diff --git "a/FineTuning - TinyLlama.ipynb" "b/FineTuning - TinyLlama.ipynb" new file mode 100644--- /dev/null +++ "b/FineTuning - TinyLlama.ipynb" @@ -0,0 +1,6428 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 18624, + "status": "ok", + "timestamp": 1731936696141, + "user": { + "displayName": "Letícia Silva", + "userId": "03927092514722669722" + }, + "user_tz": 180 + }, + "id": "kH18jD5cR_Ks", + "outputId": "5feea107-90a1-43a9-8a56-3b3f57f9b984" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.4/122.4 MB\u001b[0m \u001b[31m6.9 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[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m480.6/480.6 kB\u001b[0m \u001b[31m14.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.0 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[31m11.9 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[31m9.4 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[31m11.9 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": { + "executionInfo": { + "elapsed": 38546, + "status": "ok", + "timestamp": 1731936734682, + "user": { + "displayName": "Letícia Silva", + "userId": "03927092514722669722" + }, + "user_tz": 180 + }, + "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": { + "executionInfo": { + "elapsed": 10, + "status": "ok", + "timestamp": 1731936734684, + "user": { + "displayName": "Letícia Silva", + "userId": "03927092514722669722" + }, + "user_tz": 180 + }, + "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": { + "executionInfo": { + "elapsed": 8, + "status": "ok", + "timestamp": 1731936734685, + "user": { + "displayName": "Letícia Silva", + "userId": "03927092514722669722" + }, + "user_tz": 180 + }, + "id": "7CKnwlRfZj4V" + }, + "outputs": [], + "source": [ + "model_name = \"TinyLlama/TinyLlama-1.1B-Chat-v1.0\"\n", + "out_name = \"lleticiasilvaa/TinyLlama-schemaLinking-v0\"\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": { + "executionInfo": { + "elapsed": 1013, + "status": "ok", + "timestamp": 1731936748136, + "user": { + "displayName": "Letícia Silva", + "userId": "03927092514722669722" + }, + "user_tz": 180 + }, + "id": "PZdnxs8k-Cgl" + }, + "outputs": [], + "source": [ + "spider_id= \"NESPED-GEN/spider_selector_schemaReduzido\" #\"NESPED-GEN/spider_variacoes_formato_schema\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xT2iRdCN_MFH" + }, + "source": [ + "### Load Data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 368, + "referenced_widgets": [ + "954d0a42584d4d45988b6781dccd2721", + "882971ab369a496e9bafe407e9c6806e", + "6098cb4f75a942a2bc70c6a4990b96c7", + "91551a62023443e6a4c61ae4274fb3bf", + "2b83ce00a20b41929354dc4d6a116728", + "d8567c4ba3fc44c99c09e304ebd3590c", + "47cc3cad3b784da4a129e64d2e1ee24a", + "96b2ee95a84d4d20ae46a3cd79502ebd", + "0b286b4dc7ae4f5ba2c3fb5dde679d21", + "f0e75ea194e446f2aca9c758aea9ee7b", + "4930228f747c4f08bf113a59b67bf0d1", + "7705fbeca8d047f68dcfdb63e187e227", + "628862d69c59474abc993ba2b0a1e4a9", + "cb0cd611ada542d194c1c5b37d49c2a9", + "958a0f10d33e4d8bbd0f99a8fc5f977b", + "bb972db2c6694ce6aaebcec812fb7e1e", + "4dff216ac8d84f0f9bddee1f33487460", + "7a1c00f596e54deebc2a681b91d11448", + "54e1ada8506a4433b80e7b3ea71c6e69", + "dba7d8ba65ef4b239ce9f549036fd7bc", + "e2006b4423c8493a9b1ac993f59030f0", + "64430c8fab104287aba9e4712193b7d9", + "7a153beddcbc48abaac1bc873495bb89", + "bda393faad884464958d809342154ccc", + "90256975588c40c8ba05537bd85d0ade", + "1f447a033b5042ea9d4d061efcc128a8", + "6930ae4a42b445659651804