{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "5e06060e-f3d7-4e1e-b97e-dc57d8d17ce5", "showTitle": false, "title": "" } }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "6831b89a-0776-4014-a3db-9e1860a4c80c", "showTitle": false, "title": "" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "workding dir: /home/inflaton/code/projects/courses/novel-translation\n" ] } ], "source": [ "import os\n", "import sys\n", "from pathlib import Path\n", "\n", "workding_dir = str(Path.cwd().parent)\n", "os.chdir(workding_dir)\n", "sys.path.append(workding_dir)\n", "print(\"workding dir:\", workding_dir)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "1bdd4cdb-cb26-4527-862d-66ea2a7a1f05", "showTitle": false, "title": "" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loading env vars from: /home/inflaton/code/projects/courses/novel-translation/.env\n" ] }, { "data": { "text/plain": [ "True" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dotenv import find_dotenv, load_dotenv\n", "\n", "found_dotenv = find_dotenv(\".env\")\n", "\n", "if len(found_dotenv) == 0:\n", " found_dotenv = find_dotenv(\".env.example\")\n", "print(f\"loading env vars from: {found_dotenv}\")\n", "load_dotenv(found_dotenv, override=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "14807e21-2648-48a3-9916-6c576fc61d2e", "showTitle": false, "title": "" } }, "outputs": [ { "data": { "text/plain": [ "('unsloth/Qwen2-1.5B-Instruct',\n", " True,\n", " 'models/Qwen2-1.5B-Instruct-MAC-',\n", " 'Qwen2-1.5B-Instruct-MAC-',\n", " 2048,\n", " 10,\n", " None,\n", " 'datasets/mac/mac.tsv')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "\n", "model_name = os.getenv(\"MODEL_NAME\")\n", "token = os.getenv(\"HF_TOKEN\") or None\n", "load_in_4bit = os.getenv(\"LOAD_IN_4BIT\") == \"true\"\n", "local_model = os.getenv(\"LOCAL_MODEL\")\n", "hub_model = os.getenv(\"HUB_MODEL\")\n", "num_train_epochs = int(os.getenv(\"NUM_TRAIN_EPOCHS\") or 0)\n", "data_path = os.getenv(\"DATA_PATH\")\n", "\n", "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n", "dtype = (\n", " None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", ")\n", "\n", "model_name, load_in_4bit, local_model, hub_model, max_seq_length, num_train_epochs, dtype, data_path" ] }, { "cell_type": "markdown", "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "bc44b98b-6394-4b2c-af2f-8caa40b28453", "showTitle": false, "title": "" }, "id": "r2v_X2fA0Df5" }, "source": [ "* We support Llama, Mistral, Phi-3, Gemma, Yi, DeepSeek, Qwen, TinyLlama, Vicuna, Open Hermes etc\n", "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n", "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n", "* With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models.\n", "* [**NEW**] We make Phi-3 Medium / Mini **2x faster**! See our [Phi-3 Medium notebook](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "b952e9b9-edf1-4bb8-b52b-bb714852c721", "showTitle": false, "title": "" }, "colab": { "base_uri": "https://localhost:8080/", "height": 353, "referenced_widgets": [ "98c58f23f4d549518832cb2d18f796e8", "09b76013aa9e45efb6deb23a7a0d0925", "39b29a75374b45c0a22506010be2b84e", "78e5400bff924a92a4cc61c4ff18b182", "2a58d04b428c46f4b3dbadd3bc6cd529", "dea41c5260884aa6879b5e1d1697b14f", "89965917796a4f81b899fdc7685f33df", "30cdc32298134cb0be4d41615b9e5774", "47928317548c454bba6358ab132e8dee", "b9b313fd861948f5aba25b24b1518d30", "4c666f4ace3943f8b80ecd20e7503236", "c22f71b1f85843209d7e5321506b9cb9", "1f44c9ce1adf470cbb19784493ed209f", "f1addc4479d849879e743cf9089e6540", "8b3505352a5a42bf910428c40ce40465", "4c4c88d4c701450692fa0f6b0c5764b0", "0c34be936c8145d3ab41282f30a70713", "0a92c56bfa134ef583220d7ef0b13e17", "43dec2ede91341f5af60eb522e18e984", "d8e5318cead340c4adbeaccc05d39225", "49277aeeac16434a865a4d12308b1abc", "2157f01726d748f8a9ae4a00664430da", "fce7a61c25ec4390af43d92b7c473a45", "30307300bc4e4baf96560e30969a82b6", "8fc142b628fb40568730234de1cafde2", "a8464a4c711e4e00aafdfc919b60d07e", "5f40db8173dd4d76b6ef5ed6d9ec8b6e", "e36a3f9eff0e4cf68834d66b0213ae96", "a0037bdccf254159becde630bee3d1db", "4ae7e449e4ea4c729b5f34607c18ebae", "3572201bd4d74a58b7a665f9bdfdcdba", "fb995c740590427b882572c81d4e848c", "201b59ccd9f845e197029b57e424aefc", "cf245afeb1c04f29a24d291608c3d157", "b518dcee69074b87be73957cd810e7ed", "e29104486d594b2992d7285e0ef77371", "6578fd7acdb54c4c93528ea431fd0144", "d35db8148a354c56aaac56dbae22536f", "d891f8d0b1fc462f8008d02bb2a15692", "cced8fd7e998472794f3f3e3018956a5", "a9f0cc51fc3d4d7b874c32dcf1c5bdf2", "2f6c70dd266c4816bfad3fd3d192929a", "370692d819df41828b48c4ad446f977b", "a0bf9160eb2647409b3200270914b90f", "2d18ddf6482c4d97829ac0e5a7b9868f", "9f679ad3ec7f4fe8ad0510ffb57bc2ab", "f2df530d22c74977b249dd9fb5f4829b", "89b2ef0dbfea47ab8e6f8d659e3351d1", "3056b148aa9f4e6e8aa3b61d26886255", "4ea63adfce694725bdba878aef709dd3", "74501720ac7e4dbb911a4a99b3633bc6", "21db8a77b00d4a4e82fdfa608657531f", "6dbbedeca9314e66ae50e44ffa31a414", "b8908fa0df3743ecb9d12983a739104f", "177c78fce95d4b4ab33057c5a048d693", "27155728b6b84cb199c91c940095d0a8", "6b91feeed5464877991ac2c207aebe7c", "cca8113c54c0495daedce1327bf9c68b", "2e63a29e2f7247bba5beede9a568c99f", "5c9d781c28944f3eb86e2a6d44efdf18", "4b2061b8a73c43ffb0c2f83daf0d0183", "69ac12aec0714318bf2c83d4f4e745f5", "e02f9b7849c64531835eb77b860d1c93", "56aee4853b7740e6a977254f5d1fa66d", "b993eaec6b224440bf80c0958c6fb536", "de868e26e7154f62aa86223a539ad421" ] }, "id": "QmUBVEnvCDJv", "outputId": "a0e2d781-4934-415a-90b4-35165b9e44c5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a44529371839466cae7797d068873634", "version_major": 2, "version_minor": 0 }, "text/plain": [ "config.json: 0%| | 0.00/707 [00:00 0 ! Suggested 8, 16, 32, 64, 128\n", " target_modules=[\n", " \"q_proj\",\n", " \"k_proj\",\n", " \"v_proj\",\n", " \"o_proj\",\n", " \"gate_proj\",\n", " \"up_proj\",\n", " \"down_proj\",\n", " ],\n", " lora_alpha=16,\n", " lora_dropout=0, # Supports any, but = 0 is optimized\n", " bias=\"none\", # Supports any, but = \"none\" is optimized\n", " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", " use_gradient_checkpointing=\"unsloth\", # True or \"unsloth\" for very long context\n", " random_state=3407,\n", " use_rslora=False, # We support rank stabilized LoRA\n", " loftq_config=None, # And LoftQ\n", ")" ] }, { "cell_type": "markdown", "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "16e3c2ff-36ff-4895-bfd0-59ab1b2130cc", "showTitle": false, "title": "" }, "id": "vITh0KVJ10qX" }, "source": [ "\n", "### Data Prep\n", "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n", "\n", "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", "\n", "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n", "\n", "If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing).