diff --git "a/FineTuning_Llama_3_2_SQL_e_SL.ipynb" "b/FineTuning_Llama_3_2_SQL_e_SL.ipynb" new file mode 100644--- /dev/null +++ "b/FineTuning_Llama_3_2_SQL_e_SL.ipynb" @@ -0,0 +1,6033 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "kH18jD5cR_Ks", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1a299e09-f24b-4742-f0ac-ab51cef1c5b8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.4/122.4 MB\u001b[0m \u001b[31m7.8 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[31m7.2 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[31m14.2 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[31m5.7 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[31m4.4 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[31m15.3 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-3.2-1B-text2SQL-schemaLinking\"\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": [ + "673b4cb9d2a14650a8a9ef3539cd4781", + "9f46f7376bf64327b7d56e8df8ff64b6", + "a6cd5af382a54e1a8d40e155b6c16854", + "07f75b4038ee47cc86c1b2f995262b03", + "64b5edb236174f3bb799e974d9aa93e7", + "ca1e8877a3dd405286fcefaf68dbbf0a", + "dddc7193c5bb4c4191464ad605742779", + 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"e35e18298ee34a2895c3fea9d125bdf8", + "48fe83566caf4d6281c28702b6bc399a", + "0ea19607361244bebc459e56a7ad5a36", + "0a087ffb8eb047279fcb8ef828f551e1", + "ee95fb7c36e247c7b7fa6571d0c9e439", + "b01de71ab1b241508ebc05b553df9ad5", + "38785f594cf64ef2bfbec15ecca169c5", + "1bd6c714e08c494b847a3f1de38d0981", + "1fa7b3baafb94959a2f67e445908d45a", + "46857abaa93f45e1bd03d650f174caaa", + "aaf1397a53854fb9a21c0e74d67b9838", + "36588c5d6dc344ed92b5af29ac363651", + "4285576559504cb5a03a67946300fb5a", + "345bccf9259947d9ac3b6808307667ca", + "cad45fd1ae6d42c3905c06fd98fc6f6e", + "0444a7b08ce348fa936b97289cae5346", + "a6f61ce1ee9a48a68c0ff1b8437b5f3c", + "f8aff4451d194e62a3e27a91acd0b336", + "8f8f13e8872d48be9c3315cfc1262884", + "0495dd6544414389b8626c5444775c52", + "97ac0577464d414f9c597e4f2ab3ab5b", + "bb005a5bd5c74136922f4104aeee146e" + ] + }, + "outputId": "caaa8586-6456-4899-e6eb-89a4135277ff" + }, + "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_SQL['text'][df_SQL.index[70]])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "leGEqxURAL4r" + }, + "source": [ + "#### Chat Template - Schema Linking" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "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_correct']\n", + "\n", + " system = \"Given a user question and the schema of a database, your task is to generate an JSON with 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": 16, + "metadata": { + "id": "yvcMZAL3E3TB" + }, + "outputs": [], + "source": [ + "df_SL = df_SL.apply(apply_template, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "TxrNQ8OGvToc", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "262101f5-c923-41ac-818b-d6c584e2ad02" + }, + "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: 30 Nov 2024\n", + "\n", + "Given a user question and the schema of a database, your task is to generate an JSON with 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_SL['text'][df_SL.index[70]])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DWpXeuO_KlLS" + }, + "source": [ + "### Finetuning" + ] + }, + { + "cell_type": "code", + "source": [ + "_df_SQL = pd.DataFrame(columns=['text'])\n", + "_df_SQL['text'] = df_SQL.sample(frac=1, random_state=14).reset_index(drop=True)['text']\n", + "_df_SQL = Dataset.from_pandas(_df_SQL)\n", + "_df_SQL" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4g_zJe_mTyU6", + "outputId": "8799c9da-138b-422a-ddae-689d6abc5aeb" + }, + "execution_count": 21, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Dataset({\n", + " features: ['text'],\n", + " num_rows: 8656\n", + "})" + ] + }, + "metadata": {}, + "execution_count": 21 + } + ] + }, + { + "cell_type": "code", + "source": [ + "_df_SL = pd.DataFrame(columns=['text'])\n", + "_df_SL['text'] = df_SL.sample(frac=1, random_state=14).reset_index(drop=True)['text']\n", + "_df_SL = Dataset.from_pandas(_df_SL)\n", + "_df_SL" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ibzh2yBbTys5", + "outputId": "d46f6985-86b0-4c6a-bbc1-091e0d73b239" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Dataset({\n", + " features: ['text'],\n", + " num_rows: 8656\n", + "})" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from datasets import concatenate_datasets\n", + "concatenated_dataset = concatenate_datasets([_df_SQL, _df_SL])\n", + "concatenated_dataset" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nlIvLoj0T2sC", + "outputId": "57e3f1d9-8f7f-4e5a-890e-546829118c64" + }, + "execution_count": 23, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Dataset({\n", + " features: ['text'],\n", + " num_rows: 17312\n", + "})" + ] + }, + "metadata": {}, + "execution_count": 23 + } + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "jgNv1q3IA4J2", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1c8ee498-20d5-4182-8d70-f9bbd98f6f99" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Dataset({\n", + " features: ['text'],\n", + " num_rows: 17138\n", + "})" + ] + }, + "metadata": {}, + "execution_count": 25 + } + ], + "source": [ + "_df = concatenated_dataset.train_test_split(test_size=0.01, shuffle=True, seed=14)\n", + "train_dataset, valid_dataset = _df[\"train\"], _df[\"test\"]\n", + "train_dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "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": 27, + "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": 28, + "metadata": { + "id": "L0qqP5Y9PtRh", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1bffb266-8f30-48ae-a4a6-3912504d04bd" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found 7 modules to quantize: ['o_proj', 'down_proj', 'gate_proj', 'v_proj', 'up_proj', 'k_proj', 'q_proj']\n" + ] + } + ], + "source": [ + "modules = find_all_linear_names(model)\n", + "print(f\"Found {len(modules)} modules to quantize: {modules}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "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": "98cf7759-60e7-463c-ff29-8d1222555433" + }, + "execution_count": 30, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'lleticiasilvaa/Llama-3.2-1B-text2SQL-schemaLinking'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 30 + } + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "buh0o2P2jwbx" + }, + "outputs": [], + "source": [ + "torch.cuda.empty_cache()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "9bD7ea0F-GQn", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "9be750d1e84b40239fe6d187dd560b1e", + "a40b8a16bb0d4c0a81e279359f96e573", + "4b7de78be13c4fcc97e858fc9929f205", + "93a5190839ae4c009f7b91dc921eeec5", + 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max_seq_length. 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%| | 0/17138 [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", + "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": 33, + "metadata": { + "id": "i39Ov4dsGv77", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 139 + }, + "outputId": "74ca11a1-8fe1-48e4-ac56-2983e974b498" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + 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