diff --git "a/FineTuning_Llama_3_2.ipynb" "b/FineTuning_Llama_3_2.ipynb" new file mode 100644--- /dev/null +++ "b/FineTuning_Llama_3_2.ipynb" @@ -0,0 +1,5944 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "kH18jD5cR_Ks", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "56adc2c0-872f-4c88-d9d0-bdafb8bc9604" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.4/122.4 MB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m310.9/310.9 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "# !pip install -q accelerate peft bitsandbytes transformers trl faiss-gpu langchain_community wandb flash-attn\n", + "!pip install -q accelerate peft bitsandbytes transformers trl datasets\n", + "\n", + "# flash-attn" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "cgVNTbBa-D3j" + }, + "outputs": [], + "source": [ + "# load the required packages.\n", + "import torch\n", + "from datasets import load_dataset, Dataset\n", + "from peft import LoraConfig, AutoPeftModelForCausalLM, PeftModel, get_peft_model\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments, AutoConfig, set_seed\n", + "from trl import SFTTrainer\n", + "import bitsandbytes as bnb\n", + "import transformers\n", + "\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import sqlparse\n", + "import re\n", + "import json\n", + "\n", + "from huggingface_hub import hf_hub_download\n", + "from huggingface_hub import HfFileSystem" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "basaX_55Yf_D" + }, + "outputs": [], + "source": [ + "#transformers.logging.set_verbosity_info()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "bkkjgGdlrNcq" + }, + "outputs": [], + "source": [ + "WRITE_TOKEN = userdata.get('hf_write')\n", + "READ_TOKEN = userdata.get('hf_read')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "7CKnwlRfZj4V" + }, + "outputs": [], + "source": [ + "model_name = \"meta-llama/Llama-3.2-1B-Instruct\"\n", + "out_name = \"lleticiasilvaa/Llama-3.2-schemaLinking-min\"\n", + "prev_checkpoint = None #\"checkpoint-1000\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9zs7nCmt-pMC" + }, + "outputs": [], + "source": [ + "#!huggingface-cli login" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "PZdnxs8k-Cgl" + }, + "outputs": [], + "source": [ + "spider_id= \"NESPED-GEN/spider_selector_schemaReduzido\" #\"NESPED-GEN/spider_variacoes_formato_schema\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xT2iRdCN_MFH" + }, + "source": [ + "### Load Data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "lLKgY40efdJo", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 479, + "referenced_widgets": [ + "267f71c7f9f84b66a2dbbce6ea920d22", + "686798c8f5b342839a7527542e0c372d", + "14f0b95ad472409395605e63f59daf08", + "df17e8609150419aa06092244e41bfb8", + "67eed0ba17874e3d800fa15c6f5ed5ac", + "b54c4b982c36468891d6a41dd6ab7dc5", + "bae055768ad84b02a700f1f00d05c173", + "a344586617d5482d8747d720610d2a71", + "9072273b59474529950bef9c843c4a26", + "79abc79b298940a580220bc4ed9de8f6", + "6f93ef3db4b946e383fa6e3bf9fc4645", + "fc66bc833ff44459935d2a0f7f460a9d", + "73d321e50ac642a3b1238e4636e09c88", + "f5ff6ec349ad4908a23a611492295d4a", + "a1961e2d0e3c4f8c9300a6091836e10b", + "6f24168e97a643f88aa73fd9ca257d89", + "2d3056a4ab94462c9acfe0335456a311", + "fbc97cba0daa4f47906c52578fd38333", + "eb2537c04d214ea4997199e7f7428a37", + "2b1a606cafb04f0fbc03623ccc6ff042", + "cb683fe8f87548d6b8c0f461019cec1c", + "1c5ce7b66f804570a7754fd7d8190d38", + "42426e5fce4f4276a8c0e4ac0592b29f", + "275e9fc6deac483d8385808ceca90ddb", + "003b367deb01432a8c02682882605221", + "9b4de6910183410896c956229dec0290", + "2d285a1c8d1849a8af90de56de3d1ca5", + "82013e95e2014e70b6498ca827b5258e", + "81b71dcb6afc4f67902a8bb0cd23a5a7", + "556dc72847b440edae8e8059e0bc933e", + "90e8a0305861416495bd073651e7d00e", + "cd48fe65db134