diff --git "a/FineTuning_Llama_3_2-GerarSQL.ipynb" "b/FineTuning_Llama_3_2-GerarSQL.ipynb" new file mode 100644--- /dev/null +++ "b/FineTuning_Llama_3_2-GerarSQL.ipynb" @@ -0,0 +1,7489 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "kH18jD5cR_Ks", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0295fe2b-fc00-40d4-abbf-8d2608c45eb5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.4/122.4 MB\u001b[0m \u001b[31m5.9 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[31m14.3 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[31m12.1 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[31m5.6 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[31m9.1 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[31m9.0 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[31m14.4 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-3.2-3B-Instruct\"\n", + "out_name = \"lleticiasilvaa/Llama-3.2-3B-text2SQL-alias-indentacao\"\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": 405, + "referenced_widgets": [ + "1ee5cdfeb6ae4bf881e25e9da3843705", + "fd03318ab0f1498aa3579b1a5c0d0a47", + "c1e38b0220d642cea0c3c3c048df3cef", + "e4e9cae537ef4677831c14ad8edab58e", + "f58a728cc6964b109ae18a42dd7ab898", + "7d0bf82e099e4a408c64e7754d6823be", + "f9706a9f32a6447388d1a70143622486", + 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"6275544d78be4874991053b1eaabd7ad", + "bf126033aa1e47d5a2c8eb9e988b988d", + "ec2c80c92b184c628dfdf667dccc6808", + "c6cbec3b282d465fa5ed84c0128183ca", + "c60f21dd5999485cb2a82599941aeacb", + "55a676a518454da7b6d3163f0c73b00b", + "79e3f21db4c54c2b8ada0a5e24c5c421", + "346c79f7cdf84c17ac15a1b1c103fb26", + "aa6012c67ab643b8bf5a6c96fb2dfb00", + "d39b5b24ccdd44b38b1c06ba0a128e2f", + "1387d57609944a9a9b0c8263ed93bd65", + "b71d694f87174ee6bc01f50aea480b91", + "a7d205c5801f4751810223dbc5c12e17", + "404406ed87a74293a1cbad4b3a3ba9c7", + "6b8b79036d2c4632a02a18655ee112cc", + "27eb92aaf52748bc9fde488bca03c77d", + "3adbab1d19674500908fda7d497a728b", + "4bea4fc874434e0da4b5b53d5849876d", + "94c7c2710da84be3997820b673db3d04", + "e0adc81d1d264642a7414efd76726695", + "79a9de5626a04e6b9e088b079d8f2d31", + "1430d0818fff4df596feade7c34b3e03" + ] + }, + "outputId": "ca436c05-d2c5-449d-8ae9-560467d941f0" + }, + "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%| | 0.00/1.02k [00:00<|start_header_id|>system<|end_header_id|>\n", + "\n", + "Cutting Knowledge Date: December 2023\n", + "Today Date: 30 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 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", + "```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": 14, + "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": null, + "metadata": { + "id": "aHGTJNq2AmUs" + }, + "outputs": [], + "source": [ + "def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n", + " question = row['question_en']\n", + " schema = row['schema_SQLDatabase']\n", + " schema_linking = row['selector_parser']\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": null, + "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": null, + "metadata": { + "id": "TxrNQ8OGvToc", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9d8d3038-e85a-48da-c11a-9f4bd091dc05" + }, + "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: 23 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': ['course_id']\n", + "}\n", + "```<|eot_id|>\n" + ] + } + ], + "source": [ + "print(df['text'][df.index[70]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": 15, + "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": 16, + "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": 17, + "metadata": { + "id": "L0qqP5Y9PtRh", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "bbde95a0-d70e-410c-fe01-c5398dcf6440" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found 7 modules to quantize: ['v_proj', 'q_proj', 'o_proj', 'k_proj', 'gate_proj', 'down_proj', 'up_proj']\n" + ] + } + ], + "source": [ + "modules = find_all_linear_names(model)\n", + "print(f\"Found {len(modules)} modules to quantize: {modules}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "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": "2e321acb-e0bf-4631-dcc5-8b89dcaee197" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'lleticiasilvaa/Llama-3.2-3B-text2SQL-alias-indentacao'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 19 + } + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "buh0o2P2jwbx" + }, + "outputs": [], + "source": [ + "torch.cuda.empty_cache()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "9bD7ea0F-GQn", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 843, + "referenced_widgets": [ + "15a6dcedb741424e86f2e08f4bab642f", + "3d8b0f0eec354eb6874dcc5e05c38aef", + "4e90f39c8e414654b8c50949e5115143", + "9e3e2836f23548f59296e1c206fde7b9", + "56ef159580c64ddba4725093cb8f5744", + "4c7a76d2726d44b2b7ae3f114c50dcb4", + "41446f2ef5b249bdaa89878d95f490a6", + "a6ea1b2b2da648478bae639975aad050", + "5fc91d34d21c4b679b60855a76bd1ffa", + "23b6164c276045b9ab39934fea531a34", + "7c5eda1d79f641378304d9655adfba8d", + "bd739feda5a84830b447d24cf9c57064", + "430ddcaf8396459f8bb08fa008743bc5", + "0004ef4c46f24175b999a29b089fca92", + "24131e625e8e4fa4a76e8d556315d438", + "e19c89c43eeb41fcbcdb4356d6bc8082", + 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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" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "15a6dcedb741424e86f2e08f4bab642f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/8569 [00:00\n", + " \n", + " \n", + " [ 414/1071 1:22:07 < 2:10:57, 0.08 it/s, Epoch 0.39/1]\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \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" + ] + } + ], + "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": 22, + "metadata": { + "id": "i39Ov4dsGv77", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 104 + }, + "outputId": "b1b29e4a-14aa-49cf-9df3-bcd47ec3abec" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + 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