{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", "
\n", "\n", "\n", " Join Discord if you need help + ⭐ Star us on Github ⭐\n", "
\n", "\n", "To install Unsloth on your local device, follow [our guide](https://unsloth.ai/docs/get-started/install). This notebook is licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).\n", "\n", "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & how to save it" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### News" ] }, { "cell_type": "markdown", "metadata": {}, "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": {}, "source": [ "### Installation" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "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 --upgrade \"torchao>=0.16.0\"\n!pip install transformers==5.3.0\n!pip install --no-deps trl==0.22.2" }, { "cell_type": "markdown", "metadata": { "id": "9xLDGk41C7IF" }, "source": [ "### Unsloth" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 549, "referenced_widgets": [ "40eaa2864f724a76bcce5831bac6c07c", "1a68a146b92f4d3cbbb111b082c3975d", "5282b7880a2a4c359107cff08cd6c6a6", "07578806c48347a793be54779c111207", "a01453c835c549fc9493a6c248a2222d", "a5216f04685544938ad0159f3bb6212f", "52162180bc9346818f3e44b7bb6ad5be", "1b4d574a30f84cc99a07e24a19640bc1", "617a00d6737445f09e778835475cbb7b", "1ca5e965d5154b45acd8411ec3d2ffea", "0604cb7387354cf49f859af34705091d", "f3d88dba74234a23bc837ef7ae3651b2", "d09eb069224f4b399736c7400c24d003", "006ba5c54c1e43f8920c9a4f6039025e", "096cbacb38274251b1463fa6431dce23", "42d8884146284cfb962a03e0b9c86555", "2df59d962c754307bcf86ab950ade608", "9cffa40449284277aa1241c917c5c707", "742b4b65647f4478a84103d8f973114b", "c2cc8a5f5c12477185116faec5910c21", "9bea829af9394953b4d2ef594c06b448", "5da646ff3a264ed18a7701fc9d01b942", "81ca4e3df1174e218d19a9469a64ab27", "dee94b551d384c3dabe5ff809158508c", "242b3cdc57cf47ce816c013abbbde1ad", "178a07392f2a41b5bafa16ff135527e5", "6c1f8d90eda949198d15e32b239b2d92", "1f2dc8ca3bb9487fa5c69ae9bb4d7d57", "2f71ad2acfe2424bb09158a8a716880c", "37c1b208d2d5402cbb9318258d55e88f", "9d4dcb3c4d5b4b1cb39e5ed64de3c7d9", "076cfaed8e7c4bad9184b28197da23ad", "5b4f4b4101e14d9b9072807b38db2eff", "8d42a28b82484093bd6491b9e1274ca1", "1bade1aa86354d13b37876a49c1bf8c9", "480a68ce58b84dcea45ca1cb6875039d", "a0bde8a6d1b74f12a7a95b0be4f2aa33", "733ec3b803f440ae90bc3320c1107f10", "4414084b70434d5688bb5fcbcedc4e8a", "3129adecfccd49bbba84ae1b31f7eaa8", "c4a55b0f0f2f45c19ee8da88425fd468", "44d4629638994a72b0a2430618648e17", "c00716f990de40b5880977f09f21e4c5", "bd91399957d94360a8b803c86391ae92", "a3db032d41164076952c3386a4ac0334", "9da901165b73479c801be5673740c1d7", "b0df9b77bad64ab083f925271f3e83ec", "bc6380db75834812ae60909fc6ccae3f", "fffb788626324f3a9d81e059494c12c8", "11ff7df80173493ebae792edb1c34f52", "1256462fe8574100b9d65df1e07d1c08", "930695144ca8461ea59fbc0d6a743329", "2b597d58f71b4cb9b4124dec25bf8bdc", "a7c1a66919a64259a8fd5e2bd102a019", "2a184f987a8849eb8d26546c0ef8f994", "5d350281eb5c4d26a01997fdac891e82", "59035637038a4c6eaf9c6265a5a2073d", "6c941b37e1d64455b652c004cc60b20b", "84d2e9b9b44f4c5ba23dca445991b3d6", "729c779fb97b45bcae9422696645f1ef", "6176a8c7248f4c72a67c81ddfb16059c", "5f64719a983b438ca1a4baf186804e12", "b9db5da17bdf471d8290101b617acaa9", "0406cc7eaf904da5853352afcc1248db", "5ff36a7d685a4be8887f63b0a3be948f", "c374328d4a2740ba82fb6030deb780d2", "f1659caa12e9404c8a6be00b856402ab", "158a95a19c10487c9b43b47d73243118", "bfcb8f63614240059b18526008