ee1a60f5ed8f28c4473", + "6478ed6e69024696878317a72e160556", + "dcdcca701d51497783a6149a322e4e83", + "135ae5749d08476c8309f88eb6f6417f", + "437a726a5a714d1fa2948075e35bff7d", + "9913900812ca4de7befce7efa365f898", + "ca8c21853fde4a3aa668b9a55a953c93", + "70af8eb8744f40c4b74b299ecf309998", + "7ea5d717d07643fc99c52b1249c695d9", + "485d137c08354dc0ba737f20b935b1d8", + "7b8245f2499f4259b99f73546809dc02", + "a238c91c94544730a027f9cd4913f8ae", + "f02f3ce840a1489b92d9d26f9cc12b54", + "17ced407dc634c3b87ca6a0c06172c76", + "aae0b83be0e84468822aebf7d75d7ec1", + "24a260362545475dabf187e2e032bf67", + "c161b2db2ea4466f945bc685024228b3", + "16add3be15a44cadae7c60915e265499", + "5dacf180ab894d0a9d8daaa0235af3be", + "a28c10ef3c264a4da313448b5e846297", + "302af4e839aa41619696f4b6f8334622", + "ca2dc21812654a96be962af38f276a64", + "23e178297cd14460a51f06788eed4253", + "7a188ca65ef14711a5f0d758a1e54317" + ] + }, + "outputId": "15a3692b-c27c-40d9-91f9-05bba5f63e58" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "README.md: 0%| | 0.00/980 [00:00/n/' + system_message + '/n//n/' }}\n", + "# {% if messages|selectattr(\"role\", \"equalto\", \"example\")|list %}\n", + "# Below are some examples of question and their corresponding SQL queries:/n//n/\n", + "# {% else %}\n", + "# /n/\n", + "# {% endif %}\n", + "# {% for message in loop_messages %}\n", + "# {% if message['role'] == 'example' %}\n", + "# {{ message['content'] }}/n//n/\n", + "# {% elif message['role'] == 'schema' %}\n", + "# # <|schema|>/n/The query will run on a database with the following schema:/n/{{ message['content'] }}/n//n/\n", + "# {% elif message['role'] == 'user' %}\n", + "# # <|user|>/n/[QUESTION]{{ message['content'] }}[/QUESTION]/n//n/\n", + "# {% elif message['role'] == 'assistant' %}\n", + "# # <|assistant|>/n/[SQL]{{ message['content'] }}[/SQL]\n", + "# {% endif %}\n", + "# {% endfor %}\n", + "# {% if add_generation_prompt %}\n", + "# # <|assistant|>/n/[SQL]\n", + "# {% endif %}\n", + "# \"\"\".replace(\"\\n\",\"\").replace(\" \", \"\").replace(\"/n/\", \"\\n\")" + ], + "metadata": { + "id": "cYVA3Q7ZCzHi" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import re\n", + "\n", + "def replace_alias_with_table(query):\n", + " # Expressão regular para encontrar tabelas com alias, capturando o nome da tabela e o alias\n", + " alias_pattern = re.compile(r'(\\bFROM\\b|\\bJOIN\\b)\\s+(\\w+)\\s+AS\\s+(\\w+)', re.IGNORECASE)\n", + "\n", + " # Substituições de aliases encontrados no padrão\n", + " aliases = {match.group(3): match.group(2) for match in alias_pattern.finditer(query)}\n", + "\n", + " # Substituir cada alias pelo nome da tabela correspondente\n", + " for alias, table in aliases.items():\n", + " query = re.sub(r'\\b' + alias + r'\\b', table, query)\n", + "\n", + " # Remover 'AS' e alias das cláusulas 'FROM' e 'JOIN'\n", + " query = re.sub(r'\\bAS\\s+\\w+', '', query, flags=re.IGNORECASE)\n", + " return query" + ], + "metadata": { + "id": "NDD-URc1cbFx" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "count = 0\n", + "for i in spider:\n", + " if 'AS ' in i['query_llm']:\n", + " count += 1\n", + " print(count)\n", + " print('--------------------------')\n", + " print('query = ', i['query'])\n", + " print('--------------------------')\n", + " print('query_llm = ', i['query_llm'])\n", + " print('--------------------------')\n", + " print(replace_alias_with_table(i['query']))\n", + " print('--------------------------')\n", + "print(count)" + ], + "metadata": { + "id": "n8lBUu3acgfg" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6RDDdVgP5gqT" + }, + "outputs": [], + "source": [ + "def to_sql(query):\n", + " return sqlparse.format(replace_alias_with_table(query), reindent=True, keyword_case='upper')\n", + "\n", + "def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n", + " question = row['question_en']\n", + " schema = row['schema_SQLDatabase_reduzido_tabelas']\n", + " sql = to_sql(row['query'])\n", + "\n", + " system = \"Given a user question and the schema of a database, your task is to generate an SQL query that accurately answers the question based on the provided schema.