diff --git "a/FineTuning_Llama_3_2.ipynb" "b/FineTuning_Llama_3_2.ipynb"
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+++ "b/FineTuning_Llama_3_2.ipynb"
@@ -0,0 +1,9160 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "kH18jD5cR_Ks",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "2be6a11d-3305-4902-b089-a16092e12b0b"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.4/122.4 MB\u001b[0m \u001b[31m5.2 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[31m10.7 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[31m19.2 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[31m6.7 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[31m11.1 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[31m7.6 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[31m9.3 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": "s7RtbLxPwTx2"
+ },
+ "outputs": [],
+ "source": [
+ "# from google.colab import drive\n",
+ "# from google.colab import userdata\n",
+ "# drive.mount('/content/drive')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "basaX_55Yf_D"
+ },
+ "outputs": [],
+ "source": [
+ "#transformers.logging.set_verbosity_info()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "id": "bkkjgGdlrNcq"
+ },
+ "outputs": [],
+ "source": [
+ "WRITE_TOKEN = userdata.get('hf_write')\n",
+ "READ_TOKEN = userdata.get('hf_read')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "id": "7CKnwlRfZj4V"
+ },
+ "outputs": [],
+ "source": [
+ "model_name = \"meta-llama/Llama-3.2-1B-Instruct\"\n",
+ "out_name = \"lleticiasilvaa/Llama-3.2-text2SQL-schemaReduzidoTabelas\"\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": [
+ "bird_id=\"NESPED-GEN/bird\"\n",
+ "spider_id= \"NESPED-GEN/spider_selector_schemaReduzido\" #\"NESPED-GEN/spider_variacoes_formato_schema\"\n",
+ "\n",
+ "selector_id = \"NESPED-GEN/selector_testes\""
+ ]
+ },
+ {
+ "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": 368,
+ "referenced_widgets": [
+ "1cdfe53ab5a640c180dcfa594c580d9e",
+ "6e11c23b6986401dad323b0d107dd302",
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+ "b3d6cd9b55464771b2fec0617f97f35d",
+ "85dff97955b54522ade6f27e30d5c4d5",
+ "9f2a16a1c1e143659c0e0d8593fc9cd3",
+ "0dc664b1b4c44f86a8e6de50c8b757ad",
+ "40589b7b5f964c62b396066b16e19b27",
+ "99391d0054064c999cc5f04a3fe342b1",
+ "9b35463725124defafdf1a6d66e7424f",
+ "50f940e30bb148d79f942c5f9c164e92",
+ "c3bf68b1602442e4b0606c1444757cb4",
+ "21c28d8e5be649efa4da0ebeba96cabe",
+ "e297e576911f42619532b1478e8801f3",
+ "c3fca402dd1f48ae9a094cdec0c96090"
+ ]
+ },
+ "outputId": "91614753-63d5-4dc9-d38e-cf4631350ccf"
+ },
+ "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%| | 0.00/885 [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\")\n"
+ ],
+ "metadata": {
+ "id": "Jiq-S5I1KOBr"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "aHGTJNq2AmUs"
+ },
+ "outputs": [],
+ "source": [
+ "def to_sql(query):\n",
+ " return sqlparse.format(query, reindent=True, keyword_case='upper')\n",
+ "\n",
+ "def replace_last_sql_content(text, new_content):\n",
+ " matches = list(re.finditer(r'```sql (.*?)```', text, re.DOTALL))\n",
+ " if not matches:\n",
+ " return text\n",
+ "\n",
+ " last_match = matches[-1]\n",
+ "\n",
+ " start, end = last_match.span(1)\n",
+ " return text[:start] + new_content + text[end:]\n",
+ "\n",
+ "# def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n",
+ "# question = row['question_en']\n",
+ "# schema = row['schema_llm']\n",
+ "# sql = to_sql(row['query_llm'])\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",
+ "\n",
+ "# chat = [{'role': 'user', 'content': f\"# System:\\n{system}\\n\\n# Schema:\\n```sql\\n{schema}\\n```\\n\\n# Question: {question}\"},\n",
+ "# {'role': 'assistant', 'content': f\"```sql\\n{sql}\\n```\"}\n",
+ "# ]\n",
+ "\n",
+ "# row['text'] = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False)\n",
+ "\n",
+ "# return row\n",
+ "\n",
+ "def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n",
+ " question = row['question_en']\n",
+ " schema = row['schema_llm_t']\n",
+ " schema_linking = to_sql(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 the names of tables and columns of the schema that the question is referring to.\"\n",
+ "\n",