faa117", "ea1c3899138b4136b7eb799c4b200e51", "9cdb8d42c38641cc9ad12df63ed8e8fe", "7c552cb5372d49efbd5a9f8572084433", "26b8e8ada5154d5288812fdfbca71e09", "3a3a4a3afc1f4330af300a5dbe35d43e", "b956e290495448eabbf8b7d19eedeccb", "1d10042ecb6a448e839b3165840da23a", "195d0f34f0544837929eb1426b87f950", "df1c8947e7e94963961dcef09395199d", "9114c4464d0e4a94820b8413580d61bd", "1e13c35b08984b26a515879c01b75aef", "be59f3f738b54314ae4749ad88c797bf", "2d48bcd9b6b54db7af8b5926cb042891", "4d127cf94513420a94df47b34175244b", "855e0c61052a470484306bdf2d3a47b2", "ddeb9add45da4bd0ae448ffc8a24ca5c", "c350e2b6da00410b84565812828dd4e4", "4062b7797eba4e56b85a75516195f5c8", "e2e7c2d4b2d3460a89d41161dabb7dec", "e25f245cde1d4e1aa0d54fead7e2dafb", "077d69df53834dd89643e5f678bc4607", "5c86b38512284c8783d8bb4380229780", "d3deb84bbae94df4845724a5b720afc2", "de75eb0fc2cb43ab8bbe1082003cae7c", "eecedccefca1420397a91982cb3dcee7", "c18e33fc06da4540b8a95eb0881b3e7b", "387b4485a94b41638156bba9397141f6", "d06814f043a34074b0787e92c926d001", "17867b48d30849a189c88f4dee5ef501", "78c4d81e45ff49659bc2a1d24b881ade", "1d1161439b8a4bc8b86213404cf313b5", "511ab59960c644d1b36500caedf5893b", "26d8d4730130437e8455fb31d9182891", "3ec8c86e797d433c8c3556dce39542fa", "df6ac540add1422ca7bf1dd710713dd1", "23f82917ef3c428498a5b8be57c13ee6", "654e780ab0df471dba28a6e420f85054", "dabe792021d94d6e825b14707850b37e", "ac19a81f175f40509af9047d80330ecb", "acfa19ada0ad404e88862494074f0096", "7c39d332aaec48f3829ff5a2ce782123" ] }, "id": "QmUBVEnvCDJv", "outputId": "7062d4fb-109e-45e6-ae96-8a6e931cebcc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "🦥 Unsloth Zoo will now patch everything to make training faster!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Unrecognized keys in `rope_parameters` for 'rope_type'='yarn': {'max_position_embeddings'}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "==((====))== Unsloth 2025.11.6: Fast Ministral3 patching. Transformers: 5.0.0.dev0.\n", " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.9.0+cu126. CUDA: 7.5. CUDA Toolkit: 12.6. Triton: 3.5.0\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.33.post1. FA2 = False]\n", " \"-____-\" Free license: http://github.com/unslothai/unsloth\n", "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Unrecognized keys in `rope_parameters` for 'rope_type'='yarn': {'max_position_embeddings'}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Unsloth: QLoRA and full finetuning all not selected. Switching to 16bit LoRA.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Unrecognized keys in `rope_parameters` for 'rope_type'='yarn': {'max_position_embeddings'}\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "40eaa2864f724a76bcce5831bac6c07c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "model.safetensors.index.json: 0%| | 0.00/45.6k [00:00\n", "### Data Prep\n", "We'll be using a sampled dataset of handwritten maths formulas. The goal is to convert these images into a computer readable form - ie in LaTeX form, so we can render it. This can be very useful for complex formulas.