{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "2vQXvnUUsTzI" }, "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": { "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": null, "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": null, "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": null, "metadata": { "id": "QmUBVEnvCDJv", "outputId": "9920a66c-4176-44e9-d8a4-f6bbf2e37f48", "colab": { "base_uri": "https://localhost:8080/", "height": 397, "referenced_widgets": [ "9dbba5cc07984692a5cb9310c37294d1", "3ac2b9eb631b49bab460d80e68135ab2", "9a1b953f27b2419eb3302fee6ecbcc17", "2a4ecf86f482417081d8645efdbe864d", "b0f65d2867744352a1ff5fb2551cab67", "a5b07bff219c4a33b0060c3ff3a12320", "9d2f16071d4a429c8c9dbc588ec71b56", "ec43f7e43c54441a8e13f42b8d21c306", "7947ae62e3814e139e33e889e4753d24", "aa18b4a0d9964a15a8699f461491a50f", "4eca3234dd45475d8daa4d38ad818413", "c45ec80780064eb698252b9f8268d18a", "3008a3d0e4dc44a79304f6a398d914a7", "c1e1699a15934316981527944b1acc1c", "49a5c555942842ecb8c52d085c97a72c", "a19a4d1df8db4cdc963d0374b0a3ead0", "29be362f9fec42398fa8c358b0c9671c", "008f1fd0286e4192adb7f8ac89f3191a", "3af0e66e54f54a75906e08c9a6f2442e", "c5bbad21d0eb4fd288dfc16c468be6da", "07bec6bd94cd4c43b5c4f691969f9e4b", "86f337af3a05478593337b916a108e5f", "7edbbaac43b349be94f3a35f0c6d77d1", "2a207602d4e04defa6294fd60cdcde4d", "f3ef6cdedf1d49919d7fff94eb744b12", "d6dd94e831ae4bb68380e6b1ec616c6a", "ec11bc623d2d4f69bb514ffdac4bea7e", "fcf6969b6ca04e74b57a1f0c5b857a3c", "a63ad4bb24d34dfa9d7758bb17c76656", "aa962a031a974a5c9c0995f01ee44d3d", "86b32de59f744db8957f6830aa78882c", "b6c332d8dbf149b38350f20728eb2d1b", "ef8855bc019c406a8139203673af4a65", "d1f0e4cdafd14357a9cb8dcac07f9da2", "671fd02e35244c7ba8253a9d8a214bfa", "b5c12d22f2c8436984be989f04dc1b82", "329aef97734640fe858eb10cd898c1f0", "96406b4153124e8ba124145e30bd74dd", "8d320330f70248688d5acb3ec09a382c", "6226c59b27fc4cf38d62ce5c7c69e375", "344ea1b499e94bce91ae471cb39c6267", "686d888d3aa24aa1acc943a78e7ab394", "cb9060f87c9a44b18b1370dd8ccfec6a", "5ee05bbd6830432a9f1a92c71e8f870f", "acff987614e2454182fdd285f84cbfe2", "b2bdbe39bdde4891958840f88e74ed7b", "cd8bca8f57c84bfdaa8f76002d01ca50", "e9c068d524664334b01fb6f1db19295e", "8dac8e16ac9145ca9aae00456f0b9c74", "09d2ce198cbd4148ad721aa825d62d6d", "4789199cf5964285b8e550ad4fbed48e", "3b8b062634424ea69075e48a06c6dc8f", "a8637a53374d4a37b3b410e4408bf4ee", "0ab3c43906724b08bcf869a5655bff30", "aa515530e9494a28a24f90fdd9204a96", "a60cd51dbd1d4404989c69a5b7e95f96", "4f47d85c7a2c414e9cfb8f9e6e87df4c", "e237c0a6a6814576a3100f68b329d4cb", "a5705ad4707d4a3faacdd2d37eb63fbc", "42bd62697c1649ec98f504930e56c396", "0661e6df71fd4df88a185d9cadab56f0", "e615ee8b03ce4bd494315a4ad2df8b9a", "766ffc619cf04a378d7c1186c74f87ba", "efadf09207c0427cb0d29f48bebe2ac7", "d2a87fd0f3d04145a5bd9dd1ed75395b", "d38821cad5ac4f62bdbbfed1bbd3ffb6", "418feb293e1143c7bd3a057697e02b88", "7ca38ff83b274ddf908d1250c613b590", "526d7159b777407ea1c3fd34d44024df", "28b52765c091458d8c411a5aa3d3c4ed", "0501889c836b49bf90e4a9f3cca16b44", "df2390fa32f0482a9b7d3fd10f4d19b0", "67cfb5f857da465cae4575ce0e12449d", "1dcda7554eb342cbb660bf8a78272ecc", "1d23ab2873814345a58a8c12438196df", "b2a50fc0c2a44c82a7015f11713c6181", "8e47b9fa36ee4c128414a714d3ae9547" ] } }, "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", "Unsloth: QLoRA and full finetuning all not selected. Switching to 16bit LoRA.