diff --git "a/FineTuning_StableCode.ipynb" "b/FineTuning_StableCode.ipynb" new file mode 100644--- /dev/null +++ "b/FineTuning_StableCode.ipynb" @@ -0,0 +1,7326 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kH18jD5cR_Ks", + "outputId": "6af84e44-f00a-4888-fa83-587d770e7704" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.4/122.4 MB\u001b[0m \u001b[31m5.6 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[31m6.9 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[31m17.1 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[31m9.9 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[31m7.9 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[31m10.0 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[31m11.1 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 = \"stabilityai/stable-code-instruct-3b\"\n", + "out_name = \"lleticiasilvaa/StableCode-schemaLinking-dict\"\n", + "prev_checkpoint = None #\"checkpoint-500\"" + ] + }, + { + "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": [ + "540d101a3af44fc1aace9bdc809cf773", + "1b522fa9e1684ed0819ea51e94edf59b", + "5ea3156e78824294a30a35f8302619e1", + "b8f1d6ec8336460f8cff7317fc5daf21", + "f86ec1cc89a5424fae07edd12478d8bc", + "a60ad3a856ce4ef092bfeeb036a0df45", + "0de6356d3a8244e7b27d2ff0a2c91603", + "13db90fe1d9a47e5af26263831f32b68", + "4c6c287debf64d9ebd1560b604881188", + "8293e24ed42c4886b564d8f0ceb8038b", + "c748830d64aa41408aeeea8d5712590f", + "f10d450652d64b24b1ced5308d7aa57f", + "e83bb63dbbc14690b51d9de489c7b2bd", + "7d1d254ec7c24769adc9db596e4227f0", + "fe27060930b64800a417ab6ee2299ac1", + "09dbfc02e4f04eec83554d447675e501", + "c5c1f571e0494833a5d26e90fe0ffe8d", + "57738cf153b04906a1bdebd4f2bbde04", + "7dbabdab462848998232a85a4569eb51", + "1895578e02714ca2ab86923957102e98", + "b6e8502f5d2947f29aed1db1157d5cfe", + "77dabff26d0e4e78a65c08b883959254", + "b77f9a754c3247d0a82316724626cbd7", + "67370064cae34ee584657fc43bd846b9", + "c78567dcdef9453eba2af56b9f8a1dcc", + "e78f69cc50e34048adb894a4e4557b43", + "5d0476c68b4044d5a3c3674208a9d81d", + "74a6467b5bf346c7bef33e3c147d443f", + "5cf613a7a34c43639954f11e60236e12", + "a205a36e94cf4743851aaaa021f22bc1", + "95532691a3524318a46c99b4e3fc9ae5", + "30477fcc1f3146419adbd6a88114231c", + "edca0a76b0a74e88974b42d31c29670f", + "51ecafd0f5ab41ea8cbde60f8009f424", + "c539388e05c44159971f14c22ab6cf57", + "de9e215d3018461e8f12aadbbb789579", + "41864f70088a414d8aea9b7b998f1e5d", + "19f6085f95e74340a94df9a5cde69057", + "099ecb37253e45b28f7f6fa5f0011222", + "ffff0d9c9a0446759280165164d5f227", + "8eda9ccd836845b2952ccbd6f8b2d1f0", + "ec64b51f80ea4d40a8bacb6cae65c9e0", + "47c71f351d7f4cc3b6642d4572bb2189", + "b0df2ddfad164a7c9fe66e529932f5bd", + "577a692b24a841f3bd64cfec2d5df5bd", + "0f53a1d52cd749c89195b01d44858be1", + "3e292a11ac344a0aa953c7c2c6b4a9a1", + "70a182f7562647ee9fdaf249ad7dedf6", + "1d9aad7035e747b6b8d38de8dff282eb", + "a800ecc53e384c179234926d1becd07e", + "2b5a9c69b262420da795b483a51acc8e", + "31d83c615c644f3a973f061ae58e943d", + "4ff4a89b7ec6437782e6caf940d7ba70", + "4a633c15213d443097230f53b6d54b31", + "674c7709fe1b41838e144ed5cb89aa4c" + ] + }, + "id": "lLKgY40efdJo", + "outputId": "12ee4166-51b4-4b04-f2fa-c925e55dac04" + }, + "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%| | 0.00/885 [00:00/n/' + system_message + '/n//n/' }}\n", + "# {% if messages|selectattr(\"role\", \"equalto\", \"example\")|list %}\n", + "# Below are some examples of question and their corresponding SQL queries:/n//n/\n", + "# {% else %}\n", + "# /n/\n", + "# {% endif %}\n", + "# {% for message in loop_messages %}\n", + "# {% if message['role'] == 'example' %}\n", + "# {{ message['content'] }}/n//n/\n", + "# {% elif message['role'] == 'schema' %}\n", + "# # <|schema|>/n/The query will run on a database with the following schema:/n/{{ message['content'] }}/n//n/\n", + "# {% elif message['role'] == 'user' %}\n", + "# # <|user|>/n/[QUESTION]{{ message['content'] }}[/QUESTION]/n//n/\n", + "# {% elif message['role'] == 'assistant' %}\n", + "# # <|assistant|>/n/[SQL]{{ message['content'] }}[/SQL]\n", + "# {% endif %}\n", + "# {% endfor %}\n", + "# {% if add_generation_prompt %}\n", + "# # <|assistant|>/n/[SQL]\n", + "# {% endif %}\n", + "# \"\"\".replace(\"\\n\",\"\").replace(\" \", \"\").replace(\"/n/\", \"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": 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']\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": "2trHEegL5gqU" + }, + "outputs": [], + "source": [ + "# spider_chain = json.load(open(\"/content/drive/Shareddrives/LLMs/Datasets/spider/spider_chain_of_thought.json\", \"r\"))\n", + "# bird_chain = json.load(open(\"/content/drive/Shareddrives/LLMs/Datasets/bird/bird_chain_of_thought.json\", \"r\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "N4jWrC7s5gqU" + }, + "outputs": [], + "source": [ + "# df['CoT'] = spider_chain + bird_chain" + ] + }, + { + "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": "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": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "PIvSnr6Y5gqW", + "outputId": "2d3c87b0-eafc-487a-d563-8dc708283d32" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "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", + " Status TEXT,\n", + " Population REAL,\n", + " PRIMARY KEY (City_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": 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": 10, + "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": 11, + "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": 12, + "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": 13, + "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": 14, + "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": 15, + "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": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "L0qqP5Y9PtRh", + "outputId": "de7a6177-f6b4-4006-a47c-4068ea4e7e25" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Found 7 modules to quantize: ['gate_proj', 'o_proj', 'v_proj', 'up_proj', 'q_proj', 'down_proj', 'k_proj']\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": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 889, + "referenced_widgets": [ + 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"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": "e48aaa0d622c489eab223f16e83ae50e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/8569 [00:00\n", + " \n", + " \n", + " [ 869/1071 2:05:20 < 29:12, 0.12 it/s, Epoch 0.81/1]\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", + "
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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" + ] + } + ], + "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": 20, + "metadata": { + "id": "i39Ov4dsGv77", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 69 + }, + "outputId": "52dab757-db98-4aca-bff6-0cce382632e1" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + 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