gemma-3.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"tOvDRvBT-NKB"},"source":["To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n","<div class=\"align-center\">\n","<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n","<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n","<a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a></a> Join Discord if you need help + ⭐ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐\n","</div>\n","\n","To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n","\n","You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n"]},{"cell_type":"markdown","metadata":{"id":"Db9cA9Tq-NKD"},"source":["### News"]},{"cell_type":"markdown","metadata":{"id":"O_9BU-q4-NKE"},"source":["**Read our [Gemma 3 blog](https://unsloth.ai/blog/gemma3) for what's new in Unsloth and our [Reasoning blog](https://unsloth.ai/blog/r1-reasoning) on how to train reasoning models.**\n","\n","Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n"]},{"cell_type":"markdown","metadata":{"id":"AOHXmccO-NKE"},"source":["### Installation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lCpROVM--NKE"},"outputs":[],"source":["%%capture\n","import os\n","if \"COLAB_\" not in \"\".join(os.environ.keys()):\n","    !pip install unsloth vllm\n","else:\n","    # [NOTE] Do the below ONLY in Colab! Use [[pip install unsloth vllm]]\n","    !pip install --no-deps unsloth vllm"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"R9TDCKNY-NKF"},"outputs":[],"source":["#@title Colab Extra Install { display-mode: \"form\" }\n","%%capture\n","import os\n","if \"COLAB_\" not in \"\".join(os.environ.keys()):\n","    !pip install unsloth vllm\n","else:\n","    !pip install --no-deps unsloth vllm\n","    # [NOTE] Do the below ONLY in Colab! Use [[pip install unsloth vllm]]\n","    # Skip restarting message in Colab\n","    import sys, re, requests; modules = list(sys.modules.keys())\n","    for x in modules: sys.modules.pop(x) if \"PIL\" in x or \"google\" in x else None\n","    !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft \"trl==0.15.2\" triton cut_cross_entropy unsloth_zoo\n","    !pip install sentencepiece protobuf datasets huggingface_hub hf_transfer\n","\n","    # vLLM requirements - vLLM breaks Colab due to reinstalling numpy\n","    f = requests.get(\"https://raw.githubusercontent.com/vllm-project/vllm/refs/heads/main/requirements/common.txt\").content\n","    with open(\"vllm_requirements.txt\", \"wb\") as file:\n","        file.write(re.sub(rb\"(transformers|numpy|xformers)[^\\n]{1,}\\n\", b\"\", f))\n","    !pip install -r vllm_requirements.txt"]},{"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":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":432,"referenced_widgets":["ef76d2770d9346eb8d1a0e1e145db3fb","7c6fa57d124944b4840f3d3ec540098b","96ee61528dde4b2a90972f9e04a70f85","e30a2d2cd7744976becf2f822c0c77ae","4c34f401b3ab423c99e48f7165e26043","f3a03a6997ad43868a300341737a2a84","623b6ef43ab14d5e8b4cfdb63cabc4bf","9c18299226b0465d8d2ac8f95b259cd7","253b09a4d67c403eaaa80dea35dab05d","0e810f8337b147d98def7d732c56f806","8f60790c054f42b1b483bee933e2230a","477ac908e4e74f63a4d0e8fd21ceeb9d","1d854462c41246d6a5d29f79a44008d3","3a4a30ee55984adbb09ca8da38df1c3a","4c7650a6b53d49f3b1e2f66dd8411315","9865c43c048a47c9a57ab3886930ab0c","9614e284f1d44f1cb216907d638d3e6b","ef8709ccce924c8aa0c6bd0ad7976322","054df2ca501f4136a98a01160dea760a","2fe2538502c940648e96e0119af503e6","95486119c1be46f3807011c0524aecd3","6bb31305f504453ca6a4e8be83f84ca7","67b6d3b184024958b3ec3c019e030899","c592679bd2d7404c912aa8e172214ab2","3a6ebc13942b4398aece086081c5026b","edde204ffb96409e8c2e91eb418d8737","170323e81ba74967a2b5adf0760411b5","d3caa18ea9e04702bc0c337b273363e6","3c5c1207164d4f59b4aa9e28fd3645dc","6b82480932374e72bc84ad5b7c22fbfa","e03c305395b9457b9865d448ad977380","4b5d871cb8234e869d97cad1ebf6019d","54603f38ddab46d69b189bcdd05ce024","ade90e78de6b4373a5e38d36ce3257b0","86ad825d2e234db1ab5a2f5b0d72c8fc","0303ee28e80d448fbe29a8e250d47318","1732186cd096409cb72f1c6cf83ef6d2","944bc15a28724d94b8e0367b48e5f675","0724f2c5a00241b19603bdfea378f7c5","d8b3c42a4f854eab9eaf085f656f7c8e","a7cf5e93c81f4d319cec26a5932efde8","0d6dffa372d34f7791debd37797b4894","2616833ccf9145