celsowm

from fastapi import FastAPI, HTTPException, Depends  # pip install fastapi uvicorn
from pydantic import BaseModel
import mysql.connector # pip install mysql-connector-python

db_config = {
    "host": "localhost",
    "user": "root",
    "password": "",
    "database": "exemplo"
}

celsowm

<?php
namespace FastPHP\Attributes;

#[\Attribute(\Attribute::TARGET_METHOD)]
class Route
{
    public string $method;
    public string $path;

    public function __construct(string $method, string $path)

celsowm

<?php
namespace FastPHP;

use FastPHP\Attributes\Get;
use FastPHP\Attributes\Post;

class FastPHP
{
    private array $routes = [];

celsowm

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name = "output/lei_n_15_022_2024_full_training"
model = AutoModelForCausalLM.from_pretrained(model_name, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(model_name)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

messages = [
    {

celsowm

from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import SFTTrainer, SFTConfig
import os, torch

torch.cuda.empty_cache()

os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'
os.environ['WANDB_MODE'] = 'disabled'

celsowm

"Create a vibrant and eye-catching YouTube thumbnail. The background should be split into two sections: one side featuring a futuristic AI theme with glowing neural networks, circuits, or lines of code, and the other side showing a legal theme with symbols like a justice scale ⚖️, a gavel, or a law book. In the center, include a person or avatar with closely shaved hair (like a buzz cut, machine level 1), wearing glasses, and pointing at the divide with a confident or excited expression. Add bold, large text that reads 'TREINE SUA IA' in bright yellow and white, with a smaller subtitle 'Usando Python' below in a clear, professional font. Use dynamic colors like blue, yellow, and red to make the design pop, and include subtle effects like a glow or burst around the text to draw attention."

celsowm

// Seleciona todos os elementos <code> na página
var finalResult = JSON.stringify(
    Array.from(document.querySelectorAll('code'))
        .map(function(el) {
            var textContent = el.textContent;

            // Usa regex para encontrar todos os pares "prompt" e "response" válidos
            var matches = [...textContent.matchAll(/"prompt"\s*:\s*"([^"]*?)",\s*"response"\s*:\s*"([^"]*?)"/g)];

            // Converte os pares encontrados em objetos

celsowm

import numpy as np
import urllib.request

# Ler os dados do CSV
url = 'https://gist.githubusercontent.com/celsowm/35fb4f6d5a9590fce2c79367e6f31a13/raw/853288d17afc03e6cc67c819659d0da60d74b2f5/diabetes.csv'
dados = np.genfromtxt(urllib.request.urlopen(url), delimiter=',', skip_header=1)
X = dados[:, :3]  # Entradas: fumante, obeso, pratica_exercicios
y = dados[:, 3:]  # Saída: diabetico

# Inicializar pesos aleatórios

celsowm

pip install playwright
python -m playwright install

celsowm

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name = "Qwen/Qwen2.5-0.5B-Instruct"

model = AutoModelForCausalLM.from_pretrained(model_name, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(model_name)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

messages = [