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/10.2G [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": null, "metadata": { "id": "LjY75GoYUCB8", "outputId": "e5477a1e-acac-46ce-8c00-bb48ace0df50", "colab": { "base_uri": "https://localhost:8080/", "height": 177, "referenced_widgets": [ "87fb532c204143a6957b498382749619", "76e88b170834473aa4e41f8ecede6545", "594a65dcffb442cb9a7d264e17d26bea", "5eca7ef2432f43fc9ff2bb88a017c0e7", "4a703d9591cf467ba73f98a0bad9b169", "690042bf19924111809748d2f3fdf5e2", "19dbff1fba52476aa320072b0c5ca0ad", "446de647345c4d96a4f0bc20527a9239", "24c3d3519e5e4203801f6db21b174d75", "b630883c3e984eea892ecf0c23942c74", "5f93397ffbde4cbf8c494f0e6495deab", "f04af098beb745dd83eeebb4a129df16", "16642b8f9da245e58595e8b59a898803", "1b39506c9fa943f18c99e9a9ab29f2e1", "5c14213926ab404a815a6a6fb0b10e5d", "abcb4e51c128476b97a5cabcb51925ae", "99c7aa8c8a96453dbcc5ed8ae8d55e39", "94b4e20a08f741d89475873612d9f014", "eb8b4370227e4969a1c8a2649f219b6a", 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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": null, "metadata": { "id": "lXjfJr4W6z8P", "outputId": "3d2c1834-b446-436e-81d9-0375434fd9a6", "colab": { "base_uri": "https://localhost:8080/", "height": 52 } }, "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": null, "metadata": { "id": "nPopsxAC1CrS", "outputId": "ec105cce-33b7-4bc1-f4db-45de7e8befb3", "colab": { "base_uri": "https://localhost:8080/", "height": 52 } }, "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": null, "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": null, "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": null, "metadata": { "id": "gGFzmplrEy9I", "outputId": "b38a50f0-7526-4be4-8a1c-97558df918b4", "colab": { "base_uri": "https://localhost:8080/" } }, "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": null, "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": null, "metadata": { "id": "vcat4UxA81vr", "outputId": "d033736e-e80f-490f-f189-6e09ea98817f", "colab": { "base_uri": "https://localhost:8080/" } }, "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 \\xi^\\dagger \\partial_\\lambda \\xi + V(\\lambda) \\xi^\\dagger \\xi \\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": null, "metadata": { "id": "95_Nn-89DhsL", "outputId": "13282f82-d3fe-438b-d0f8-e47f4582fb17", "colab": { "base_uri": "https://localhost:8080/" } }, "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": null, "metadata": { "cellView": "form", "id": "2ejIt2xSNKKp", "outputId": "54133bfb-63f2-4d2d-bbe1-cea95d077a83", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPU = Tesla T4. Max memory = 14.563 GB.\n", "10.307 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": null, "metadata": { "id": "yqxqAZ7KJ4oL", "outputId": "3d8a59ac-ec41-419e-f25f-ddee3f9dea89", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 } }, "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 = 59,719,680 of 5,182,897,696 (1.15% trained)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
\n", " \n", " \n", " [60/60 03:09, Epoch 0/1]\n", "
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StepTraining Loss
113.119604
214.200603
314.389761
414.693236
514.118143
610.960572
78.097441
86.393641
94.937364
104.203208
113.456124
123.272719
132.971413
142.495770
152.417672
162.748857
172.540635
182.330746
192.280551
202.012815
212.011085
222.002847
231.918156
241.896314
251.834707
261.854128
271.651750
281.860624
291.869892