e1a20dff9790df11b4","e95f88a9101f4084ab89288d02a83401","88e0c9d873494d0688ad34bb0ed487f5","1eb58a76ae634104b4144a176b76e26b","e55347eb7eb84209a6720a05e781af14","6c7d0a9d22804afaa9ad61ee6699aa5d","51a6d229962a47798de89deabe9c6e98","8fe298634de944e39dc5b70981168ea5","6316b727dc914c699fa502185866d312","6c528c621bc442c5881db8c0e8c87c4f","fe5eca8b017d4fc59a9edbe91d5bd07f","dd180121959e4c0d92a25c74d016e9c0","c2d1cdd528f3422bbefb77be4c3a3d70","ae7be54d9b2349938cb6d723c7d8009b","1b6e68ac0a3d47ffbec1bb1d3c7267c7","e535bf323720400fb171bbb671265243","df693d0db8ed44ed8ef4ca59258d935b","96457a0a77754d6897f81ffd4cc339a9","608649511f964042b682eb33c4447dba","48cef38fd0d34ef5bca580b137972cca","b74a1fb9cceb49e4b0cbf60ef3a967d0","4e593897ae154a6ba4d00bcdd23228ad","4e580eb550dd41bea8dffff0b70eb63b","1edb4306da42403da241cf76dcc5d8b9","8c228efb3c504020b627b96fe4e517ad","c7058eb7c2df4da7b638a79cbcb2f24b","46b49001241d400bae600d7ed63e76f0","3679f4ebf47d48a3a40716f1b207bf3c","71d1f0abc9034e33b7d23c25288bd8ed","b450dca9821646b08dcfc92be8f5b75e","0b6ac10f8262424397cc4487ee9bcc1c","241f85fd6e204b6ab2d22aef1ebeae9c","ac8d1ef88f6140ac917411f2875d310c","adffa0bdb23c4332ab4c1988929f86f3","e8abc0d0c6344df6a0e11c80fdf9cea5"]},"id":"-Xbb0cuLzwgf","outputId":"d04127d2-c180-48e3-d699-652697c07620","executionInfo":{"status":"ok","timestamp":1746051806879,"user_tz":-330,"elapsed":95649,"user":{"displayName":"Kavan Mevada","userId":"15076178667420285336"}}},"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","INFO 04-30 22:22:34 [importing.py:53] Triton module has been replaced with a placeholder.\n","INFO 04-30 22:22:34 [__init__.py:239] Automatically detected platform cuda.\n","==((====))==  Unsloth 2025.4.3: Fast Gemma3 patching. Transformers: 4.51.3. vLLM: 0.8.5.\n","   \\\\   /|    Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n","O^O/ \\_/ \\    Torch: 2.6.0+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.2.0\n","\\        /    Bfloat16 = FALSE. FA [Xformers = 0.0.29.post3. 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"]},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/1.00G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ef76d2770d9346eb8d1a0e1e145db3fb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/233 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"477ac908e4e74f63a4d0e8fd21ceeb9d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/1.16M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"67b6d3b184024958b3ec3c019e030899"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.model:   0%|          | 0.00/4.69M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ade90e78de6b4373a5e38d36ce3257b0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/33.4M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"88e0c9d873494d0688ad34bb0ed487f5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["added_tokens.json:   0%|          | 0.00/35.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ae7be54d9b2349938cb6d723c7d8009b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/670 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8c228efb3c504020b627b96fe4e517ad"}},"metadata":{}}],"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 = \"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\", # use one if using 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":null,"metadata":{"id":"6bZsfBuZDeCL","outputId":"043f5ed1-d67a-4457-fba5-31f66852f5bd","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1746051824119,"user_tz":-330,"elapsed":5210,"user":{"displayName":"Kavan Mevada","userId":"15076178667420285336"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Unsloth: Making `model.base_model.model.model` require gradients\n"]}],"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":["<a name=\"Data\"></a>\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","<bos><start_of_turn>user\n","Hello!<end_of_turn>\n","<start_of_turn>model\n","Hey there!