\n", "\n", "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "4426fdab-78f7-4a28-abf7-dc55b19db864", "showTitle": false, "title": "" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loading train/test data files\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "95a1b6aa815f461a8281e33633a28a9b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map: 0%| | 0/4528 [00:00system\\nYou are an expert in translating Chinese into English.<|im_end|>\\n<|im_start|>user\\nTranslate from Chinese to English.\\n全仗着狐仙搭救。<|im_end|>\\n<|im_start|>assistant\\nBecause I was protected by a fox fairy.<|im_end|>',\n", " 'prompt': '<|im_start|>system\\nYou are an expert in translating Chinese into English.<|im_end|>\\n<|im_start|>user\\nTranslate from Chinese to English.\\n全仗着狐仙搭救。<|im_end|>\\n<|im_start|>assistant\\n'},\n", " {'chinese': '老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞迸着，嚓嚓有声。',\n", " 'english': 'Old Geng picked up his shotgun, squinted, and pulled the trigger. Two sparrows crashed to the ground like hailstones as shotgun pellets tore noisily through the branches.',\n", " 'text': '<|im_start|>system\\nYou are an expert in translating Chinese into English.<|im_end|>\\n<|im_start|>user\\nTranslate from Chinese to English.\\n老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞迸着，嚓嚓有声。<|im_end|>\\n<|im_start|>assistant\\nOld Geng picked up his shotgun, squinted, and pulled the trigger. Two sparrows crashed to the ground like hailstones as shotgun pellets tore noisily through the branches.<|im_end|>',\n", " 'prompt': '<|im_start|>system\\nYou are an expert in translating Chinese into English.<|im_end|>\\n<|im_start|>user\\nTranslate from Chinese to English.\\n老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞迸着，嚓嚓有声。<|im_end|>\\n<|im_start|>assistant\\n'})" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datasets[\"train\"][0], datasets[\"test\"][0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "3e839830-d2da-48e3-b6f4-63da7a7b9dab", "showTitle": false, "title": "" } }, "outputs": [ { "data": { "text/plain": [ "({'chinese': '周瑞家的道：“太太说：‘他们原不是一家子； 当年他们的祖和太老爷在一处做官，因连了宗的。',\n", " 'english': \"'She said they don't really belong to the family but were adopted into the clan years ago when your grandfather and theirs were working in the same office.\",\n", " 'text': \"<|im_start|>system\\nYou are an expert in translating Chinese into English.<|im_end|>\\n<|im_start|>user\\nTranslate from Chinese to English.\\n周瑞家的道：“太太说：‘他们原不是一家子； 当年他们的祖和太老爷在一处做官，因连了宗的。<|im_end|>\\n<|im_start|>assistant\\n'She said they don't really belong to the family but were adopted into the clan years ago when your grandfather and theirs were working in the same office.<|im_end|>\",\n", " 'prompt': '<|im_start|>system\\nYou are an expert in translating Chinese into English.<|im_end|>\\n<|im_start|>user\\nTranslate from Chinese to English.\\n周瑞家的道：“太太说：‘他们原不是一家子； 当年他们的祖和太老爷在一处做官，因连了宗的。<|im_end|>\\n<|im_start|>assistant\\n'},\n", " {'chinese': '“听到了吗？',\n", " 'english': \"'Did you hear that?'\",\n", " 'text': \"<|im_start|>system\\nYou are an expert in translating Chinese into English.<|im_end|>\\n<|im_start|>user\\nTranslate from Chinese to English.\\n“听到了吗？<|im_end|>\\n<|im_start|>assistant\\n'Did you hear that?'<|im_end|>\",\n", " 'prompt': '<|im_start|>system\\nYou are an expert in translating Chinese into English.<|im_end|>\\n<|im_start|>user\\nTranslate from Chinese to English.\\n“听到了吗？