{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Zi65RoJIkMKI" }, "source": [ "# Installing Dependencies" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pwMj-8TYjcw6", "outputId": "9f7bdbc0-1f69-43db-9f29-f95907ad3f59" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n", "\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n" ] } ], "source": [ "! pip install -q datasets transformers trl peft accelerate bitsandbytes auto-gptq optimum pandas scikit-learn matplotlib" ] }, { "cell_type": "markdown", "metadata": { "id": "YgiSwLTjd7Pm" }, "source": [ "# Loading Dataset" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 215, "referenced_widgets": [ "ecf7e9bf414944f5bd595b40a8ff8887", "3200d015c5c447a09d14c19f5cfe87cb", "6c690d2f392f4b0bbde78b8f257e55a3", "b1da0aacfcfc4cf9806d41d4ec30ed76", "efa5f7b2994443309aa789aa68b873a1", "7d37b85ed4a94453bed1ef673c557f66", "aa5f6423a21e404e90a3c41f29c10d03", "733828a60ec945878f3905c872186b30", "19bcbecf169744bbb82282fb022ffcf7", "94431376ceda486c9db942cf9fbd33ba", "86a77f0ec0c94b2e87cac21acfc38606", "7a8bc6f5c12e4dfcbad16ba8efe312d6", "2f4713ea007741a582bb71cdd263f2fe", "528abb4364c147ca8e62922fdf5d8c96", "7303d8f11e244451894a29272ff0c54f", "dcbf8abb61ed40c8b59df4694733e3f0", "b8b79b0b9b03414180cc365a97b1ff3b", "77e6f9636d714572ad199962758bf746", "00c67909f4e44cb5a9633817cff5bab7", "e23c2ab2165f43bd94f435987f9e0dd4", "b04227f7011e4a53ae98442eb145e295", "ed4de2bf3c4543ad9894ab1996a08bc6", "109a9e434d9643588be8e1f7daa664ef", "4786789860264b33aae6361cfe6bfe21", "474a17567aae487f999ea69db76b1ece", "ecdc0179ea184b3ab4eb9a1357fff3d8", "34bebb0e98d34261aa9b7d58173a2550", "2e23899ecedc46519d3503bf098f6356", "43818c0e38254d81bdc8ba049e3aef2d", "ce3d7fc830af46a6999f2c5191ad3418", "6e9cd4e5d2d549a5a155db7b2cae5af8", "5eed37c4cc034924a091766d37962eda", "c0a5a53e12ff44c6a9bce0812e9af79f" ] }, "id": "IReclv6-d0n5", "outputId": "6f59f553-7d91-476f-858c-44dcc48f7fb5" }, "outputs": [], "source": [ "from datasets import load_dataset\n", "dataset = load_dataset(\"Harshvardhan27/Wikicorpus_Fine_Tuned_Mistral_FinalCheckpoint\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lbuq9jdke-p8", "outputId": "15da09f0-6fd7-4305-8b7c-938b68d3166c" }, "outputs": [ { "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['id', 'title', 'text', 'input_prompt', 'output_text', 'output_length', 'output_cleaned'],\n", " num_rows: 1000\n", " })\n", " test: Dataset({\n", " features: ['id', 'title', 'text', 'input_prompt', 'output_text', 'output_length', 'output_cleaned'],\n", " num_rows: 200\n", " })\n", "})" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VeCk49xOKn8x", "outputId": "7601d42b-d8ec-4404-e3e8-be6dd9ad5c8d" }, "outputs": [ { "data": { "text/plain": [ "{'id': '5599879',\n", " 'title': 'Amine (singer)',\n", " 'text': 'Amine Mounder is a French R&B singer born in Casablanca, Morocco. He is known for his singular music style, Raï\\'n\\'B, which is a mix of R&B and Raï.\\n\\nHis biggest hits are \"Sobri (notre Destin),\" a duet with French singer Leslie, and \"J\\'voulais.\" Both reached the #1 spot on the French singles chart.\\n\\n\\n\\nDiscography.\\n\\nAlbums.\\n\\nAu delà des rêves (31 December 2005) #16 FR;\\n\\n\\n\\nSingles.\\n\\n2005 \"Ma Vie\" #15 FR;\\n\\n2006 \"J\\'voulais\" #1 FR;\\n\\n2006 \"My Girl\" #29;\\n\\n\\n\\nExternal links.\\n\\nOfficial website (in French);\\n\\nEMI Music France;',\n", " 'input_prompt': 'Amine Mounder is a French R&B singer born',\n", " 'output_text': \"ень 1982 in mohammedia algeria he is best known for his 2009 single which has over 16 million views on youtube as of 2013 he has released three albums and several singles since 2004 and has collaborated with french rappers including saïd and mc solaar and singers like kenza farah and zied he has also acted in several films including 2006's la and 2007's 36 quai des orfèvres in ень 2009 he was sentenced to a year in prison for ень 2010 he was sentenced to another year in prison for drug possession the united states army corps of engineers ( usace ) is a major agency of the united states department of the army responsible for implementing civilian works such as river and flood control navigable waterways and military construction the corps also operates locks and dams in the united states and around the world and manages more than 1 @@ 200 miles ( 1 @@ 900 km ) of the chicago san die\",\n", " 'output_length': 258,\n", " 