ddb593ae9", + "b097342684dc458fac80ebf4ab63c166", + "ee0a5ec6ec9c426993f77188e55bb9ae", + "ba9b64b057194d7f9244c1f77b2b9adb", + "f10fff8306d84e86a6af152480873514", + "8d56070740ae4f7da131e2be3c37fb6b", + "8b3ad462348645bd80010e054ca928f1", + "e5d6190fb02242ca8f0415d8f8a443f4", + "5106589ddf594b80a046e02b67caf394", + "7951dfed0ebe492ca8385f00e8d99fb1", + "6150da9b770b4deaa199292928e7c37b", + "7c09d9cb5118429da08fd306e3debe5d", + "0a226111af6f4e6fb4beb9e48dec06c8", + "054c9e5c38c94cd4beac7d3820ed9d26", + "7f48eba4b40a47e8bf1ec8fbbc553be9", + "a257398c261c4d89bc7e827b35a1b5a8", + "a4113770044d46d6858b0b366c88180c", + "8e2705bc99e04d80b03dc79e976be5da", + "760d55aa8260454dac44713de2731376", + "86e2ab3a72314e9b9cc07375ae97288a", + "d916bdba31694485879bd83dbe580dfd", + "0d65a517e6944cc490ac2166a8bbc7d4", + "a9aa9df1faca4157abb9952d776660c3", + "1ffea1b63ed84e6caef1116932fae078", + "ff1bab0e996f403d80fd0cb45ab046a6", + "1f22779c5d3145fba24390ac446d27f5", + "3811df5017ad4b68a091e6f24c1b07f8", + "6129d30e24ab45c39424bfa2bf4e741d", + "185818ef42464b0d8546b968fce348ef" + ] + }, + "executionInfo": { + "elapsed": 7537, + "status": "ok", + "timestamp": 1731936757971, + "user": { + "displayName": "Letícia Silva", + "userId": "03927092514722669722" + }, + "user_tz": 180 + }, + "id": "lLKgY40efdJo", + "outputId": "af220bbe-1f0b-4887-83fc-837c9ee92b8e" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "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" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "954d0a42584d4d45988b6781dccd2721", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "README.md: 0%| | 0.00/885 [00:00\n", + "' + message['content'] + eos_token }}\n", + "{% elif message['role'] == 'system' %}\n", + "{{ '<|system|>\n", + "' + message['content'] + eos_token }}\n", + "{% elif message['role'] == 'assistant' %}\n", + "{{ '<|assistant|>\n", + "' + message['content'] + eos_token }}\n", + "{% endif %}\n", + "{% if loop.last and add_generation_prompt %}\n", + "{{ '<|assistant|>' }}\n", + "{% endif %}\n", + "{% endfor %}\n" + ] + } + ], + "source": [ + "print(tokenizer.chat_template)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_I7-bFfm5gqS" + }, + "source": [ + "#### Chat Template - Gerar SQL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "luwUn-oueM1C" + }, + "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": null, + "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_reduzido_tabelas']\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", + " # chat = [\n", + " # {\"role\": \"schema\", \"content\": schema},\n", + " # {\"role\": \"user\", \"content\": question},\n", + " # {\"role\": \"assistant\", \"content\": sql},\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": "bTF0pBsw5gqU" + }, + "outputs": [], + "source": [ + "df = df.apply(apply_template, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DfJvLaGR5gqV" + }, + "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/" + }, + "executionInfo": { + "elapsed": 5, + "status": "ok", + "timestamp": 1731590359389, + "user": { + "displayName": "Helena Barcelos", + "userId": "07137541013699470373" + }, + "user_tz": 180 + }, + "id": "PIvSnr6Y5gqW", + "outputId": "37f9f97d-23ec-4305-a8d3-ee2b3ccfe9ab" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<|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.