{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kH18jD5cR_Ks", "outputId": "17769649-6b3c-4e3a-98d4-a56e0cb2fe06" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.1/69.1 MB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m293.4/293.4 kB\u001b[0m \u001b[31m23.2 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[31m37.3 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[31m8.0 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[31m11.8 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[31m13.4 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[31m17.7 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')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "7CKnwlRfZj4V" }, "outputs": [], "source": [ "model_name = \"stabilityai/stable-code-instruct-3b\"\n", "out_name = \"lleticiasilvaa/StableCode-text2SQL-withoutquantization\"\n", "prev_checkpoint = None" ] }, { "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\"" ] }, { "cell_type": "markdown", "metadata": { "id": "xT2iRdCN_MFH" }, "source": [ "### Load Data\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 368, "referenced_widgets": [ "9b58c410cffb4c87a29e5c6f6a70e9ad", "2fb63b1952ac49a2850bb7dc50c54605", "c1b2d4eb841d479b909a202005c979b0", "51f76fa61af14d4b9dec07b0548972e1", "8ef2693eed4848bf86d77c3dc22497e3", "dded2b91fd0e4c0f8ff9d576a6fb7965", "6d0d7c54921b4f6e8aaa55c8d63e4730", "98042f5eae90417dbc642872f1b69e64", "3caac23b7dca4f9f8e5b4f6079ef43ff", "5c832fde73cb46229a603882e9a63f57", "13726f8593cc48b0b9f6f6acb306e65f", "b5be12da681c4078af59c902c295911a", "8c29d6e99f5048739f2337d96c2624fe", "49a2083cc1d14d198bccebf0b2aa4805", "8e3c59bb4b4a4c31a3d4e179720fbb77", "ca418fb231a543e5ba8167e878acc1a1", "4f47eba328604aacb94214a0990a21d0", "be9ca409d9a346b384355744ec44fba1", "2c713f48c66a4119aa4aea281553b399", "402e1d9981194c69b9fbb7b0a1c60e21", "7f7f4ce8ca414e7087f66dcb1a536e5a", "fdc1ee094612498c9647126acaf5afed", "1f4692572b6b4c848bfe8bff9cf95283", "1eac6180fe2941d88a42108edfccfb22", "8ba3f9fb2252437fb2c79b24793d13a3", "5176885c522e44769e5c238396b04fdd", "826e90401db54e4abc7766388200bca1", "903b4aba0e7b4dc5be4a6fcef7435af5", "e4721a0a336d4e0daedf32f9e60686d6", "de298595f3b3475cbbfbceecd96e99e4", "1b1b9924a640446e8de7764b4d85632d", "0e99f153c79f46a6b8916d4527088a96", "36689b2cd1144b09b31f790fae4b1d44", "7e0c6c2be1394f489ff8b9b381e9b09c", "61284d1e9160433fa5ed1d7cf8e57f9e", "5726194a309d42d0bf17021f45fc8574", "6bed4bfe3a2a44668e31b0f05da94e36", "861299fd23f24929a7976f4eb65a0140", "fc31f5db4c064be091e5a36c3550acf8", "4e8c529cb2454fbfad8a30a5878c29e2", "75d1c673353f4f3cb113fdb606ad737c", "0818a942aa9b4329b5a75d4e7cbe93f4", "1b262509fa5c4543b3d3eb073405cd00", "4ce326e8ff7e4a82aeac63d7863898b4", "5f3aed312e7d415fa2a76bfbd8071f43", "f1a44163a7fb4b4b8434bcbfe2cc4754", "c9895290197944c6b1f7a0658464e224", "03705fd79e184229abca04ad474af251", "6605cffd7ee3488c9c4dcb8523154861", "bf2a1aba5a8c41ef9a842277952b48a6", "70cbc71c003d46c1b928e17acc99fff5", "3910c8f092e6474799ce1c710e8409fe", "02ad3cb881ac49f8b2fd9eac702b0e00", "ba649d326060407fa8f0729d7d99897e", "317fc61b9b6a4d3c8befe41d9b03bec5" ] }, "id": "lLKgY40efdJo", "outputId": "f1845191-e592-447e-e6d9-af2b68a9f9e4" }, "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/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\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "id": "IE87Vlt1hrje" }, "outputs": [], "source": [ "import re\n", "\n", "def replace_alias_with_table(query):\n", " # Expressão regular para encontrar tabelas com alias, capturando o nome da tabela e o alias\n", " alias_pattern = re.compile(r'(\\bFROM\\b|\\bJOIN\\b)\\s+(\\w+)\\s+AS\\s+(\\w+)', re.IGNORECASE)\n", "\n", " # Substituições