diff --git "a/FineTuning_StableCode.ipynb" "b/FineTuning_StableCode.ipynb"
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+++ "b/FineTuning_StableCode.ipynb"
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+{
+ "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",
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+ "ca418fb231a543e5ba8167e878acc1a1",
+ "4f47eba328604aacb94214a0990a21d0",
+ "be9ca409d9a346b384355744ec44fba1",
+ "2c713f48c66a4119aa4aea281553b399",
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+ ]
+ },
+ "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<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "9b58c410cffb4c87a29e5c6f6a70e9ad"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "dev-00000-of-00001.parquet: 0%| | 0.00/429k [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "b5be12da681c4078af59c902c295911a"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "train-00000-of-00001.parquet: 0%| | 0.00/3.07M [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "1f4692572b6b4c848bfe8bff9cf95283"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating dev split: 0%| | 0/1034 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "7e0c6c2be1394f489ff8b9b381e9b09c"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0%| | 0/8656 [00:00<?, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "5f3aed312e7d415fa2a76bfbd8071f43"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "Dataset({\n",
+ " features: ['db_id', 'question_en', 'query', 'answer', 'hardness', 'query_llm', 'schema_SQLDatabase', 'schema_SQLDatabase_min', 'schema_our', 'schema_our_min', 'schema_dict', 'selector', 'selector_correct', 'schema_SQLDatabase_reduzido', 'schema_SQLDatabase_reduzido_tabelas', 'selector_parser'],\n",
+ " num_rows: 8656\n",
+ "})"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 6
+ }
+ ],
+ "source": [
+ "spider = load_dataset(spider_id, split=\"train\")\n",
+ "spider"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "id": "po1gNdLjFKdZ"
+ },
+ "outputs": [],
+ "source": [
+ "df = spider.to_pandas()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ON-hPsEYM1Bu"
+ },
+ "source": [
+ "# Load Base Model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "id": "yEAZpfzlNOHW"
+ },
+ "outputs": [],
+ "source": [
+ "def download_checkpoint(adapter_model_id, checkpoint):\n",
+ " fs = HfFileSystem()\n",
+ " for file in fs.ls(f'{adapter_model_id}/{checkpoint}', detail=False):\n",
+ " file_name = file.split(checkpoint)[-1]\n",
+ "\n",
+ " hf_hub_download(repo_id=adapter_model_id, filename=(f'{checkpoint}{file_name}'), local_dir='out')\n",
+ "\n",
+ " for file in fs.ls(f'{adapter_model_id}/logs', detail=False):\n",
+ " file_name = file.split(checkpoint)[-1]\n",
+ "\n",
+ " hf_hub_download(repo_id=adapter_model_id, filename=(f'logs/{file_name.split(\"/\")[-1]}'), local_dir='out')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "enUxjGXEqHxg"
+ },
+ "outputs": [],
+ "source": [
+ "# download_checkpoint(out_name, prev_checkpoint)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 337,
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "d1e3a5fd31fa4bcca9376ead155d17d3"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "generation_config.json: 0%| | 0.00/132 [00:00<?, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "18673b14a4b9403fb47cc74e12a06e85"
+ }
+ },
+ "metadata": {}
+ }
+ ],
+ "source": [
+ "seed=14\n",
+ "\n",
+ "\n",
+ "if (prev_checkpoint != None):\n",
+ " try:\n",
+ " download_checkpoint(out_name, prev_checkpoint)\n",
+ " except:\n",
+ " pass\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, token=READ_TOKEN, map_device=\"auto\", add_eos_token=True, use_fast=True)\n",
+ "\n",
+ "new_tokens = {'additional_special_tokens': ['[SQL]','[/SQL]', '[QUESTION]','[/QUESTION]']}\n",
+ "#adicionar tokens especiais:\n",