'output_cleaned': \"ень 1982 in mohammedia algeria he is best known for his 2009 single which has over 16 million views on youtube as of 2013 he has released three albums and several singles since 2004 and has collaborated with french rappers including saïd and mc solaar and singers like kenza farah and zied he has also acted in several films including 2006's la and 2007's 36 quai des orfèvres in ень 2009 he was sentenced to a year in prison for ень 2010 he was sentenced to another year in prison for drug possession the united states army corps of engineers ( usace ) is a major agency of the united states department of the army responsible for implementing civilian works such as river and flood control navigable waterways and military construction the corps also operates locks and dams in the united states and around the world and manages more than 1 @@ 200 miles ( 1 @@ 900 km ) of the chicago san die\"}" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset['train'][0]" ] }, { "cell_type": "markdown", "metadata": { "id": "rb1y_o_8dJ_I" }, "source": [ "# Truncating output_text to 126\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 247, "referenced_widgets": [ "6bbfdd0314734c0fb73551e3100ff04a", "fc8896f2838643ceb0c0cfa5c3e6f74a", "f1181b8559f840b9865767f0a6a5c5f5", "65a1568514a94b048c7bb4d607d51cdb", "bb066b6253b943ef802f839e5c0e67fa", "c3de7ff51cb542e5be84fd7f3dfffd65", "ff9248327ee04a8b82ad8af3759d0ec0", "6aa97b4fa988476cbe0959fbd034adfb", "31d2db83a59f463ab42e8ce4f8de0192", "b6186ccbf0f9424a85e615022aff1c7f", "eb818ba68f1c494bba065ceb3c4b52fa", "08a5524c34c8449299a8969b3fa35d58", "6e47279f09704cc399aa28add35b27f5", "88e069b0bcd043058e8f676fedbe0541", "928405824f1e4be29a7aee26ebd6e128", "001923ee86ba442091918d473ccbe4dc", "dc441e9a4ca84de380eb98f4be9a5aca", "79f9e34aa2ab49f1bfb7394f5ce3fb4f", "a30aa991f7524fa488e3beb99fab2122", "67c8ea0d4eb24492865d25843db801c3", "b3c95eee07304aedacc6ef2f2a336886", "ff827fba68e449a88a85d3c26eca0b10", "f01094cfb58e4fd1ab8f984c0579e44e", "a76b06f5ff434f6baea2703f22a4f366", "ea091764ee874ff4b9df6447431fca0a", "8d08c66ac869481a98bb0ac302104733", "0814b3af892a4410af378c72c53db309", "eb67ef65fddd411b81d175c69e56a6cc", "20e5e85076b84330aeee265284aa50ee", "e7beacdf69494450b61806edbeaea3ed", "cbe9dda4a6f74e859a3e69e45ad035ee", "460d894b311a4298a918bbc06a1e069f", "c369243a4f1f47608130e90ed72029e8", "9bce4ff9441d4d2c9e60469500209c02", "1ebde148dd7140ceb0fb31beb54e042d", "ae85d2d127444be69074218ce3e209d9", "d8e62650094646a4a49e70d66923bc81", "5b6d4cb16df64629aee303a31b453d47", "75142341108145769fbc1e7d5812ce7c", "8532a3f227bf4beb8ff5c144be07213a", "b92063569dba463880a6a36e5928cd34", "2ecc1c8631284461850dfe7d7606840e", "8d02fa1507834a809dfe797531b1df3c", "1ba724f1001945278566a95d32b2a232" ] }, "id": "ckjk3R9_dRgn", "outputId": "a77aabc2-7ad8-44c8-a813-73c33e806540" }, "outputs": [ { "data": { "text/plain": [ "LlamaTokenizerFast(name_or_path='mistralai/Mistral-7B-v0.1', vocab_size=32000, model_max_length=1000000000000000019884624838656, is_fast=True, padding_side='left', truncation_side='right', special_tokens={'bos_token': '', 'eos_token': '', 'unk_token': ''}, clean_up_tokenization_spaces=False), added_tokens_decoder={\n", "\t0: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t1: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "\t2: AddedToken(\"\", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True),\n", "}" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_checkpoint = \"mistralai/Mistral-7B-v0.1\"\n", "from transformers import AutoTokenizer\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)\n", "tokenizer" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000 200\n" ] } ], "source": [ "train, test = 0, 0\n", "for i in range(len(dataset['train'])):\n", " initial_token_count = len(tokenizer.tokenize(dataset['train'][i]['output_cleaned']))\n", " if initial_token_count > 126:\n", " train += 1\n", "\n", "for i in range(len(dataset['test'])):\n", " initial_token_count = len(tokenizer.tokenize(dataset['test'][i]['output_cleaned']))\n", " if initial_token_count > 126:\n", " test += 1\n", "print(train, test)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'id': '5599879',\n", " 'title': 'Amine (singer)',\n", " 'text': 'Amine Mounder is a French R&B singer born in Casablanca, Morocco. He is known for his singular music style, Raï\\'n\\'B, which is a mix of R&B and Raï.