\n", + "<|user|>\n", + "# Schema:\n", + "```sql\n", + "CREATE TABLE Courses (\n", + " course_id VARCHAR(100),\n", + " course_name VARCHAR(120),\n", + " course_description VARCHAR(255),\n", + " other_details VARCHAR(255),\n", + " PRIMARY KEY (course_id)\n", + ");\n", + "\n", + "CREATE TABLE Student_Course_Attendance (\n", + " student_id INTEGER,\n", + " course_id INTEGER,\n", + " date_of_attendance DATETIME,\n", + " PRIMARY KEY (student_id)\n", + ");\n", + "```\n", + "\n", + "# Question: How many students are attending English courses?\n", + "<|assistant|>\n", + "```sql\n", + "SELECT count(*)\n", + "FROM courses\n", + "JOIN student_course_attendance ON courses.course_id = student_course_attendance.course_id\n", + "WHERE courses.course_name = \"English\"\n", + "```\n", + "\n", + "\n" + ] + } + ], + "source": [ + "print(df['text'][df.index[70]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": 11, + "metadata": { + "executionInfo": { + "elapsed": 472, + "status": "ok", + "timestamp": 1731936866951, + "user": { + "displayName": "Letícia Silva", + "userId": "03927092514722669722" + }, + "user_tz": 180 + }, + "id": "opbdu1g6eMuq" + }, + "outputs": [], + "source": [ + "def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n", + " question = row['question_en']\n", + " schema = row['schema_SQLDatabase']\n", + " schema_linking = row['selector_correct']\n", + "\n", + " system = \"Given a user question and the schema of a database, your task is to generate an JSON with the 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", + "execution_count": 12, + "metadata": { + "executionInfo": { + "elapsed": 5753, + "status": "ok", + "timestamp": 1731936874927, + "user": { + "displayName": "Letícia Silva", + "userId": "03927092514722669722" + }, + "user_tz": 180 + }, + "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": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 902, + "status": "ok", + "timestamp": 1731936877432, + "user": { + "displayName": "Letícia Silva", + "userId": "03927092514722669722" + }, + "user_tz": 180 + }, + "id": "EFY_pTEteMut", + "outputId": "e9eb0324-3e6f-4556-8943-f8fe29814987" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<|user|>\n", + "# 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.\n", + "\n", + "# Schema:\n", + "```sql\n", + "CREATE TABLE Addresses (\n", + " address_id INTEGER,\n", + " line_1 VARCHAR(80),\n", + " line_2 VARCHAR(80),\n", + " city VARCHAR(50),\n", + " zip_postcode CHAR(20),\n", + " state_province_county VARCHAR(50),\n", + " country VARCHAR(50),\n", + " PRIMARY KEY (address_id)\n", + ");\n", + "\n", + "CREATE TABLE People (\n", + " person_id INTEGER,\n", + " first_name VARCHAR(255),\n", + " middle_name VARCHAR(255),\n", + " last_name VARCHAR(255),\n", + " cell_mobile_number VARCHAR(40),\n", + " email_address VARCHAR(40),\n", + " login_name VARCHAR(40),\n", + " password VARCHAR(40),\n", + " PRIMARY KEY (person_id)\n", + ");\n", + "\n", + "CREATE TABLE Students (\n", + " student_id INTEGER,\n", + " student_details VARCHAR(255),\n", + " PRIMARY KEY (student_id),\n", + " FOREIGN KEY (student_id) REFERENCES People(person_id)\n", + ");\n", + "\n", + "CREATE TABLE Courses (\n", + " course_id VARCHAR(100),\n", + " course_name VARCHAR(120),\n", + " course_description VARCHAR(255),\n", + " other_details VARCHAR(255),\n", + " PRIMARY KEY (course_id)\n", + ");\n", + "\n", + "CREATE TABLE People_Addresses (\n", + " person_address_id INTEGER,\n", + " person_id INTEGER,\n", + " address_id INTEGER,\n", + " date_from DATETIME,\n", + " date_to DATETIME,\n", + " PRIMARY KEY (person_address_id),\n", + " FOREIGN KEY (address_id) REFERENCES