{ "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": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-Xbb0cuLzwgf", "outputId": "184e9d8a-6a99-4ecf-c20b-de1b341fa0ea" }, "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 2026.7.4: 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": 3, "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": 4, "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": 5, "metadata": { "id": "Mkq4RvEq7FQr", "colab": { "base_uri": "https://localhost:8080/", "height": 141, "referenced_widgets": [ "5d76d6c149c04b9f9f288089011a0091", "b5cac975bd964d9391d7ab4859dd6a2c", "346dc76fd6cc45559ce3ad8961f1d9e4", "35da48d72d4748c2b6e5513048f9c0a0", "e14f27a17a4349399c9731446e140b45", "dba47c5610a2422ba605048864c3b70a", "961d8130cfcf4fe8b949a37e3ee1d275", "4a669ae9543d4c078e50dd781eae8112", "87f62c624482497c943440f770939337", "6fab717a87784b4f83199fbe45259ced", "d203161b2885436aa397a504d0ba6b28", "8d8cb75c9abe4694a7bff7e742925f23", "f346f7e4b0954c6f9b8d01d142ad32dd", "7dedc6b1ad024b4daf14609851ce6e0d", "6d01398f0e084e6d880271d1c274e71f", "1b77b2af4d1241bc8d31d6ba16181838", "8353491603864c4eab9b4101144d33e4", "05c6c2b938bf4ef789e96bb9ded015b6", "3dacfc003adc4580967ca90e229bc501", "8e298c1a7c4b4228a6c8b427244df03c", "533fc61c296742c8b326f80c0a7f6172", "26d51d10ec5849c98ddfb533b845e926", "70307cbf16e7490cbe23459350812ee9", "6579d79123454dffb69d8ad234c815e9", "747bf6cb17fe4830b68f2cabd5bc4e33", "f255dc8920f5443d8b33a5755b92a50b", "75c7c35ebf0840bda6976e191722c28e", "e4ef9e05fb194267927354cf8cadc34b", "8de1a10d12334cc9a60bff55c0ea57dd", "f8aacb6d84344645b3a94bd15cd198b6", "ae7fab596014428c9cf67425ceb7dee7", "ef335ca686df4f5db440e79f1ba85223", "398b1075862948eca55b507295ae8119" ] }, "outputId": "904c12be-38ec-4850-9b34-86a8958e03eb" }, "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": "5d76d6c149c04b9f9f288089011a0091" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "data/train-00000-of-00001.parquet: 0%| | 0.00/46.2k [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": 8, "metadata": { "id": "1ahE8Ys37JDJ", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "b9d40b8a2c294f72957cd16f3f8b9136", "34a9643f6b884f6ba4ddded003287f2d", "27b3aa4f1ff04c53841280e1406c689b", "f99c6aa682a446f28e69c9e10a69d43e", "5c6e6c7591c04bccbef202115ba4d368", "0f5d67db58f440a183fb195f8d39a955", "471b114e09b4467e895146d4595c6d20", "eb7b8bd6d4b54a8dacb6195d78618f82", "753eafd9e52b4a9fb711df3dd1517cac", "86a588da7ad24191b6625b58682b2251", "3ec691683e7446acbd086c0bc43141e0" ] }, "outputId": "46fd6855-3219-4aba-e8eb-fb0d2b0ea388" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/120 [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": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 89 }, "id": "gGFzmplrEy9I", "outputId": "2e145afa-1020-4e29-d48e-cab9963d5ed4" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'user\\nTeknik bir tanıtım yap.\\nmodel\\nBen Berk Birkan AI; doğal dil üzerinden gereksinim analizi, kodlama, hata ayıklama, dokümantasyon, veri hazırlama ve AI agent mimarileri gibi görevlerde destek vermek üzere Berk Birkan tarafından özelleştirilen bir yapay zekâ asistanıyım.\\n'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 9 } ], "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": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 85, "referenced_widgets": [ "09f3df77dc2b470d800e82a06a172c91", "9879e1eeb3074653b2585a1468506621", "1b97aaf1fe7f48bc89801175fd1ecad9", "3c60b260d9c04209a2b81e91ff1a7e79", "de1ab813fa1142a293e99a1063f6b35f", "b25ecf2238604be0b8091dba4ac52c9e", "aba0d880cdec43d3b4cbb32d6c4903b5", "c10f0c17fcb944229c859ae4886e2679", "ff14e3d03f204b54acde0c832f957936", "8fb316a1f98946ab97425f4705885a67", "f43c59a2c17a4148a670fc7d64fe24c7" ] }, "id": "95_Nn-89DhsL", "outputId": "d2630e51-3d7a-4755-e1d9-9e22e67824b0" }, "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%| | 0/120 [00:00user\\n' and response_part = 'model\\n'\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/120 [00:00` as expected!" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 89 }, "id": "LtsMVtlkUhja", "outputId": "58931221-e613-4d93-f308-de15f071c61f" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'user\\nTeknik bir tanıtım yap.\\nmodel\\nBen Berk Birkan AI; doğal dil üzerinden gereksinim analizi, kodlama, hata ayıklama, dokümantasyon, veri hazırlama ve AI agent mimarileri gibi görevlerde destek vermek üzere Berk Birkan tarafından özelleştirilen bir yapay zekâ asistanıyım.\\n'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 12 } ], "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": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 89 }, "id": "_rD6fl8EUxnG", "outputId": "17910e8c-5417-4c64-ac1f-e15a34dd0bc4" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "' Ben Berk Birkan AI; doğal dil üzerinden gereksinim analizi, kodlama, hata ayıklama, dokümantasyon, veri hazırlama ve AI agent mimarileri gibi görevlerde destek vermek üzere Berk Birkan tarafından özelleştirilen bir yapay zekâ asistanıyım.\\n'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 13 } ], "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": 14, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "2ejIt2xSNKKp", "outputId": "1d7d6a14-fe93-4a19-b185-6ef154ab7866" }, "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": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "yqxqAZ7KJ4oL", "outputId": "972ada20-92d8-4e46-9773-feb4a76e0844" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n", " \\\\ /| Num examples = 120 | Num Epochs = 2 | 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", "
\n", " \n", " \n", " [30/30 03:05, Epoch 2/2]\n", "
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StepTraining Loss
14.470100
24.289600
34.394900
44.256000
54.088800
63.125200
73.593900
83.479800
93.205700
102.972100
113.025600
122.806000
133.064600
143.195500
152.516000
162.907000
172.411700
182.796900
192.648000
202.447200
212.405600
222.520300
232.535700
242.529900
252.521000
262.237500
272.692600
282.038400
292.809700
302.388800

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "pCqnaKmlO1U9", "outputId": "f6fed16b-f81a-41d4-f49d-00c0a827e11f" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "385.8073 seconds used for training.\n", "6.43 minutes used for training.\n", "Peak reserved memory = 1.512 GB.\n", "Peak reserved memory for training = 0.0 GB.\n", "Peak reserved memory % of max memory = 10.382 %.\n", "Peak reserved memory for training % of max memory = 0.0 %.\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": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kR3gIAX-SM2q", "outputId": "856eaec7-4809-44fd-fced-8b5aebf2aa0c" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "['user\\nContinue the sequence: 1, 1, 2, 3, 5, 8,\\nmodel\\n1 1 2 3 5 8 13 21 34 55 89 \\n\\nThis is the Fibonacci sequence, where each number is the sum of the two preceding numbers.\\n']" ] }, "metadata": {}, "execution_count": 19 } ], "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": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e2pEuRb1r2Vg", "outputId": "4ec73793-8d17-4142-8a1c-46b3bdd0ab0a" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Berk Birkan, bağımsız bir mühendislik mühendisi, yazılım mühendisliği, proje yönetimi ve kişisel finansal biriktirme danışmanıdır. AI ve AI araçlarının şirketleri için dijital bir araç olarak işbirliği yapmasını hedeflemiştir ve kişisel bir finansal uzman\n" ] } ], "source": [ "messages = [{\n", " \"role\": \"user\",\n", " \"content\": [{\"type\" : \"text\", \"text\" : \"Berk Birkan kimdir?\",}]\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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