de aliases encontrados no padrão\n", " aliases = {match.group(3): match.group(2) for match in alias_pattern.finditer(query)}\n", "\n", " # Substituir cada alias pelo nome da tabela correspondente\n", " for alias, table in aliases.items():\n", " query = re.sub(r'\\b' + alias + r'\\b', table, query)\n", "\n", " # Remover 'AS' e alias das cláusulas 'FROM' e 'JOIN'\n", " query = re.sub(r'\\bAS\\s+\\w+', '', query, flags=re.IGNORECASE)\n", " return query" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "6RDDdVgP5gqT" }, "outputs": [], "source": [ "def to_sql(query):\n", " return sqlparse.format(replace_alias_with_table(query), reindent=True, keyword_case='upper')\n", "\n", "def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n", " question = row['question_en']\n", " schema = row['schema_SQLDatabase']\n", " sql = to_sql(row['query'])\n", "\n", " system = \"Given a user question and the schema of a database, your task is to generate an SQL query that accurately answers the question based on the provided schema.\"\n", "\n", " chat = [\n", " {'role': 'system', 'content': system},\n", " {'role': 'user', 'content': f\"# Schema:\\n```sql\\n{schema}\\n```\\n\\n# Question: {question}\"},\n", " {'role': 'assistant', 'content': f\"```sql\\n{sql}\\n```\\n\"}\n", " ]\n", "\n", " row['text'] = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False)\n", "\n", " return row" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "bTF0pBsw5gqU" }, "outputs": [], "source": [ "df = df.apply(apply_template, axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "L4tjUv7o5gqV" }, "outputs": [], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "DfJvLaGR5gqV" }, "outputs": [], "source": [ "# df['n_tokens'] = df['text'].apply(lambda x: len(tokenizer.encode(x)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vJseOHIu5gqW" }, "outputs": [], "source": [ "# import seaborn as sns\n", "# sns.histplot(df['n_tokens'])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PIvSnr6Y5gqW", "outputId": "078b70e8-252b-48c2-d4e0-812a5cf8561d" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "<|im_start|>system\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.<|im_end|>\n", "<|im_start|>user\n", "# Schema:\n", "```sql\n", "CREATE TABLE city (\n", " City_ID INT,\n", " Official_Name TEXT,\n", " Status TEXT,\n", " Area_km_2 REAL,\n", " Population REAL,\n", " Census_Ranking TEXT,\n", " PRIMARY KEY (City_ID)\n", ");\n", "\n", "CREATE TABLE farm (\n", " Farm_ID INT,\n", " Year INT,\n", " Total_Horses REAL,\n", " Working_Horses REAL,\n", " Total_Cattle REAL,\n", " Oxen REAL,\n", " Bulls REAL,\n", " Cows REAL,\n", " Pigs REAL,\n", " Sheep_and_Goats REAL,\n", " PRIMARY KEY (Farm_ID)\n", ");\n", "\n", "CREATE TABLE farm_competition (\n", " Competition_ID INT,\n", " Year INT,\n", " Theme TEXT,\n", " Host_city_ID INT,\n", " Hosts TEXT,\n", " PRIMARY KEY (Competition_ID),\n", " FOREIGN KEY (Host_city_ID) REFERENCES city(City_ID)\n", ");\n", "\n", "CREATE TABLE competition_record (\n", " Competition_ID INT,\n", " Farm_ID INT,\n", " Rank INT,\n", " PRIMARY KEY (Competition_ID),\n", " FOREIGN KEY (Farm_ID) REFERENCES farm(Farm_ID),\n", " FOREIGN KEY (Competition_ID) REFERENCES farm_competition(Competition_ID)\n", ");\n", "```\n", "\n", "# Question: Show the status shared by cities with population bigger than 1500 and smaller than 500.