\\n\\nHis biggest hits are \"Sobri (notre Destin),\" a duet with French singer Leslie, and \"J\\'voulais.\" Both reached the #1 spot on the French singles chart.\\n\\n\\n\\nDiscography.\\n\\nAlbums.\\n\\nAu delà des rêves (31 December 2005) #16 FR;\\n\\n\\n\\nSingles.\\n\\n2005 \"Ma Vie\" #15 FR;\\n\\n2006 \"J\\'voulais\" #1 FR;\\n\\n2006 \"My Girl\" #29;\\n\\n\\n\\nExternal links.\\n\\nOfficial website (in French);\\n\\nEMI Music France;',\n", " 'input_prompt': 'Amine Mounder is a French R&B singer born',\n", " 'output_text': \"ень 1982 in mohammedia algeria he is best known for his 2009 single which has over 16 million views on youtube as of 2013 he has released three albums and several singles since 2004 and has collaborated with french rappers including saïd and mc solaar and singers like kenza farah and zied he has also acted in several films including 2006's la and 2007's 36 quai des orfèvres in ень 2009 he was sentenced to a year in prison for ень 2010 he was sentenced to another year in prison for drug possession the united states army corps of engineers ( usace ) is a major agency of the united states department of the army responsible for implementing civilian works such as river and flood control navigable waterways and military construction the corps also operates locks and dams in the united states and around the world and manages more than 1 @@ 200 miles ( 1 @@ 900 km ) of the chicago san die\",\n", " 'output_length': 258,\n", " 'output_cleaned': \"ень 1982 in mohammedia algeria he is best known for his 2009 single which has over 16 million views on youtube as of 2013 he has released three albums and several singles since 2004 and has collaborated with french rappers including saïd and mc solaar and singers like kenza farah and zied he has also acted in several films including 2006's la and 2007's 36 quai des orfèvres in ень 2009 he was sentenced to a year in prison for ень 2010 he was sentenced to another year in prison for drug possession the united states army corps of engineers ( usace ) is a major agency of the united states department of the army responsible for implementing civilian works such as river and flood control navigable waterways and military construction the corps also operates locks and dams in the united states and around the world and manages more than 1 @@ 200 miles ( 1 @@ 900 km ) of the chicago san die\"}" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset['train'][0]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "list1 = []\n", "max_token_count = 126\n", "for i in range(1000):\n", " tokens = tokenizer.tokenize(dataset['train'][i]['output_cleaned'])\n", " initial_token_count = len(tokens)\n", " truncated_tokens = tokens[:max_token_count]\n", " text = tokenizer.convert_tokens_to_string(truncated_tokens)\n", " updated_token_count = len(tokenizer.tokenize(text))\n", " list1.append(text)\n", " #print(f\"Original Token Count: {initial_token_count}, Updated Token Count: {updated_token_count}\")" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "list2 = []\n", "max_token_count = 126\n", "for i in range(200):\n", " tokens = tokenizer.tokenize(dataset['test'][i]['output_cleaned'])\n", " initial_token_count = len(tokens)\n", " truncated_tokens = tokens[:max_token_count]\n", " text = tokenizer.convert_tokens_to_string(truncated_tokens)\n", " updated_token_count = len(tokenizer.tokenize(text))\n", " list2.append(text)\n", " #print(f\"Original Token Count: {initial_token_count}, Updated Token Count: {updated_token_count}\")" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "train = pd.DataFrame(list1, columns=['text'])\n", "test = pd.DataFrame(list2, columns=['text'])" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "from datasets import Dataset, DatasetDict\n", "import pandas as pd\n", "\n", "train = Dataset.from_pandas(train)\n", "test = Dataset.from_pandas(test)\n", "\n", "dataset = DatasetDict({\n", " 'train': train,\n", " 'test': test\n", "})" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "126" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(tokenizer.tokenize(dataset['train'][0]['text']))" ] }, { "cell_type": "markdown", "metadata": { "id": "JmzIdl7ohwtN" }, "source": [ "# Dataset saving" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "id": "F7ATJioEiNzF" }, "outputs": [], "source": [ "from datasets import load_dataset\n", "import pandas as pd\n", "for split, save_data in