\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": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 177, "referenced_widgets": [ "1e8ce62ce8b547c599af9cacf328db8a", "cd5bceb3ad26496590c20bbb64dc958a", "b4a22580c3824a4585543e6c729874ee", "71aeaffbd226440eaf08a5e6ce523413", "c97ef84526f54a68afbc309e23794bd8", "270955d1f47c497182db41efc9e8a1b4", "c5288bde069a4731995a5f037191ebb0", "c086d9d88946477d8fe8eed7cc18d960", "ebbcd3053d0040b094a8fc0667c1441c", "529056c4dd3a4d778ab6c483d8709b9a", "f8f8573071ff4bffb66fad6943d42f07", "09c2a17fd20e493e80d80ea2886ee135", "aa8d6fa680e749469d53fbc541bbf19e", "edd655c3ecc146af97203959d16a92d0", "47bbef15d7ff45a4ab2ad21c476a9be1", "2cbc7d8629124954b5ef50a6048ef924", "7086d83684294077bc2598f1e93494af", "ba83a6039c2549fc9fd79b8c38b6dd9c", "a3c8aa944fe044c09d2d8a0f46d4a07d", "528ccc508d214e31988897fe26401f20", "29a17d5a47054b9a860908e26127ccf7", "46baf0e755474455a100ab63fa59c9a8", "c46eec8566c4454aaef2a565955115a9", "b8b2184633ba4695b7c782163cd8e643", "48d36eabdabe47fa8e87297af201f518", "4b6788e23ea84ede9ce2d66755fcc5e7", "e5b0a6c84a75440a9aa1b3fad0ea9181", "6dbd6e5deb4a4ac2908e6a9651b44666", "4fb16e6831d841f5a15e2a5787312840", "5a5376f182814dbe9a413aa265ec4145", "959df897a9694c76a07ed37d2740111b", "d3a5d547cce342b0af5df11aa7dd53c1", "df44b38a61d344eaab1523ab97ddc353", "1a860be3e7df4da98c5f999de93f9ede", "ef9901047567467ca0c907c35311b083", "20e41835e51443cbb0489e5216744737", "8eaeb4ebb97d4215909d8ac858840b5a", "efe554ccbb4f47a987fab994c525636d", "f26515fe48124b60a6e8358bd9c66b84", "64aa5043e9a449f69df5c570f91ae2dd", "52381204188d4eb0875f7082371c0edf", "1acef6b3e36f4aa192fc351c60c28a57", "770e8bb1d6e64b9b9a26e7a5d9e2237e", "153e606216ff4bc589bcc6cea25faf09", "99e9cb61794d49a2993b1fc7954899c5", "8da15a82762044028e8b5887fc0aad71", "6b1211e260bb41b09aa1ce45945f2f9f", "8282bcd04bdf470d98cf44a7579fa4d1", "0d73dd2c22d24ad48e16fedf2ede4045", "3fb2654a9e7b43f5805535639c0e6604", "0e65083a75ea473bad5ee6b4fed7f214", "26ec9c25ba3d41e6b9043d307ed12a71", "787fb1ca807c47a1afdf3b38e4a805ae", "3ec8cdee5ed8486eab7139e5f01ce7d0", "e4e97254c9d14141b26167d9a4751691" ] }, "id": "LjY75GoYUCB8", "outputId": "ade17417-3944-46ce-c6b2-70fa5972896c" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1e8ce62ce8b547c599af9cacf328db8a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "README.md: 0%| | 0.00/519 [00:00" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[2][\"image\"]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 53 }, "id": "VTzhtzNRAEL1", "outputId": "82c9918a-2098-4c5a-a3b6-71fa75404b32" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "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 \\\\} \\\\ .'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset[2][\"text\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "NAeQ9LXCAEkW" }, "source": [ "We can also render the LaTeX in the browser directly!" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 58 }, "id": "lXjfJr4W6z8P", "outputId": "b9bf5aea-3844-4612-8a35-9d2194ae7adb" }, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle H ^ { \\prime } = \\beta N \\int d \\lambda \\biggl \\{ \\frac { 1 } { 2 \\beta ^ { 2 } N ^ { 2 } } \\partial _ { \\lambda } \\zeta ^ { \\dagger } \\partial _ { \\lambda } \\zeta + V ( \\lambda ) \\zeta ^ { \\dagger } \\zeta \\biggr \\} \\ .$" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.display import display, Math, Latex\n", "\n", "latex = dataset[2][\"text\"]\n", "display(Math(latex))" ] }, { "cell_type": "markdown", "metadata": { "id": "K9CBpiISFa6C" }, "source": [ "To format the dataset, all vision finetuning tasks should be formatted as follows:\n", "\n", "```python\n", "[\n", "{ \"role\": \"user\",\n", " \"content\": [{\"type\": \"text\", \"text\": Q}, {\"type\": \"image\", \"image\": image} ]\n", "},\n", "{ \"role\": \"assistant\",\n", " \"content\": [{\"type\": \"text\", \"text\": A} ]\n", "},\n", "]\n", "```" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "oPXzJZzHEgXe" }, "outputs": [], "source": [ "instruction = \"Write the LaTeX representation for this image.