+ " chat = [{'role': 'user', 'content': f\"# System:\\n{system}\\n\\n# 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",
+ "source": [
+ "# spider_chain = json.load(open(\"/content/drive/Shareddrives/LLMs/Datasets/spider/spider_chain.json\", \"r\"))\n",
+ "# bird_chain = json.load(open(\"/content/drive/Shareddrives/LLMs/Datasets/bird/bird_chain.json\", \"r\"))"
+ ],
+ "metadata": {
+ "id": "QlA1kMj83Vql"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# df['chain'] = spider_chain + bird_chain"
+ ],
+ "metadata": {
+ "id": "pCcD4TiC3rpb"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "yvcMZAL3E3TB"
+ },
+ "outputs": [],
+ "source": [
+ "df = df.apply(apply_template, axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "df.head()"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 539
+ },
+ "id": "rQ0KUl2w58K9",
+ "outputId": "664b4e86-fe9f-45ee-bd2b-ea021a31b08f"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " db_id schema \\\n",
+ "0 department_management \\nCREATE TABLE department (\\n\\t\"Department_ID\"... \n",
+ "1 department_management \\nCREATE TABLE department (\\n\\t\"Department_ID\"... \n",
+ "2 department_management \\nCREATE TABLE department (\\n\\t\"Department_ID\"... \n",
+ "3 department_management \\nCREATE TABLE department (\\n\\t\"Department_ID\"... \n",
+ "4 department_management \\nCREATE TABLE department (\\n\\t\"Department_ID\"... \n",
+ "\n",
+ " question_en hardness \\\n",
+ "0 How many heads of the departments are older th... easy \n",
+ "1 List the name, born state and age of the heads... medium \n",
+ "2 List the creation year, name and budget of eac... medium \n",
+ "3 What are the maximum and minimum budget of the... medium \n",
+ "4 What is the average number of employees of the... easy \n",
+ "\n",
+ " schema_llm \\\n",
+ "0 Table department (\\n department.Department_... \n",
+ "1 Table department (\\n department.Department_... \n",
+ "2 Table department (\\n department.Department_... \n",
+ "3 Table department (\\n department.Department_... \n",
+ "4 Table department (\\n department.Department_... \n",
+ "\n",
+ " query_llm \\\n",
+ "0 SELECT count(*) FROM head WHERE age > 56 \n",
+ "1 SELECT name , born_state , age FROM head ORD... \n",
+ "2 SELECT creation , name , budget_in_billions ... \n",
+ "3 SELECT max(budget_in_billions) , min(budget_i... \n",
+ "4 SELECT avg(num_employees) FROM department WHER... \n",
+ "\n",
+ " selector \\\n",
+ "0 {\\n 'head': ['head_id', 'age']\\n} \n",
+ "1 {\\n 'head': ['head_id', 'name', 'born_state',... \n",
+ "2 {\\n 'department': ['department_id', 'name', '... \n",
+ "3 {\\n 'department': ['department_id', 'budget_i... \n",
+ "4 {\\n 'department': ['department_id', 'ranking'... \n",
+ "\n",
+ " schema_llm_ct \\\n",
+ "0 CREATE TABLE department (\\n department.Depa... \n",
+ "1 CREATE TABLE department (\\n department.Depa... \n",
+ "2 CREATE TABLE department (\\n department.Depa... \n",
+ "3 CREATE TABLE department (\\n department.Depa... \n",
+ "4 CREATE TABLE department (\\n department.Depa... \n",
+ "\n",
+ " schema_llm_columns_min \\\n",
+ "0 Table department (\\n department.department_... \n",
+ "1 Table department (\\n department.department_... \n",
+ "2 Table department (\\n department.department_... \n",
+ "3 Table department (\\n department.department_... \n",
+ "4 Table department (\\n department.department_... \n",
+ "\n",
+ " schema_llm_columns_min_ct \\\n",
+ "0 CREATE TABLE department (\\n department.depa... \n",
+ "1 CREATE TABLE department (\\n department.depa... \n",
+ "2 CREATE TABLE department (\\n department.depa... \n",
+ "3 CREATE TABLE department (\\n department.depa... \n",
+ "4 CREATE TABLE department (\\n department.depa... \n",
+ "\n",
+ " schema_llm_all_min \\\n",
+ "0 Table department (\\n department.department_... \n",
+ "1 Table department (\\n department.department_... \n",
+ "2 Table department (\\n department.department_... \n",
+ "3 Table department (\\n department.department_... \n",
+ "4 Table department (\\n department.department_... \n",
+ "\n",
+ " schema_llm_all_min_ct \\\n",
+ "0 CREATE TABLE department (\\n department.depa... \n",
+ "1 CREATE TABLE department (\\n department.depa... \n",
+ "2 CREATE TABLE department (\\n department.depa... \n",
+ "3 CREATE TABLE department (\\n department.depa... \n",
