{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "M9SutP3lHNkD" }, "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": "2dvMamdWHNkI" }, "source": [ "### News" ] }, { "cell_type": "markdown", "metadata": { "id": "puCrQmlLHNkJ" }, "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": "va6es98AHNkL" }, "source": [ "### Installation" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "iuvAEF6ZHNkM" }, "outputs": [], "source": [ "%%capture\n", "import os, re\n", "if \"COLAB_\" not in \"\".join(os.environ.keys()):\n", " !pip install unsloth # Do this in local & cloud setups\n", "else:\n", " import torch; v = re.match(r'[\\d]{1,}\\.[\\d]{1,}', str(torch.__version__)).group(0)\n", " xformers = 'xformers==' + {'2.10':'0.0.34','2.9':'0.0.33.post1','2.8':'0.0.32.post2'}.get(v, \"0.0.34\")\n", " !pip install sentencepiece protobuf \"datasets==4.3.0\" \"huggingface_hub>=0.34.0\" hf_transfer\n", " !pip install --no-deps unsloth_zoo bitsandbytes accelerate {xformers} peft trl triton unsloth\n", " !pip install --no-deps --upgrade \"torchao>=0.16.0\"\n", "!pip install transformers==4.56.2\n", "!pip install --no-deps trl==0.22.2" ] }, { "cell_type": "markdown", "metadata": { "id": "TGMWlrRdzwgf" }, "source": [ "### Unsloth\n", "\n", "`FastModel` supports loading nearly any model now! This includes Vision and Text models!" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-Xbb0cuLzwgf", "outputId": "f8077426-54be-47df-945e-c742586f09e0" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/unsloth/__init__.py:1432: UserWarning: WARNING: Unsloth should be imported before [trl, transformers, peft] to ensure all optimizations are applied. Your code may run slower or encounter memory issues without these optimizations.\n", "\n", "Please restructure your imports with 'import unsloth' at the top of your file.\n", " from ._gpu_init import *\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Unsloth: UnslothAlignPropTrainer is already patched.\n", "==((====))== Unsloth 2026.7.4: Fast Gemma3 patching. Transformers: 4.56.2.\n", " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.563 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.11.0+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: Using float16 precision for gemma3 won't work! Using float32.\n" ] } ], "source": [ "from unsloth import FastModel\n", "import torch\n", "\n", "fourbit_models = [\n", " # 4bit dynamic quants for superior accuracy and low memory use\n", " \"unsloth/gemma-3-1b-it-unsloth-bnb-4bit\",\n", " \"unsloth/gemma-3-4b-it-unsloth-bnb-4bit\",\n", " \"unsloth/gemma-3-12b-it-unsloth-bnb-4bit\",\n", " \"unsloth/gemma-3-27b-it-unsloth-bnb-4bit\",\n", "\n", " # Other popular models!\n", " \"unsloth/Llama-3.1-8B\",\n", " \"unsloth/Llama-3.2-3B\",\n", " \"unsloth/Llama-3.3-70B\",\n", " \"unsloth/mistral-7b-instruct-v0.3\",\n", " \"unsloth/Phi-4\",\n", "] # More models at https://huggingface.co/unsloth\n", "\n", "model, tokenizer = FastModel.from_pretrained(\n", " model_name = \"unsloth/gemma-3-1b-it\",\n", " max_seq_length = 2048, # Choose any for long context!\n", " load_in_4bit = True, # 4 bit quantization to reduce memory\n", " load_in_8bit = False, # [NEW!] A bit more accurate, uses 2x memory\n", " full_finetuning = False, # [NEW!] We have full finetuning now!\n", " # token = \"YOUR_HF_TOKEN\", # HF Token for gated models\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "SXd9bTZd1aaL" }, "source": [ "We now add LoRA adapters so we only need to update a small amount of parameters!" