{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "WE5GJ6s7y0Xo" }, "source": [ "## Fine-tune large models using 🤗 `peft` adapters, `transformers` & `bitsandbytes`\n", "\n", "In this tutorial we will cover how we can fine-tune large language models using the very recent `peft` library and `bitsandbytes` for loading large models in 8-bit.\n", "The fine-tuning method will rely on a recent method called \"Low Rank Adapters\" (LoRA), instead of fine-tuning the entire model you just have to fine-tune these adapters and load them properly inside the model. \n", "After fine-tuning the model you can also share your adapters on the 🤗 Hub and load them very easily. Let's get started!" ] }, { "cell_type": "markdown", "metadata": { "id": "TfBzP8gWzkpv" }, "source": [ "### Install requirements\n", "\n", "First, run the cells below to install the requirements:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "otj46qRbtpnd", "outputId": "2aa109f6-3f4e-4887-a16e-336f51e7cc9a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m10.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m25.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.7/199.7 KB\u001b[0m \u001b[31m25.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m26.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m20.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m72.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", " Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" ] } ], "source": [ "!pip install -q bitsandbytes datasets accelerate\n", "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git" ] }, { "cell_type": "markdown", "metadata": { "id": "FOtwYRI3zzXI" }, "source": [ "### Model loading\n", "\n", "Here let's load the `opt-6.7b` model, its weights in half-precision (float16) are about 13GB on the Hub! If we load them in 8-bit we would require around 7GB of memory instead." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 408, "referenced_widgets": [ "d4de260ffd8a440eb87eb900fc1bb1d3", "8602b545a9f8474dbb3cc178ac0b8e60", "b46919912ee54f6f9f2ce9080be1c61a", "50374e3ab81c4626a182e61fc03b94ce", "2144bc2897dc40b29f060e30ace12275", "949ca70002ca4472bbc21fea4d7ac745", "49943c9dadca43a584b3f354ba45280c", "6123e53fb26b41f0af9a3a3348ae1afd", "285ef943d540400ab827c462945a259c", "95727290446244ccb9626f4594949675", "61b54aa6c9e94ee1bc45c15a9e3f7917", "fc2d5ffe254d425b939252ec46ec27cc", "f65af2e868244edeb0cc9402534874a8", "e466054f08004bbcabb24e400cb3c7fc", "6ea40800dfd849e3b106bae71fc53ae3", "722a01f42b7d4c38836a4546ecb38108", "1bd5179cdb474b65aa06eca3520ad37b", "04d367124a3b419ab1fa1dfd4f9004c3", "4ea667f48b9f4e1f9da7c5a0d3025b85", "9b96c63630654773acd38b8b88371f28", "cb69ae47666a4603a07a8778e2ae7d6e", 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StepTraining Loss
12.364400
22.200400
32.302300
42.184700
51.878700
62.307200
72.193800
82.446200
92.458900
102.020000
111.941200
121.931000
132.055900
141.975100
152.015100
162.095600
171.768300
182.155700
192.402300
202.124600
212.314900
221.908500
232.078800
241.941900
251.879800
261.927500
271.371400
281.977600
292.055000
301.915800
311.958100
322.195900
332.001000
342.025000
351.576900
361.879800
371.821600
381.727800
391.995700
401.698600
412.129300
422.025800
431.696500
441.984700
452.051100
462.054400
471.765600
482.063100
491.746900
501.873000
512.391300
522.494100
532.072300
541.808000
551.911900
562.168100
572.166100
581.921500
591.856000
601.652800
611.605000
622.032500
631.822100
641.623600
651.923200
662.053200
672.114300
681.807700
691.857800
701.854600
712.023000
721.864900
731.769300
741.837700
751.742200
761.895900
771.922800
782.325300
792.231200
802.309500
811.945700
822.072100
831.917400
842.004600
851.951700
861.450600
871.785600
881.668000
891.903100
901.709800
912.312900
922.092100
932.319600
941.603100
951.740000
961.670500
971.611600
981.728900
992.285200
1001.957800
1011.676700
1021.656300
1031.612400
1041.848900
1051.870000
1061.954000
1072.192200
1081.637600
1091.208700
1102.254200
1111.832100
1122.119600
1132.126400
1141.915700
1151.587500
1161.564800
1171.742700
1181.712600
1191.727900
1202.361500