+ "# if (prev_checkpoint == None):\n",
+ "# tokenizer.add_special_tokens(new_tokens)\n",
+ "\n",
+ "\n",
+ "if torch.cuda.is_bf16_supported():\n",
+ " compute_dtype = torch.bfloat16\n",
+ " attn_implementation = 'flash_attention_2'\n",
+ "else:\n",
+ " compute_dtype = torch.float16\n",
+ " attn_implementation = 'sdpa'\n",
+ "\n",
+ "tokenizer.pad_token = tokenizer.eos_token\n",
+ "tokenizer.padding_side = \"right\"\n",
+ "\n",
+ "\n",
+ "bnb_config = BitsAndBytesConfig(\n",
+ " load_in_4bit=True,\n",
+ " bnb_4bit_quant_type=\"nf4\",\n",
+ " bnb_4bit_compute_dtype=compute_dtype,\n",
+ " bnb_4bit_use_double_quant=False,\n",
+ ")\n",
+ "\n",
+ "model = AutoModelForCausalLM.from_pretrained(\n",
+ " model_name,\n",
+ " torch_dtype=compute_dtype,\n",
+ " device_map=\"auto\",\n",
+ " # quantization_config=bnb_config,\n",
+ "\n",
+ " trust_remote_code=True,\n",
+ " token=READ_TOKEN,\n",
+ " # attn_implementation=attn_implementation\n",
+ ")\n",
+ "\n",
+ "# se adicionar special_tokens tem que fazer resize do tokenizer:\n",
+ "# model.resize_token_embeddings(len(tokenizer))\n",
+ "\n",
+ "## model.resize_token_embeddings(max(len(tokenizer), model.config.vocab_size))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "model"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "9_UCZVaQTgWg",
+ "outputId": "e7807893-f689-464e-a367-cb9ddde0bc2f"
+ },
+ "execution_count": 10,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "StableLmForCausalLM(\n",
+ " (model): StableLmModel(\n",
+ " (embed_tokens): Embedding(50304, 2560)\n",
+ " (layers): ModuleList(\n",
+ " (0-31): 32 x StableLmDecoderLayer(\n",
+ " (self_attn): StableLmSdpaAttention(\n",
+ " (q_proj): Linear(in_features=2560, out_features=2560, bias=False)\n",
+ " (k_proj): Linear(in_features=2560, out_features=2560, bias=False)\n",
+ " (v_proj): Linear(in_features=2560, out_features=2560, bias=False)\n",
+ " (o_proj): Linear(in_features=2560, out_features=2560, bias=False)\n",
+ " (attention_dropout): Dropout(p=0.0, inplace=False)\n",
+ " (rotary_emb): StableLmRotaryEmbedding()\n",
+ " )\n",
+ " (mlp): StableLmMLP(\n",
+ " (gate_proj): Linear(in_features=2560, out_features=6912, bias=False)\n",
+ " (up_proj): Linear(in_features=2560, out_features=6912, bias=False)\n",
+ " (down_proj): Linear(in_features=6912, out_features=2560, bias=False)\n",
+ " (act_fn): SiLU()\n",
+ " )\n",
+ " (input_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
+ " (post_attention_layernorm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
+ " (dropout): Dropout(p=0.0, inplace=False)\n",
+ " )\n",
+ " )\n",
+ " (norm): LayerNorm((2560,), eps=1e-05, elementwise_affine=True)\n",
+ " (rotary_emb): StableLmRotaryEmbedding()\n",
+ " )\n",
+ " (lm_head): Linear(in_features=2560, out_features=50304, bias=False)\n",
+ ")"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 10
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "fLuqzhSJBvi8",
+ "outputId": "65293049-fcc8-491b-aaca-579bca0686c7"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "The new embeddings will be initialized from a multivariate normal distribution that has old embeddings' mean and covariance. As described in this article: https://nlp.stanford.edu/~johnhew/vocab-expansion.html. To disable this, use `mean_resizing=False`\n",
+ "The new lm_head weights will be initialized from a multivariate normal distribution that has old embeddings' mean and covariance. As described in this article: https://nlp.stanford.edu/~johnhew/vocab-expansion.html. To disable this, use `mean_resizing=False`\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "Embedding(32004, 2048)"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# se adicionar special_tokens tem que fazer resize do tokenizer:\n",
+ "#model.resize_token_embeddings(len(tokenizer))\n",
+ "\n",