<end_of_turn>\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":null,"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":null,"metadata":{"id":"Mkq4RvEq7FQr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1746052517810,"user_tz":-330,"elapsed":2543,"user":{"displayName":"Kavan Mevada","userId":"15076178667420285336"}},"outputId":"e566aa55-fa5e-4c96-a540-45ac20daef6a"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}],"source":["#from datasets import load_dataset\n","#dataset = load_dataset(\"mlabonne/FineTome-100k\", split = \"train\")\n","\n","from google.colab import drive\n","drive.mount('/content/drive')\n","\n","#dataset = load_dataset('csv', data_files='drive/MyDrive/all-data.csv', encoding='latin-1')[\"train\"]"]},{"cell_type":"markdown","metadata":{"id":"K9CBpiISFa6C"},"source":["We now use `standardize_data_formats` to try converting datasets to the correct format for finetuning purposes!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"reoBXmAn7HlN"},"outputs":[],"source":["from unsloth.chat_templates import standardize_data_formats\n","dataset = standardize_data_formats(dataset)"]},{"cell_type":"markdown","metadata":{"id":"6i5Sx9In7vHi"},"source":["Let's see how row 100 looks like!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dzE1OEXi7s3P","outputId":"80df0763-108c-48ed-c770-526d112c61c5","executionInfo":{"status":"ok","timestamp":1746052612857,"user_tz":-330,"elapsed":27,"user":{"displayName":"Kavan Mevada","userId":"15076178667420285336"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'neutral': 'positive',\n"," 'According to Gran , the company has no plans to move all production to Russia , although that is where the company is growing .': 'Circulation revenue has increased by 5 % in Finland and 4 % in Sweden in 2008 .'}"]},"metadata":{},"execution_count":22}],"source":["dataset[100]"]},{"cell_type":"markdown","metadata":{"id":"8Xs0LXio7rfd"},"source":["We now have to apply the chat template for `Gemma-3` onto the conversations, and save it to `text`. We remove the `<bos>` token using removeprefix(`'<bos>'`) since we're finetuning. The Processor will add this token before training and the model expects only one."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1ahE8Ys37JDJ","colab":{"base_uri":"https://localhost:8080/","height":371,"referenced_widgets":["9a880f41637f4e2280ca5f2acbc9087f","5e730ca00ea348e3b41e7d6b5b0ad559","56e8f0ccaf60446fb3d33ca2e1c37626","9315107209cd454b9c198a32a8c1a0d0","ecf0ec4c59db4b13be5a78cb50f6565c","341f2d064bcf474f860aa385f3ef35ea","acccfb400e5d4d72a2b1f88dff300aeb","a932aefb8a0747e195319a134cd5649f","e6151e29f0d944cfa864c0846531308a","010943cd20c64081902a70fd633775c6","c44465720eee440ca25500146f28f227"]},"executionInfo":{"status":"error","timestamp":1746052740491,"user_tz":-330,"elapsed":132,"user":{"displayName":"Kavan Mevada","userId":"15076178667420285336"}},"outputId":"b0b1f572-5e3d-4278-ff4b-a504ae21a0f0"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/4845 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9a880f41637f4e2280ca5f2acbc9087f"}},"metadata":{}},{"output_type":"error","ename":"KeyError","evalue":"1","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m<ipython-input-25-39dad95f53f4>\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m \u001b[0;34m\"text\"\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mexamples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mformatting_prompts_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    555\u001b[0m         }\n\u001b[1;32m    556\u001b[0m         \u001b[0;31m# apply actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 557\u001b[0;31m         \u001b[0mout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"DatasetDict\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    558\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    559\u001b[0m         \u001b[0;31m# re-apply format to the output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc, try_original_type)\u001b[0m\n\u001b[1;32m   3077\u001b[0m                     \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdesc\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"Map\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3078\u001b[0m                 ) as pbar:\n\u001b[0;32m-> 3079\u001b[0;31m                     \u001b[0;32mfor\u001b[0m \u001b[0mrank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m 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Notice there is no `<bos>` token as the processor tokenizer will be adding one."