<|im_end|>\\n<|im_start|>assistant\\n'})" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "datasets[\"train\"][1000], datasets[\"test\"][1000]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "03a3c02c-d3d9-49f4-87b5-2e568c174175", "showTitle": false, "title": "" }, "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "26e4202cca81496a90d15a0dd4ca9cf1", "ba90fdb8822d47dab7ba203bee297f37", "61560ff6a36b44f4a9dfdae5c52791d4", "95fbe66647904c06a20f640630d6dc0e", "57182a263d324a3dbf1471c74290a0d5", "0f8b6bfe16894500838793f2491d403f", "bb19f6c747754682a514373a3a0535ba", "db19fc8d37db4e45a5790a876836d8c4", "36166c7bcb854b34aca1f41a5d6ea50b", "b0a370dc20654b279b9680692e34418e", "cfeb365ddf7548d58b2557f22737fcf5", "73e352a3404f4c7dad0737f57d29e92f", "988a0e8c1f89446086858da0a891a79c", "4ccedf0d93094e63b57a0f8a434fba06", "6b2012c3f88547af8884a9ea90e3164b", "7e29cb8dd4df4d5b94407cd8fd3f2011", "ad2be500fc164c0f86f33e914ef8e6a0", "5234566b1bfc4655b8d582ea5b46ed9f", "4463edd481c1467f914c7dcd6c6e6ffc", "6d3b9a05db0b4dadb638c686faa0c40a", "938f45f1b3e24118b815d96ae34ba86a", "9367047a800747f79c6b225d92397846", "d1b47d39450d4019ae85c9b2f943eeaf", "4dcf6ff672d24983a1877a8431709aa9", "7975adbc2ec5489ea7fa0167e620d85c", "71ce208e20d6483abb9ed923510c86d7", "cfe8cae0e22b495bafa221a63d13b283", "5807d5fb827d490fb3bc698f801ffff5", "c4f2b06a82fd4987b8b659524a7b503b", "6e34619b45934040b6092e6fb01ea7fe", "271ddaa553a042d09b6db7b450643d8f", "d69dc491b3ab44d7852b21873ed7bb7f", "f401d53bf28e44eb906bce6c05412662", "daf4cd890b35422683d22fd30bc71e83", "b0240cd9a4554b29ae11f8051984a1c6", "bc883d4cf13e4f8b8a4fe5f410cb6efd", "99fdbb0300c14c139d1937c646f0cfe7", "c161d94df0f04feba9542237e0856c22", "edaf890370314a218f138015faa0b05d", "697f027529b54ee9956bae78a11e0611", "e9159e03e61f4f56978ece9c3bca49b2", "810ff6c0e17d4fa09a30fef27eacff90", "7358cdad832342c983e31efb8754ab78", "e9adf418296e436fb48bb9f78885598b" ] }, "id": "LjY75GoYUCB8", "outputId": "7e2045fb-9ce9-49b1-b6e7-d5c9bc92455c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<|im_start|>system\n", "You are an expert in translating Chinese into English.<|im_end|>\n", "<|im_start|>user\n", "Translate from Chinese to English.\n", "“听到了吗？<|im_end|>\n", "<|im_start|>assistant\n", "\n", "----------------------------------------\n", "<|im_start|>system\n", "You are an expert in translating Chinese into English.<|im_end|>\n", "<|im_start|>user\n", "Translate from Chinese to English.\n", "“听到了吗？<|im_end|>\n", "<|im_start|>assistant\n", "Did you hear that?<|im_end|>\n", "CPU times: user 1.8 s, sys: 873 ms, total: 2.68 s\n", "Wall time: 2.72 s\n" ] } ], "source": [ "%%time\n", "\n", "prompt1 = datasets[\"test\"][\"prompt\"][1000]\n", "print(prompt1)\n", "print(\"--\" * 20)\n", "test_model(model, tokenizer, prompt1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "22ad05ed-04e7-420f-82bf-8f990efce37c", "showTitle": false, "title": "" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1133/1133 [30:01<00:00, 1.59s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 27min 10s, sys: 2min 52s, total: 30min 2s\n", "Wall time: 30min 1s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "%%time\n", "\n", "predictions = eval_model(model, tokenizer, datasets[\"test\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "eeba4278-d952-4391-8f63-c123e6098ffd", "showTitle": false, "title": "" } }, "outputs": [ { "data": { "text/plain": [ "{'accuracy': 0.00176522506619594,\n", " 'correct_ids': [658, 659],\n", " 'bleu_scores': {'bleu': 0.08285381577653864,\n", " 'precisions': [0.40636974021865224,\n", " 0.12583290620194773,\n", " 0.051405438435685916,\n", " 0.02290685609386224],\n", " 'brevity_penalty': 0.9405675222192741,\n", " 'length_ratio': 0.9422656508777741,\n", " 