diff --git "a/Qwen2_Vision_Finetuning_Unsloth_Maths_OCR.ipynb" "b/Qwen2_Vision_Finetuning_Unsloth_Maths_OCR.ipynb" new file mode 100644--- /dev/null +++ "b/Qwen2_Vision_Finetuning_Unsloth_Maths_OCR.ipynb" @@ -0,0 +1,6534 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "IqM-T1RTzY6C" + }, + "source": [ + "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", + "
\n", + " \n", + " \n", + " Join Discord if you need help + ⭐ Star us on Github ⭐\n", + "
\n", + "\n", + "To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth?tab=readme-ov-file#-installation-instructions).\n", + "\n", + "**[NEW] As of Novemeber 2024, Unsloth now supports vision finetuning!**\n", + "\n", + "You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n", + "\n", + "**This notebook finetunes Qwen2 VL 7B to allow handwritten maths formulas be converted into machine LaTeX format.**" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "2eSvM9zX_2d3" + }, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install unsloth\n", + "# Also get the latest nightly Unsloth!\n", + "!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r2v_X2fA0Df5" + }, + "source": [ + "* We support Llama 3.2 Vision 11B, 90B; Pixtral; Qwen2VL 2B, 7B, 72B; and any Llava variants like Llava NeXT!\n", + "* We support 16bit LoRA or 4bit QLoRA. Both are accelerated and use much less memory!" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 517, + "referenced_widgets": [ + "1507168018f24b7b8b944181e8b49899", + "d8fd19b87a004ac491d4a57bcd9bb353", + "dae1aac4ad194dc9864f2aec01a3a8eb", + "584fed0c63e144bd89dc8c95b10eca89", + "87837a98960d4f80bd14415bb18c9fd1", + "694692f333544779aa0b9327133ef082", + "eebf9082100f4ef29b61ba6ef65cb45a", + "3afce26a64434ac58225077412f975e2", + "7257b6fac84747cbb5f384d1f73a5347", + "dff9bd8b495149ab8bbc472ee0eb4173", + "920582d339fb4a2e89e07aec7d4c693c", + "966fdc83c62f40449f78dab4171bdd51", + "2c32d63d344147cb8f97668f92690c85", + "91630458c5354a3fbcd37d3fa1932390", + "ea85c91ae874498f97c9b19af3465bb0", + "91fcc553aa8944459657e8fad68255b4", + "60b37e78bf334e92b5a09edddbe6a709", + "f0519e36febd4dd28b3101f7773464ba", + "9b819f42ac0c42f1ad6f146aed80565b", + "96dd78ae6496466b915400a339693306", + "7af99d95312f4d809e9a270865560555", + "1e83277cfd5b47adbcf454ecce8d842a", + "eb0d6ac0ca924bc091d8d30ffa59b45b", + "fb317d0493ad48abbe1a98feae0bd3d0", + "03f3ab8e64774eb09fbed194f1a7689c", + "dac10fbbc662401ca6dd1d4e1ded99d6", + "c1ca433fb87a4a5b9a468a2860c113ed", + "c0e6072669cd401ba2c515185621194b", + "1a9ccb2dc3164f1a962effdaab634870", + "b03d38c35ae443b086a8900ab62ee3ce", + "90a68a302b2542678301a6aad9d31201", + "77db2bd697454d31bed57d25dd388b89", + "38f1e6ca5fa944ad96c9336c3e57516f", + "edcf8fb5479b416f86f3a2fbeb6c525a", + "9f5f218077a04d1896f08796096ac829", + "64d834af5bf541b795812a02f9073a59", + "b0e621c7c4b340648b8a694d4f85ed29", + "64ffb5eac24b429eb7ee3bd1e44ae7ca", + "6d6b3c6f77414ff2a33265df7779c3c1", + "9b2418b98f9b4445a11df529b129a7ba", + "191220f0b54a42c5bafbfe8c87231664", + "8a24a72d2a07405aa563dbe9ae7cdbac", + "224c27bb990f4f7e838dbc16f3ee2436", + "23a4e1ed62ce4c1f8f582daf9cb4a611", + "2655454eff384ed685278cc283050c53", + "aee3036d9d124d869a9e125e99485729", + "391f1b30d4314a83a6a3649ce8d1be05", + "f1b2c382fe864479a964632ca00326d2", + "63af604ea1ed449aaee2e751a5d6ed31", + "35a50bc424c249a8ad6a4c646ba02c74", + "dda78abb7182407fa7fe58613469cf7a", + "ec4935b999844fa58860526dc55a9352", + "14118c70472d44fc9ff7758d871e9479", + "9c53fb54bdc14b1e9712223fffbda452", + "92642df3d51b475ebf826bbfe016dbd1", + "99a4fcc4819c4f7b92aeab02e0773169", + "f260377b572f42a9bdc85dca2c262ae1", + "36721d56621e4fc2afa2a9e93b891a83", + "99a4940014fb42959acbbe32c91f8afb", + "095e83f2da21428babca1ba1149be8dd", + "10b84d4e54294018951303ef7d02d384", + "75c66d13075e4c2082f52949de11d6b2", + "d9b406e289264068bdd605a78325b101", + "11cd31eed683425a8cfef77d0e604ac0", + "95d99dc295c24863a493a7baa8376976", + "a40992b7410048fa8ee75a16c33e373d", + "5432b1d9a353433ab5f0f91e4230dcb9", + "1ce09f5fa0b442d580d8bce0b361b0c5", + "fba8604830f745039e94715bf6d26bf9", + "c316eef102ba4f6c9b4f4989b8f5ee15", + "97642965dd044538b8965535be5488c1", + "1419b98009634170990ca72783bcdabd", + "2d168db572324