Rajiv Mehta rajivmehtaflex

/// script

requires-python = ">=3.10"

dependencies = [

"vllm",

"transformers",

"datasets",

]

///

Your Python code here

How LLMs Actually Work: A Plain-English Guide to Transformer Architecture

A from-scratch walkthrough of what happens between typing a prompt and getting a response — no ML background assumed.

What This Guide Covers

Every large language model — GPT-4, Claude, Gemini, Llama — is built on the same core idea. This guide follows that idea in the exact order data actually flows through a real model: text in, tokens, vectors, 80 stacked layers, and a word out — then loops back to explain how the model got trained in the first place. Read it top to bottom; each section is built to need only what came before it.

The One Job an LLM Has
Vectors — Turning Words Into Math

How to get oobabooga/text-generation-webui running on Windows or Linux with LLaMa-30b 4bit mode via GPTQ-for-LLaMa on an RTX 3090 start to finish.

This guide actually works well for linux too. Just don't bother with the powershell envs

Download prerequisites
- Download and install miniconda
- (Windows Only) Download and install Visual Studio 2019 Build Tools
  - Click on the latest BuildTools link, Select Desktop Environment with C++ when installing)
(Windows Only) Open the Conda Powershell.

	import asyncio
	import base64
	import json
	import os
	import pyaudio
	from websockets.asyncio.client import connect


	class SimpleGeminiVoice:
	def __init__(self):

	import concurrent.futures
	from typing import Any, Callable, List
	from tqdm import tqdm

	def concurrent_task_executor(task: Callable[[Any], None], data_list: List[Any], max_workers: int = 32, description: str = None) -> None:
	"""
	Execute tasks concurrently on a list of data objects using ThreadPoolExecutor.

	Args:
	task (Callable): The function to apply to each data object.

	import autogen

	config_list = [
	{
	'model': 'gpt-4',
	'api_key': 'API_KEY'
	}
	]

	llm_config={

	#! /usr/bin/python

	# 02/27/2015, Kenial
	# Just simple scripts for instant use of SQLAlchemy.

	import sqlalchemy as sa
	sa.__version__ # '0.9.4' for me

	# Sample DB connection string
	# engine = sa.create_engine('postgresql://scott:tiger@localhost/mydatabase')

	<!DOCTYPE html>
	<html>
	<head>
	<meta charset="utf-8" />
	<title>Remote terminal</title>

	<link rel="stylesheet" href="xterm.css" />

	<script src="terminal.class.js"></script>

	# main
	llama-index
	langchain