\"\n", "\n", "def convert_to_conversation(sample):\n", " conversation = [\n", " { \"role\": \"user\",\n", " \"content\" : [\n", " {\"type\" : \"text\", \"text\" : instruction},\n", " {\"type\" : \"image\", \"image\" : sample[\"image\"]} ]\n", " },\n", " { \"role\" : \"assistant\",\n", " \"content\" : [\n", " {\"type\" : \"text\", \"text\" : sample[\"text\"]} ]\n", " },\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": 10, "metadata": { "id": "gFW2qXIr7Ezy" }, "outputs": [], "source": [ "converted_dataset = [convert_to_conversation(sample) for sample in dataset]" ] }, { "cell_type": "markdown", "metadata": { "id": "ndDUB23CGAC5" }, "source": [ "We look at how the conversations are structured for the first example:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gGFzmplrEy9I", "outputId": "54ff6ea3-95de-4275-ca4f-1de3891a1c7e" }, "outputs": [ { "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 }'}]}]}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "converted_dataset[0]" ] }, { "cell_type": "markdown", "metadata": { "id": "FecKS-dA82f5" }, "source": [ "Let's first see before we do any finetuning what the model outputs for the first example!" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vcat4UxA81vr", "outputId": "3cc8e17b-e51c-4184-bf61-daee9e1717f3" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "The expression in the image can be written in LaTeX as follows:\n", "\n", "```latex\n", "H' = \\beta N \\int \\frac{1}{2 \\beta^2 N^2} \\partial_\\lambda \\zeta \\partial_\\lambda \\zeta + V(\\lambda) \\zeta^2 \\, d\\lambda\n", "```\n", "\n", "However, if you want to make it more compact and standard for mathematical notation (assuming the integral is over \\(\\lambda\\)):\n", "\n", "```latex\n", "H' = \\beta N \\int \\left( \\frac{1}{2 \\beta^2 N^2} \\partial_\\lambda \\zeta \\partial_\\lambda \\zeta + V(\\lambda) \\zeta^2 \\right) d\\lambda\n", "```\n", "\n", "If you want to emphasize the structure with a clear separation of terms, you can also write it as:\n", "\n", "```latex\n", "H' = \\beta N \\int \\left[ \\frac{1}{2 \\beta^2 N^2} \\left( \\frac{\\partial \\zeta}{\\partial \\lambda} \\right)^2 + V(\\lambda) \\zeta^2 \\right] d\\lambda\n", "```\n" ], "text/html": [ "
The expression in the image can be written in LaTeX as follows:\n",
              "\n",
              "```latex\n",
              "H' = \\beta N \\int \\frac{1}{2 \\beta^2 N^2} \\partial_\\lambda \\zeta \\partial_\\lambda \\zeta + V(\\lambda) \\zeta^2 \\, d\\lambda\n",
              "```\n",
              "\n",
              "However, if you want to make it more compact and standard for mathematical notation (assuming the integral is over \\(\\lambda\\)):\n",
              "\n",
              "```latex\n",
              "H' = \\beta N \\int \\left( \\frac{1}{2 \\beta^2 N^2} \\partial_\\lambda \\zeta \\partial_\\lambda \\zeta + V(\\lambda) \\zeta^2 \\right) d\\lambda\n",
              "```\n",
              "\n",
              "If you want to emphasize the structure with a clear separation of terms, you can also write it as:\n",
              "\n",
              "```latex\n",
              "H' = \\beta N \\int \\left[ \\frac{1}{2 \\beta^2 N^2} \\left( \\frac{\\partial \\zeta}{\\partial \\lambda} \\right)^2 + V(\\lambda) \\zeta^2 \\right] d\\lambda\n",