+ "#model.resize_token_embeddings(max(len(tokenizer), model.config.vocab_size))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_I7-bFfm5gqS"
+ },
+ "source": [
+ "#### Chat Template - Gerar SQL"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "cYVA3Q7ZCzHi"
+ },
+ "outputs": [],
+ "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 SQL query that accurately answers the question based on the provided schema.' %}\n",
+ "# {% endif %}\n",
+ "# {{ '# <|system|>/n/' + system_message + '/n//n/' }}\n",
+ "# {% if messages|selectattr(\"role\", \"equalto\", \"example\")|list %}\n",
+ "# Below are some examples of question and their corresponding SQL queries:/n//n/\n",
+ "# {% else %}\n",
+ "# /n/\n",
+ "# {% endif %}\n",
+ "# {% for message in loop_messages %}\n",
+ "# {% if message['role'] == 'example' %}\n",
+ "# {{ message['content'] }}/n//n/\n",
+ "# {% elif message['role'] == 'schema' %}\n",
+ "# # <|schema|>/n/The query will run on a database with the following schema:/n/{{ message['content'] }}/n//n/\n",
+ "# {% elif message['role'] == 'user' %}\n",
+ "# # <|user|>/n/[QUESTION]{{ message['content'] }}[/QUESTION]/n//n/\n",
+ "# {% elif message['role'] == 'assistant' %}\n",
+ "# # <|assistant|>/n/[SQL]{{ message['content'] }}[/SQL]\n",
+ "# {% endif %}\n",
+ "# {% endfor %}\n",
+ "# {% if add_generation_prompt %}\n",
+ "# # <|assistant|>/n/[SQL]\n",
+ "# {% endif %}\n",
+ "# \"\"\".replace(\"\\n\",\"\").replace(\" \", \"\").replace(\"/n/\", \"\\n\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "id": "IE87Vlt1hrje"
+ },
+ "outputs": [],
+ "source": [
+ "import re\n",
+ "\n",
+ "def replace_alias_with_table(query):\n",
+ " # Expressão regular para encontrar tabelas com alias, capturando o nome da tabela e o alias\n",
+ " alias_pattern = re.compile(r'(\\bFROM\\b|\\bJOIN\\b)\\s+(\\w+)\\s+AS\\s+(\\w+)', re.IGNORECASE)\n",
+ "\n",
+ " # Substituições de aliases encontrados no padrão\n",
+ " aliases = {match.group(3): match.group(2) for match in alias_pattern.finditer(query)}\n",
+ "\n",
+ " # Substituir cada alias pelo nome da tabela correspondente\n",
+ " for alias, table in aliases.items():\n",
+ " query = re.sub(r'\\b' + alias + r'\\b', table, query)\n",
+ "\n",
+ " # Remover 'AS' e alias das cláusulas 'FROM' e 'JOIN'\n",
+ " query = re.sub(r'\\bAS\\s+\\w+', '', query, flags=re.IGNORECASE)\n",
+ " return query"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "id": "6RDDdVgP5gqT"
+ },
+ "outputs": [],
+ "source": [
+ "def to_sql(query):\n",
+ " return sqlparse.format(replace_alias_with_table(query), reindent=True, keyword_case='upper')\n",
+ "\n",
+ "def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n",
+ " question = row['question_en']\n",
+ " schema = row['schema_SQLDatabase']\n",
+ " sql = to_sql(row['query'])\n",
+ "\n",
+ " system = \"Given a user question and the schema of a database, your task is to generate an SQL query that accurately answers the question based on the provided schema.\"\n",
+ "\n",
+ " chat = [\n",
+ " {'role': 'system', 'content': system},\n",
+ " {'role': 'user', 'content': f\"# Schema:\\n```sql\\n{schema}\\n```\\n\\n# Question: {question}\"},\n",
+ " {'role': 'assistant', 'content': f\"```sql\\n{sql}\\n```\\n\"}\n",
+ " ]\n",
+ "\n",
+ " row['text'] = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False)\n",
+ "\n",
+ " return row"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "id": "bTF0pBsw5gqU"
+ },
+ "outputs": [],
+ "source": [
+ "df = df.apply(apply_template, axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "L4tjUv7o5gqV"
+ },
+ "outputs": [],
+ "source": [
+ "df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "DfJvLaGR5gqV"
+ },
+ "outputs": [],
+ "source": [
+ "# df['n_tokens'] = df['text'].apply(lambda x: len(tokenizer.encode(x)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "vJseOHIu5gqW"
+ },
+ "outputs": [],
+ "source": [
+ "# import seaborn as sns\n",
