAHClzTnoI/005hDZ2CL6NZnyFBLBpXSFXs4G8msUIcJ7h0LNnZaqv8gEx2wzHdohAaMxsx2+//fb2QqRB2YbMykg4y3gYe0tKOPm2nQmBPCdv3BUV7oQBnOAntsXfDJhX1IOhykNa7IFRsdAen6+BFTzCpIMo2xumGPZpDJ4NNh1zrA1/8Ni/QUJ+CzbrMXt4hZ9CPV+9CjppDIeoR1EcxlpA0VcjkWF2NGJhKdBoiX0Ac6qZRWMwaLGCZYWezHerrbZq7Ii6+YJzWr7hq6M+sSZswJzb4KZOaREuiUxSLYTldeEwXz2LimQw+0BwCrLmqMJEbNT+XzKnWAY/DMSR0GT4iRMnYlhjwabiVYngk933gAPKaw0Mfqq2N2HyzOqik8DoosJgnEeYXefVXK/VkAGQ5+V27CHdnsPI1UYzIUcDZamCtyrz6mmRDLDkWVpqYKP3OtWBjahdEBZseaPCPjzFM2Yk/Fv8YCIiNeWGJK1KsZWozGUMokLoaMGpBvwFoUYUw0DpVb42pZ5GmHkFG9W3KZ/BZrQcIaDC5YRVkKw5dk/ewDgstRXkE5wk0p7kZiCrmANsciHiWNqZiEKlUYmYtuJMzgUpIcMJLcaMsW0kSCwfZDMVyQT/whaFOJFmcCGhxam7mYgAauamY45/SkcWDxIhgR8JkcgZkhbc8gHLBI4qLOpos127wsd4mumoRstadEmXT3niXIUUOWUJq0jAKdfpZXExfvx4yIFFRrGVk/skfCRL18YoJNz0UvSqUdSiBBUxlfI65cvkR7rYTlPyFTO5hVIbspAWak211Dkw+OlyCKaVZoJQLIjG6Kcp/zDgBJineo/LVAfuAeHIb0/CJlaVXEyn2lKt02NUWW0s9TL8soHYDLKVlttQafUpqOx4Sd1GCNFwgqgKV3//+99vxc558rNv4U+7osWc0MF1+JTzJVIhI6sJXpTwYchDwjqflSiZvevldBfyfJ0wYUKtY1MZccWLINExdB2/ueSESqxZI2O1xKUo9UYkNT0kf5qFwpn56k1TTsvjabFXocQNZLEmgZjsIZ1nRgfz4T+vVYBavdbd13Thh5JwkUJ7jdXgMa/BqlHwOnkYpjDTnsOCp1Ez6W7cmRYlwNmosdDt43MaB54i8v8fRQV57wqLlCXnuP0bQ6cOwxM1FfyFh9LSvhKdZlrlWAVwGy0j52tIxP60KFUS1ZagkuWYRdWBY0kylXZTO93t96iXFakkoFfQOiHnzElQWri616RQnGBDwT8f1m7TBSHtkMfNQrexoxZgOEyJIVKvE7JI5GlV5sYbsLiiPG9qPW7cONEk3T1rRa8gLHqQyeVeYFqgsrrRXUUxkREsTByEGAwQpIZt6K+FXGMX/Gi02LH9Llx6rRHySkDbGQIWeQGHVZXpcqgvQZpqRjsM9G9kM5rhRGNjCXBj+2i3TOPAo00MfsNsDGJtXSEXnfIcWi6htyuYIQnyRgdmH77KKg5gpSavll7FgUnHyhVrBFu49i3j5NpNOE2n9XXY7hk8Zte2HtAy740U/NC61w1nZqGX4pJZtaMW3duUpvYkavA9qaZNx1afwmojWh5i1dPGsiHUN91bIW/fHiVwJDe0nJIAromfV5P/Mu2vIhwKh001E2GH6MBVir2s99qBuysbFbMM2rcZZg2cZNVoZB0TbeXAjQjtJ/G97BSwmNVWW+0HP/iB1antJROwmJ0rfmBYoQWhe7yQJJPbwdpiiy28OpwEkHNXW6lZz3Nmy85axzYOk5QSDqc4zjTzi7QP61lDUsU/LDy9AR5JpKhxOEM48Owso8eFmth0t4jaNWVS1mYcw46ousOkbiFvj4cgyDkvtafiX89Ks7m96CajzOzkUIHB7w1sQY0bN841I7f2uLcVI4/1CQZPe/5LLLGEug0Y57pSNzxuiQlJcgsAedv9qmrHNqeO1fPVHI3CUEooDkv/NSRV/NB2gLAwM6wKQpwTM61kj7/VuB06hz0TZFhSTxe4Dw48LOuZrgAAZGBWZcu365jbU2dPvNcUmjfa3zbLlYGdgbl/ixO+J8GKJtZmiSmZ51dxsjZIfHWCoovixqJ9Y3dI/TNxlipAiErq1V7qjWZaA2j1ikSrT521dx1hKzYQah+aWzn2EDkcIlgr9vrV3qfrI90Ql3HzEJj8NJ8b5ApKLT90g05zHPyTPdn2lFT90IL3urWS3+5Y9DoE4mN2m++55x5enVNZgQbPKUkXuAVmi9VPLJCRBFwCF4l05MNaIs5rnSb/1bE5Q4PW0dFAFD523ZvRzOjFhq01Jx9WuitLlpqOkdiGtSjkcTyEVJz0xmbKVX6nRz65+MH9+K09FT9b5b06ZqOlsKc7SDD+HwUk7soDCLnsqPN2MErpMqj0WAPRv3HhvWP2GKkPU+gRBkqjSKFykc0eZxiuFvABN5alMo0dzy3bcOUKR5DHUROMPWVaVyMkSb5t/mau6zKGnzG7deTQ0qzYRWsZdcKECUwhvQoVUujilrVr4QKEnze6pBH8bmXI5+qBKV0GlR5rgOsaXFsYBsJ+RI+pD5Uc5maskrjo2gAJXZDI/5SSytLeXVmS0nM9QIyAvJZIG8nJn25uaXda1vh10DKjaCDmZLhd+7Wi6foJZbf0MOOtgaUyyhUR7RtJuTJYrrPSSC3LDTWGtYaTeNFyRGQT2LV+u81W3QY1PXzCQCkmvT4BkHV5viQMzNfW6Cf/SBMkGM8CRpBCojQOKr3UQLzLuLij4lqru4Nu6RqjrhtYF4TqViToC56clCLNE0aJAZi5k8Hz0zxRw2Wv+G1TcubS/gFAdbXcFGzQOMY1YMTtR/h/YJZFthjVGcDY5HlWbHUhDPQDBS1nxVsqo82Febud5/woZ7RpDfD3VwPCtEVQTgT6y0l76jOwAxNM9OnNrAYhYSKHOu0VKlQPBaw9ksHXsaMBntzqhHksMDljO3CPNdizeNFjuQbkmmogk9PeZIimDAylceDAQ9HSAGaggTGqgRlvF3qMKnLA1kAD/dDAwIH7ofUBzYEGuqSBgQN3SZEDNAMN9EMDAwfuh9YHNAca6JIGBg7cJUUO0Aw00A8NDBy4H1of0BxooEsaGDhwlxQ5QDPQQD80MHDgfmh9QHOggS5pYODAXVLkAM1AA/3QwP8AGMg7qICuIqsAAAAASUVORK5CYII=\n", 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FFAH/2Q==\n" }, "metadata": {}, "execution_count": 7 } ], "source": [ "dataset[2][\"image\"]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "id": "lXjfJr4W6z8P", "colab": { "base_uri": "https://localhost:8080/", "height": 70 }, "outputId": "cb5eb325-8bd4-46f1-e3a9-f565d5bd289a" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'H ^ { \\\\prime } = \\\\beta N \\\\int d \\\\lambda \\\\biggl \\\\{ \\\\frac { 1 } { 2 \\\\beta ^ { 2 } N ^ { 2 } } \\\\partial _ { \\\\lambda } \\\\zeta ^ { \\\\dagger } \\\\partial _ { \\\\lambda } \\\\zeta + V ( \\\\lambda ) \\\\zeta ^ { \\\\dagger } \\\\zeta \\\\biggr \\\\} \\\\ .'