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":34},"id":"gGFzmplrEy9I","outputId":"e214d7fc-97d6-44cd-db0e-9177f5006290","executionInfo":{"status":"ok","timestamp":1746052731646,"user_tz":-330,"elapsed":4,"user":{"displayName":"Kavan Mevada","userId":"15076178667420285336"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'positive'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":24}],"source":["dataset[100][\"text\"]"]},{"cell_type":"markdown","metadata":{"id":"idAEIeSQ3xdS"},"source":["<a name=\"Train\"></a>\n","### Train the model\n","Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112,"referenced_widgets":["730aa679b5ac483b929a3646bb5947fa","1f333859babd4c1abac69cacda6df864","e7f8d2c781a64e83988b0bdd090bdb97","3e3feb4fcca74c87abb608c0543236b1","2970cbc657d244bab22715bcb788be6a","c5b1d1476ddc45249e037df07a96ae37","19c27988e01d47e79319f89b5cfd73e2","5e48531593d741eaa3669f9118ba8afc","62838ffab83d486f86e86417caf0b498","7e5378838c114195ba3919fdd683fd7d","3350d22f463643ef9a726227f262ac49","a8559112c61949318ef9e4ab4cadfeda"]},"id":"95_Nn-89DhsL","outputId":"2ffe3c70-8c46-41a1-ef51-07c220b5935d"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a8559112c61949318ef9e4ab4cadfeda","version_major":2,"version_minor":0},"text/plain":["Unsloth: Tokenizing [\"text\"] (num_proc=2):   0%|          | 0/100000 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"}],"source":["from trl import SFTTrainer, SFTConfig\n","trainer = SFTTrainer(\n","    model = model,\n","    tokenizer = tokenizer,\n","    train_dataset = dataset,\n","    eval_dataset = None, # Can set up evaluation!\n","    args = SFTConfig(\n","        dataset_text_field = \"text\",\n","        per_device_train_batch_size = 2,\n","        gradient_accumulation_steps = 4, # Use GA to mimic batch size!\n","        warmup_steps = 5,\n","        # num_train_epochs = 1, # Set this for 1 full training run.\n","        max_steps = 30,\n","        learning_rate = 2e-4, # Reduce to 2e-5 for long training runs\n","        logging_steps = 1,\n","        optim = \"adamw_8bit\",\n","        weight_decay = 0.01,\n","        lr_scheduler_type = \"linear\",\n","        seed = 3407,\n","        report_to = \"none\", # Use this for WandB etc\n","        dataset_num_proc=2,\n","    ),\n",")"]},{"cell_type":"markdown","metadata":{"id":"C_sGp5XlG6dq"},"source":["We also use Unsloth's `train_on_completions` method to only train on the assistant outputs and ignore the loss on the user's inputs. This helps increase accuracy of finetunes!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["30918e50f2174d1c8e7af3eef332b6ee","f4b34bc9a62f405383c3b81dc87792b9","1be74564b60c48d6b21615adf29009fb","148125f955954041a8f5631f9338c43e","91693f16da7b421885fe8474cf533327","28de62814a6847e0a0b41ec6bf8fdc66","c43cef665c9542f982986a74dc50ca98","ded71beadafd438ebff07bb0594771e4","677e4d7a08ab408e9430d67a2870f707","54d2fb7e7c2b4107ba758a7c7ef4f382","34da5010faa749e0940c2821f2f46e59","d94e1d86e3274277a9d908a7498ef1fa"]},"id":"juQiExuBG5Bt","outputId":"6f017a0d-2ccc-429e-8a27-ab76c6c23b57"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d94e1d86e3274277a9d908a7498ef1fa","version_major":2,"version_minor":0},"text/plain":["Map (num_proc=255):   0%|          | 0/100000 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"}],"source":["from unsloth.chat_templates import train_on_responses_only\n","trainer = train_on_responses_only(\n","    trainer,\n","    instruction_part = \"<start_of_turn>user\\n\",\n","    response_part = \"<start_of_turn>model\\n\",\n",")"]},{"cell_type":"markdown","metadata":{"id":"Dv1NBUozV78l"},"source":["Let's verify masking the instruction part is done! Let's print the 100th row again.  Notice how the sample only has a single `<bos>` as expected!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"LtsMVtlkUhja","outputId":"aebb55c2-3883-4494-e9f8-78d60b9b08e8"},"outputs":[{"data":{"text/plain":["'<bos><start_of_turn>user\\nWhat is the modulus operator in programming and how can I use it to calculate the modulus of two given numbers?