1212.070300
1221.878500
1231.846600
1242.061700
1252.149700
1261.940600
1272.098300
1281.734100
1292.111700
1301.887600
1311.716300
1322.070000
1331.782200
1341.955200
1351.762900
1361.954700
1371.687100
1381.979100
1391.634600
1401.801200
1411.954100
1421.833900
1432.051400
1441.921200
1451.787500
1461.825400
1471.363400
1481.977400
1491.768300
1502.226700
1511.945500

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import transformers\n", "from datasets import load_dataset\n", "\n", "data = load_dataset(\"Abirate/english_quotes\")\n", "data = data.map(lambda samples: tokenizer(samples[\"quote\"]), batched=True)\n", "\n", "trainer = transformers.Trainer(\n", " model=model,\n", " train_dataset=data[\"train\"],\n", " args=transformers.TrainingArguments(\n", " per_device_train_batch_size=4,\n", " gradient_accumulation_steps=4,\n", " warmup_steps=100,\n", " max_steps=200,\n", " learning_rate=2e-4,\n", " fp16=True,\n", " logging_steps=1,\n", " output_dir=\"outputs\",\n", " ),\n", " data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),\n", ")\n", "model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n", "trainer.train()" ] }, { "cell_type": "markdown", "metadata": { "id": "Duak7T_B3VpJ" }, "source": [ "## Share adapters on the 🤗 Hub" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 331, "referenced_widgets": [ "262f01ffc5824b5faa8a61afac12ff67", "c1af10b599da43a3a848f3ba816d7acc", "be673691e713472980fa1132465714b4", "01f8b90f0f184dfdb92c0c3bffb28b0f", "9ad2bd0e92174d339a3a91a38253a180", "bff17ef6aabd4aa681bfd5ad64b808d9", "55633800b60a4336abea6a4adfcfdec1", "73d5c6b4034d49b392b103d889bfb3b4", "a55954b8d7bf4057b0d7aa6a1cb9e91a", "4da42eb3846f423d88e2a6462a0cfce8", "dab39ef354a84be3b37b6f151f9d9b9d", "b88b03326f464c96a5656eef774e36d5", "4eccb670e98043b3b2702821a3060ece", "a333501a50df4b9fa9546d8d965e0dc3", "1f173cb95c5c44f4b32f6cfe10ee3b03", "52c8a7e673f24276a07042388a13b58f", "a0f323ccfbc14fc4b7a5e7046b221ce3" ] }, "id": "DpYr24pR8T_0", "outputId": "20186456-1bd4-4655-b2f2-8f24f9f37fcc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Token is valid.\n", "Your token has been saved to /root/.cache/huggingface/token\n", "Login successful\n" ] } ], "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 133, "referenced_widgets": [ "3dbe077ed0c34e4eb1628418138ccbc6", "a579cb7804774ca3aa9efd800d1af57e", "9f44e3175835470aba43e239661037b2", "549bd12e3af64256a7534903688835a8", "a18d9beb34b848ff8cc541d2cb290c4c", "876cb42184f54b749c442163290c2c45", "43b14e0e1263499dbd592b280cff21b0", "7164feaf360d4300a5083f95064b144a", "bd7547126a874a45b608afed7ab2958b", "26e4b6b94e4540728c59df8e481ce43d", "9d1b77500f1c45308d4791a9c443e307", "b2693135b6954d35afb3120f3caf4000", "aeca2429ee48450a814515cb06acbc3e", "434c09950be04d728cd7ca8d6c134dc6", "34f58c132d2e4249b1e62a0b57c85999", "d148369be26a43949257790cb202728d", "0ecf58c5cbcd40908595fccddff1c6d4", "fc2314656a2745eb921f636cc3451381", "7817f8669b7f449fadf02d7145fa89e2", "06e012eea9714ed589344a362b7421bb", "504e9e5ced0348cc87aafae0c1c372eb", "5b538e8389fb4574a5dfdc554624e3c8" ] }, "id": "VxB6UV5XAvvP", "outputId": "c3b0133b-f5b1-4283-8367-f06524bea46c" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Uploading the following files to ybelkada/opt-6.7b-lora: adapter_config.json,adapter_model.bin\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3dbe077ed0c34e4eb1628418138ccbc6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Upload 1 LFS files: 0%| | 0/1 [00:00Pro Tip: If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. " } }, "c006e62ee6d04831b2b89273ef04a8ac": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_9e5f870ef80242f5af09fad70f84ea62", "placeholder": "​", "style": "IPY_MODEL_6f173ca73dd545deb22b8cd0470d925f", "value": " 685/685 [00:00<00:00, 34.4kB/s]" } }, "c1af10b599da43a3a848f3ba816d7acc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_73d5c6b4034d49b392b103d889bfb3b4", "placeholder": "​", "style": "IPY_MODEL_a55954b8d7bf4057b0d7aa6a1cb9e91a", "value": "


Copy a token from your Hugging Face\ntokens page and paste it below.
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