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 8 } ], "source": [ "dataset[2][\"text\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "rKHxfZua1CrS" }, "source": [ "We can also render LaTeX directly in the browser!" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "nPopsxAC1CrS", "colab": { "base_uri": "https://localhost:8080/", "height": 52 }, "outputId": "df37c129-7a34-43ce-962f-183f266bdb80" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/latex": "$\\displaystyle \\sigma ^ { \\mu } \\frac { \\lambda ^ { a } } { 2 } A _ { \\mu } ^ { a } .$" }, "metadata": {} } ], "source": [ "from IPython.display import display, Math, Latex\n", "\n", "latex = dataset[3][\"text\"]\n", "display(Math(latex))" ] }, { "cell_type": "markdown", "metadata": { "id": "K9CBpiISFa6C" }, "source": [ "To format the dataset, all vision fine-tuning tasks should follow this format:\n", "\n", "```python\n", "[\n", " {\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\"type\": \"text\", \"text\": instruction},\n", " {\"type\": \"image\", \"image\": sample[\"image\"]},\n", " ],\n", " },\n", " {\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\"type\": \"text\", \"text\": instruction},\n", " {\"type\": \"image\", \"image\": sample[\"image\"]},\n", " ],\n", " },\n", "]\n", "```" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "oPXzJZzHEgXe" }, "outputs": [], "source": [ "instruction = \"Write the LaTeX representation for this image.\"\n", "\n", "def convert_to_conversation(sample):\n", " conversation = [\n", " {\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\"type\": \"text\", \"text\": instruction},\n", " {\"type\": \"image\", \"image\": sample[\"image\"]},\n", " ],\n", " },\n", " {\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": sample[\"text\"]}]},\n", " ]\n", " return {\"messages\": conversation}\n", "pass" ] }, { "cell_type": "markdown", "metadata": { "id": "FY-9u-OD6_gE" }, "source": [ "Let's convert the dataset into the \"correct\" format for finetuning:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "id": "gFW2qXIr7Ezy" }, "outputs": [], "source": [ "converted_dataset = [convert_to_conversation(sample) for sample in dataset]" ] }, { "cell_type": "markdown", "metadata": { "id": "ndDUB23CGAC5" }, "source": [ "The first example is now structured like below:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "gGFzmplrEy9I", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "df44e584-c376-4d2d-c483-4285428edc37" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'messages': [{'role': 'user',\n", " 'content': [{'type': 'text',\n", " 'text': 'Write the LaTeX representation for this image.'},\n", " {'type': 'image',\n", " 'image': }]},\n", " {'role': 'assistant',\n", " 'content': [{'type': 'text',\n", " 'text': '{ \\\\frac { N } { M } } \\\\in { \\\\bf Z } , { \\\\frac { M } { P } } \\\\in { \\\\bf Z } , { \\\\frac { P } { Q } } \\\\in { \\\\bf Z }'}]}]}" ] }, "metadata": {}, "execution_count": 12 } ], "source": [ "converted_dataset[0]" ] }, { "cell_type": "markdown", "metadata": { "id": "MsRPBIb0JJ6c" }, "source": [ "Lets take the Gemma 4 instruction chat template and use it in our base model" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "exoDVEvmJN-6" }, "outputs": [], "source": [ "from unsloth import get_chat_template\n", "\n", "processor = get_chat_template(\n", " processor,\n", " \"gemma-4-thinking\"\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "FecKS-dA82f5" }, "source": [ "Before fine-tuning, let us evaluate the base model's performance. We do not expect strong results, as it has not encountered this chat template before." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "vcat4UxA81vr", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "d5089c79-8359-4dbe-bd66-2b1253310113" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "<|channel>thought\n", "The LaTeX code for the equation in the image is:\n", "\n", "```latex\n", "H' = \\beta N \\int d\\lambda \\left\\{ \\frac{1}{2\\beta^2 N^2} \\partial_\\lambda \\zeta^\\dagger \\partial_\\lambda \\zeta + \\mathcal{V}(\\lambda) \\zeta^\\dagger \\zeta \\right\\} .\n", "```\n", "\n", "**Rendered Equation:**\n", "$$H' = \\beta N \\int d\\lambda \\left\\{ \\frac{1}{2\\beta^2 N^2} \\partial_\\lambda \\zeta^\\dagger \\partial_\\\n" ] } ], "source": [ "image = dataset[2][\"image\"]\n", "instruction = \"Write the LaTeX representation for this image.\"\n", "\n", "messages = [\n", " {\n", " \"role\": \"user\",\n", " \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": instruction}],\n", " }\n", "]\n", "input_text = processor.apply_chat_template(messages, add_generation_prompt = True)\n", "inputs = processor(\n", " image,\n", " input_text,\n", " add_special_tokens = False,\n", " return_tensors = \"pt\",\n", ").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "\n", "text_streamer = TextStreamer(processor, skip_prompt = True)\n", "result = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n", " use_cache = True, temperature = 1.0, top_p = 0.95, top_k = 64)" ] }, { "cell_type": "markdown", "metadata": { "id": "FeAiMlQ71CrS" }, "source": [ "You can see it's absolutely terrible! It doesn't follow instructions at all" ] }, { "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`. We also support `DPOTrainer` and `GRPOTrainer` for reinforcement learning!\n", "\n", "We use our new `UnslothVisionDataCollator` which will help in our vision finetuning setup." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "id": "95_Nn-89DhsL", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "1bedc6bd-3573-43d6-b930-46f1312d0870" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Unsloth: Model does not have a default image size - using 512\n" ] } ], "source": [ "from unsloth.trainer import UnslothVisionDataCollator\n", "from trl import SFTTrainer, SFTConfig\n", "\n", "trainer = SFTTrainer(\n", " model = model,\n", " train_dataset = converted_dataset,\n", " processing_class = processor.tokenizer,\n", " data_collator = UnslothVisionDataCollator(model, processor),\n", " args = SFTConfig(\n", " per_device_train_batch_size = 1,\n", " gradient_accumulation_steps = 4,\n", " max_grad_norm = 0.3,\n", " warmup_ratio = 0.03,\n", " max_steps = 60,\n", " # num_train_epochs = 2, # Set this instead of max_steps for full training runs\n", " learning_rate = 2e-4,\n", " logging_steps = 1,\n", " save_strategy = \"steps\",\n", " optim = \"adamw_8bit\",\n", " weight_decay = 0.001,\n", " lr_scheduler_type = \"cosine\",\n", " seed = 3407,\n", " output_dir = \"outputs\",\n", " report_to = \"none\", # For Weights and Biases or others\n", "\n", " # You MUST put the below items for vision finetuning:\n", " remove_unused_columns = False,\n", " dataset_text_field = \"\",\n", " dataset_kwargs = {\"skip_prepare_dataset\": True},\n", " max_length = 2048,\n", " )\n", ")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "cellView": "form", "id": "2ejIt2xSNKKp", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "30eaeb52-6380-4bef-fe5f-5fab21c3c0d2" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPU = NVIDIA A100-SXM4-40GB. Max memory = 39.494 GB.