              "```</s>\n",
              "
" ] }, "metadata": {} } ], "source": [ "FastVisionModel.for_inference(model) # Enable for inference!\n", "\n", "image = dataset[2][\"image\"]\n", "instruction = \"Write the LaTeX representation for this image.\"\n", "\n", "messages = [\n", " {\"role\": \"user\", \"content\": [\n", " {\"type\": \"image\"},\n", " {\"type\": \"text\", \"text\": instruction}\n", " ]}\n", "]\n", "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt = True)\n", "inputs = tokenizer(\n", " image,\n", " input_text,\n", " add_special_tokens = False,\n", " return_tensors = \"pt\",\n", ").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 1000,\n", " use_cache = True, temperature = 1.5, min_p = 0.1)" ] }, { "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": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "95_Nn-89DhsL", "outputId": "ec662cf0-c2a4-4de2-c19e-7a2e342ad00a" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead.\n" ] } ], "source": [ "from unsloth.trainer import UnslothVisionDataCollator\n", "from trl import SFTTrainer, SFTConfig\n", "from unsloth import is_bf16_supported\n", "\n", "trainer = SFTTrainer(\n", " model = model,\n", " tokenizer = tokenizer,\n", " data_collator = UnslothVisionDataCollator(model, tokenizer), # Must use!\n", " train_dataset = converted_dataset,\n", " args = SFTConfig(\n", " per_device_train_batch_size = 4,\n", " gradient_accumulation_steps = 2,\n", " warmup_steps = 5,\n", " max_steps = 30,\n", " # num_train_epochs = 1, # Set this instead of max_steps for full training runs\n", " learning_rate = 2e-4,\n", " logging_steps = 1,\n", " optim = \"adamw_8bit\",\n", " fp16 = not is_bf16_supported(), # Use fp16 if bf16 is not supported\n", " bf16 = is_bf16_supported(), # Use bf16 if supported\n", " weight_decay = 0.001,\n", " lr_scheduler_type = \"linear\",\n", " seed = 3407,\n", " output_dir = \"outputs\",\n", " report_to = \"tensorboard\", # For Weights and Biases\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": 14, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "2ejIt2xSNKKp", "outputId": "cf2b5231-b1b8-4564-e3f5-31d7ecc0332a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GPU = Tesla T4. Max memory = 14.741 GB.\n", "7.697 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": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "yqxqAZ7KJ4oL", "outputId": "b1ad1dc6-75df-4d59-e405-61f69e1ca5a0" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "The model is already on multiple devices. Skipping the move to device specified in `args`.\n", "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n", " \\\\ /| Num examples = 68,686 | Num Epochs = 1 | Total steps = 30\n", "O^O/ \\_/ \\ Batch size per device = 4 | Gradient accumulation steps = 2\n", "\\ / Data Parallel GPUs = 1 | Total batch size (4 x 2 x 1) = 8\n", " \"-____-\" Trainable parameters = 67,502,080 of 3,916,592,128 (1.72% trained)\n", "Unrecognized keys in `rope_parameters` for 'rope_type'='yarn': {'max_position_embeddings'}\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Unsloth: Will smartly offload gradients to save VRAM!\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " \n", " [30/30 06:09, Epoch 0/1]\n", "
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StepTraining Loss
12.674496
22.714989
32.170414
41.810329
51.432709
61.483366
71.070037
80.685739
90.397824
100.228164
110.174792
120.139204
130.126111
140.074174
150.093727