<end_of_turn>\\n<start_of_turn>model\\nIn programming, the modulus operator is represented by the \\'%\\' symbol. It calculates the remainder when one number is divided by another. To calculate the modulus of two given numbers, you can use the modulus operator in the following way:\\n\\n```python\\n# Calculate the modulus\\nModulus = a % b\\n\\nprint(\"Modulus of the given numbers is: \", Modulus)\\n```\\n\\nIn this code snippet, the variables \\'a\\' and \\'b\\' represent the two given numbers for which you want to calculate the modulus. By using the modulus operator \\'%\\', we calculate the remainder when \\'a\\' is divided by \\'b\\'. The result is then stored in the variable \\'Modulus\\'. Finally, the modulus value is printed using the \\'print\\' statement.\\n\\nFor example, if \\'a\\' is 10 and \\'b\\' is 4, the modulus calculation would be 10 % 4, which equals 2. Therefore, the output of the above code would be:\\n\\n```\\nModulus of the given numbers is: 2\\n```\\n\\nThis means that the modulus of 10 and 4 is 2.<end_of_turn>\\n'"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"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":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":175},"id":"_rD6fl8EUxnG","outputId":"e9012c2a-60eb-437e-f145-3e11e9a0dd34"},"outputs":[{"data":{"text/plain":["'                               In programming, the modulus operator is represented by the \\'%\\' symbol. It calculates the remainder when one number is divided by another. To calculate the modulus of two given numbers, you can use the modulus operator in the following way:\\n\\n```python\\n# Calculate the modulus\\nModulus = a % b\\n\\nprint(\"Modulus of the given numbers is: \", Modulus)\\n```\\n\\nIn this code snippet, the variables \\'a\\' and \\'b\\' represent the two given numbers for which you want to calculate the modulus. By using the modulus operator \\'%\\', we calculate the remainder when \\'a\\' is divided by \\'b\\'. The result is then stored in the variable \\'Modulus\\'. Finally, the modulus value is printed using the \\'print\\' statement.\\n\\nFor example, if \\'a\\' is 10 and \\'b\\' is 4, the modulus calculation would be 10 % 4, which equals 2. Therefore, the output of the above code would be:\\n\\n```\\nModulus of the given numbers is: 2\\n```\\n\\nThis means that the modulus of 10 and 4 is 2.<end_of_turn>\\n'"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"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":null,"metadata":{"cellView":"form","colab":{"base_uri":"https://localhost:8080/"},"id":"2ejIt2xSNKKp","outputId":"ba6de9bc-35f1-48ed-8552-5cf1943d0478"},"outputs":[{"name":"stdout","output_type":"stream","text":["GPU = Tesla T4. Max memory = 14.741 GB.\n","4.283 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":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"yqxqAZ7KJ4oL","outputId":"b44425bc-2ccf-4683-ce72-a837e5a07e9e"},"outputs":[{"name":"stderr","output_type":"stream","text":["==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = 1\n","   \\\\   /|    Num examples = 100,000 | 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 = 14,901,248/4,000,000,000 (0.37% trained)\n","It is strongly recommended to train Gemma3 models with the `eager` attention implementation instead of `sdpa`. Use `eager` with `AutoModelForCausalLM.from_pretrained('<path-to-checkpoint>', attn_implementation='eager')`.\n"]},{"name":"stdout","output_type":"stream","text":["Unsloth: Will smartly offload gradients to save VRAM!\n"]},{"data":{"text/html":["\n","    <div>\n","      \n","      <progress value='30' max='30' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [30/30 16:18, Epoch 0/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>1.237700</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>1.636400</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>1.766300</td>\n","    </tr>\n","    <tr>\n","      <td>4</td>\n","      <td>1.420700</td>\n","    </tr>\n","    <tr>\n","      <td>5</td>\n","      <td>1.235700</td>\n","    </tr>\n","    <tr>\n","      <td>6</td>\n","      <td>1.806600</td>\n","    </tr>\n","    <tr>\n","      <td>7</td>\n","      <td>1.010100</td>\n","    </tr>\n","    <tr>\n","      <td>8</td>\n","      <td>1.896600</td>\n","    </tr>\n","    <tr>\n","      <td>9</td>\n","      <td>1.464700</td>\n","    </tr>\n","    <tr>\n","      <td>10</td>\n","      <td>1.309700</td>\n","    </tr>\n","    <tr>\n","      <td>11</td>\n","      <td>1.461600</td>\n","    </tr>\n","    <tr>\n","      <td>12</td>\n","      <td>1.867400</td>\n","    </tr>\n","    <tr>\n","      <td>13</td>\n","      <td>1.854700</td>\n","    </tr>\n","    <tr>\n","      <td>14</td>\n","      <td>1.394800</td>\n","    </tr>\n","    <tr>\n","      <td>15</td>\n","      <td>1.633000</td>\n","    </tr>\n","    <tr>\n","      <td>16</td>\n","      <td>1.238600</td>\n","    </tr>\n","    <tr>\n","      <td>17</td>\n","      <td>2.174100</td>\n","    </tr>\n","    <tr>\n","      <td>18</td>\n","      <td>1.488000</td>\n","    </tr>\n","    <tr>\n","      <td>19</td>\n","      <td>1.521400</td>\n","    </tr>\n","    <tr>\n","      <td>20</td>\n","      <td>1.816000</td>\n","    </tr>\n","    <tr>\n","      <td>21</td>\n","      <td>1.694700</td>\n","    </tr>\n","    <tr>\n","      <td>22</td>\n","      <td>1.614100</td>\n","    </tr>\n","    <tr>\n","      <td>23</td>\n","      <td>1.829700</td>\n","    </tr>\n","    <tr>\n","      <td>24</td>\n","      <td>1.616300</td>\n","    </tr>\n","    <tr>\n","      <td>25</td>\n","      <td>1.269200</td>\n","    </tr>\n","    <tr>\n","      <td>26</td>\n","      <td>1.291600</td>\n","    </tr>\n","    <tr>\n","      <td>27</td>\n","      <td>1.743000</td>\n","    </tr>\n","    <tr>\n","      <td>28</td>\n","      <td>1.525200</td>\n","    </tr>\n","    <tr>\n","      <td>29</td>\n","      <td>1.999000</td>\n","    </tr>\n","    <tr>\n","      <td>30</td>\n","      <td>1.858200</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"}],"source":["trainer_stats = trainer.train()"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","colab":{"base_uri":"https://localhost:8080/"},"id":"pCqnaKmlO1U9","outputId":"5d5d33ee-7a84-4418-b038-bd15fb4614e4"},"outputs":[{"name":"stdout","output_type":"stream","text":["1068.4322 seconds used for training.\n","17.81 minutes used for training.\n","Peak reserved memory = 13.561 GB.\n","Peak reserved memory for training = 9.278 GB.\n","Peak reserved memory % of max memory = 91.995 %.\n","Peak reserved memory for training % of max memory = 62.94 %.\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":["<a name=\"Inference\"></a>\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":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kR3gIAX-SM2q","outputId":"407daa07-ae31-4771-8c31-779665e53bd8"},"outputs":[{"data":{"text/plain":["['<bos><start_of_turn>user\\nContinue the sequence: 1, 1, 2, 3, 5, 8,<end_of_turn>\\n<start_of_turn>model\\n13, 21, 34, 55, 89...\\n\\nThis is the Fibonacci sequence, where each number is the sum of the two preceding ones.\\n<end_of_turn>']"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"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","text = tokenizer.apply_chat_template(\n","    messages,\n","    add_generation_prompt = True, # Must add for generation\n",")\n","outputs = model.generate(\n","    **tokenizer([text], return_tensors = \"pt\").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","text = tokenizer.apply_chat_template(\n","    messages,\n","    add_generation_prompt = True, # Must add for generation\n",")\n","\n","from transformers import TextStreamer\n","_ = model.generate(\n","    **tokenizer([text], return_tensors = \"pt\").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":["<a name=\"Save\"></a>\n","### Saving, loading finetuned models\n","To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n","\n","**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"upcOlWe7A1vc","outputId":"a99a1086-5a2d-4828-d599-7e3634a069cd"},"outputs":[{"data":{"text/plain":["['gemma-3/processor_config.json']"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["model.save_pretrained(\"gemma-3\")  # Local saving\n","tokenizer.save_pretrained(\"gemma-3\")\n","# model.push_to_hub(\"HF_ACCOUNT/gemma-3\", token = \"...