dataset.items():\n", " # Convert to pandas DataFrame\n", " df = pd.DataFrame(save_data)\n", "\n", " # Export to CSV\n", " df.to_csv(f\"{split}_data.csv\", index=False)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-ZQ3FNgFkZU6", "outputId": "4a3db4ad-8250-4c22-a482-ed5b9efe1ba7" }, "outputs": [ { "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['text'],\n", " num_rows: 1000\n", " })\n", " test: Dataset({\n", " features: ['text'],\n", " num_rows: 200\n", " })\n", "})" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "B06WSdvvlQEo", "outputId": "98de9d35-80bb-4946-cb4f-a51a4f149862" }, "outputs": [ { "data": { "text/plain": [ "{'text': 'щем by the irish recorded music association ( irma ) and published by the интернет на в интервью с ёжиком 2009 года из 100 самых знаменитых людей по версии газеты « Комсомольская правда» занял 22 @@ е место на 2 @@ е месте удона ( 1 @@ е место в ноябре 2008 года ) находится 4 @@ е место среди самых'}" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset['train'][10]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kfb9yVOTlVsB", "outputId": "e2f617bd-a5df-4a8f-d4e5-b4465c0dfadf" }, "outputs": [ { "data": { "text/plain": [ "{'text': 'й team that plays in the Arizona League the Giants are affiliated with the San Francisco Giants and play their home games at Scottsdale Stadium in Scottsdale Arizona the Giants franchise began play in 2007 as the Scottsdale Phillies when it was affiliated with the philadelphia phillies the team became the Scottsdale Giants in 2010 when the franchise affiliation switched to the san francisco giants the arizona league season runs from june through august the team is owned and operated by the й corporation which also owns and operates the major league spring '}" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset['test'][10]" ] }, { "cell_type": "markdown", "metadata": { "id": "yLfVtVBekXpK" }, "source": [ "# Importing Dependencies" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "id": "V1l9lXtFkTpg" }, "outputs": [], "source": [ "import torch\n", "import pandas as pd\n", "from datasets import load_dataset, Dataset\n", "from peft import LoraConfig, prepare_model_for_kbit_training, get_peft_model\n", "from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig, BitsAndBytesConfig, TrainingArguments, TrainerCallback\n", "from trl import SFTTrainer\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "metadata": { "id": "KlxU4Y-cl0KX" }, "source": [ "# Chatbot Config" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "id": "ZQp4uYEtl2cl" }, "outputs": [], "source": [ "class Config:\n", " MODEL_ID = \"mistralai/Mistral-7B-v0.1\" # mistralai/Mistral-7B-v0.1 TheBloke/Mistral-7B-v0.1-GPTQ\n", " # DATASET_ID = dataset_dict\n", " CONTEXT_FIELD= \"\"\n", " INSTRUCTION_FIELD = \"text\"\n", " TARGET_FIELD = \"label\"\n", " DATASET_TEXT_FIELD = \"text\"\n", "\n", " BITS = 4\n", " DISABLE_EXLLAMA = True\n", " DEVICE_MAP = \"auto\"\n", " USE_CACHE = False\n", "\n", " LORA_R = 16\n", " LORA_ALPHA = 32\n", " LORA_DROPOUT = 0.05\n", " BIAS = \"none\"\n", " TARGET_MODULES = ['q_proj','k_proj','v_proj','o_proj']\n", " TASK_TYPE = \"CAUSAL_LM\"\n", "\n", " OUTPUT_DIR = \"./Mistral_finetuned_adapters\"\n", "\n", " BATCH_SIZE = 16\n", " GRAD_ACCUMULATION_STEPS = 1\n", " OPTIMIZER = \"paged_adamw_32bit\"\n", " LR = 1e-4\n", " LR_SCHEDULER = \"cosine\"\n", " LOGGING_STEPS = 50\n", " SAVE_STRATEGY = \"epoch\"\n", " EVALUATION_STRATEGY = \"epoch\"\n", " NUM_TRAIN_EPOCHS = 4 # epochs setting different for reference model\n", "\n", " # MAX_STEPS = 250\n", " FP16 = False\n", " BF16 = True\n", " PUSH_TO_HUB = False\n", " MAX_SEQ_LENGTH = 128\n", " PACKING = False\n", "\n", " use_4bit = True\n", " bnb_4bit_compute_dtype = \"float16\"\n", " bnb_4bit_quant_type = \"nf4\"\n", " bnb_4bit_use_double_quant=True" ] }, { "cell_type": "markdown", "metadata": { "id": "fVxiX-UllcKd" }, "source": [ "# Model Trainer" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "id": "jpj9gBxula4Z" }, "outputs": [], "source": [ "class ModelTrainer:\n", "\n", " def __init__(self):\n", "\n", " '''\n", " A Trainer used to train the Zephyr 7B model which beats Llama2-70b-chat model for your custom usecase\n", "\n", " Initialized:\n", " config: Parameters required for the trainer to create and process dataset, train and save model finally\n", " tokenizer: Tokenizer required in training loop\n", " '''\n", "\n", " self.config = Config()\n", " self.tokenizer = AutoTokenizer.from_pretrained(self.config.MODEL_ID)\n", " self.tokenizer.pad_token = self.tokenizer.unk_token\n", "\n", " def process_data_sample(self, example):\n", "\n", " processed_example =f'''{example[self.config.INSTRUCTION_FIELD]} '''\n", " return processed_example\n", "\n", " def create_dataset(self):\n", "\n", "\n", " # data = load_dataset(self.config.DATASET_ID, split=\"train\")\n", " data = dataset\n", " print(\"\\n====================================================================\\n\")\n", " print(\"\\t\\t\\tDOWNLOADED DATASET\")\n", " print(\"\\n====================================================================\\n\")\n", "\n", " # Access the 'train' dataset\n", " temp_train_dataset = data['train']\n", "\n", " # Convert the 'train' dataset to a list of dictionaries\n", " temp_data = [item for item in temp_train_dataset]\n", "\n", " # Create a DataFrame from the list\n", " big_df = pd.DataFrame(temp_data)\n", " df = big_df #[:1000]\n", " # df = data.to_pandas()\n", " df[self.config.DATASET_TEXT_FIELD] = df[[self.config.INSTRUCTION_FIELD]].apply(lambda x: self.process_data_sample(x), axis=1)\n", "\n", " print(\"\\n====================================================================\\n\")\n", " print(\"\\t\\t\\tPROCESSED DATASET\")\n", " print(df.iloc[0])\n", " print(\"\\n====================================================================\\n\")\n", "\n", " processed_data = Dataset.from_pandas(df[[self.config.DATASET_TEXT_FIELD]])\n", " return processed_data\n", "\n", " def create_test_dataset(self):\n", "\n", " '''\n", " Downloads and processes the dataset\n", "\n", " Returns:\n", " processed_data: Training ready processed dataset\n", " '''\n", "\n", " # data = load_dataset(self.config.DATASET_ID, split=\"train\")\n", " data = dataset\n", " print(\"\\n====================================================================\\n\")\n", " print(\"\\t\\t\\tDOWNLOADED DATASET\")\n", " print(\"\\n====================================================================\\n\")\n", "\n", " # Access the 'train' dataset\n", " temp_test_dataset = data['test']\n", "\n", " # Convert the 'train' dataset to a list of dictionaries\n", " temp_data = [item for item in temp_test_dataset]\n", "\n", " # Create a DataFrame from the list\n", " big_df = pd.DataFrame(temp_data)\n", " df = big_df\n", " # df = data.to_pandas()\n", " df[self.config.DATASET_TEXT_FIELD] = df[[self.config.INSTRUCTION_FIELD]].apply(lambda x: self.process_data_sample(x), axis=1)\n", "\n", " print(\"\\n====================================================================\\n\")\n", " print(\"\\t\\t\\tPROCESSED DATASET\")\n", " print(df.iloc[0])\n", " print(\"\\n====================================================================\\n\")\n", "\n", " processed_data = Dataset.from_pandas(df[[self.config.DATASET_TEXT_FIELD]])\n", " return processed_data\n", "\n", " def prepare_model(self):\n", "\n", " '''\n", " Prepares model for finetuning by quantizing it and attaching lora modules to the model\n", "\n", " Returns:\n", " model - Model ready for finetuning\n", " peft_config - LoRA Adapter config\n", " '''\n", "\n", " bnb_config = BitsAndBytesConfig(\n", " load_in_4bit=self.config.use_4bit,\n", " bnb_4bit_quant_type=self.config.bnb_4bit_quant_type,\n", " bnb_4bit_compute_dtype=self.config.bnb_4bit_compute_dtype,\n", " bnb_4bit_use_double_quant=self.config.bnb_4bit_use_double_quant,\n", " )\n", " # bnb_config = GPTQConfig(\n", " # bits=self.config.BITS,\n", " # disable_exllama=self.config.DISABLE_EXLLAMA,\n", " # tokenizer=self.tokenizer\n", " # )\n", "\n", " model = AutoModelForCausalLM.from_pretrained(\n", " self.config.MODEL_ID,\n", " quantization_config=bnb_config,\n", " device_map=self.config.DEVICE_MAP\n", " )\n", "\n", " print(\"\\n====================================================================\\n\")\n", " print(\"\\t\\t\\tDOWNLOADED MODEL\")\n", " print(model)\n", " print(\"\\n====================================================================\\n\")\n", "\n", " if torch.cuda.device_count() > 1: # If more than 1 GPU\n", " model.is_parallelizable = True\n", " model.model_parallel = True\n", "\n", " model.config.use_cache=self.config.USE_CACHE\n", " model.config.pretraining_tp=1\n", " model.gradient_checkpointing_enable()\n", " model = prepare_model_for_kbit_training(model)\n", "\n", " print(\"\\n====================================================================\\n\")\n", " print(\"\\t\\t\\tMODEL