BexTuychiev

#!/usr/bin/env python3
"""
Notebook to DOCX Converter

Converts Jupyter notebooks to Word documents with proper formatting:
- Markdown formatting preserved as Word styles
- Backticks preserved around inline code
- Code blocks with triple backticks visible, Courier New font
- Non-code text in Poppins font
- Images with alt text built-in

BexTuychiev

"""
Voice assistant with Gemini Live API, Firecrawl web search, and Gmail integration
"""

import asyncio
import os
import smtplib
from email.mime.text import MIMEText

from livekit.agents import (

BexTuychiev

name: mem0-selfhost

services:
  mem0:
    build: .
    image: mem0-selfhost:latest
    ports:
      - "8888:8000"
    env_file:
      - .env

BexTuychiev

"""
PyTorch Built-in LSTM for IMDB Sentiment Classification
Demonstrates nn.LSTM with a real NLP task.
"""

import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
from datasets import load_dataset
from collections import Counter

BexTuychiev

import asyncio
from pyppeteer import launch
import json


async def scrape_quotes():
    browser = await launch(headless=True)
    page = await browser.newPage()
    await page.setViewport({"width": 1920, "height": 1080})

BexTuychiev

from playwright.sync_api import sync_playwright
import json

with sync_playwright() as p:
    browser = p.chromium.launch(headless=True)
    page = browser.new_page(viewport={"width": 1920, "height": 1080})

    base_url = "https://quotes.toscrape.com/js/page/{}/"
    all_quotes = []

BexTuychiev

from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from webdriver_manager.chrome import ChromeDriverManager
import json

# Configure headless Chrome

BexTuychiev

{
  "metadata": {
    "finetuned_checkpoint": "tinker://8f13c8d2-d406-4533-810a-268360972ff6/sampler_weights/fincot-checkpoint-400",
    "base_model": "qwen/qwen3-8b",
    "judge_model": "openai/gpt-4o",
    "total_questions": 10
  },
  "summary": {
    "avg_score_finetuned": 8.5,
    "avg_score_base": 6.5,

BexTuychiev

"""
Model Comparison Script: Fine-tuned Tinker Model vs Base Qwen3-8B
Uses Kimi K2 Thinking as an unbiased judge to evaluate responses to financial questions.
"""

import os
import json
import time
import requests
from dotenv import load_dotenv

BexTuychiev

"""
Tinker Financial Q&A Fine-Tuning with FinCoT Dataset
Uses chain-of-thought reasoning dataset for improved answer quality
Includes validation tracking, warmup, and proper checkpoint management
"""

import time
import numpy as np
from dotenv import load_dotenv
from datasets import load_dataset