'translation_length': 28447,\n", " 'reference_length': 30190},\n", " 'rouge_scores': {'rouge1': 0.38844471682897896,\n", " 'rouge2': 0.14120062297432684,\n", " 'rougeL': 0.3280668137668106,\n", " 'rougeLsum': 0.3280344032501499}}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "calc_metrics(datasets[\"test\"][\"english\"], predictions, debug=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "2485caac-9b06-42f5-a4da-213d3e522a06", "showTitle": false, "title": "" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Unnamed: 0 chinese \\\n", "0 0 老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞... \n", "\n", " english \\\n", "0 Old Geng picked up his shotgun, squinted, and ... \n", "\n", " unsloth/Qwen2-0.5B-Instruct(finetuned) \\\n", "0 Old Geng lifted his rifle and narrowed his eye... \n", "\n", " unsloth/Qwen2-1.5B-Instruct \n", "0 Old Geng took up his gun, squinted one of its ... \n" ] } ], "source": [ "save_results(\n", " model_name,\n", " \"results/mac-results.csv\",\n", " datasets[\"test\"],\n", " predictions,\n", " debug=True,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "5c3f9939-9068-4edf-b057-e4898efeb94e", "showTitle": false, "title": "" }, "id": "idAEIeSQ3xdS" }, "source": [ "\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`. We also support TRL's `DPOTrainer`!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "053bd880-409c-4ae0-a5a5-06084ada19d5", "showTitle": false, "title": "" }, "colab": { "base_uri": "https://localhost:8080/", "height": 122, "referenced_widgets": [ "3cf2dd993b5e4d3daecf61e4bab5a404", "087b76a8b7514269b1f0ab29b062e444", "35b0e8c26d6640e9bd0ed7b242a423d8", "54ad89e05fd74576b9b8b5b5a10eaf8d", "a41dc44766444a998bec2d777f249d23", "a069d2ab23824f29aa320ac256e2cfe9", "06e806c82c7b4cbea31c5358dd9c3434", "2e5087c76f98437cb5dc729230358cba", "036fc5746f43416db18c19ad8fd36677", "fdb1941405ed4e4aa06019933892deb3", "668d5377ca56426a99753867e6e24862" ] }, "id": "95_Nn-89DhsL", "outputId": "bce9db22-b022-4e43-de3f-c7ea4c9c3c4e" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6b952d520d494e58811bae80cf5ae883", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map (num_proc=2): 0%| | 0/4528 [00:00\n", " \n", " \n", " [5660/5660 1:32:43, Epoch 10/10]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StepTraining Loss
1001.919100
2001.774900
3001.722600
4001.721900
5001.695700
6001.612500
7001.473700
8001.518000
9001.452100
10001.454900
11001.509600
12001.272200
13001.128400
14001.161200
15001.165600
16001.169700
17001.140900
18000.796500
19000.812800
20000.815000
21000.806600
22000.850100
23000.737200
24000.533900
25000.521600
26000.562600
27000.557700
28000.563000
29000.418500
30000.343000
31000.353900
32000.368300
33000.367600
34000.361000
35000.230000
36000.244000
37000.246400
38000.245400
39000.256800
40000.232000
41000.178700
42000.186600
43000.189200
44000.189600
45000.190100
46000.160900
47000.155000
48000.155300
49000.157400
50000.159500
51000.157000
52000.138300
53000.138600
54000.139500
55000.141400
56000.144900