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "6bZsfBuZDeCL" }, "outputs": [], "source": [ "model = FastModel.get_peft_model(\n", " model,\n", " finetune_vision_layers = False, # Turn off for just text!\n", " finetune_language_layers = True, # Should leave on!\n", " finetune_attention_modules = True, # Attention good for GRPO\n", " finetune_mlp_modules = True, # Should leave on always!\n", "\n", " r = 8, # Larger = higher accuracy, but might overfit\n", " lora_alpha = 8, # Recommended alpha == r at least\n", " lora_dropout = 0,\n", " bias = \"none\",\n", " random_state = 3407,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "vITh0KVJ10qX" }, "source": [ "\n", "### Data Prep\n", "We now use the `Gemma-3` format for conversation style finetunes. We use [Maxime Labonne's FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset in ShareGPT style. Gemma-3 renders multi turn conversations like below:\n", "\n", "```\n", "user\n", "Hello!\n", "model\n", "Hey there!\n", "```\n", "\n", "We use our `get_chat_template` function to get the correct chat template. We support `zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, phi3, llama3, phi4, qwen2.5, gemma3` and more." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "LjY75GoYUCB8" }, "outputs": [], "source": [ "from unsloth.chat_templates import get_chat_template\n", "tokenizer = get_chat_template(\n", " tokenizer,\n", " chat_template = \"gemma-3\",\n", ")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "Mkq4RvEq7FQr", "colab": { "base_uri": "https://localhost:8080/", "height": 325, "referenced_widgets": [ "5fffd44f467d4685a46ed74d1d477f61", "f7c0b17786b5484e8b9920c6eec170c7", "e5019659a420410fb79e28d074a7e87e", "5c335a6701c945ee8ffda7c29ea4a374", "5ef8e085640c498bb7ba972dd8ac246a", "47d7b4be121d4368952d5ca95c90f54e", "7e4e4ce609044b7abd9ff4e8a418bc51", "c574dc6bae5741e486fca52dfe7a0e3d", "ee6ed88f393340d790be0646761f9a57", "b0f827f77d8548f9a06f62e212b5f32d", "6e8c6d565e7940d2abfd221b4360afec", "9dc7142b11d542e1adf0bfc7099e5c55", "d34efac1e03544ed83dd824a19f92211", "07cca18590fa419dbe75ad833c058cff", "c74c25a8b5d34076b3d3845739ca3ea7", "fbd907f333b548c6b6c83821116addde", "d7eebf1395974ee393e68f54a71e87d1", "e0f4c68383564fe6984b3a7c8dad1fde", "1e1d8769e4f24a71babdf4c5b3cb38fa", "6a9016d8adde48d9b8a21d0365283a15", "3e6b687fd3f847eeb00cab8abfe68cd1", "e7201f87d6a74902961b4a824e96f59b", "0e5eded51ed6432a8fac590d4de1e769", "d8775bc904d847649f2ceeedf0552a83", "deb7a37418f346629928c48f2884c8c0", "42bbea8a92a144ff88380ff5df9d5d60", "eda62c66798c4271b414ef7bea343b05", "b8e26ad39175435982d46aa326268879", "8db79f93cc6b467eabaa2faec6e0f92b", "c2be5118b3ff4934908007064aea5160", "aacc60f970404fbb8628aa82b5879b49", "d1355809c3454488873ce7a8d6493a29", "507a8c3745d94657b6f70692a10ebd1a", "7f4afc4c62b24ef993f1d3c097fff51c", "4fd99e59f2a5484687c1a4eeb841e90b", "40036917423b402e80658399c1a88fa6", "57bfffb254b7460e8c646eee2a816ccc", "dcb24aca8f2c4fc892abbb2161904d2e", "e48efa4b6c1d4296a4ba8158c16d39cf", "d70bb847a0a642948937cff8e5fd8694", "5d0f8c74a64f4607ab15ed2abef52665", "f22cfb291bee415c8368e04d306837bf", "67778d8b0e1b4bb1ab3dbd53aaa41eab", "d8f9e2736c684ccba8a51729dbb4c645", "8444000f3e4945a5a0423dfe654b89fa", "ef86ef3005244b91ac6bfe05e37fc528", "2632f1b65e7643479c814c5dd6e4606c", "fb2e2f95aef340e6be69ce7e0b7b599b", "0d883ff5b89c4b1bba1ef569b7f9e58b", "912a1012d2014c119bf41a9a421ab645", "64677e1bc60b4