+ "# sns.histplot(df['n_tokens'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "PIvSnr6Y5gqW",
+ "outputId": "078b70e8-252b-48c2-d4e0-812a5cf8561d"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "<|im_start|>system\n",
+ "Given a user question and the schema of a database, your task is to generate an SQL query that accurately answers the question based on the provided schema.<|im_end|>\n",
+ "<|im_start|>user\n",
+ "# Schema:\n",
+ "```sql\n",
+ "CREATE TABLE city (\n",
+ " City_ID INT,\n",
+ " Official_Name TEXT,\n",
+ " Status TEXT,\n",
+ " Area_km_2 REAL,\n",
+ " Population REAL,\n",
+ " Census_Ranking TEXT,\n",
+ " PRIMARY KEY (City_ID)\n",
+ ");\n",
+ "\n",
+ "CREATE TABLE farm (\n",
+ " Farm_ID INT,\n",
+ " Year INT,\n",
+ " Total_Horses REAL,\n",
+ " Working_Horses REAL,\n",
+ " Total_Cattle REAL,\n",
+ " Oxen REAL,\n",
+ " Bulls REAL,\n",
+ " Cows REAL,\n",
+ " Pigs REAL,\n",
+ " Sheep_and_Goats REAL,\n",
+ " PRIMARY KEY (Farm_ID)\n",
+ ");\n",
+ "\n",
+ "CREATE TABLE farm_competition (\n",
+ " Competition_ID INT,\n",
+ " Year INT,\n",
+ " Theme TEXT,\n",
+ " Host_city_ID INT,\n",
+ " Hosts TEXT,\n",
+ " PRIMARY KEY (Competition_ID),\n",
+ " FOREIGN KEY (Host_city_ID) REFERENCES city(City_ID)\n",
+ ");\n",
+ "\n",
+ "CREATE TABLE competition_record (\n",
+ " Competition_ID INT,\n",
+ " Farm_ID INT,\n",
+ " Rank INT,\n",
+ " PRIMARY KEY (Competition_ID),\n",
+ " FOREIGN KEY (Farm_ID) REFERENCES farm(Farm_ID),\n",
+ " FOREIGN KEY (Competition_ID) REFERENCES farm_competition(Competition_ID)\n",
+ ");\n",
+ "```\n",
+ "\n",
+ "# Question: Show the status shared by cities with population bigger than 1500 and smaller than 500.<|im_end|>\n",
+ "<|im_start|>assistant\n",
+ "```sql\n",
+ "SELECT Status\n",
+ "FROM city\n",
+ "WHERE Population > 1500 INTERSECT\n",
+ " SELECT Status\n",
+ " FROM city WHERE Population < 500\n",
+ "```\n",
+ "<|im_end|>\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(df['text'][df.index[50]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "id": "roZzKNOj5gqW"
+ },
+ "outputs": [],
+ "source": [
+ "_df = pd.DataFrame(columns=['text'])\n",
+ "_df['text'] = df.sample(frac=1, random_state=14).reset_index(drop=True)['text']\n",
+ "_df = Dataset.from_pandas(_df)\n",
+ "_df = _df.train_test_split(test_size=0.01, shuffle=True, seed=14)\n",
+ "train_dataset, valid_dataset = _df[\"train\"], _df[\"test\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "b6mjOblXeMup"
+ },
+ "source": [
+ "#### Chat Template - Schema Linking"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "opbdu1g6eMuq"
+ },
+ "outputs": [],
+ "source": [
+ "def apply_template(row, tokenizer=tokenizer, n_examplea=0):\n",
+ " question = row['question_en']\n",
+ " schema = row['schema_dict']\n",
+ " schema_linking = row['selector_correct']\n",
+ "\n",
+ " system = \"Given a user question and the schema of a database, your task is to generate an JSON with the the names of tables and columns of the schema that the question is referring to.\"\n",
+ "\n",
+ " chat = [\n",
+ " {'role': 'system', 'content': system},\n",
+ " {'role': 'user', 'content': f\"# Schema:\\n```sql\\n{schema}\\n```\\n\\n# Question: {question}\"},\n",
+ " {'role': 'assistant', 'content': f\"```json\\n{schema_linking}\\n```\"}\n",
+ " ]\n",
+ "\n",
+ " row['text'] = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=False)\n",
+ "\n",
+ " return row"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "x_HHKJ9VeMur"
+ },
+ "outputs": [],
+ "source": [
+ "df = df.apply(apply_template, axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "oF0-4l8DeMus"
+ },
+ "outputs": [],
+ "source": [
+ "# df['n_tokens'] = df['text'].apply(lambda x: len(tokenizer.encode(x)))\n",
+ "# import seaborn as sns\n",
+ "# sns.histplot(df['n_tokens'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "EFY_pTEteMut",