CONFIG UPDATED\")\n", " print(\"\\n====================================================================\\n\")\n", "\n", " peft_config = LoraConfig(\n", " r=self.config.LORA_R,\n", " lora_alpha=self.config.LORA_ALPHA,\n", " lora_dropout=self.config.LORA_DROPOUT,\n", " bias=self.config.BIAS,\n", " task_type=self.config.TASK_TYPE,\n", " target_modules=self.config.TARGET_MODULES\n", " )\n", "\n", " model = get_peft_model(model, peft_config)\n", " model.print_trainable_parameters()\n", "\n", " print(\"\\n====================================================================\\n\")\n", " print(\"\\t\\t\\tPREPARED MODEL FOR FINETUNING\")\n", " print(model)\n", " print(\"\\n====================================================================\\n\")\n", "\n", " return model, peft_config\n", "\n", " def set_training_arguments(self):\n", "\n", " '''\n", " Sets the arguments for the training loop in TrainingArguments class\n", " '''\n", "\n", " training_arguments = TrainingArguments(\n", " output_dir=self.config.OUTPUT_DIR,\n", " per_device_train_batch_size=self.config.BATCH_SIZE,\n", " gradient_accumulation_steps=self.config.GRAD_ACCUMULATION_STEPS,\n", " optim=self.config.OPTIMIZER,\n", " learning_rate=self.config.LR,\n", " lr_scheduler_type=self.config.LR_SCHEDULER,\n", " save_strategy=self.config.SAVE_STRATEGY,\n", " evaluation_strategy=self.config.EVALUATION_STRATEGY,\n", " logging_steps=self.config.LOGGING_STEPS,\n", " num_train_epochs=self.config.NUM_TRAIN_EPOCHS,\n", " # max_steps=self.config.MAX_STEPS,\n", " fp16=self.config.FP16,\n", " bf16=self.config.BF16,\n", " push_to_hub=self.config.PUSH_TO_HUB\n", " )\n", "\n", " return training_arguments\n", "\n", " class PerplexityCallback(TrainerCallback):\n", " def __init__(self):\n", " # Initialize any necessary variables\n", " pass\n", "\n", " def on_evaluate(self, args, state, control, metrics=None, **kwargs):\n", " # This is a new method that you can call at the end of evaluation to calculate perplexity\n", " if metrics and 'eval_loss' in metrics:\n", " eval_loss = metrics['eval_loss']\n", " perplexity = torch.exp(torch.tensor(eval_loss)).item()\n", " # Update the metrics with the Perplexity value\n", " metrics['perplexity'] = perplexity\n", " else:\n", " print(\"Eval loss not found in metrics at this step.\")\n", "\n", " def train(self):\n", "\n", " '''\n", " Trains the model on the specified dataset in config\n", " '''\n", "\n", " train_data = self.create_dataset()\n", " test_data = self.create_test_dataset()\n", " model, peft_config = self.prepare_model()\n", " training_args = self.set_training_arguments()\n", " ppl_callback = self.PerplexityCallback()\n", "\n", " print(\"\\n====================================================================\\n\")\n", " print(\"\\t\\t\\tPREPARED FOR FINETUNING\")\n", " print(\"\\n====================================================================\\n\")\n", "\n", " trainer = SFTTrainer(\n", " model=model,\n", " train_dataset=train_data,\n", " eval_dataset=test_data,\n", " peft_config=peft_config,\n", " dataset_text_field=self.config.DATASET_TEXT_FIELD,\n", " args=training_args,\n", " tokenizer=self.tokenizer,\n", " packing=self.config.PACKING,\n", " max_seq_length=self.config.MAX_SEQ_LENGTH,\n", " callbacks=[ppl_callback]\n", " )\n", " trainer.train()\n", "\n", " print(\"\\n====================================================================\\n\")\n", " print(\"\\t\\t\\tFINETUNING COMPLETED\")\n", " print(\"\\n====================================================================\\n\")\n", "\n", " # trainer.push_to_hub()\n", " # Save the tokenizer and model in the same directory\n", " output_dir = \"./Mistral_finetuned_Final_Adapter\"\n", " # Save model's state dictionary and configuration\n", " model.save_pretrained(output_dir)\n", " # Save tokenizer's configuration and vocabulary\n", " self.tokenizer.save_pretrained(output_dir)\n", "\n", " print(\"\\n====================================================================\\n\")\n", " print(\"\\t\\t\\tFINETUNED MODEL