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1h 23min 59s, sys: 8min 44s, total: 1h 32min 43s\n", "Wall time: 1h 32min 45s\n" ] } ], "source": [ "%%time\n", "\n", "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "e843842b-295f-4020-accf-393934732322", "showTitle": false, "title": "" }, "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "pCqnaKmlO1U9", "outputId": "98f78253-86cf-4673-ff2b-923460c2b3fd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5564.5261 seconds used for training.\n", "92.74 minutes used for training.\n", "Peak reserved memory = 4.152 GB.\n", "Peak reserved memory for training = 0.519 GB.\n", "Peak reserved memory % of max memory = 34.617 %.\n", "Peak reserved memory for training % of max memory = 4.327 %.\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": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "72e8aeca-cd4c-44ee-82cd-04fa6728b40a", "showTitle": false, "title": "" } }, "source": [ "\n", "### Inference\n", "Let's run the model! You can change the instruction and input - leave the output blank!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "3a76b619-1a84-4852-9be7-0f9b2bfa4c05", "showTitle": false, "title": "" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<|im_start|>system\n", "You are an expert in translating Chinese into English.<|im_end|>\n", "<|im_start|>user\n", "Translate from Chinese to English.\n", "老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞迸着，嚓嚓有声。<|im_end|>\n", "<|im_start|>assistant\n", "\n", "----------------------------------------\n", "<|im_start|>system\n", "You are an expert in translating Chinese into English.<|im_end|>\n", "<|im_start|>user\n", "Translate from Chinese to English.\n", "老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞迸着，嚓嚓有声。<|im_end|>\n", "<|im_start|>assistant\n", "Old Geng raised the pistol, squinted one eye, squeezed the trigger, and let a shower of jumbo pigeons drop down from skyward, coursing through the willows as though carried on silkworm tails, tossing tin cans in the air as they fell.<|im_end|>\n", "CPU times: user 3.71 s, sys: 352 ms, total: 4.07 s\n", "Wall time: 4.04 s\n" ] } ], "source": [ "%%time\n", "\n", "prompt1 = datasets[\"test\"][\"prompt\"][0]\n", "print(prompt1)\n", "print(\"--\" * 20)\n", "test_model(model, tokenizer, prompt1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "55d0f54b-a9fc-4eb5-970e-0e9c118619bf", "showTitle": false, "title": "" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Old Geng picked up his shotgun, squinted, and pulled the trigger. Two sparrows crashed to the ground like hailstones as shotgun pellets tore noisily through the branches.\n" ] } ], "source": [ "print(datasets[\"test\"][\"english\"][0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "1aa24900-40c4-45de-b8af-3d1da7070ff7", "showTitle": false, "title": "" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1133/1133 [34:09<00:00, 1.81s/it] " ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 30min 48s, sys: 3min 21s, total: 34min 10s\n", "Wall time: 34min 9s\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "%%time\n", "\n", "predictions = eval_model(model, tokenizer, datasets[\"test\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "68c4520d-e356-491c-9aa9-ecc57316d177", "showTitle": false, "title": "" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Downloading package wordnet to /home/inflaton/nltk_data...\n", "[nltk_data] Package wordnet is already up-to-date!\n", "[nltk_data] Downloading package punkt to /home/inflaton/nltk_data...\n", "[nltk_data] Package punkt is already up-to-date!\n", "[nltk_data] Downloading package omw-1.4 to /home/inflaton/nltk_data...\n", "[nltk_data] Package omw-1.4 is already up-to-date!