{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "8fe0634c-c88a-4c89-b956-1f4247c2d503", "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": "e2fc659d-8a38-4a94-bf3c-81b2778c780a", "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": "6601e3ff-e856-4353-98d8-42fcb158f230", "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": 8, "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": "f30d283f-4759-403b-8cc4-94e360a76c04", "showTitle": false, "title": "" } }, "outputs": [ { "data": { "text/plain": [ "('unsloth/Qwen2-0.5B-Instruct',\n", " True,\n", " 'models/Qwen2-0.5B-Instruct-MAC-',\n", " 'Qwen2-0.5B-Instruct-MAC-',\n", " 2048,\n", " 10,\n", " None,\n", " 'datasets/mac/mac.tsv')" ] }, "execution_count": 9, "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": "6f80b3a1-a6e6-43c7-b54f-da10ef37df32", "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": "9df0e65d-07a4-4d5e-8848-c41872280e6f", "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", "==((====))== Unsloth: Fast Qwen2 patching release 2024.5\n", " \\\\ /| GPU: NVIDIA GeForce RTX 4080 Laptop GPU. Max memory: 11.994 GB. Platform = Linux.\n", "O^O/ \\_/ \\ Pytorch: 2.2.2+cu121. CUDA = 8.9. CUDA Toolkit = 12.1.\n", "\\ / Bfloat16 = TRUE. Xformers = 0.0.25.post1. FA = False.\n", " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 10.6 s, sys: 2.07 s, total: 12.6 s\n", "Wall time: 51.9 s\n" ] } ], "source": [ "%%time\n", "\n", "from llm_toolkit.translation_engine import *\n", "\n", "model, tokenizer = load_model(model_name, load_in_4bit)" ] }, { "cell_type": "markdown", "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "adfadd0f-8c01-4f67-b643-cff930c1ce00", "showTitle": false, "title": "" }, "id": "SXd9bTZd1aaL" }, "source": [ "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "2cd85242-237f-4cca-a706-b7664ec9d3e5", "showTitle": false, "title": "" }, "colab": { "base_uri": "https://localhost:8080/" }, "id": "6bZsfBuZDeCL", "outputId": "bc6d9ce7-f82a-4191-d0c5-ec8247d9b9eb" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Unsloth 2024.5 patched 24 layers with 0 QKV layers, 24 O layers and 24 MLP layers.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 9.31 s, sys: 0 ns, total: 9.31 s\n", "Wall time: 2.12 s\n" ] } ], "source": [ "%%time\n", "\n", "model = FastLanguageModel.get_peft_model(\n", " model,\n", " r=16, # Choose any number > 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": "2c3fdf26-130f-4ce7-9c51-d62e1ce17629", "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": "a378fd31-1620-42bc-b97a-82f4ffbdcb11", "showTitle": false, "title": "" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loading train/test data files\n", "DatasetDict({\n", " train: Dataset({\n", " features: ['chinese', 'english', 'text', 'prompt'],\n", " num_rows: 4528\n", " })\n", " test: Dataset({\n", " features: ['chinese', 'english', 'text', 'prompt'],\n", " num_rows: 1133\n", " })\n", "})\n" ] } ], "source": [ "import os\n", "from llm_toolkit.translation_engine import *\n", "\n", "datasets = load_translation_dataset(data_path, tokenizer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "6ac9ac82-aaf3-482b-8cdb-8eab19fde5ae", "showTitle": false, "title": "" } }, "outputs": [ { "data": { "text/plain": [ "({'chinese': '全仗着狐仙搭救。',\n", " 'english': 'Because I was protected by a fox fairy.',\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": 13, "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": "87f73aab-12df-4e4e-b758-ee055e17ed58", "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": 14, "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": "dd425707-88cc-43db-8bc0-e858c8084e16", "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 it?<|im_end|>\n", "CPU times: user 1.62 s, sys: 160 ms, total: 1.78 s\n", "Wall time: 1.8 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": "markdown", "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "53cc91a1-1623-4197-bf82-78cdadad933e", "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": "f1053974-9253-4d4d-a172-1f5fea046745", "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": "c9df9c466cc24f5e8715c02eb6764c3c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map (num_proc=2): 0%| | 0/4528 [00:00\n", " \n", " \n", " [5660/5660 1:02:58, 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