472b842a74163bbbd31", + "e7ce93c1a1ea4fae95a1c5dc36796e0c", + "d2e19537f30f4f94899092ba7cc65bdd", + "864e35d964bb4367bbbf60465c056fef", + "b40e2e8794db429cb461fa0e4e3c7abb", + "db9a65e5c9224f3fa75f2f840aba80e1", + "75b2ffa363104eddb5690b97b6851afa", + "4f46071e1c88406490b5a604619a8e00", + "8dbc1da3ec2949859a2b1c050755a2ac", + "7d39f9a19d1a443a89382f40860de987", + "3f811d559d384b37afcfc499760e8881", + "8616576ee309425d9155a47c298acc69", + "a124574846b1409d86612daea9d43e8f", + "cdb6f7ca7ff6425caa3bad15843bbeb6", + "3bfc77d6763f4a599c3bee2b4c30d44f", + "7e8b0545f43d4093bfc54e68661d4beb", + "12851f23c63f401db977c566540579a4", + "941a8ca86af646acaff677ce13f4e941", + "711d7de3a4df4f95b3448fce843b86e2", + "e337b9e043d64a979fbb340d0f22f232", + "118dd4848b2d4bbca866e6a2250d5bcb", + "70ebf37e74cb49b7866df5a84a19e735", + "8e74f2295ebf4f18b0351e1ad5ae4942", + "d9b0cff2e4df4ba2b7b031e952684d06", + "5f8325d07ebc4850b335e1b73a794624", + "2a3d9fa41ff2420a933603251be0630f", + "604e5e4af4f34262ac766cbf289bf511", + "fe05d72b362348ff84385a4a2388f888", + "b684a6bc70c547ff90d8dbd85b509cd3", + "046f9f53e7114436b0b7067180957f68", + "f985a6e40d064a218972594095ee79ea", + "73d90b319b74463a958e282651dfb3e6", + "211710af8f974c47b4ce92901bd984fb", + "ac93a7b9a74c4635a2d72a88a25635c8", + "d283d3ee082d4dccbb457e7c0344fe34", + "fe3179046ca44f948f3302f5d2531441", + "424af709554e4082af7e3eaf68289a89", + "5ef23ad9af8144e5af67b026e71d568d" + ] + }, + "id": "QmUBVEnvCDJv", + "outputId": "deef5f33-ca87-4e9f-fac9-7ef2863a8913" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", + "🦥 Unsloth Zoo will now patch everything to make training faster!\n", + "==((====))== Unsloth 2024.11.9: Fast Qwen2_Vl vision patching. Transformers = 4.46.2.\n", + " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", + "O^O/ \\_/ \\ Pytorch: 2.5.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", + "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.28.post3. FA2 = False]\n", + " \"-____-\" Free Apache 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: 0%| | 0.00/5.90G [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": { + "id": "LjY75GoYUCB8", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 177, + "referenced_widgets": [ + "78e081ae6f3a4ef1b19ef41063bfa23b", + "4ff8ae0b69d54fcf90f5efa67396cdbb", + "074b54462cec41f08f8d889ce474421f", + "6a8dfa3d64ec4f97910729bf9dc5504e", + "3f04b73a0e154f6ebb1bed2582b70683", + "30f8f18a85634085a6c09eb26fe5cd31", + "43e239e845554e6d86934385ecd4654d", + "4631ee905003446990a10e83cd7fbf10", + "5da8f7b0032a4d6b90e75312abdbbaa0", + "4b2c6115d0944db78c5f85e3e2d59580", + "50d7bd1a6dbc434dac7980fc55fa8f2b", + "9b1da2b9830d4f3e9940330101a61791", + "75133fb90e0845199756d7da24a4be33", + "ae17e6b18ac946c286a2f8299cc5bc17", + "d8a92bef58d14baeab38844796330585", + "d6f0425e66fc4ed19e351792778a94a3", + "d98624dc14124de19a6af9f94dffb1bb", + "9509c3536d384018b463d334c844495e", + 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\n", 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FFAH/2Q==\n" + }, + "metadata": {}, + "execution_count": 6 + } + ] + }, + { + "cell_type": "code", + "source": [ + "dataset[2][\"text\"]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 53 + }, + "id": "VTzhtzNRAEL1", + "outputId": "ef3b8978-e85f-4727-8e8c-01a6fc13280f" + }, + "execution_count": 7, + "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": 7 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "We can also render the LaTeX in the browser directly!" + ], + "metadata": { + "id": "NAeQ9LXCAEkW" + } + }, + { + "cell_type": "code", + "source": [ + "from IPython.display import display, Math, Latex\n", + "latex = dataset[2][\"text\"]\n", + "display(Math(latex))" + ], + "metadata": { + "id": "lXjfJr4W6z8P", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 58 + }, + "outputId": "b23693c2-fe2f-4f91-eb0c-0cb3c5886f1b" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/latex": "$\\displaystyle H ^ { \\prime } = \\beta N \\int d \\lambda \\biggl \\{ \\frac { 1 } { 2 \\beta ^ { 2 } N ^ { 2 } } \\partial _ { \\lambda } \\zeta ^ { \\dagger } \\partial _ { \\lambda } \\zeta + V ( \\lambda ) \\zeta ^ { \\dagger } \\zeta \\biggr \\} \\ .$" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "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", + "```" + ], + "metadata": { + "id": "K9CBpiISFa6C" + } + }, + { + "cell_type": "code", + "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" + ], + "metadata": { + "id": "oPXzJZzHEgXe" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Let's convert the dataset into the \"correct\" format for finetuning:" + ], + "metadata": { + "id": "FY-9u-OD6_gE" + } + }, + { + "cell_type": "code", + "source": [ + "converted_dataset = [convert_to_conversation(sample) for sample in dataset]" + ], + "metadata": { + "id": "gFW2qXIr7Ezy" + }, + "execution_count": 10, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "We look at how the conversations are structured for the first example:" + ], + "metadata": { + "id": "ndDUB23CGAC5" + } + }, + { + "cell_type": "code", + "source": [ + "converted_dataset[0]" + ], + "metadata": { + "id": "gGFzmplrEy9I", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "18ea9a0e-3f8c-401b-cd3e-1dd19adfa7ed" + }, + "execution_count": 11, + "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": 11 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Let's first see before we do any finetuning what the model outputs for the first example!" + ], + "metadata": { + "id": "FecKS-dA82f5" + } + }, + { + "cell_type": "code", + "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)" + ], + "metadata": { + "id": "vcat4UxA81vr", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d2982b7a-f45e-4f8a-9bc2-416f50bf753a" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "$$\\mathrm { ~ n a ~ }$$<|im_end|>\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "idAEIeSQ3xdS" + }, + "source": [ + "\n", + "### Train the model\n", + "Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!\n", + "\n", + "We use our new `UnslothVisionDataCollator` which will help in our vision finetuning setup." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "95_Nn-89DhsL", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "791f88b0-0a38-4471-bcac-4e16019aa4fd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "max_steps is given, it will override any value given in num_train_epochs\n" + ] + } + ], + "source": [ + "from unsloth import is_bf16_supported\n", + "from unsloth.trainer import UnslothVisionDataCollator\n", + "from trl import SFTTrainer, SFTConfig\n", + "\n", + "FastVisionModel.for_training(model) # Enable for training!\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 = 2,\n", + " gradient_accumulation_steps = 4,\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", + " fp16 = not is_bf16_supported(),\n", + " bf16 = is_bf16_supported(),\n", + " logging_steps = 1,\n", + " optim = \"adamw_8bit\",\n", + " weight_decay = 0.01,\n", + " lr_scheduler_type = \"linear\",\n", + " seed = 3407,\n", + " output_dir = \"outputs\",\n", + " report_to = \"none\", # 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", + " dataset_num_proc = 4,\n", + " max_seq_length = 2048,\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "cellView": "form", + "id": "2ejIt2xSNKKp", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4a7af16e-71dd-42ac-a7ab-72da06502605" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "GPU = Tesla T4. Max memory = 14.748 GB.\n", + "6.131 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": { + "id": "yqxqAZ7KJ4oL", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "90ed99c3-80cf-467a-c63b-3f4c6e88a0b1" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n", + " \\\\ /| Num examples = 68,686 | Num Epochs = 1\n", + "O^O/ \\_/ \\ Batch size per device = 2 | Gradient Accumulation steps = 4\n", + "\\ / Total batch size = 8 | Total steps = 30\n", + " \"-____-\" Number of trainable parameters = 50,855,936\n", + "🦥 Unsloth needs about 1-3 minutes to load everything - please wait!\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " \n", + " \n", + " [30/30 04:20, Epoch 0/1]\n", + "
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StepTraining Loss
11.840500
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51.716300
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81.027400