160.077184
170.082082
180.069264
190.060772
200.084713
210.065397
220.084037
230.081809
240.068134
250.080953
260.073309
270.051272
280.101226
290.057514
300.067599

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "pCqnaKmlO1U9", "outputId": "6a5c476b-7e7b-4e43-e34e-f8d04e387e99" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "454.3304 seconds used for training.\n", "7.57 minutes used for training.\n", "Peak reserved memory = 9.73 GB.\n", "Peak reserved memory for training = 2.033 GB.\n", "Peak reserved memory % of max memory = 66.006 %.\n", "Peak reserved memory for training % of max memory = 13.791 %.\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 change the instruction and input - leave the output blank!\n", "\n", "We use `min_p = 0.1` and `temperature = 1.5`. Read this [Tweet](https://x.com/menhguin/status/1826132708508213629) for more information on why." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kR3gIAX-SM2q", "outputId": "5ae94857-10cc-4df4-f6db-05d207cec014" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "H ^ { \\prime } = \\beta N \\int d \\lambda \\left\\{ \\frac { 1 } { 2 \\beta ^ { 2 } N ^ { 2 } } \\partial _ { \\lambda } \\zeta ^ { \\dagger } \\partial _ { \\lambda } \\zeta + V ( \\lambda ) \\zeta ^ { \\dagger } \\zeta \\right\\} .\n" ], "text/html": [ "

H ^ { \\prime } = \\beta N \\int d \\lambda \\left\\{ \\frac { 1 } { 2 \\beta ^ { 2 } N ^ { 2 } } \\partial _ { \\lambda } \\zeta ^ { \\dagger } \\partial _ { \\lambda } \\zeta + V ( \\lambda ) \\zeta ^ { \\dagger } \\zeta \\right\\} .</s>\n",
              "
" ] }, "metadata": {} } ], "source": [ "FastVisionModel.for_inference(model) # Enable for inference!\n", "\n", "image = dataset[2][\"image\"]\n", "instruction = \"Write the LaTeX representation for this image.\"\n", "\n", "messages = [\n", " {\"role\": \"user\", \"content\": [\n", " {\"type\": \"image\"},\n", " {\"type\": \"text\", \"text\": instruction}\n", " ]}\n", "]\n", "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt = True)\n", "inputs = tokenizer(\n", " image,\n", " input_text,\n", " add_special_tokens = False,\n", " return_tensors = \"pt\",\n", ").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n", " use_cache = True, temperature = 1.5, min_p = 0.1)" ] }, { "cell_type": "markdown", "metadata": { "id": "uMuVrWbjAzhc" }, "source": [ "\n", "### Saving, loading finetuned models\n", "To save the final model as LoRA adapters, either use 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": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "upcOlWe7A1vc", "outputId": "8dbffde0-340d-4be1-8437-27a4cb5893c7" }, "outputs": [ { "data": { "text/plain": [ "['lora_model/processor_config.json']" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.save_pretrained(\"ministral_lora\") # Local saving\n", "tokenizer.save_pretrained(\"ministral_lora\")\n", "# model.push_to_hub(\"your_name/ministral_lora\", token = \"YOUR_HF_TOKEN\") # Online saving\n", "# tokenizer.push_to_hub(\"your_name/ministral_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": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MKX_XKs_BNZR", "outputId": "f1d22bd8-512a-4b0f-eaca-7394e7ffc40d" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "\\frac { N } { M } \\in \\mathbb { Z } , \\frac { M } { P } \\in \\mathbb { Z } , \\frac { P } { Q } \\in \\mathbb { Z }\n" ], "text/html": [ "
\\frac { N } { M } \\in \\mathbb { Z } , \\frac { M } { P } \\in \\mathbb { Z } , \\frac { P } { Q } \\in \\mathbb { Z }</s>\n",
