{ "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", "dee1d94b6a9242d2b79a24da0cb4db40", "d66d4762305343f9be72fb3612368ad7", 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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
22.024400
32.159600
41.707200
51.716300
61.622400
71.353500
81.027400
90.938900
101.077900
110.983700
120.947200
130.780700
140.798400
150.831000
160.713200
170.758600
180.726300
190.669600
200.756500
210.659100
220.878700
230.634900
240.713000
250.773500
260.843500
270.739800
280.846000
290.740200
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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