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101.077900
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120.947200
130.780700
140.798400
150.831000
160.713200
170.758600
180.726300
190.669600
200.756500
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260.843500
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300.752500

" + ] + }, + "metadata": {} + } + ], + "source": [ + "trainer_stats = trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "cellView": "form", + "id": "pCqnaKmlO1U9", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "dc6eb1e6-ccb5-4a18-d0a8-a975e9383e21" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "314.6248 seconds used for training.\n", + "5.24 minutes used for training.\n", + "Peak reserved memory = 7.508 GB.\n", + "Peak reserved memory for training = 1.377 GB.\n", + "Peak reserved memory % of max memory = 50.909 %.\n", + "Peak reserved memory for training % of max memory = 9.337 %.\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(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\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": { + "id": "kR3gIAX-SM2q", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "982326e0-f9b5-41ef-83b5-1129ad4d7cc5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\\left( \\frac { \\partial } { \\partial x } \\right) ^ { 2 } \\phi = \\frac { 1 } { 2 } \\left( \\frac { \\partial } { \\partial x } \\right) ^ { 2 } \\phi _ { 0 } + \\frac { 1 } { 2 } \\left( \\frac { \\partial } { \\partial x } \\right) ^ { 2 } \\phi _ { 1 } + \\frac { 1 } { 2 } \\left( \\frac { \\partial } { \\partial x\n" + ] + } + ], + "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 Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", + "\n", + "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "upcOlWe7A1vc", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "30ad83fb-2b8f-4a80-dbe0-bef5e6da2b75" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[]" + ] + }, + "metadata": {}, + "execution_count": 18 + } + ], + "source": [ + "model.save_pretrained(\"lora_model\") # Local saving\n", + "tokenizer.save_pretrained(\"lora_model\")\n", + "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n", + "# tokenizer.push_to_hub(\"your_name/lora_model\", 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": { + "id": "MKX_XKs_BNZR", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "be6801bc-53c4-4bda-9202-08cfc0a05d9c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\\left( \\frac { \\partial _ { \\mu } \\phi } { \\partial _ { \\mu } \\phi ^ { * } } \\right) ^ { 2 } = \\frac { 1 } { 2 } \\left( \\frac { \\partial _ { \\mu } \\phi } { \\partial _ { \\mu } \\phi ^ { * } } \\right) ^ { 2 } \\left( \\frac { \\partial _ { \\mu } \\phi ^ { * } } { \\partial _ { \\mu } \\phi } \\right) ^ { 2 }\n" + ] + } + ], + "source": [ + "if False:\n", + " from unsloth import FastVisionModel\n", + " model, tokenizer = FastVisionModel.from_pretrained(\n", + " model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n", + " load_in_4bit = load_in_4bit,\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." + ] + }, + { + "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 = \"PUT_HERE\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Zt9CHJqO6p30" + }, + "source": [ + "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", + "\n", + "Some other links:\n", + "1. Llama 3.2 Conversational notebook. [Free Colab](https://colab.research.google.com/drive/1T5-zKWM_5OD21QHwXHiV9ixTRR7k3iB9?usp=sharing)\n", + "2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)\n", + "3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/drive/1j0N4XTY1zXXy7mPAhOC1_gMYZ2F2EBlk?usp=sharing)\n", + "4. Qwen 2 VL Vision finetuning - Maths OCR to LaTeX. [Free Colab](https://colab.research.google.com/drive/1whHb54GNZMrNxIsi2wm2EY_-Pvo2QyKh?usp=sharing)\n", + "5. Pixtral 12B Vision finetuning - General QA datasets. [Free Colab](https://colab.research.google.com/drive/1K9ZrdwvZRE96qGkCq_e88FgV3MLnymQq?usp=sharing)\n", + "6. More notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [Github](https://github.com/unslothai/unsloth)!\n", + "\n", + "

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