              "
" ] }, "metadata": {} } ], "source": [ "if False:\n", " from unsloth import FastVisionModel\n", " model, tokenizer = FastVisionModel.from_pretrained(\n", " model_name = \"ministral_lora\", # YOUR MODEL YOU USED FOR TRAINING\n", " load_in_4bit = True, # Set to False for 16bit LoRA\n", " )\n", " FastVisionModel.for_inference(model) # Enable for inference!\n", "\n", "image = dataset[0][\"image\"]\n", "instruction = \"Write the LaTeX representation for this image.\"\n", "\n", "messages = [\n", " {\"role\": \"user\", \"content\": [\n", " {\"type\": \"image\"},\n", " {\"type\": \"text\", \"text\": instruction}\n", " ]}\n", "]\n", "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt = True)\n", "inputs = tokenizer(\n", " image,\n", " input_text,\n", " add_special_tokens = False,\n", " return_tensors = \"pt\",\n", ").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n", " use_cache = True, temperature = 1.5, min_p = 0.1)" ] }, { "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": 20, "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\", tokenizer,)\n", "\n", "# To export and save to your Hugging Face account\n", "if False: model.push_to_hub_merged(\"YOUR_USERNAME/unsloth_finetune\", tokenizer, token = \"YOUR_HF_TOKEN\")" ] }, { "cell_type": "markdown", "metadata": { "id": "qjuPsiqcOYaA" }, "source": [ "### GGUF / llama.cpp Conversion\n", "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n", "\n", "Some supported quant methods (full list on our [docs page](https://unsloth.ai/docs/basics/inference-and-deployment/saving-to-gguf)):\n", "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n", "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n", "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n", "\n", "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "At1T2hJnOdGM" }, "outputs": [], "source": [ "# Save to 8bit Q8_0\n", "if False: model.save_pretrained_gguf(\"ministral_finetune\", tokenizer,)\n", "# Remember to go to https://huggingface.co/settings/tokens for a token!\n", "# And change hf to your username!\n", "if False: model.push_to_hub_gguf(\"HF_USERNAME/ministral_finetune\", tokenizer, token = \"YOUR_HF_TOKEN\")\n", "\n", "# Save to 16bit GGUF\n", "if False: model.save_pretrained_gguf(\"ministral_finetune\", tokenizer, quantization_method = \"f16\")\n", "if False: model.push_to_hub_gguf(\"HF_USERNAME/ministral_finetune\", tokenizer, quantization_method = \"f16\", token = \"YOUR_HF_TOKEN\")\n", "\n", "# Save to q4_k_m GGUF\n", "if False: model.save_pretrained_gguf(\"ministral_finetune\", tokenizer, quantization_method = \"q4_k_m\")\n", "if False: model.push_to_hub_gguf(\"HF_USERNAME/ministral_finetune\", tokenizer, quantization_method = \"q4_k_m\", token = \"YOUR_HF_TOKEN\")\n", "\n", "# Save to multiple GGUF options - much faster if you want multiple!\n", "if False:\n", " model.push_to_hub_gguf(\n", " \"HF_USERNAME/ministral_finetune\", # Change hf to your username!\n", " tokenizer,\n", " quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n", " token = \"YOUR_HF_TOKEN\",\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "IrcXHZRkX0ST" }, "source": [ "Now, use the `ministral_finetune.Q8_0.gguf` file or `ministral_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", " This notebook and all Unsloth notebooks are licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).\n", "
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