bb4ac653c02d100c33b", "9b0f68db638247cd8a06c3847d18a93c", "58d4738d6bee4772a753ee959dac166d", "ccd7107fec2e417c84fa6fc7bc873b95", "126283ebb95e4c409a25d574195365a6", "98df5dc2e1e64eec856318907f9b93e4", "096dba0e6b77412f848c3c120da7aabf", "7c30bb008d82485084308aa35c546c2b", "ef07bb19b6224a55a10d4acd82b6849c", "85ccbd693e9c416bb19a285b777ddb73", "3d64c6d2c3b245cfb1e466f0f5437161", "385da33ce0684a4d8c4b2e6127b6ff36", "8b8b18d7d0244638815ff3abaa5f5938", "710953102b084491a9441cf7653a1f3d", "45dfb988ea914e2e95bd42ea187c834e", "a368250bd76145cab332b3e0f3f61f39", "802a9719b58f4429a7d728cac1b441c1", "f0c939acb8614328aa0fbab03ddd9569", "f27108f74c0644d0a3e37ae5152528cd", "37fb022501c146ea9bd000975a42ee31", "43447607fc9c4cb3934f350e5f942bdc", "d93af29738684f41b0eb06f34a0f1553", "c41cfc027ae64db38262bf07d5abda9f", "d9cdd303d14543aabac4991d23208d42", "d4c90c1f66714519bd76e0b2cf1377de", "b79e7fd2c6b24a6ba32ee0a12155cf81", "68bf0761c88643e2b82a3725e07e04f0" ] }, "outputId": "c5e6efd7-54ea-4be1-c065-cbf2985cd989" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "README.md: 0.00B [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "5fffd44f467d4685a46ed74d1d477f61" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "data/train-00000-of-00001.parquet: 0%| | 0.00/439k [00:00` token using removeprefix(`''`) since we're finetuning. The Processor will add this token before training and the model expects only one." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "id": "1ahE8Ys37JDJ", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "06541714699e432ab1675456c703c6d6", "4fadb96e79cb4799a5ccc797dd3d6125", "4ef108efaef94441bd5d2e2b9b22aae7", "d31ac1454b9d49d9af24c889261ad364", "6979488129b745f998765b67b7cb72ca", "4be52eb81b1e4393a8cb01541d62492b", "ce9dfe62ac514a1eaabc7e8c3c49aca8", "24547aa4e9734294a0c439c70920358c", "10fa13a66d3145b68aee52b5a14efbba", "d70eb238c1cb410194b38f7888a1abc8", "75265d5533e84d1a81dbb5f7ebc4b980" ] }, "outputId": "174e4af0-e076-471d-f22a-ccf2eb774903" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/823 [00:00') for convo in convos]\n", " return { \"text\" : texts, }\n", "\n", "dataset = dataset.map(formatting_prompts_func, batched = True)" ] }, { "cell_type": "markdown", "metadata": { "id": "ndDUB23CGAC5" }, "source": [ "Let's see how the chat template did! Notice there is no `` token as the processor tokenizer will be adding one." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "id": "gGFzmplrEy9I", "outputId": "6a4a32b8-b326-4031-8448-5c46ce2cefbf" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'user\\nAşağıdaki X gönderisine bağlama uygun, doğal ve kısa bir reply yaz:\\n\\n18 yaş altındaki kardeşlerime hayatın sırrını veriyorum:\\n\\n18 yaşına bastığınızda ne yapın edin muhakkak evlenin. \\n\\nBir gün bile kaybetmeyin. \\n\\nAynı yıl içinde muhakkak en az 3 tane çocuk yapın. \\n\\nBunu tüp bebek ile yapabilirsiniz. Öbür türlü manuel yapmaya çalışırsanız üç tane yapamayabilirsiniz ilk atışta.\\n\\nYaptınız diyelim. Şimdi 18 yaşındasınız,\\nmodel\\n@sengpt 3 çocuk 27 ay yapar bu 2 sene demek 20 yaşındasın\\n'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 12 } ], "source": [ "dataset[100][\"text\"]" ] }, { "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`." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "fcaa637fffd341b28aea5991bba37b50", "6f7af89f3c454ba98c3de3c72706d98c", "621fcdf2a83241919af2a69c8e9844d7", "df1f802778564951b7c101ec1c3e1830", "7b66bc3e492d483399752bb0604880af", "bccd607f3db949b9a7d168e292dfefbb", "fee4c568c6cd43f88107c04872943b8e", "ec1519acdcc3431885e5d0001f7e25eb", "a49af103a6af4a07885e3292268cefd3", "257d3b76ea84468898f8514c884af36a", "fc952a4ae47f4e68a630fbb39563bed1" ] }, "id": "95_Nn-89DhsL", "outputId": "d02ce816-309f-4bf8-e94c-200e94f59097" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Unsloth: Switching to float32 training since model cannot work with float16\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Unsloth: Tokenizing [\"text\"]: 0%| | 0/823 [00:00user\\n' and response_part = 'model\\n'\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/823 [00:00` as expected!" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "id": "LtsMVtlkUhja", "outputId": "e9ba7310-e3d0-4f08-bf0e-d3c35a87ba3f" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'user\\nAşağıdaki X gönderisine bağlama uygun, doğal ve kısa bir reply yaz:\\n\\n18 yaş altındaki kardeşlerime hayatın sırrını veriyorum:\\n\\n18 yaşına bastığınızda ne yapın edin muhakkak evlenin. \\n\\nBir gün bile kaybetmeyin. \\n\\nAynı yıl içinde muhakkak en az 3 tane çocuk yapın. \\n\\nBunu tüp bebek ile yapabilirsiniz. Öbür türlü manuel yapmaya çalışırsanız üç tane yapamayabilirsiniz ilk atışta.\\n\\nYaptınız diyelim. Şimdi 18 yaşındasınız,\\nmodel\\n@sengpt 3 çocuk 27 ay yapar bu 2 sene demek 20 yaşındasın\\n'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 19 } ], "source": [ "tokenizer.decode(trainer.train_dataset[100][\"input_ids\"])" ] }, { "cell_type": "markdown", "metadata": { "id": "4Kyjy__m9KY3" }, "source": [ "Now let's print the masked out example - you should see only the answer is present:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 53 }, "id": "_rD6fl8EUxnG", "outputId": "e329bee2-334f-4d3f-9a96-6b870b9b32bb" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "' @sengpt 3 çocuk 27 ay yapar bu 2 sene demek 20 yaşındasın\\n'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 20 } ], "source": [ "tokenizer.decode([tokenizer.pad_token_id if x == -100 else x for x in trainer.train_dataset[100][\"labels\"]]).replace(tokenizer.pad_token, \" \")" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "2ejIt2xSNKKp", "outputId": "f970d38d-d947-44df-c1fd-632e56573660" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "GPU = Tesla T4. Max memory = 14.563 GB.\n", "1.512 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": "markdown", "metadata": { "id": "CNP1Uidk9mrz" }, "source": [ "Let's train the model! To resume a training run, set `trainer.train(resume_from_checkpoint = True)`" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "yqxqAZ7KJ4oL", "outputId": "83f17fc0-c0ff-43be-863e-832775bc7d23" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n", " \\\\ /| Num examples = 823 | Num Epochs = 1 | Total steps = 30\n", "O^O/ \\_/ \\ Batch size per device = 2 | Gradient accumulation steps = 4\n", "\\ / Data Parallel GPUs = 1 | Total batch size (2 x 4 x 1) = 8\n", " \"-____-\" Trainable parameters = 6,522,880 of 1,006,408,832 (0.65% trained)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "
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StepTraining Loss
17.661600
27.060800
37.261900
46.779300
55.642700
66.280500
75.706200
85.260500
94.839000
105.534400
114.481400
124.790600
134.891800
145.307900
154.856300
164.687300
174.697400
184.768600
194.695900
204.713100
215.112300
224.513100
234.628400
245.072300