301.532480
311.548356
322.402746
331.781458
341.850275
351.509472
361.659287
371.609388
381.703836
391.961015
402.099207
411.570554
421.179479
431.736481
441.550761
451.548448
461.569731
471.550730
481.422475
491.556145
501.559651
511.485411
521.749113
531.348081
541.384408
551.508071
562.067807
571.268674
581.504525
591.869724
601.731446

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "pCqnaKmlO1U9", "outputId": "b505ad75-49e6-416d-8f9b-6673d1d98d64", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "217.1113 seconds used for training.\n", "3.62 minutes used for training.\n", "Peak reserved memory = 10.764 GB.\n", "Peak reserved memory for training = 0.457 GB.\n", "Peak reserved memory % of max memory = 73.913 %.\n", "Peak reserved memory for training % of max memory = 3.138 %.\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": null, "metadata": { "id": "kR3gIAX-SM2q", "outputId": "1da42901-c48a-4396-f463-386ff4bf2685", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\\[\\left[[B_{n}^{\\pm}, b_{2}^{\\pm}\\right], b_{2}^{\\mp}\\right] = nB_{n}^{+}, \\quad \\left[[B_{n}^{-}, b_{2}^{\\pm}\\right], b_{2}^{\\mp}\\right] = nB_{n}^{-}.\\]\n" ] } ], "source": [ "image = dataset[10][\"image\"]\n", "instruction = \"Write the LaTeX representation for this image.\"\n", "\n", "messages = [\n", " {\n", " \"role\": \"user\",\n", " \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": instruction}],\n", " }\n", "]\n", "\n", "input_text = processor.apply_chat_template(messages, add_generation_prompt = True)\n", "\n", "inputs = processor(\n", " image,\n", " input_text,\n", " add_special_tokens = False,\n", " return_tensors = \"pt\",\n", ").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "\n", "text_streamer = TextStreamer(processor, skip_prompt = True)\n", "result = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n", " use_cache = True, temperature = 1.0, top_p = 0.95, top_k = 64)" ] }, { "cell_type": "markdown", "metadata": { "id": "uMuVrWbjAzhc" }, "source": [ "\n", "### Saving, loading finetuned models\n", "To save the final model as LoRA adapters, use Hugging Face’s `push_to_hub` for online saving, or `save_pretrained` for local storage.\n", "\n", "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "upcOlWe7A1vc", "outputId": "32663620-c6cf-4ce5-8e16-12ac5c196988", "colab": { "base_uri": "https://localhost:8080/" } }, "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": null, "metadata": { "id": "MKX_XKs_BNZR", "outputId": "ac64eeb1-fb27-44c8-8a6d-198ad3daac3d", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The image shows a mathematical equation:\n", "\n", "$$D_{\\mu}^{\\alpha \\beta} \\bar{A}_{\\mu}^{\\alpha \\beta} = 0,$$\n", "\n", "This equation is a condition or a constraint within a specific physical theory, likely related to quantum field theory, particle physics, or general relativity, given the use of indices ($\\mu, \\alpha, \\beta$) and the notation $\\bar{A}$.\n", "\n", "**Interpretation of the terms (based on common physics notation):**\n", "\n", "* **$D_{\\mu}^{\\alpha \\beta}$:** This likely represents a covariant derivative (or a related tensor/operator) acting on some field\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": null, "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": { "9dbba5cc07984692a5cb9310c37294d1": { "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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