\") # Online saving\n","# tokenizer.push_to_hub(\"HF_ACCOUNT/gemma-3\", 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":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MKX_XKs_BNZR","outputId":"d016d936-4bd5-40f8-dffa-bcfad987f489"},"outputs":[{"name":"stdout","output_type":"stream","text":["Okay, let's break down what Gemma-3 is. It's a fascinating development in the world of AI, and here's a comprehensive overview:\n","\n","**1. What it is:**\n","\n","* **A Family of Open-Weight Language Models:** Gemma-3 isn't just *one* model\n"]}],"source":["if False:\n","    from unsloth import FastModel\n","    model, tokenizer = FastModel.from_pretrained(\n","        model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n","        max_seq_length = 2048,\n","        load_in_4bit = True,\n","    )\n","\n","messages = [{\n","    \"role\": \"user\",\n","    \"content\": [{\"type\" : \"text\", \"text\" : \"What is Gemma-3?\",}]\n","}]\n","text = tokenizer.apply_chat_template(\n","    messages,\n","    add_generation_prompt = True, # Must add for generation\n",")\n","\n","from transformers import TextStreamer\n","_ = model.generate(\n","    **tokenizer([text], return_tensors = \"pt\").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":"f422JgM9sdVT"},"source":["### Saving to float16 for VLLM\n","\n","We also support saving to `float16` directly for deployment! We save it in the folder `gemma-3-finetune`. Set `if False` to `if True` to let it run!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iHjt_SMYsd3P"},"outputs":[],"source":["if False: # Change to True to save finetune!\n","    model.save_pretrained_merged(\"gemma-3-finetune\", tokenizer)"]},{"cell_type":"markdown","metadata":{"id":"z6O48DbNIAr0"},"source":["If you want to upload / push to your Hugging Face account, set `if False` to `if True` and add your Hugging Face token and upload location!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZV-CiKPrIFG0"},"outputs":[],"source":["if False: # Change to True to upload finetune\n","    model.push_to_hub_merged(\n","        \"HF_ACCOUNT/gemma-3-finetune\", tokenizer,\n","        token = \"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\"\n","    )"]},{"cell_type":"markdown","metadata":{"id":"TCv4vXHd61i7"},"source":["### GGUF / llama.cpp Conversion\n","To save to `GGUF` / `llama.cpp`, we support it natively now for all models! For now, you can convert easily to `Q8_0, F16 or BF16` precision. `Q4_K_M` for 4bit will come later!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FqfebeAdT073"},"outputs":[],"source":["if False: # Change to True to save to GGUF\n","    model.save_pretrained_gguf(\n","        \"gemma-3-finetune\",\n","        quantization_type = \"Q8_0\", # For now only Q8_0, BF16, F16 supported\n","    )"]},{"cell_type":"markdown","metadata":{"id":"Q974YEVPI7JS"},"source":["Likewise, if you want to instead push to GGUF to your Hugging Face account, set `if False` to `if True` and add your Hugging Face token and upload location!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZgcJIhJ0I_es"},"outputs":[],"source":["if False: # Change to True to upload GGUF\n","    model.push_to_hub_gguf(\n","        \"gemma-3-finetune\",\n","        quantization_type = \"Q8_0\", # Only Q8_0, BF16, F16 supported\n","        repo_id = \"HF_ACCOUNT/gemma-finetune-gguf\",\n","        token = \"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\",\n","    )"]},{"cell_type":"markdown","metadata":{"id":"bB0kj4Q1-NKV"},"source":["Now, use the `gemma-3-finetune.gguf` file or `gemma-3-finetune-Q4_K_M.gguf` file in llama.cpp or a UI based system like Jan or Open WebUI. You can install Jan [here](https://github.com/janhq/jan) and Open WebUI [here](https://github.com/open-webui/open-webui)\n","\n","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 links:\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","6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n","\n","<div class=\"align-center\">\n","  <a href=\"https://unsloth.ai\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n","  <a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n","  <a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a>\n","\n","  Join Discord if you need help + ⭐️ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐️\n","</div>\n"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[{"file_id":"https://github.com/unslothai/notebooks/blob/main/nb/Gemma3_(4B).ipynb","timestamp":1745926411219}]},"kernelspec":{"display_name":"Python 3 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