{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "2vQXvnUUsTzI" }, "source": [ "To run this, press \"*Runtime*\" and press \"*Run all*\" on a Google Colab L4 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": 380, "referenced_widgets": [ "99bff77887c742b188878c8375844c00", "69e590ac68fc4967b08d7b03f8d1029c", "4a9ba035d1384ebe9c80163ac262e4e8", "b24ef9781b824950bec1ec219867eee7", "3a6e1f1e42cc4ef199c7d2f727f1077f", "56bb57f481f3478cb724c8ba1f2b6cbe", "cc4c86caf7c0411887432e63c5f7390e", "123af53e4ce64b43ac0039ca8fd8c093", "cc4c9812b7f74cde97abf1e2296913ad", "b1a460f7070744e38f66bef5c8113442", "e827a7825283426f80d9a1e1f86019a9", "e9caa0bb6c3f4e69b32d74144283097d", "b2dc300bcb304343a6e719211bfa16b5", "bbc1086d0d8d44a68e8896bba354f67a", "a8259ae181f94a7dbe728e4fbea13b50", "56e6d46ada2748f79cddbb862e547475", "247d66307c12403aa152e6afaa3fb772", "2e205311dd6f46b78d6c7f0fac475d27", "260ff38c4cd848b2948ecaea9abb2de3", "d22d7e4650e443fe9ce1b84520c3894e", "16373d8ce24c4be095e520fc2f063a81", "51ec6a2c40dd46d385befe3e4cb32dce", "7f0f2a4a7eda4176b544a876ca46aa0c", "ca6772730e004e2ea6941ddd5e8965be", "2798c261bb4a449da37c73d428aa750d", "45927d38b34747a8969ff764a170a379", "cb22f737eb1b4d429c9c9518f570fc93", "738c3ace1bf64104ae3befe93d700da7", "b75576ddcc674375b650e80d32f9fa41", "4c714057645546e5820d31b2526952ea", "6b474dc0a25b4b13ab021bf2410444aa", "1f6feb3ff4bf41bfa77798588dd1a047", "27dbf6293941491f883256f5b4459d8f", "4a974128152b4403b5b7e56dcef91cf1", "d77aa9e72d2d4597982e3ce7c8a12747", "6f591057c35645288199e5be61eef098", "d177d754f6184f5181725a174bfa6f59", "bfaaf16915b24273b45e96e6ca41d3ed", "0c3660a4769a4962b6f73a8d7012678b", "812dec41c5bc40f2998564edefc32f48", "9ab59d54123049eaac41b2ca3f69e8dd", "3a1d8ef8f52647c1a050fd1e0d67e274", "4c79283275d142c5af38f80004cd92cf", "69138bb51f074fac972a2a9a1b13492f", "13540698510845829fd4792cce58ae6e", "a4d69faae483435e8221c908875ecb24", "7b3f7e7018794b49859332e2ddb7b2d9", "80b79b9be9e6449c90e0128e87a08c7f", "6e2a733507bf40c78f2e1af4793bc7ae", "aa16b740ad28497f8096e7e12237674e", "0cb1d77cf7fe4f14ae87abf2fbd5e60f", "f047301402d945fe81700ec820533cac", "d889d44b9db54983b4b3c401a44a71f0", "7645f6c36b524741a6f6e0958bb0ef00", "b48f0117b8574fdeb20b215a949eb975", "ee5cfb4e1026483e8af39bce3177d2bb", "20d8eb28f98a4c819a18f4babe0ecd62", "1b1406ea56b34090b49af6d0704553c8", "0dc0d388e53e4b59ae99b5bf4b9ace5b", "c817149cda9d4f089e073bcbe5eca9fb", "ec0cd0c6725d4ceeaeb9c20be5e3e7cd", "db5f4fdbf18345ebb272a6dfadcf7d61", "2f8c6139246e44c5a2699ef58403d450", "0a943edc0c0d4910b9c222ec26b4040f", "7ae7c6205f4c4717bd2678ce309d4406", "1ca73957b7ab4f08a4fa7a7144b0adc2", "c8e3c78a0a5a4ecdb68edfd34bf5ba57", "6be09e28388f41c684674d4e7ade2177", "853884f9eb58491db1f6605a940a12b8", "1801a1c03c3448da87284353ac0e3cc2", "89fdbc7a3ff54f618cd73e3e687050f4", "7521c7d02e4c4dc1b21fe062055f6e81", "cc7710b22b1a4e16a4cfb1e588bc7f58", "58c01f28e9c94c59aa9636f33de6ecac", "4eb1db5ea16d4c7bbd9feadad505c5f4", "57dd8104c01c43259d3b7b7998ef3393", "022cc22ac0d6416e87d32a400656882f" ] }, "outputId": "dbb6527a-1f29-4b7d-b338-7568d7a7ae00" }, "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 2026.4.4: Fast Gemma4 patching. Transformers: 5.5.0.\n", " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.563 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.10.0+cu128. CUDA: 7.5. CUDA Toolkit: 12.8. Triton: 3.6.0\n", "\\ / Bfloat16 = FALSE. 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: 0%| | 0.00/16.0G [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": [ "5951a9e9c2e947c68add360a7dc5ce92", "50b86bc5bc844b8eaad80cedd9fde48b", "75f39de184674a5c9a1e5e91230d7c1b", "473ad0b6598f4b7bb9a482fb43e5df9d", "089308801ffd44d4a4910133252ac4ac", "a699c8aa0ba1493f8acda292a7ed5baf", "d97dc62af95d4724927c811b5d7e29ec", "9fec8f47396045dd8db0098630bca7f9", "decf15485d6b43b9bd9a8e2c0b716e05", "ee418cb7012d410e9ac926454c0c07be", "abd9f5c7c77646248fc205f392432130", "4be36a52100f4428bdde5c677f97da30", "9e7726767acd484ebae1afdd654a05a4", "a31415c76913402189bc790b97a26c96", "91d72c119af74d01ba75649e6a5373c0", "ccae1e6a6d734ba8a10877f0e6f7ab62", "45fba48641cf445fadc58d19b3f6c23e", "50aef54f7eb74226a77cb441dfbe325c", "c55cdf1c16094a1daf8d88c607fadde4", "19a2453531bc4f6aaa93924719db78e6", 