Addresses(address_id),\n", + " FOREIGN KEY (person_id) REFERENCES People(person_id)\n", + ");\n", + "\n", + "CREATE TABLE Student_Course_Registrations (\n", + " student_id INTEGER,\n", + " course_id INTEGER,\n", + " registration_date DATETIME,\n", + " PRIMARY KEY (student_id),\n", + " FOREIGN KEY (course_id) REFERENCES Courses(course_id),\n", + " FOREIGN KEY (student_id) REFERENCES Students(student_id)\n", + ");\n", + "\n", + "CREATE TABLE Student_Course_Attendance (\n", + " student_id INTEGER,\n", + " course_id INTEGER,\n", + " date_of_attendance DATETIME,\n", + " PRIMARY KEY (student_id),\n", + " FOREIGN KEY (student_id) REFERENCES Student_Course_Registrations(student_id),\n", + " FOREIGN KEY (course_id) REFERENCES Student_Course_Registrations(course_id)\n", + ");\n", + "\n", + "CREATE TABLE Candidates (\n", + " candidate_id INTEGER,\n", + " candidate_details VARCHAR(255),\n", + " PRIMARY KEY (candidate_id),\n", + " FOREIGN KEY (candidate_id) REFERENCES People(person_id)\n", + ");\n", + "\n", + "CREATE TABLE Candidate_Assessments (\n", + " candidate_id INTEGER,\n", + " qualification CHAR(15),\n", + " assessment_date DATETIME,\n", + " asessment_outcome_code CHAR(15),\n", + " PRIMARY KEY (candidate_id),\n", + " FOREIGN KEY (candidate_id) REFERENCES Candidates(candidate_id)\n", + ");\n", + "```\n", + "\n", + "# Question: How many students are attending English courses?\n", + "<|assistant|>\n", + "```json\n", + "{\n", + " 'Courses': ['course_id', 'course_name'],\n", + " 'Student_Course_Attendance': ['student_id', 'course_id']\n", + "}\n", + "```\n", + "\n" + ] + } + ], + "source": [ + "print(df['text'][df.index[70]])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "executionInfo": { + "elapsed": 1025, + "status": "ok", + "timestamp": 1731936939803, + "user": { + "displayName": "Letícia Silva", + "userId": "03927092514722669722" + }, + "user_tz": 180 + }, + "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": 15, + "metadata": { + "executionInfo": { + "elapsed": 441, + "status": "ok", + "timestamp": 1731936943786, + "user": { + "displayName": "Letícia Silva", + "userId": "03927092514722669722" + }, + "user_tz": 180 + }, + "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": 16, + "metadata": { + "executionInfo": { + "elapsed": 1178, + "status": "ok", + "timestamp": 1731936946753, + "user": { + "displayName": "Letícia Silva", + "userId": "03927092514722669722" + }, + "user_tz": 180 + }, + "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": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "executionInfo": { + "elapsed": 9, + "status": "ok", + "timestamp": 1731936948654, + "user": { + "displayName": "Letícia Silva", + "userId": "03927092514722669722" + }, + "user_tz": 180 + }, + "id": "L0qqP5Y9PtRh", + "outputId": "7a98bf2b-bd0b-43a6-8a10-0022ef625b94" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 7 modules to quantize: ['gate_proj', 'down_proj', 'q_proj', 'v_proj', 'k_proj', 'o_proj', 'up_proj']\n" + ] + } + ], + "source": [ + "modules = find_all_linear_names(model)\n", + "print(f\"Found {len(modules)} modules to quantize: {modules}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "executionInfo": { + "elapsed": 449, + "status": "ok", + "timestamp": 1731936953049, + "user": { + "displayName": "Letícia Silva", + "userId": "03927092514722669722" + }, + "user_tz": 180 + }, + "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": 19, + "metadata": { + "executionInfo": { + "elapsed": 452, + "status": "ok", + "timestamp": 