\n", "19.482 GB of memory reserved.\n" ] } ], "source": [ "# @title Show current memory stats\n", "gpu_stats = torch.cuda.get_device_properties(0)\n", "start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", "max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n", "print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n", "print(f\"{start_gpu_memory} GB of memory reserved.\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "yqxqAZ7KJ4oL", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "9842dd7e-0b1f-4f83-de92-982c021bc768" }, "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 = 68,686 | 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 = 266,963,456 of 31,540,049,968 (0.85% 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 06:15, Epoch 0/1]\n", "
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StepTraining Loss
13.049649
23.714972
33.310120
43.078652
52.219565
61.768622
71.029011
80.927911
90.854767
100.424313
110.466835
120.503272
130.467726
140.267040
150.208669
160.212328
170.198571
180.158870
190.196536
200.138079
210.152322
220.187750
230.286993
240.156409
250.143032
260.183632
270.215106
280.153457
290.236711
300.231810
310.184119
320.195166
330.162600
340.160010
350.181705
360.173868
370.118509
380.167147
390.144002
400.210267
410.188537
420.131262
430.186414
440.167026
450.114353
460.124952
470.200051
480.136990
490.224131
500.128734
510.124725
520.211169
530.180625
540.172517
550.232271
560.448664
570.129194
580.109717
590.223892
600.146485

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "cellView": "form", "id": "pCqnaKmlO1U9", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "dc83f244-231c-483d-83d0-c2d0ebe620f4" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "410.1801 seconds used for training.\n", "6.84 minutes used for training.\n", "Peak reserved memory = 21.363 GB.\n", "Peak reserved memory for training = 1.881 GB.\n", "Peak reserved memory % of max memory = 54.092 %.\n", "Peak reserved memory for training % of max memory = 4.763 %.\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! You can modify the instruction and input—just leave the output blank.\n", "\n", "We'll use the best hyperparameters for inference on Gemma: `top_p=0.95`, `top_k=64`, and `temperature=1.0`." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "kR3gIAX-SM2q", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "46954df8-cab3-4a85-f4ad-176d7251c998" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "<|channel>thought\n", "\\left[ \\left[ B _ { n } ^ { + } , b _ { 2 } ^ { - } \\right] , b _ { 2 } ^ { + } \\right] = n B _ { n } ^ { + } , \\quad \\left[ \\left[ B _ { n } ^ { - } , b _ { 2 } ^ { + } \\right] , b _ { 2 } ^ { - } \\right] = n B _ { n } ^ { - } .\n" ] } ], "source": [ "image = dataset[10][\"image\"]\n", "instruction = \"Write the LaTeX representation for this image.\"\n", "\n", "messages = [\n", " {\n", " \"role\": \"user\",\n", " \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": instruction}],\n", " }\n", "]\n", "\n", "input_text = processor.apply_chat_template(messages, add_generation_prompt = True)\n", "\n", "inputs = processor(\n", " image,\n", " input_text,\n", " add_special_tokens = False,\n", " return_tensors = \"pt\",\n", ").