SAVED\")\n", " print(\"\\n====================================================================\\n\")" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "6cca0c90726d4e51adeadfd228e7c885", "49c1ddf4d9004f68b38169056cf05a72", "88d7182125d6491eb1896c4417d11c7a", "9a8b06ec50204437be6478709459eea8", "51a2a35d359b4086bfd1194e64a8f439", "9c5de661bf0646958928b7b51a0e2fb2", "09e880647f7b4eb18749c99e94a41b1e", "b073697a0b234d42b5c394d82adc6fa3", "436563a1774e4f0ea68357ac8413bfb8", "f13f54869d4949b287e18193a349b6fe", "a1386a2ebb0b4a4b8d07ab74e79bbd0a", "6256ceeb5ffd4cdabbc92c086910141d", "52010b2ade314bd08dbfeb1a1595815d", "b27c749031404182932d458679e08a4c", "61cf24eb48474ed8abab3a5910f8577f", "73e06af7c36849ed9fc1f7946b9f32f0", "f5f4a9235aa84ac9a00a668d837d536e", "05e804e15df34204bb3e5d2e756aace5", "0f1bab3fec7d42e9a87fe3a2791ec69c", "12f67b59e5a44f2db9ade0ed43da5d25", "ea16e693aa9b4f619ab31c188761116d", "88210d95729b45d2a56364fb086910f3", "3ef6524992484415ae87afc4e2943ebf", "cf3903ab728e428c9b74779a38c75d0a", "867db00e4d8e49ef906ee74e51b74f57", "366615a86fed474491ad8e01fe263577", "6200a01cf2154b9291df27f81198e81c", "7efe73b92dd5406282ee5d3d63a3abaf", "af33cf8af0c4485fa7af82bfca947ae7", "80947a8b4d6e42a6a8e01e6089eb2619", "a4572976b6a54216a2d76d91c0cdb934", "6dd98e1dbfd5430baa53cc7a3b1c553c", "6da5f7a0b2c24f64867dd0e56978963a", "6cb2fd45436e49fca03f792e1feb194f", "5a32e1a990ec45548bdedae3653c4075", "ab56d2436fab4e988c6852f1fd118eac" ] }, "id": "jUhYereLF380", "outputId": "01e77dc7-11ea-4c3f-c7c7-8b933a236355" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "====================================================================\n", "\n", "\t\t\tDOWNLOADED DATASET\n", "\n", "====================================================================\n", "\n", "\n", "====================================================================\n", "\n", "\t\t\tPROCESSED DATASET\n", "text ень 1982 in mohammedia algeria he is best know...\n", "Name: 0, dtype: object\n", "\n", "====================================================================\n", "\n", "\n", "====================================================================\n", "\n", "\t\t\tDOWNLOADED DATASET\n", "\n", "====================================================================\n", "\n", "\n", "====================================================================\n", "\n", "\t\t\tPROCESSED DATASET\n", "text ол the city of cincinnati ohio in hamilton cou...\n", "Name: 0, dtype: object\n", "\n", "====================================================================\n", "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "050e3b0535674be9a18531175406a167", "version_major": 2, "version_minor": 0 }, "text/plain": [ "config.json: 0%| | 0.00/571 [00:00\n", " \n", " \n", " [252/252 09:48, Epoch 4/4]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation Loss
11.8565001.6724145.325006
21.5298001.6357225.133161
31.3719001.6649735.285533
41.1578001.7135355.548539

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "====================================================================\n", "\n", "\t\t\tFINETUNING COMPLETED\n", "\n", "====================================================================\n", "\n", "\n", "====================================================================\n", "\n", "\t\t\tFINETUNED MODEL SAVED\n", "\n", "====================================================================\n", "\n" ] } ], "source": [ "if __name__ == \"__main__\":\n", " Model_trainer = ModelTrainer()\n", " Model_trainer.train()" ] }, { "cell_type": "markdown", "metadata": { "id": "P2YWYpbMhjYl" }, "source": [ "# Inference Code" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "id": "RLOyiJwwSHkW", "outputId": "5b32f547-7adb-4087-ae66-cb9dc370fa5e" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "826ef10c5806439db6e0687656f814ab", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Loading checkpoint shards: 0%| | 0/2 [00:00'''\n", " return processed_example\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\"./Mistral_finetuned_Final_Adapter\")\n", "tokenizer.padding_side='left'\n", "\n", "model = AutoPeftModelForCausalLM.from_pretrained(\n", " \"./Mistral_finetuned_Final_Adapter\",\n", " low_cpu_mem_usage=True,\n", " return_dict=True,\n", " torch_dtype=torch.float16,\n", " device_map=\"cuda\")\n", "\n", "generation_config = GenerationConfig(\n", " do_sample=True,\n", " # top_k=1,\n", " # temperature=0.1,\n", " temperature=0.5,\n", " top_p=0.95,\n", " top_k=40,\n", " max_new_tokens=128,\n", " pad_token_id=tokenizer.eos_token_id\n", " # temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512\n", ")" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "id": "g-i8YJ5zSHkW" }, "outputs": [], "source": [ "inp_str = process_data_sample(\n", " {\n", " #\"text\": 'What are animals?'