1731936955578, + "user": { + "displayName": "Letícia Silva", + "userId": "03927092514722669722" + }, + "user_tz": 180 + }, + "id": "buh0o2P2jwbx" + }, + "outputs": [], + "source": [ + "torch.cuda.empty_cache()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 754, + "referenced_widgets": [ + "ad3ac767a2c248808dee99fdbc1d2937", + "b5052a4d41034f62bee7339dc4999e1e", + "5aef8ebf74e44fa9a02a4ff4829c56cc", + "0e6a937064d141408991844313281cc9", + "e5cd83f2fd7a457bbb59ef07834ca7bb", + "159f0280e4054f489fa743febe30bf9d", + "2778aa7ff5d74de9a15e8d4e4b42041d", + "2e01bb63b48942df9a776ed3a9d44b7e", + "fdc6fc25703046d895a0acebae84383a", + "23cfbb0f5ff3488d8e99ecbe9c6059ad", + "c6e1891def5d4d2db0d4081cf9293f06", + "4a3618a5780d48a293c544a2de033055", + "9f0e188f83fe470dab4dab18ff358512", + "e3f5bc1354c342a0a13ce96a72f9d568", + "eab8c8f550524a17859428c8216c9e9f", + "91ae7904841c44e4a61b608a4e960c50", + "dfe9b54e2db0411580b8151b7dad22d1", + "40696d24b5594853a1ad7f8a2663a581", + "76e3edf9e227489a93823bd089b66635", + "beb84ff766404983b02e0809098522a1", + "aa01b2f327cd4d6699206771a0a49456", + "d66a7b38c5a343fd91a707e0830de86f" + ] + }, + "executionInfo": { + "elapsed": 6412790, + "status": "ok", + "timestamp": 1731943371078, + "user": { + "displayName": "Letícia Silva", + "userId": "03927092514722669722" + }, + "user_tz": 180 + }, + "id": "9bD7ea0F-GQn", + "outputId": "42806561-3e72-4768-f2f7-637b40df9267" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_deprecation.py:100: FutureWarning: Deprecated argument(s) used in '__init__': dataset_text_field, max_seq_length. Will not be supported from version '0.13.0'.\n", + "\n", + "Deprecated positional argument(s) used in SFTTrainer, please use the SFTConfig to set these arguments instead.\n", + " warnings.warn(message, FutureWarning)\n", + "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:300: UserWarning: You passed a `max_seq_length` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:328: UserWarning: You passed a `dataset_text_field` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ad3ac767a2c248808dee99fdbc1d2937", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/8569 [00:00\n", + " \n", + " \n", + " [1071/1071 1:45:47, Epoch 0/1]\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "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: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": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 69 + }, + "executionInfo": { + "elapsed": 25298, + "status": "ok", + "timestamp": 1731944414315, + "user": { + "displayName": "Letícia Silva", + "userId": "03927092514722669722" + }, + "user_tz": 180 + }, + "id": "i39Ov4dsGv77", + "outputId": "0a376762-8c5a-4ccc-a06b-bd7fbcbfe467" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "CommitInfo(commit_url='https://huggingface.co/lleticiasilvaa/TinyLlama-schemaLinking-v0/commit/569af23fa19993d320137cea1ec3559453fb84dd', commit_message='End of training', commit_description='', oid='569af23fa19993d320137cea1ec3559453fb84dd', pr_url=None, repo_url=RepoUrl('https://huggingface.co/lleticiasilvaa/TinyLlama-schemaLinking-v0', endpoint='https://huggingface.co', repo_type='model', repo_id='lleticiasilvaa/TinyLlama-schemaLinking-v0'), pr_revision=None, pr_num=None)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "trainer.push_to_hub()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [ + "leGEqxURAL4r" + ], + "gpuType": "T4", + 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