\n" ] }, { "data": { "text/plain": [ "{'accuracy': 0.00264783759929391,\n", " 'correct_ids': [147, 170, 194],\n", " 'meteor': {'meteor': 0.35503843183028994},\n", " 'bleu_scores': {'bleu': 0.09734851870184895,\n", " 'precisions': [0.38486636126948554,\n", " 0.12903115371448134,\n", " 0.05879839025606325,\n", " 0.030757244091566802],\n", " 'brevity_penalty': 1.0,\n", " 'length_ratio': 1.0050679032792316,\n", " 'translation_length': 30343,\n", " 'reference_length': 30190},\n", " 'rouge_scores': {'rouge1': 0.3809259470501297,\n", " 'rouge2': 0.1543849804952549,\n", " 'rougeL': 0.32312000381943484,\n", " 'rougeLsum': 0.32320284655253784}}" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "calc_metrics(datasets[\"test\"][\"english\"], predictions, debug=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "580351cd-ed04-47eb-82d7-d9fdda0dbeea", "showTitle": false, "title": "" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " index chinese \\\n", "0 0 老耿端起枪，眯缝起一只三角眼，一搂扳机响了枪，冰雹般的金麻雀劈哩啪啦往下落，铁砂子在柳枝间飞... \n", "\n", " english \\\n", "0 Old Geng picked up his shotgun, squinted, and ... \n", "\n", " unsloth/Qwen2-0.5B-Instruct(finetuned) \\\n", "0 Old Geng lifted his rifle and narrowed his eye... \n", "\n", " unsloth/Qwen2-1.5B-Instruct \\\n", "0 Old Geng took up his gun, squinted one of its ... \n", "\n", " unsloth/Qwen2-1.5B-Instruct(finetuned) \n", "0 Old Geng raised the rifle, squeezed one tiny t... \n" ] } ], "source": [ "from llm_toolkit.translation_engine import save_results\n", "\n", "save_results(model_name + \"(finetuned)\", \"results/mac-results.csv\", datasets[\"test\"], predictions, debug=True)" ] }, { "cell_type": "markdown", "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "c510db20-d3c0-4fcc-a5db-ccda6b022f68", "showTitle": false, "title": "" }, "id": "uMuVrWbjAzhc" }, "source": [ "\n", "### Saving, uploading 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." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "5c8ebb0f-88a4-4fd6-ba0d-d3fe2ebcca51", "showTitle": false, "title": "" } }, "outputs": [], "source": [ "def save_model(model, tokenizer, save_method, publish=True):\n", " model.save_pretrained_merged(\n", " local_model + save_method,\n", " tokenizer,\n", " save_method=save_method,\n", " )\n", "\n", " if publish:\n", " model.push_to_hub_merged(\n", " hub_model + save_method,\n", " tokenizer,\n", " save_method=save_method,\n", " token=token,\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "d8315f14-d351-42e6-8215-be7e39033e02", "showTitle": false, "title": "" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Unsloth: Merging 4bit and LoRA weights to 4bit...\n", "This might take 5 minutes...\n", "Done.\n", "Unsloth: Saving tokenizer... Done.\n", "Unsloth: Saving model... This might take 10 minutes for Llama-7b... Done.\n", "Unsloth: Merging 4bit and LoRA weights to 4bit...\n", "This might take 5 minutes...\n", "Done.\n", "Unsloth: Saving 4bit Bitsandbytes model. Please wait...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4888e1237402445b809b3b4bbab4ac25", "version_major": 2, "version_minor": 0 }, "text/plain": [ "README.md: 0%| | 0.00/575 [00:00\n", " \n", " \n", " Support our work if you can! Thanks!\n", "" ] } ], "metadata": { "accelerator": "GPU", "application/vnd.databricks.v1+notebook": { "dashboards": [], "environmentMetadata": null, "language": "python", "notebookMetadata": {}, "notebookName": "06_MAC_+_Qwen2-1.5B_Unsloth_train", "widgets": {} }, "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.9" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "036fc5746f43416db18c19ad8fd36677": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": 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