rahuldshetty

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

rahuldshetty

import logo from './logo.svg';
import './App.css';
import {useState, useEffect} from "react";
import * as tf from '@tensorflow/tfjs';

function App() {
  const [result, setResult] = useState();
  const [model,setModel] = useState();
  const [prob, setProb] = useState();
  const c2i = {" ": 0, "a": 1, "b": 2, "c": 3, "d": 4, "e": 5, "f": 6, "g": 7, "h": 8, "i": 9, "j": 10, "k": 11, "l": 12, "m": 13, "n": 14, "o": 15, "p": 16, "q": 17, "r": 18, "s": 19, "t": 20, "u": 21, "v": 22, "w": 23, "x": 24, "y": 25, "z": 26};

rahuldshetty

import tensorflow as tf 

model = tf.keras.Sequential([
      tf.keras.layers.Embedding(len(char2id) + 1, 100, input_length=max_len),
      tf.keras.layers.Flatten(),
      tf.keras.layers.Dense(512, activation='relu'),
      tf.keras.layers.Dense(1, activation='sigmoid')
])

model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

rahuldshetty

import re
import numpy as np

c2i = {' ': 0, 'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'g': 7, 'h': 8, 'i': 9, 'j': 10, 'k': 11, 'l': 12, 'm': 13, 'n': 14, 'o': 15, 'p': 16, 'q': 17, 'r': 18, 's': 19, 't': 20, 'u': 21, 'v': 22, 'w': 23, 'x': 24, 'y': 25, 'z': 26}
max_len = 10

def process_text(name):
  name = name.lower()
  name = re.sub("[^a-z ]", "", name)
  name = " ".join([x for x in name.split() if len(x)!=1])

rahuldshetty

from bst import BST

bst = BST()

values = [2,1,5,44,15]

for x in values:
    bst.insert(x)

bst.inorder()

rahuldshetty

from node import Node

class BST:
    
    __root = None

    def insert(self, value):

        def _insert(root, value):
            if root is None:

rahuldshetty

class Node:
    __left = None
    __right = None

    def __init__(self, value) -> None:
        super().__init__()
        self.__value = value

    @property
    def value(self):

rahuldshetty

import pickle

class LabelEncoder:
    '''
    Utility function to encode labels to keys and vice versa
    '''
    def __init__(self,label_list):
        self.label_list = list(set(label_list))
        self.map_key = {index:self.label_list[index] for index in range(len(self.label_list))}
        self.unmap_key = {self.map_key[idx]:idx for idx in range(len(self.label_list))}