diff --git "a/FineTuning_StableCode.ipynb" "b/FineTuning_StableCode.ipynb"
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+++ "b/FineTuning_StableCode.ipynb"
@@ -0,0 +1,8058 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "kH18jD5cR_Ks",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "bab0bba5-68fd-47d0-ea8f-d256c3d1fa15"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.1/44.1 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m320.7/320.7 kB\u001b[0m \u001b[31m17.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.4/122.4 MB\u001b[0m \u001b[31m7.0 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[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.0/10.0 MB\u001b[0m \u001b[31m53.2 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[31m24.8 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[31m10.4 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[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.0/40.0 MB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m43.1 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[31m12.0 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",
+ "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 18.0.0 which is incompatible.\n",
+ "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 18.0.0 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",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "43d5ff02-3e33-439c-e3a9-c18ce99d237d"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Mounted at /content/drive\n"
+ ]
+ }
+ ],
+ "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": 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 = \"stabilityai/stable-code-instruct-3b\"\n",
+ "out_name = \"lleticiasilvaa/StableCode-text2SQL-schemaReduzidoTabelas\"\n",
+ "prev_checkpoint = None # \"checkpoint-3000\""
+ ]
+ },
+ {
+ "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\"\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": 281,
+ "referenced_widgets": [
+ "62b5a4803207499da82677b9b146b97b",
+ "1c006910a42e40728867969135b6d848",
+ "1a5af1ef864e41d99a79d5b94d982711",
+ "a84dab2f85094fb3997adb2d3c2ecd0a",
+ "bcc0fee091bb4a308e9c39e2ae091acd",
+ "349dd7e2bfd141a583b8ec25b786cae3",
+ "29717b10d1bd45cba1a785123bd2a0b4",
+ "319f0c7c10c546b3a1be237d93ebf5ae",
+ "7c0a09f10b59412ba4271ba77a1378f0",
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+ "11d0433884ca4e46b7cb033b5e546ad4",
+ "eb2518b1c019478daec2d2c058898096",
+ "c25c3c6ca8f343c2b7519abca165c779",
+ "207cc561423a413c975d54acfdfd73a4",
+ "d86de6918ba94a159b0c48b33bc9d90c",
+ "22531ea2c5fe493ca2a87b8432424371",
+ "964dfefdf21648a6a2ab33a935efb4e7",
+ "6d9d4727e4734795a65a12306e1db3e9",
+ "cfa5cebbda604836bfb3daf1a0a9eb5b",
+ "bf0121739fac4cab8a6a56f607e99067",
+ "2cf8d94d40d14f9ab02389fe857a44e7",
+ "ca3279d8e8d14ae3958efd4990d760f5",
+ "3b861b5eb85a4eb1a5800dd33e199b88",
+ "f60ecb97d13d4056a7295306b4a726f3",
+ "7ed9c3225ff34256bb3d105a541a2dbb",
+ "8ed8b8ef97fd4b4cbb03f6954fa70367",
+ "4651ad7190a047f98bbf3817e529f9f8",
+ "22d3f097275c42869bc2bc07b70b99f4",
+ "82b145ecd4994698b56223f44c695054",
+ "ec2be65f2cdb476ea1717ef7040f839a",
+ "35c2672c5c8542dd80d92167dd9d9163",
+ "aa21408da1eb4d1bb1a2aa00eedc9412",
+ "938befa273d944d6932eafd292376a7b",
+ "d786b4589eaa4a9d8dd1a17beb71a42c",
+ "3f0ddb4d9e5346b8b6d46e3dd1f5a848",
+ "0ec766ee8a12493492e591aae846821e",
+ "ee72b1d5917e4377b4140d04b6238961",
+ "1a9cdc5f9aa84a3abe8a93d51bda8cf4",
+ "8ba0c5c19e7b40e3b1987a274f04e780",
+ "cad63d5f62d04a0987898969ec0bc4fb",
+ "30a5dcee7cc142ec8a8f6b2888853fab",
+ "69f97bec934b444993afa7056655ee4d",
+ "485a0a650475469686655c0326849223",
+ "bd7f555c54e84745b3ee0bf5d3bbe65f",
+ "5abd0ad065d74e5090902ca9420e9eda",
+ "8dd111f5c4c84ef0b1213a7cf20db429",
+ "563de63f39af4f949944c2bcc79e2f62",
+ "279032b33e64433d8c5ac7d7a1bf4b2b",
+ "a7f20c1db6014748b1c9d567b29bdb76",
+ "7f1157ebb5a04a4bb3c7b83df53fd206",
+ "1c54e6b541dc40038b3d176af7949ee2",
+ "5562393293ea45c1972f520b5740cb08",
+ "42199373c5e747b1a59973878319f8e5",
+ "48ba0bcad819498282222df452525e9e",
+ "3f2265210736450d93657b7e2a6b2785"
+ ]
+ },
+ "outputId": "57b279a5-52e6-4a12-d22a-7fe5769c1e28"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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",
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\n",
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+ " \n",
+ " | Step | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ " | 250 | \n",
+ " 0.454300 | \n",
+ " 0.294916 | \n",
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+ },
+ {
+ "metadata": {
+ "tags": null
+ },
+ "name": "stderr",
+ "output_type": "stream",
+ "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:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 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",
+ "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
+ " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\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:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 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",
+ "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
+ " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\n"
+ ]
+ },
+ {
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+ " [1071/1071 2:20:02, Epoch 0/1]\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | Step | \n",
+ " Training Loss | \n",
+ " Validation Loss | \n",
+ "
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+ " 0.204900 | \n",
+ " 0.212971 | \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:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 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",
+ "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
+ " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\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:600: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 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",
+ "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:295: FutureWarning: `torch.cpu.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cpu', args...)` instead.\n",
+ " with torch.enable_grad(), device_autocast_ctx, torch.cpu.amp.autocast(**ctx.cpu_autocast_kwargs): # type: ignore[attr-defined]\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": 23,
+ "metadata": {
+ "id": "i39Ov4dsGv77",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 52
+ },
+ "outputId": "d7c5f985-a1e4-426e-db42-6049ea4f59dc"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "CommitInfo(commit_url='https://huggingface.co/lleticiasilvaa/StableCode-text2SQL-schemaReduzidoTabelas/commit/5796bc794767f39348649736c8a9b7c88f12421d', commit_message='End of training', commit_description='', oid='5796bc794767f39348649736c8a9b7c88f12421d', pr_url=None, pr_revision=None, pr_num=None)"
+ ],
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+ ]
+ }
+ ],
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