+ "outputId": "5c8b013a-78f2-4af9-9a5a-4160440eaa92"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "<|im_start|>system\n",
+ "Given a user question and the schema of a database, your task is to generate an JSON with the the names of tables and columns of the schema that the question is referring to.<|im_end|>\n",
+ "<|im_start|>user\n",
+ "# Schema:\n",
+ "```sql\n",
+ "{\n",
+ " 'Addresses': ['address_id', 'line_1', 'line_2', 'city', 'zip_postcode', 'state_province_county', 'country'],\n",
+ " 'People': ['person_id', 'first_name', 'middle_name', 'last_name', 'cell_mobile_number', 'email_address', 'login_name', 'password'],\n",
+ " 'Students': ['student_id', 'student_details'],\n",
+ " 'Courses': ['course_id', 'course_name', 'course_description', 'other_details'],\n",
+ " 'People_Addresses': ['person_address_id', 'person_id', 'address_id', 'date_from', 'date_to'],\n",
+ " 'Student_Course_Registrations': ['student_id', 'course_id', 'registration_date'],\n",
+ " 'Student_Course_Attendance': ['student_id', 'course_id', 'date_of_attendance'],\n",
+ " 'Candidates': ['candidate_id', 'candidate_details'],\n",
+ " 'Candidate_Assessments': ['candidate_id', 'qualification', 'assessment_date', 'asessment_outcome_code']\n",
+ "}\n",
+ "```\n",
+ "\n",
+ "# Question: How many students are attending English courses?<|im_end|>\n",
+ "<|im_start|>assistant\n",
+ "```json\n",
+ "{\n",
+ " 'Courses': ['course_id', 'course_name'],\n",
+ " 'Student_Course_Attendance': ['student_id', 'course_id']\n",
+ "}\n",
+ "```<|im_end|>\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(df['text'][df.index[70]])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "puYY-BqFeMuu"
+ },
+ "outputs": [],
+ "source": [
+ "_df = pd.DataFrame(columns=['text'])\n",
+ "_df['text'] = df.sample(frac=1, random_state=14).reset_index(drop=True)['text']\n",
+ "_df = Dataset.from_pandas(_df)\n",
+ "_df = _df.train_test_split(test_size=0.01, shuffle=True, seed=14)\n",
+ "train_dataset, valid_dataset = _df[\"train\"], _df[\"test\"]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DWpXeuO_KlLS"
+ },
+ "source": [
+ "### Finetuning"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "id": "0oVpZDj1AXY9"
+ },
+ "outputs": [],
+ "source": [
+ "from huggingface_hub import login, create_repo\n",
+ "from google.colab import userdata\n",
+ "import wandb\n",
+ "import os\n",
+ "\n",
+ "#token = userdata.get('hf_write')\n",
+ "token = WRITE_TOKEN\n",
+ "login(token=token)\n",
+ "set_seed(1234)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "KRhO7UJ-Q4Y8"
+ },
+ "outputs": [],
+ "source": [
+ "def find_all_linear_names(model, new_tokens=False):\n",
+ " lora_module_names = set()\n",
+ " for name, module in model.named_modules():\n",
+ " if isinstance(module, bnb.nn.Linear4bit) or isinstance(module, bnb.nn.Linear8bitLt):\n",
+ " names = name.split(\".\")\n",
+ " lora_module_names.add(names[0] if len(names) == 1 else names[-1])\n",
+ " if(new_tokens):\n",
+ " lora_module_names.add(\"lm_head\")\n",
+ " return list(lora_module_names)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "L0qqP5Y9PtRh",
+ "outputId": "a9104e5e-a151-4230-d25d-725c783e7995"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Found 0 modules to quantize: []\n"
+ ]
+ }
+ ],
+ "source": [
+ "# modules = find_all_linear_names(model)\n",
+ "# print(f\"Found {len(modules)} modules to quantize: {modules}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "id": "uFUnJrbjPwAT"
+ },
+ "outputs": [],
+ "source": [
+ "peft_config = LoraConfig(\n",
+ " lora_alpha=128, #primeira versão = 16\n",
+ " lora_dropout=0.1,\n",
+ " r=64,\n",
+ " # bias=\"none\",\n",
+ " # task_type=\"CAUSAL_LM\",\n",
+ " #target_modules=modules,\n",
+ " # modules_to_save=[\"embed_tokens\"], #quando adicionar tokens speciais\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "id": "buh0o2P2jwbx"
+ },
+ "outputs": [],