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\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": 52 }, "outputId": "8cf867a2-2e4b-4d4c-9d4d-a790b17603df" }, "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": "6a4ef07a-7a3f-4c40-8f94-f7c892a6c0fe" }, "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": "0033ce5a-b6a2-4031-e333-febdc8ee37ff" }, "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\"\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": "1184ad38-de99-49bd-d623-48b0892e8daf" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "```latex\n", "H' = \\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", "```\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": "ecdc9c7a-7177-45a5-cdee-77f06ec9842f" }, "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": "1e46dd33-1258-4978-abce-4a0cd70c28ab" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPU = Tesla T4. Max memory = 14.563 GB.\n", "10.182 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": "1611d77d-130b-4509-f49d-da4422a7a0e9" }, "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 = 82,444,288 of 8,078,600,736 (1.02% trained)\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Unsloth: Will smartly offload gradients to save VRAM!\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [60/60 05:25, Epoch 0/1]\n", "
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StepTraining Loss
114.862482
215.983265
315.897610
416.153982
516.364323
612.335323
76.774734
85.239524
94.966103
103.271874
113.070101
122.938359
132.469718
142.040107
151.912474
162.391622
172.170793
181.760992
191.899228
201.493368
211.680966
221.567590
231.590823
241.522763
251.543306
261.648688
271.278674
281.528238
291.577930
301.366876
311.197964
321.834775
331.473532
341.572434
351.176874
361.155894
371.070827
381.249489
391.477776
401.618961
411.207805
420.868922
431.415766
441.279130
451.078420
461.076866
471.207423
481.021715
491.184945
501.114685
511.116125
521.270778
530.977663
541.013628
551.137954
561.791641
570.879617
581.045508
591.497848
601.182628

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "cellView": "form", "id": "pCqnaKmlO1U9", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "308b4484-426c-4d82-b8b2-4f40918425d9" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "353.8392 seconds used for training.\n", "5.9 minutes used for training.\n", "Peak reserved memory = 10.777 GB.\n", "Peak reserved memory for training = 0.595 GB.\n", "Peak reserved memory % of max memory = 74.003 %.\n", "Peak reserved memory for training % of max memory = 4.086 %.\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": "79616e26-3a56-41d0-8306-06889f63b9fc" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\\[\\left[[B_n^{\\pm}, b_2^{\\mp}], b_2^{\\pm}\\right] = nB_n^{\\pm}, \\quad \\left[[B_n^{\\mp}, b_2^{\\pm}], b_2^{\\mp}\\right] = nB_n^{\\mp}.\\]\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": "808a9a7d-0e26-4635-c918-41f0525310eb" }, "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": "5b7d5dfd-34b8-45e0-ef9e-1378d260fd7a" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "This equation appears to be from a field of physics, likely **theoretical physics**, specifically **electrodynamics**, **gauge theories**, or **relativistic field theory**, given the use of Greek letters ($\\mu, \\alpha, \\beta$), tensor notation ($D^\\alpha_\\mu \\tilde{A}^\\alpha_\\mu$), and the structure of the equation.\n", "\n", "Here is a breakdown of the components and the likely physical meaning:\n", "\n", "### Breakdown of the Equation\n", "\n", "$$D^\\alpha_\\mu \\tilde{A}^\\alpha_\\mu = 0$$\n", "\n", "1. **$D^\\alpha_\\mu$ (Covariant Derivative or Differential\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": "T4", "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": { "99bff77887c742b188878c8375844c00": { "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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