\"\n", + "\n", + " chat = [\n", + " {'role': 'system', 'content': system},\n", + " {'role': 'user', 'content': f\"# Schema:\\n```sql\\n{schema}\\n```\\n\\n# Question: {question}\"},\n", + " {'role': 'assistant', 'content': f\"```sql\\n{sql}\\n```\\n\"}\n", + " ]\n", + "\n", + " row['text'] = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False)\n", + "\n", + " return row" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2trHEegL5gqU" + }, + "outputs": [], + "source": [ + "# spider_chain = json.load(open(\"/content/drive/Shareddrives/LLMs/Datasets/spider/spider_chain_of_thought.json\", \"r\"))\n", + "# bird_chain = json.load(open(\"/content/drive/Shareddrives/LLMs/Datasets/bird/bird_chain_of_thought.json\", \"r\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "N4jWrC7s5gqU" + }, + "outputs": [], + "source": [ + "# df['CoT'] = spider_chain + bird_chain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bTF0pBsw5gqU" + }, + "outputs": [], + "source": [ + "df = df.apply(apply_template, axis=1)" + ] + }, + { + "cell_type": "code", + "source": [ + "pd.set_option('display.max_colwidth', None) #definir a opção para mostrar todo o conteúdo das células\n", + "pd.set_option('display.max_rows', None) #definir a opção para mostrar todas as linhas" + ], + "metadata": { + "id": "H5azRSqHPoXp" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "L4tjUv7o5gqV" + }, + "outputs": [], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DfJvLaGR5gqV" + }, + "outputs": [], + "source": [ + "# df['n_tokens'] = df['text'].apply(lambda x: len(tokenizer.encode(x)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vJseOHIu5gqW" + }, + "outputs": [], + "source": [ + "# import seaborn as sns\n", + "# sns.histplot(df['n_tokens'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "dc8e1eb8-d666-4828-b348-2e20986f50dd", + "id": "PIvSnr6Y5gqW" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", + "\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 14 Nov 2024\n", + "\n", + "Given a user question and the schema of a database, your task is to generate an SQL query that accurately answers the question based on the provided schema.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "# Schema:\n", + "```sql\n", + "CREATE TABLE Courses (\n", + " course_id VARCHAR(100),\n", + " course_name VARCHAR(120),\n", + " course_description VARCHAR(255),\n", + " other_details VARCHAR(255),\n", + " PRIMARY KEY (course_id)\n", + ");\n", + "\n", + "CREATE TABLE Student_Course_Attendance (\n", + " student_id INTEGER,\n", + " course_id INTEGER,\n", + " date_of_attendance DATETIME,\n", + " PRIMARY KEY (student_id)\n", + ");\n", + "```\n", + "\n", + "# Question: How many students are attending English courses?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n", + "\n", + "```sql\n", + "SELECT count(*)\n", + "FROM courses\n", + "JOIN student_course_attendance ON courses.course_id = student_course_attendance.course_id\n", + "WHERE courses.course_name = \"English\"\n", + "```<|eot_id|>\n" + ] + } + ], + "source": [ + "print(df['text'][df.index[70]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "roZzKNOj5gqW" + }, + "outputs": [], + "source": [ + "_df = pd.DataFrame(columns=['text'])\n", + "_df['text'] = df.sample(frac=1, random_state=14).reset_index(drop=True)['text']\n", + "_df = Dataset.from_pandas(_df)\n", + "_df = _df.train_test_split(test_size=0.01, shuffle=True, seed=14)\n", + "train_dataset, valid_dataset = _df[\"train\"], _df[\"test\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "leGEqxURAL4r" + }, + "source": [ + "#### Chat Template - Schema Linking" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "id": "aHGTJNq2AmUs" + }, + "outputs": [], + "source": [ + "def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n", + " question = row['question_en']\n", + " schema = row['schema_SQLDatabase_min']\n", + " schema_linking = row['selector_correct'].lower()\n", + "\n", + " system = \"Given a user question and the schema of a database, your task is to generate an JSON with the the names of tables and columns of the schema that the question is referring to.\"\n", + "\n", + " chat = [\n", + " {'role': 'system', 'content': system},\n", + " {'role': 'user', 'content': f\"# Schema:\\n```sql\\n{schema}\\n```\\n\\n# Question: {question}\"},\n", + " {'role': 'assistant', 'content': f\"```json\\n{schema_linking}\\n```\"}\n", + " ]\n", + "\n", + " row['text'] = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False)\n", + "\n", + " return row" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "yvcMZAL3E3TB" + }, + "outputs": [], + "source": [ + "df = df.apply(apply_template, axis=1)" + ] + }, + { + "cell_type": "code", + "source": [ + "# df['n_tokens'] = df['text'].apply(lambda x: len(tokenizer.encode(x)))\n", + "# import seaborn as sns\n", + "# sns.histplot(df['n_tokens'])" + ], + "metadata": { + "id": "-8OByl30hBp7" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "TxrNQ8OGvToc", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6755bcd8-bdee-46b4-f01a-0f0adb568919" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n", + "\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 20 Nov 2024\n", + "\n", + "Given a user question and the schema of a database, your task is to generate an JSON with the the names of tables and columns of the schema that the question is referring to.<|eot_id|><|start_header_id|>user<|end_header_id|>\n", + "\n", + "# Schema:\n", + "```sql\n", + "CREATE TABLE addresses (\n", + " address_id INTEGER,\n", + " line_1 VARCHAR(80),\n", + " line_2 VARCHAR(80),\n", + " city VARCHAR(50),\n", + " zip_postcode CHAR(20),\n", + " state_province_county VARCHAR(50),\n", + " country VARCHAR(50),\n", + " PRIMARY KEY (address_id)\n", + ");\n", + "\n", + "CREATE TABLE people (\n", + " person_id INTEGER,\n", + " first_name VARCHAR(255),\n", + " middle_name VARCHAR(255),\n", + " last_name VARCHAR(255),\n", + " cell_mobile_number VARCHAR(40),\n", + " email_address VARCHAR(40),\n", + " login_name VARCHAR(40),\n", + " password VARCHAR(40),\n", + " PRIMARY KEY (person_id)\n", + ");\n", + "\n", + "CREATE TABLE students (\n", + " student_id INTEGER,\n", + " student_details VARCHAR(255),\n", + " PRIMARY KEY (student_id),\n", + " FOREIGN KEY (student_id) REFERENCES people(person_id)\n", + ");\n", + "\n", + "CREATE TABLE courses (\n", + " course_id VARCHAR(100),\n", + " course_name VARCHAR(120),\n", + " course_description VARCHAR(255),\n", + " other_details VARCHAR(255),\n", + " PRIMARY KEY (course_id)\n", + ");\n", + "\n", + "CREATE TABLE people_addresses (\n", + " person_address_id INTEGER,\n", + " person_id INTEGER,\n", + " address_id INTEGER,\n", + " date_from DATETIME,\n", + " date_to DATETIME,\n", + " PRIMARY KEY (person_address_id),\n", + " FOREIGN KEY (address_id) REFERENCES addresses(address_id),\n", + " FOREIGN KEY (person_id) REFERENCES people(person_id)\n", + ");\n", + "\n", + "CREATE TABLE student_course_registrations (\n", + " student_id INTEGER,\n", + " course_id INTEGER,\n", + " registration_date DATETIME,\n", + " PRIMARY KEY (student_id),\n", + " FOREIGN KEY (course_id) REFERENCES courses(course_id),\n", + " FOREIGN KEY (student_id) REFERENCES students(student_id)\n", + ");\n", + "\n", + "CREATE TABLE student_course_attendance (\n", + " student_id INTEGER,\n", + " course_id