+ "source": [
+ "torch.cuda.empty_cache()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000,
+ "referenced_widgets": [
+ "0d182541e1bd4a9a9a01c1e37993d6eb",
+ "8a6e84e415084782a7a1321eb858bc1f",
+ "6cafb1cea6a74e15bc66b1d27e632612",
+ "13f5af5b5eb14fbe8dab279f23ffa82a",
+ "ff3ccccecc2240208cdf3841604b8454",
+ "158fa327be944c6b817d2dde07024640",
+ "823d5f550bfa4676b2b63e39dce39e84",
+ "a9ba747f125349bf871e2e81147cad14",
+ "6e129bfbeb914599b0c3b10ae5fb3487",
+ "d447e3d5c0924f8dbf9a29e672024063",
+ "3faf18c0dc9a4adb953f901ca55bc750",
+ "af535af5693d41c7aa95d833eb23355f",
+ "f1ecc78b3b0546e082bb9a4adf2bf4a1",
+ "d5eb0fb584074dfd8801e2e2b8c5d26d",
+ "5db659c5c674416689eb8fe759e171dd",
+ "3125a380281a44c49f9f8d91c5fa7343",
+ "ba6f4855fac24e5babd0df5141ff8928",
+ "df5e2152052f46aa941d9600c7fc8704",
+ "9d86d6140d444c809d6c80bad48e98f7",
+ "ba90868d94ed493bbf8c9d86373f2864",
+ "7e07dc5c46524eac8128df99d77e14fb",
+ "f6816699c9da46fa9a4de075aa915f88"
+ ]
+ },
+ "id": "9bD7ea0F-GQn",
+ "outputId": "22119a4d-3578-41f0-e065-a86a604e5bf2"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "0d182541e1bd4a9a9a01c1e37993d6eb",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Map: 0%| | 0/8569 [00:00<?, ? examples/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "af535af5693d41c7aa95d833eb23355f",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Map: 0%| | 0/87 [00:00<?, ? examples/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " <div>\n",
+ " \n",
+ " <progress value='820' max='1071' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+ " [ 820/1071 1:41:51 < 31:15, 0.13 it/s, Epoch 0.76/1]\n",
+ " </div>\n",
+ " <table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: left;\">\n",
+ " <th>Step</th>\n",
+ " <th>Training Loss</th>\n",
+ " <th>Validation Loss</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <td>100</td>\n",
+ " <td>4.688800</td>\n",
+ " <td>0.382587</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>200</td>\n",
+ " <td>2.331600</td>\n",
+ " <td>0.250060</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>300</td>\n",
+ " <td>1.588100</td>\n",
+ " <td>0.189046</td>\n",
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+ " <tr>\n",
+ " <td>400</td>\n",
+ " <td>1.222100</td>\n",
+ " <td>0.149319</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>500</td>\n",
+ " <td>1.038000</td>\n",
+ " <td>0.136194</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>600</td>\n",
+ " <td>0.964400</td>\n",
+ " <td>0.127944</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>700</td>\n",
+ " <td>0.880600</td>\n",
+ " <td>0.124196</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>800</td>\n",
+ " <td>0.833300</td>\n",
+ " <td>0.123276</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table><p>"
+ ],
+ "text/plain": [
+ "<IPython.core.display.HTML object>"
+ ]
+ },
+ "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": [
+ "<IPython.core.display.HTML object>"
+ ],
+ "text/html": [
+ "\n",
+ " <div>\n",
+ " \n",
+ " <progress value='1071' max='1071' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+ " [1071/1071 2:11:26, Epoch 0/1]\n",
+ " </div>\n",
+ " <table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: left;\">\n",
+ " <th>Step</th>\n",
+ " <th>Training Loss</th>\n",
+ " <th>Validation Loss</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <td>100</td>\n",
+ " <td>4.688800</td>\n",
+ " <td>0.382587</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>200</td>\n",
+ " <td>2.331600</td>\n",
+ " <td>0.250060</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>300</td>\n",
+ " <td>1.588100</td>\n",
+ " <td>0.189046</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>400</td>\n",
+ " <td>1.222100</td>\n",
+ " <td>0.149319</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>500</td>\n",
+ " <td>1.038000</td>\n",