<|im_end|>\n", "<|im_start|>assistant\n", "```sql\n", "SELECT Status\n", "FROM city\n", "WHERE Population > 1500 INTERSECT\n", " SELECT Status\n", " FROM city WHERE Population < 500\n", "```\n", "<|im_end|>\n", "\n" ] } ], "source": [ "print(df['text'][df.index[50]])" ] }, { "cell_type": "code", "execution_count": 15, "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": "b6mjOblXeMup" }, "source": [ "#### Chat Template - Schema Linking" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "opbdu1g6eMuq" }, "outputs": [], "source": [ "def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n", " question = row['question_en']\n", " schema = row['schema_dict']\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 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": "x_HHKJ9VeMur" }, "outputs": [], "source": [ "df = df.apply(apply_template, axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "oF0-4l8DeMus" }, "outputs": [], "source": [ "# df['n_tokens'] = df['text'].apply(lambda x: len(tokenizer.encode(x)))\n", "# import seaborn as sns\n", "# sns.histplot(df['n_tokens'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EFY_pTEteMut", "outputId": "5c8b013a-78f2-4af9-9a5a-4160440eaa92" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "<|im_start|>system\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.<|im_end|>\n", "<|im_start|>user\n", "# Schema:\n", "```sql\n", "{\n", " 'Addresses': ['address_id', 'line_1', 'line_2', 'city', 'zip_postcode', 'state_province_county', 'country'],\n", " 'People': ['person_id', 'first_name', 'middle_name', 'last_name', 'cell_mobile_number', 'email_address', 'login_name', 'password'],\n", " 'Students': ['student_id', 'student_details'],\n", " 'Courses': ['course_id', 'course_name', 'course_description', 'other_details'],\n", " 'People_Addresses': ['person_address_id', 'person_id', 'address_id', 'date_from', 'date_to'],\n", " 'Student_Course_Registrations': ['student_id', 'course_id', 'registration_date'],\n", " 'Student_Course_Attendance': ['student_id', 'course_id', 'date_of_attendance'],\n", " 'Candidates': ['candidate_id', 'candidate_details'],\n", " 'Candidate_Assessments': ['candidate_id', 'qualification', 'assessment_date', 'asessment_outcome_code']\n", "}\n", "```\n", "\n", "# Question: How many students are attending English courses?<|im_end|>\n", "<|im_start|>assistant\n", "```json\n", "{\n", " 'Courses': ['course_id', 'course_name'],\n", " 'Student_Course_Attendance': ['student_id', 'course_id']\n", "}\n", "```<|im_end|>\n", "\n" ] } ], "source": [ "print(df['text'][df.index[70]])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "puYY-BqFeMuu" }, "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": 16, "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": null, "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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "L0qqP5Y9PtRh", "outputId": "a9104e5e-a151-4230-d25d-725c783e7995" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Found 0 modules to quantize: []\n" ] } ], "source": [ "# modules = find_all_linear_names(model)\n", "# print(f\"Found {len(modules)} modules to quantize: {modules}\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "uFUnJrbjPwAT" }, "outputs": [], "source": [ "peft_config = LoraConfig(\n", " lora_alpha=128, #primeira versão = 16\n", " lora_dropout=0.1,\n", " r=64,\n", " # bias=\"none\",\n", " # task_type=\"CAUSAL_LM\",\n", " #target_modules=modules,\n", " # modules_to_save=[\"embed_tokens\"], #quando adicionar tokens speciais\n", ")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "buh0o2P2jwbx" }, "outputs": [], "source": [ "torch.cuda.empty_cache()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "0d182541e1bd4a9a9a01c1e37993d6eb", "8a6e84e415084782a7a1321eb858bc1f", "6cafb1cea6a74e15bc66b1d27e632612", "13f5af5b5eb14fbe8dab279f23ffa82a", "ff3ccccecc2240208cdf3841604b8454", "158fa327be944c6b817d2dde07024640", "823d5f550bfa4676b2b63e39dce39e84", "a9ba747f125349bf871e2e81147cad14", "6e129bfbeb914599b0c3b10ae5fb3487", "d447e3d5c0924f8dbf9a29e672024063", "3faf18c0dc9a4adb953f901ca55bc750", "af535af5693d41c7aa95d833eb23355f", "f1ecc78b3b0546e082bb9a4adf2bf4a1", "d5eb0fb584074dfd8801e2e2b8c5d26d", "5db659c5c674416689eb8fe759e171dd", "3125a380281a44c49f9f8d91c5fa7343", "ba6f4855fac24e5babd0df5141ff8928", "df5e2152052f46aa941d9600c7fc8704", "9d86d6140d444c809d6c80bad48e98f7", "ba90868d94ed493bbf8c9d86373f2864", "7e07dc5c46524eac8128df99d77e14fb", "f6816699c9da46fa9a4de075aa915f88" ] }, "id": "9bD7ea0F-GQn", "outputId": "22119a4d-3578-41f0-e065-a86a604e5bf2" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0d182541e1bd4a9a9a01c1e37993d6eb", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map: 0%| | 0/8569 [00:00\n", " \n", " \n", " [ 820/1071 1:41:51 < 31:15, 0.13 it/s, Epoch 0.76/1]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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StepTraining LossValidation Loss
1004.6888000.382587
2002.3316000.250060
3001.5881000.189046
4001.2221000.149319
5001.0380000.136194
6000.9644000.127944
7000.8806000.124196
8000.8333000.123276

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "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", "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", "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", "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", "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", "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", "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", "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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

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StepTraining LossValidation Loss
1004.6888000.382587
2002.3316000.250060
3001.5881000.189046
4001.2221000.149319
5001.0380000.136194
6000.9644000.127944
7000.8806000.124196
8000.8333000.123276
9000.8307000.121317
10000.8138000.120976

" ] }, "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", "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" ] } ], "source": [ "from trl.trainer.sft_config import SFTConfig\n", "\n", "steps=100\n", "strategy=\"steps\"\n", "\n", "trainer = SFTTrainer(\n", " model= model,\n", " processing_class=tokenizer,\n", "\n", " train_dataset=train_dataset,\n", " eval_dataset=valid_dataset,\n", "\n", " peft_config=peft_config,\n", "\n", " args= SFTConfig(\n", " output_dir=\"out\",\n", "\n", " dataset_text_field=\"text\",\n", " max_seq_length = 2048,\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": 69 }, "outputId": "8612cb83-a7d6-48d4-dd76-3c83b56122b6" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "CommitInfo(commit_url='https://huggingface.co/lleticiasilvaa/StableCode-text2SQL-withoutquantization/commit/824d58e45c7106e0a6f1adc911c85662e2f7f3df', commit_message='End of training', commit_description='', oid='824d58e45c7106e0a6f1adc911c85662e2f7f3df', pr_url=None, repo_url=RepoUrl('https://huggingface.co/lleticiasilvaa/StableCode-text2SQL-withoutquantization', endpoint='https://huggingface.co', repo_type='model', repo_id='lleticiasilvaa/StableCode-text2SQL-withoutquantization'), pr_revision=None, pr_num=None)" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 22 } ], "source": [ "trainer.push_to_hub()" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [ "leGEqxURAL4r" ], "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "default:Python", "language": "python", "name": "conda-env-default-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16" }, 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