INTEGER,\n", + " date_of_attendance DATETIME,\n", + " PRIMARY KEY (student_id),\n", + " FOREIGN KEY (student_id) REFERENCES student_course_registrations(student_id),\n", + " FOREIGN KEY (course_id) REFERENCES student_course_registrations(course_id)\n", + ");\n", + "\n", + "CREATE TABLE candidates (\n", + " candidate_id INTEGER,\n", + " candidate_details VARCHAR(255),\n", + " PRIMARY KEY (candidate_id),\n", + " FOREIGN KEY (candidate_id) REFERENCES people(person_id)\n", + ");\n", + "\n", + "CREATE TABLE candidate_assessments (\n", + " candidate_id INTEGER,\n", + " qualification CHAR(15),\n", + " assessment_date DATETIME,\n", + " asessment_outcome_code CHAR(15),\n", + " PRIMARY KEY (candidate_id),\n", + " FOREIGN KEY (candidate_id) REFERENCES candidates(candidate_id)\n", + ");\n", + "```\n", + "\n", + "# Question: How many students are attending English courses?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n", + "\n", + "```json\n", + "{\n", + " 'courses': ['course_id', 'course_name'],\n", + " 'student_course_attendance': ['student_id', 'course_id']\n", + "}\n", + "```<|eot_id|>\n" + ] + } + ], + "source": [ + "print(df['text'][df.index[70]])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "jgNv1q3IA4J2" + }, + "outputs": [], + "source": [ + "_df = pd.DataFrame(columns=['text'])\n", + "_df['text'] = df.sample(frac=1, random_state=14).reset_index(drop=True)['text']\n", + "_df = Dataset.from_pandas(_df)\n", + "_df = _df.train_test_split(test_size=0.01, shuffle=True, seed=14)\n", + "train_dataset, valid_dataset = _df[\"train\"], _df[\"test\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DWpXeuO_KlLS" + }, + "source": [ + "### Finetuning" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "0oVpZDj1AXY9" + }, + "outputs": [], + "source": [ + "from huggingface_hub import login, create_repo\n", + "from google.colab import userdata\n", + "import wandb\n", + "import os\n", + "\n", + "#token = userdata.get('hf_write')\n", + "token = WRITE_TOKEN\n", + "login(token=token)\n", + "set_seed(1234)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "KRhO7UJ-Q4Y8" + }, + "outputs": [], + "source": [ + "def find_all_linear_names(model, new_tokens=False):\n", + " lora_module_names = set()\n", + " for name, module in model.named_modules():\n", + " if isinstance(module, bnb.nn.Linear4bit) or isinstance(module, bnb.nn.Linear8bitLt):\n", + " names = name.split(\".\")\n", + " lora_module_names.add(names[0] if len(names) == 1 else names[-1])\n", + " if(new_tokens):\n", + " lora_module_names.add(\"lm_head\")\n", + " return list(lora_module_names)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "L0qqP5Y9PtRh", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "2ad1e042-ff2c-4d53-b1e5-0d25db5fc030" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found 7 modules to quantize: ['gate_proj', 'q_proj', 'up_proj', 'v_proj', 'k_proj', 'down_proj', 'o_proj']\n" + ] + } + ], + "source": [ + "modules = find_all_linear_names(model)\n", + "print(f\"Found {len(modules)} modules to quantize: {modules}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "uFUnJrbjPwAT" + }, + "outputs": [], + "source": [ + "peft_config = LoraConfig(\n", + " lora_alpha=128, #primeira versão = 16\n", + " lora_dropout=0.1,\n", + " r=64,\n", + " # bias=\"none\",\n", + " # task_type=\"CAUSAL_LM\",\n", + " target_modules=modules,\n", + " # modules_to_save=[\"embed_tokens\"], #quando adicionar tokens speciais\n", + ")" + ] + }, + { + "cell_type": "code", + "source": [ + "out_name" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "yA_4Bz3jpCCn", + "outputId": "1556e94b-f033-4262-b564-5862173d498c" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'lleticiasilvaa/Llama-3.2-schemaLinking-min'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 18 + } + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "buh0o2P2jwbx" + }, + "outputs": [], + "source": [ + "torch.cuda.empty_cache()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "9bD7ea0F-GQn", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 754, + "referenced_widgets": [ + "190e565455504d6db7de1e84fc4a6036", + "4e2f5cda9b3443afbbf3974dae7633c0", + "5b80a1832bb44ef7933d7acc04e6a4e6", + "c8b3fc02c78247dd87a64e5eaa236c33", + "39316137dc684f24a412e21e0e88e856", + 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Will not be supported from version '0.13.0'.