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" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "pCqnaKmlO1U9", "outputId": "ce0e7228-1377-477d-bd70-0f044503918e" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "359.0106 seconds used for training.\n", "5.98 minutes used for training.\n", "Peak reserved memory = 1.869 GB.\n", "Peak reserved memory for training = 0.357 GB.\n", "Peak reserved memory % of max memory = 12.834 %.\n", "Peak reserved memory for training % of max memory = 2.451 %.\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 via Unsloth native inference! According to the `Gemma-3` team, the recommended settings for inference are `temperature = 1.0, top_p = 0.95, top_k = 64`" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kR3gIAX-SM2q", "outputId": "3ac01fba-440f-4ba4-811b-55fd41af998b" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['user\\nContinue the sequence: 1, 1, 2, 3, 5, 8,\\nmodel\\nThe sequence is the Fibonacci sequence. \\n\\nSo the next numbers in the sequence are:\\n\\n13, 21, 34, 55, 89, 144...']" ] }, "metadata": {}, "execution_count": 24 } ], "source": [ "from unsloth.chat_templates import get_chat_template\n", "tokenizer = get_chat_template(\n", " tokenizer,\n", " chat_template = \"gemma-3\",\n", ")\n", "messages = [{\n", " \"role\": \"user\",\n", " \"content\": [{\n", " \"type\" : \"text\",\n", " \"text\" : \"Continue the sequence: 1, 1, 2, 3, 5, 8,\",\n", " }]\n", "}]\n", "inputs = tokenizer.apply_chat_template(\n", " messages,\n", " add_generation_prompt = True, # Must add for generation\n", " tokenize = True,\n", " return_tensors = \"pt\",\n", " return_dict = True,\n", ")\n", "outputs = model.generate(\n", " **inputs.to(\"cuda\"),\n", " max_new_tokens = 64, # Increase for longer outputs!\n", " # Recommended Gemma-3 settings!\n", " temperature = 1.0, top_p = 0.95, top_k = 64,\n", ")\n", "tokenizer.batch_decode(outputs)" ] }, { "cell_type": "markdown", "metadata": { "id": "CrSvZObor0lY" }, "source": [ " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e2pEuRb1r2Vg", "outputId": "de757d2d-a66b-4be6-c9c9-78cf491dfeba" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Okay, let's break down why the sky is blue! It's a fascinating phenomenon that boils down to a combination of physics and light. Here's the explanation:\n", "\n", "**1. Sunlight and its Colors:**\n", "\n", "* Sunlight, which appears white to us, is actually made up of *all* the\n" ] } ], "source": [ "messages = [{\n", " \"role\": \"user\",\n", " \"content\": [{\"type\" : \"text\", \"text\" : \"Why is the sky blue?\",}]\n", "}]\n", "inputs = tokenizer.apply_chat_template(\n", " messages,\n", " add_generation_prompt = True, # Must add for generation\n", " tokenize = True,\n", " return_tensors = \"pt\",\n", " return_dict = True,\n", ")\n", "\n", "from transformers import TextStreamer\n", "_ = model.generate(\n", " **inputs.to(\"cuda\"),\n", " max_new_tokens = 64, # Increase for longer outputs!\n", " # Recommended Gemma-3 settings!\n", " temperature = 1.0, top_p = 0.95, top_k = 64,\n", " streamer = TextStreamer(tokenizer, skip_prompt = True),\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "uMuVrWbjAzhc" }, "source": [ "\n", "### Saving, loading finetuned models\n", "To save the final model as LoRA adapters, either use Hugging Face's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", "\n", "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. 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