+ "4 CREATE TABLE department (\\n department.depa... \n",
+ "\n",
+ " schema_dict \\\n",
+ "0 {\\n 'department': ['Department_ID', 'Name', '... \n",
+ "1 {\\n 'department': ['Department_ID', 'Name', '... \n",
+ "2 {\\n 'department': ['Department_ID', 'Name', '... \n",
+ "3 {\\n 'department': ['Department_ID', 'Name', '... \n",
+ "4 {\\n 'department': ['Department_ID', 'Name', '... \n",
+ "\n",
+ " selector_correct \\\n",
+ "0 {\\n 'head': ['head_ID', 'age']\\n} \n",
+ "1 {\\n 'head': ['head_ID', 'name', 'born_state',... \n",
+ "2 {\\n 'department': ['Department_ID', 'Name', '... \n",
+ "3 {\\n 'department': ['Department_ID', 'Budget_i... \n",
+ "4 {\\n 'department': ['Department_ID', 'Ranking'... \n",
+ "\n",
+ " schema_llm_t \\\n",
+ "0 TABLE department (\\n department.Department_... \n",
+ "1 TABLE department (\\n department.Department_... \n",
+ "2 TABLE department (\\n department.Department_... \n",
+ "3 TABLE department (\\n department.Department_... \n",
+ "4 TABLE department (\\n department.Department_... \n",
+ "\n",
+ " schema_llm_columns_min_t \\\n",
+ "0 TABLE department (\\n department.department_... \n",
+ "1 TABLE department (\\n department.department_... \n",
+ "2 TABLE department (\\n department.department_... \n",
+ "3 TABLE department (\\n department.department_... \n",
+ "4 TABLE department (\\n department.department_... \n",
+ "\n",
+ " schema_llm_all_min_t \\\n",
+ "0 TABLE department (\\n department.department_... \n",
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+ " selector | \n",
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+ " schema_llm_columns_min_ct | \n",
+ " schema_llm_all_min | \n",
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+ " CREATE TABLE department (\\n department.depa... | \n",
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+ " | 2 | \n",
+ " department_management | \n",
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\n",
+ " \n",
+ " | 3 | \n",
+ " department_management | \n",
+ " \\nCREATE TABLE department (\\n\\t\"Department_ID\"... | \n",
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+ " Table department (\\n department.Department_... | \n",
+ " SELECT max(budget_in_billions) , min(budget_i... | \n",
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+ " CREATE TABLE department (\\n department.Depa... | \n",
+ " Table department (\\n department.department_... | \n",
+ " CREATE TABLE department (\\n department.depa... | \n",
+ " Table department (\\n department.department_... | \n",
+ " CREATE TABLE department (\\n department.depa... | \n",
+ " {\\n 'department': ['Department_ID', 'Name', '... | \n",
+ " {\\n 'department': ['Department_ID', 'Budget_i... | \n",
+ " TABLE department (\\n department.Department_... | \n",
+ " TABLE department (\\n department.department_... | \n",
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\n",
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+ " | 4 | \n",
+ " department_management | \n",
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+ " Table department (\\n department.Department_... | \n",
+ " SELECT avg(num_employees) FROM department WHER... | \n",
+ " {\\n 'department': ['department_id', 'ranking'... | \n",
+ " CREATE TABLE department (\\n department.Depa... | \n",
+ " Table department (\\n department.department_... | \n",
+ " CREATE TABLE department (\\n department.depa... | \n",
+ " Table department (\\n department.department_... | \n",
+ " CREATE TABLE department (\\n department.depa... | \n",
+ " {\\n 'department': ['Department_ID', 'Name', '... | \n",
+ " {\\n 'department': ['Department_ID', 'Ranking'... | \n",
+ " TABLE department (\\n department.Department_... | \n",
+ " TABLE department (\\n department.department_... | \n",
+ " TABLE department (\\n department.department_... | \n",
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+ " [1071/1071 57:25, Epoch 0/1]\n",
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\n",
+ " \n",
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+ " | Step | \n",
+ " Training Loss | \n",
+ " Validation Loss | \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": 24,
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+ "outputs": [
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