\n", + "\n", + "Deprecated positional argument(s) used in SFTTrainer, please use the SFTConfig to set these arguments instead.\n", + " warnings.warn(message, FutureWarning)\n", + "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:300: UserWarning: You passed a `max_seq_length` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:328: UserWarning: You passed a `dataset_text_field` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map: 0%| | 0/8569 [00:00" + ], + "text/html": [ + "\n", + "
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" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n", + "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n", + "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n", + " return fn(*args, **kwargs)\n", + "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n", + "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n", + "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n", + " return fn(*args, **kwargs)\n", + "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n", + "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n", + "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n", + " return fn(*args, **kwargs)\n", + "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n", + "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n", + "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n", + " return fn(*args, **kwargs)\n" + ] + } + ], + "source": [ + "steps=250\n", + "strategy=\"steps\"\n", + "\n", + "trainer = SFTTrainer(\n", + " model= model,\n", + " tokenizer=tokenizer,\n", + "\n", + " train_dataset=train_dataset,\n", + " eval_dataset=valid_dataset,\n", + "\n", + " peft_config=peft_config,\n", + "\n", + " dataset_text_field=\"text\",\n", + " max_seq_length=2048,\n", + " args= TrainingArguments(\n", + " output_dir=\"out\",\n", + "\n", + " save_strategy=strategy,\n", + " save_steps= steps,\n", + "\n", + " logging_strategy=strategy,\n", + " logging_steps=steps,\n", + " logging_dir=\"out/logs\",\n", + "\n", + " eval_strategy=strategy,\n", + " eval_steps=steps,\n", + "\n", + " num_train_epochs=1,\n", + " per_device_train_batch_size=1,\n", + " per_device_eval_batch_size=1,\n", + " gradient_accumulation_steps=8, #primeira versão era 1\n", + " gradient_checkpointing=True,\n", + " fp16=True,\n", + " bf16=False,\n", + "\n", + " optim=\"paged_adamw_8bit\",\n", + " learning_rate=1e-4, #primeira versão era 2e-4,\n", + " weight_decay=0.001,\n", + " max_grad_norm=0.3,\n", + " max_steps=-1,\n", + " warmup_ratio=0.03,\n", + " group_by_length=True,\n", + " lr_scheduler_type=\"cosine\", #primeira versão era linear\n", + " seed= seed,\n", + "\n", + "\n", + " report_to=[\"tensorboard\"],\n", + " push_to_hub=True,\n", + " hub_strategy=\"all_checkpoints\",\n", + " hub_model_id=out_name,\n", + "\n", + " label_names=[\"labels\"]\n", + " )\n", + "\n", + ")\n", + "if(prev_checkpoint != None):\n", + " trainer.train(f\"out/{prev_checkpoint}\")\n", + "else:\n", + " trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "i39Ov4dsGv77", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 156 + }, + "outputId": "d1b77065-bc20-4654-ce72-c06fa0260568" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + 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