diff --git "a/FineTuning_CodeS.ipynb" "b/FineTuning_CodeS.ipynb" new file mode 100644--- /dev/null +++ "b/FineTuning_CodeS.ipynb" @@ -0,0 +1,8182 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "kH18jD5cR_Ks", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4186ce3f-d94d-4195-fd22-5d3c70e22443" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "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[31m12.8 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[31m30.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m6.8 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[31m9.3 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[31m10.2 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[31m10.1 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')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "7CKnwlRfZj4V" + }, + "outputs": [], + "source": [ + "model_name = \"seeklhy/codes-3b\"\n", + "out_name = \"lleticiasilvaa/CodeS-3B-text2SQL-alias-indentacao\"\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": [ + "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": { + "id": "lLKgY40efdJo", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 405, + "referenced_widgets": [ + "654692ea8e7d482fb6d62af77e49e6d7", + "7b6cda3188334ebf8f944e8b304f46d0", + "83ef391097c9485c8f642590b50117f4", + "3e2dec77e2a647c18f4152975336b5bd", + "0530a57c4fd245b1ada0529130f6aa9f", + "7611a7c8fbc049409539f2c57b7e4780", + "fc1851ccfd4448a1afae80439ea1a22e", + "9deacf33a01c449abd9e3467c9300991", + "ee4f08b23ab74349a32f3a99ee1bceab", + 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"63f1223c3deb445584f731f35ea8f4de", + "7d71f02ef34b4ada93cb2da1c070cae1", + "8f39de9827cb428ba52759c6187e4cba", + "0d84d96f46c44e048b881a21bfd4aa83", + "8318999fa45b44dfb6a784d59bedb6e8", + "5ad18780a6474bbca3e3540080cffaa1", + "072fc82554c742a6813f2c0acfaad543", + "2048a5ec94ba438a8b9a3a5fe9b4e851", + "2bbb8ecabbda46b5b9b6d470ee6b21a6", + "14d01af221514be9aec8cf35a23d9b62", + "9a01f2649e584492b9c81b4d2f6dc9ee", + "1ab3cdd07af2498693f9cb50e1835fdc", + "c2297484bfd64d1c813250743ea6c225", + "05ff754f4c8844fd8368cac4ea198e0f", + "20581edd68024a488e5e7addaf54da37", + "3313846f705f4c00932cacfbd94ea934", + "6f9cc7cdd21d43f09f8c445133420995", + "9796075a342b4c74b231b55ed113eaa2", + "800b64426bb748048127caf4c1e2e732", + "8a081e606b424ce985a50ef66a489d89" + ] + }, + "outputId": "b325be1a-77cd-4478-9f3a-3e24710f1054" + }, + "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", + "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 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.\n", + "<|assistant|>\n", + "```sql\n", + "SELECT Status FROM city WHERE Population > 1500 INTERSECT SELECT Status FROM city WHERE Population < 500\n", + "```\n", + "\n", + "<|assistant|>\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": "leGEqxURAL4r" + }, + "source": [ + "#### Chat Template - Schema Linking" + ] + }, + { + "cell_type": "code", + "source": [ + "tokenizer.chat_template = \"\"\"\n", + "{% if messages[0]['role'] == 'system' %}\n", + " {% set loop_messages = messages[1:] %}\n", + " {% set system_message = messages[0]['content'] %}\n", + "{% else %}\n", + " {% set loop_messages = messages %}\n", + " {% set system_message = 'Given a user question and the schema of a database, your task is to generate an JSON with the names of tables and columns of the schema that the question is referring to.' %}\n", + "{% endif %}\n", + "{{ system_message + '/n//n/' }}\n", + "\n", + "{% for message in loop_messages %}\n", + " {% if message['role'] == 'example' %}\n", + " {{ message['content'] }}/n//n/\n", + " {% elif message['role'] == 'schema' %}\n", + " {{ message['content'] }}/n//n/\n", + " {% elif message['role'] == 'user' %}\n", + " {{ message['content'] }}/n//n/\n", + " {% elif message['role'] == 'assistant' %}\n", + " {{ message['content'] }}\n", + " {% endif %}\n", + "{% endfor %}\n", + "{% if add_generation_prompt %}\n", + " /n/\n", + "{% endif %}\n", + "\"\"\".replace(\"\\n\",\"\").replace(\" \", \"\").replace(\"/n/\", \"\\n\")" + ], + "metadata": { + "id": "N_BxcydUCuro" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "aHGTJNq2AmUs" + }, + "outputs": [], + "source": [ + "def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n", + " question = row['question_en']\n", + " schema = row['schema_SQLDatabase_min']\n", + " schema_linking = row['selector_correct'].lower()\n", + "\n", + " system = \"Given a user question and the schema of a database, your task is to generate an JSON with 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": "yvcMZAL3E3TB" + }, + "outputs": [], + "source": [ + "df = df.apply(apply_template, axis=1)" + ] + }, + { + "cell_type": "code", + "source": [ + "# df['n_tokens'] = df['text'].apply(lambda x: len(tokenizer.encode(x)))\n", + "# import seaborn as sns\n", + "# sns.histplot(df['n_tokens'])" + ], + "metadata": { + "id": "-8OByl30hBp7" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TxrNQ8OGvToc", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7912efda-4a4f-407b-cfa5-eefef62234e8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Given a user question and the schema of a database, your task is to generate an JSON with 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", + "\n", + "```json\n", + "{\n", + " 'courses': ['course_id', 'course_name'],\n", + " 'student_course_attendance': ['student_id', 'course_id']\n", + "}\n", + "```\n" + ] + } + ], + "source": [ + "print(df['text'][df.index[70]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jgNv1q3IA4J2" + }, + "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": 17, + "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": 18, + "metadata": { + "id": "L0qqP5Y9PtRh", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6f2bb622-e965-473c-90eb-78502ea56ece" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found 3 modules to quantize: ['c_attn', 'c_proj', 'c_fc']\n" + ] + } + ], + "source": [ + "modules = find_all_linear_names(model)\n", + "print(f\"Found {len(modules)} modules to quantize: {modules}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "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": 20, + "metadata": { + "id": "buh0o2P2jwbx" + }, + "outputs": [], + "source": [ + "torch.cuda.empty_cache()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "9bD7ea0F-GQn", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 826, + "referenced_widgets": [ + "8a3e1c3d5b6743b79a8a9f635dabbfec", + "cee94dabb8fe40edb9986f95badf8cdb", + "104c1e6100b9485d950bc96cde9d9a3a", + 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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": "8a3e1c3d5b6743b79a8a9f635dabbfec", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/8569 [00:00\n", + " \n", + " \n", + " [ 482/1071 1:42:18 < 2:05:33, 0.08 it/s, Epoch 0.45/1]\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n", + "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n", + "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n", + " return fn(*args, **kwargs)\n", + "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n", + "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n", + "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n", + " return fn(*args, **kwargs)\n", + "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n", + "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n", + "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n", + " return fn(*args, **kwargs)\n" + ] + } + ], + "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": 22, + "metadata": { + "id": "i39Ov4dsGv77", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 121 + }, + "outputId": "11266fa7-89f8-49df-cbcd-83d4a073fe7d" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + 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