1002.137700
2002.001500
3001.938200
4001.935400
5001.899800
6001.819500
7001.689600
8001.737300
9001.665900
10001.664600
11001.723000
12001.520200
13001.381000
14001.423000
15001.419400
16001.436500
17001.401500
18001.119500
19001.130700
20001.139100
21001.120000
22001.166200
23001.062000
24000.858400
25000.846800
26000.892000
27000.887700
28000.907300
29000.728300
30000.644400
31000.652400
32000.683000
33000.673200
34000.670000
35000.460900
36000.487600
37000.501000
38000.491400
39000.501700
40000.452200
41000.352000
42000.368500
43000.368500
44000.360600
45000.374900
46000.294500
47000.270000
48000.270800
49000.285300
50000.282200
51000.285100
52000.216900
53000.228700
54000.223900
55000.226100
56000.229100

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "application/vnd.databricks.v1+cell": { "cellMetadata": {}, "inputWidgets": {}, "nuid": "ee4dfe12-ac7d-4af0-b8ee-9a3361eb9a48", "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": [ "3779.1598 seconds used for training.\n", "62.99 minutes used for training.\n", "Peak reserved memory = 1.855 GB.\n", "Peak reserved memory for training = 0.656 GB.\n", "Peak reserved memory % of max memory = 15.466 %.\n", "Peak reserved memory for training % of max memory = 5.469 %.\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": "4de3b67e-eeda-4c45-9472-35f105b4c30e", "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": "12c0141c-6a60-4fcd-a285-27eb59aa002b", "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 lifted his gun and squinted over his shoulder, because the pistol started firing.<|im_end|>\n", "CPU times: user 1.71 s, sys: 161 ms, total: 1.87 s\n", "Wall time: 1.86 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": "1ea3d5bc-314e-4c80-9419-bb5dfdd0172a", "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": "f620a94b-4106-4cc9-a2cd-10ddc5f53b7a", "showTitle": false, "title": "" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 1133/1133 [26:28<00:00, 1.40s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 23min 55s, sys: 2min 33s, total: 26min 28s\n", "Wall time: 26min 28s\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": "a4da8fd3-c055-429b-812f-0988fb7cd228", "showTitle": false, "title": "" } }, "outputs": [ { "data": { "text/plain": [ "{'accuracy': 0.00088261253309797,\n", " 'correct_ids': [147],\n", " 'bleu_scores': {'bleu': 0.06508609399238363,\n", " 'precisions': [0.3407579117113485,\n", " 0.09377291935878182,\n", " 0.03598822203642444,\n", " 0.01652015762352228],\n", " 'brevity_penalty': 0.9858565320713017,\n", " 'length_ratio': 0.9859556144418682,\n", " 'translation_length': 29766,\n", " 'reference_length': 30190},\n", " 'rouge_scores': {'rouge1': 0.32340459562777546,\n", " 'rouge2': 0.11259712507132531,\n", " 'rougeL': 0.2671219091010598,\n", " 'rougeLsum': 0.2670685844265569}}" ] }, "execution_count": 25, "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": "c65f8c79-a8fe-4256-9a9d-2451ee7164ba", "showTitle": false, "title": "" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " chinese \\\n", "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" ] } ], "source": [ "from llm_toolkit.translation_engine import save_results\n", "\n", "save_results(model_name + \"(finetuned)\", \"results/mac-results.tsv\", datasets[\"test\"], predictions, debug=True)" ] } ], "metadata": { "accelerator": "GPU", "application/vnd.databricks.v1+notebook": { "dashboards": [], "environmentMetadata": null, "language": "python", "notebookMetadata": {}, "notebookName": "05_MAC_+_Qwen2-0.5B_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": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "06e806c82c7b4cbea31c5358dd9c3434": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": 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