+ " <td>0.136194</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>600</td>\n",
+ " <td>0.964400</td>\n",
+ " <td>0.127944</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>700</td>\n",
+ " <td>0.880600</td>\n",
+ " <td>0.124196</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>800</td>\n",
+ " <td>0.833300</td>\n",
+ " <td>0.123276</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>900</td>\n",
+ " <td>0.830700</td>\n",
+ " <td>0.121317</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <td>1000</td>\n",
+ " <td>0.813800</td>\n",
+ " <td>0.120976</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table><p>"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
+ "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
+ "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n",
+ "Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from trl.trainer.sft_config import SFTConfig\n",
+ "\n",
+ "steps=100\n",
+ "strategy=\"steps\"\n",
+ "\n",
+ "trainer = SFTTrainer(\n",
+ " model= model,\n",
+ " processing_class=tokenizer,\n",
+ "\n",
+ " train_dataset=train_dataset,\n",
+ " eval_dataset=valid_dataset,\n",
+ "\n",
+ " peft_config=peft_config,\n",
+ "\n",
+ " args= SFTConfig(\n",
+ " output_dir=\"out\",\n",
+ "\n",
+ " dataset_text_field=\"text\",\n",
+ " max_seq_length = 2048,\n",
+ "\n",
+ " save_strategy=strategy,\n",
+ " save_steps= steps,\n",
+ "\n",
+ " logging_strategy=strategy,\n",
+ " logging_steps=steps,\n",
+ " logging_dir=\"out/logs\",\n",
+ "\n",
+ " eval_strategy=strategy,\n",
+ " eval_steps=steps,\n",
+ "\n",
+ " num_train_epochs=1,\n",
+ " per_device_train_batch_size=1,\n",
+ " per_device_eval_batch_size=1,\n",
+ " gradient_accumulation_steps=8, #primeira versão era 1\n",
+ " gradient_checkpointing=True,\n",
+ " fp16=True,\n",
+ " bf16=False,\n",
+ "\n",
+ " optim=\"paged_adamw_8bit\",\n",
+ " learning_rate=1e-4, #primeira versão era 2e-4,\n",
+ " weight_decay=0.001,\n",
+ " max_grad_norm=0.3,\n",
+ " max_steps=-1,\n",
+ " warmup_ratio=0.03,\n",
+ " group_by_length=True,\n",
+ " lr_scheduler_type=\"cosine\", #primeira versão era linear\n",
+ " seed= seed,\n",
+ "\n",
+ "\n",
+ " report_to=[\"tensorboard\"],\n",
+ " push_to_hub=True,\n",
+ " hub_strategy=\"all_checkpoints\",\n",
+ " hub_model_id=out_name,\n",
+ "\n",
+ " label_names=[\"labels\"]\n",
+ " )\n",
+ "\n",
+ ")\n",
+ "if(prev_checkpoint != None):\n",
+ " trainer.train(f\"out/{prev_checkpoint}\")\n",
+ "else:\n",
+ " trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "id": "i39Ov4dsGv77",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 69
+ },
+ "outputId": "8612cb83-a7d6-48d4-dd76-3c83b56122b6"
+ },
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "CommitInfo(commit_url='https://huggingface.co/lleticiasilvaa/StableCode-text2SQL-withoutquantization/commit/824d58e45c7106e0a6f1adc911c85662e2f7f3df', commit_message='End of training', commit_description='', oid='824d58e45c7106e0a6f1adc911c85662e2f7f3df', pr_url=None, repo_url=RepoUrl('https://huggingface.co/lleticiasilvaa/StableCode-text2SQL-withoutquantization', endpoint='https://huggingface.co', repo_type='model', repo_id='lleticiasilvaa/StableCode-text2SQL-withoutquantization'), pr_revision=None, pr_num=None)"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
+ },
+ "metadata": {},
+ "execution_count": 22
+ }
+ ],
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+ "trainer.push_to_hub()"
+ ]
+ }
+ ],
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+ ],
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+ },
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+ "display_name": "default:Python",
+ "language": "python",
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+ },
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+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
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