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "\n", "text_streamer = TextStreamer(processor, skip_prompt = True)\n", "result = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n", " use_cache = True, temperature = 1.0, top_p = 0.95, top_k = 64)" ] }, { "cell_type": "markdown", "metadata": { "id": "uMuVrWbjAzhc" }, "source": [ "\n", "### Saving, loading finetuned models\n", "To save the final model as LoRA adapters, use Hugging Face’s `push_to_hub` for online saving, or `save_pretrained` for local storage.\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": 20, "metadata": { "id": "upcOlWe7A1vc", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "4a815ae8-22d9-47ee-ad27-aee38c2b10e9" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['gemma_4_lora/processor_config.json']" ] }, "metadata": {}, "execution_count": 20 } ], "source": [ "model.save_pretrained(\"gemma_4_lora\") # Local saving\n", "processor.save_pretrained(\"gemma_4_lora\")\n", "# model.push_to_hub(\"your_name/gemma_4_lora\", token = \"YOUR_HF_TOKEN\") # Online saving\n", "# processor.push_to_hub(\"your_name/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": 21, "metadata": { "id": "MKX_XKs_BNZR", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "1ab27847-31a6-4565-d766-485d4c548dab" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "<|channel>thought\n", "The image contains a mathematical equation written in LaTeX notation:\n", "\n", "$$ D _ { \\mu } ^ { \\alpha \\beta } \\bar { A } _ { \\mu } ^ { \\alpha \\beta } = 0 , $$\n" ] } ], "source": [ "if False:\n", " from unsloth import FastVisionModel\n", "\n", " model, processor = FastVisionModel.from_pretrained(\n", " model_name = \"gemma_4_lora\", # YOUR MODEL YOU USED FOR TRAINING\n", " load_in_4bit = True, # Set to False for 16bit LoRA\n", " )\n", "\n", "sample = dataset[1]\n", "image = sample[\"image\"].convert(\"RGB\")\n", "messages = [\n", " {\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\n", " \"type\": \"text\",\n", " \"text\": sample[\"text\"],\n", " },\n", " {\n", " \"type\": \"image\",\n", " },\n", " ],\n", " },\n", "]\n", "input_text = processor.apply_chat_template(messages, add_generation_prompt = True)\n", "inputs = processor(\n", " image,\n", " input_text,\n", " add_special_tokens = False,\n", " return_tensors = \"pt\",\n", ").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "\n", "text_streamer = TextStreamer(processor.tokenizer, skip_prompt = True)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n", " use_cache = True, temperature = 1.0, top_p = 0.95, top_k = 64)" ] }, { "cell_type": "markdown", "metadata": { "id": "f422JgM9sdVT" }, "source": [ "### Saving to float16 for VLLM\n", "\n", "We also support saving to `float16` directly. Select `merged_16bit` for float16. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens. See [our docs](https://unsloth.ai/docs/basics/inference-and-deployment) for more deployment options." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "id": "iHjt_SMYsd3P" }, "outputs": [], "source": [ "# Select ONLY 1 to save! (Both not needed!)\n", "\n", "# Save locally to 16bit\n", "if False: model.save_pretrained_merged(\"unsloth_finetune\", processor,)\n", "\n", "# To export and save to your Hugging Face account\n", "if False: model.push_to_hub_merged(\"YOUR_USERNAME/unsloth_finetune\", processor, token = \"YOUR_HF_TOKEN\")" ] }, { "cell_type": "markdown", "metadata": { "id": "TSjNVDCYv-yr" }, "source": [ "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": "3.13.3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "cca48201ab524349b98bd7dc3ec0a371": { "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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