\n", " \"text\": 'Walker off to running start with Irish Its',\n", " }\n", ")\n", "\n", "inputs = tokenizer(inp_str, return_tensors=\"pt\").to(\"cuda\")" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aJ_G1F-LcbgH", "outputId": "cbb3ef04-19c1-40d4-f3fc-6cd043c1e0a4" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " What are animals? м 1994 the term animals was used to refer to all multicellular eukaryotes that are not plants or fungi however this definition is not universally accepted and the term is often used more restrictively to refer to a specific clade of organisms that includes all animals except the sea sponges (porifera) the term animals is sometimes used more broadly to refer to all heterotrophic organisms ( all organisms that consume other organisms for food ) including protists and some algae this broader definition is used in some textbooks and popular works \n", "\n", "\n", "Execution Time: 8.000709295272827\n" ] } ], "source": [ "import time\n", "st_time = time.time()\n", "with torch.no_grad():\n", " outputs = model.generate(**inputs, generation_config=generation_config)\n", " print(tokenizer.decode(outputs[0], skip_special_tokens=False))\n", "print(f\"\\n\\nExecution Time:\", time.time()-st_time)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Walker off to running start with Irish Its 1988 89 season the first under new head coach david wilkinson the team finished with a record of 13 @@ 15 and a third @@ place finish in the big east conference the team was led by senior guard john walker who averaged 18 @@ 6 points per game and was named to the all @@ big east second team walker was also the team's leading scorer for the 1987 88 season when он finished with a record of 14 14 и в и \n", "\n", "\n", "Execution Time: 6.86424446105957\n" ] } ], "source": [ "import time\n", "st_time = time.time()\n", "with torch.no_grad():\n", " outputs = model.generate(**inputs, generation_config=generation_config)\n", " print(tokenizer.decode(outputs[0], skip_special_tokens=False))\n", "print(f\"\\n\\nExecution Time:\", time.time()-st_time)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "id": "Jaz9n-AOfGOT", "outputId": "1c55b7e7-3303-410f-a199-de6cdbdd3a66" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "A decoder-only architecture is being used, but right-padding was detected! For correct generation results, please set `padding_side='left'` when initializing the tokenizer.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Walker off to running start with Irish Its lineage is as long as it is distinguished, conjuring up a who #39;s who among Notre Dame football greats. Some of the best players in Fighting Irish history have graced the No. position, including Paul Hornung, Joe Theismann, Tim Brown and Raghib Ismail.\n", "\n", "\n", "\n", "The latest in the line of great Irish No. 3s is junior running back Armando Allen, who has taken over the position from fellow junior Robert Hughes.\n", "\n", "\n", "\n", "Allen 5 11 \n", "\n", "\n", "Execution Time: 3.0769588947296143\n" ] } ], "source": [ "## Old execution\n", "\n", "import time\n", "st_time = time.time()\n", "with torch.no_grad():\n", " outputs = model.generate(**inputs, generation_config=generation_config)\n", " print(tokenizer.decode(outputs[0], skip_special_tokens=False))\n", "print(f\"\\n\\nExecution Time:\", time.time()-st_time)" ] }, { "cell_type": "markdown", "metadata": { "id": "IaKx1XewKn82", "jp-MarkdownHeadingCollapsed": true }, "source": [ "# Models Size Calculation Code" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8cEcKmWCKn82", "outputId": "191719d1-aa2a-45ba-db25-7d27d74a7de2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1723852637 bytes\n" ] } ], "source": [ "import os\n", "\n", "def get_size(start_path = '.'):\n", " total_size = 0\n", " for dirpath, dirnames, filenames in os.walk(start_path):\n", " for f in filenames:\n", " fp = os.path.join(dirpath, f)\n", " # skip if it is symbolic link\n", " if not os.path.islink(fp):\n", " total_size += os.path.getsize(fp)\n", "\n", " return total_size\n", "\n", "print(get_size(), 'bytes')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DozcOIPVKn82" }, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [ "P2YWYpbMhjYl", "IaKx1XewKn82" ], "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", 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