diff --git "a/FineTuning_StableCode.ipynb" "b/FineTuning_StableCode.ipynb" new file mode 100644--- /dev/null +++ "b/FineTuning_StableCode.ipynb" @@ -0,0 +1,7421 @@ +{ + "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", + 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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
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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": { + 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