tokenbender

#include <cstring>

class Disjoint_Union
{
public:
	Disjoint_Union(const int & n);
	int create();
	void merge(int i , int j);
	int find(const int & i);
	int size(){

tokenbender

#include <iostream>

using namespace std;


struct node{
	int value;
	node *left;
	node *right;
};

tokenbender

#include <iostream>
#include <math.h>
using namespace std;

template <class T>
struct Node {
    T value;
    Node *left;
    Node *right;

tokenbender

/* AVL Tree Implementation in C++   */
/* Harish R                         */


#include<iostream>

using namespace std;

class BST
{

tokenbender

/*
    Graph implementation following  tutorial http://www.geeksforgeeks.org/graph-and-its-representations/
*/
#include<iostream>
#include<cstdlib>
using namespace std;

//struct for an adjacency list node
struct AdjListNode{
    int data;

tokenbender

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

tokenbender

You have to assign tasks to a junior engineer to solve a user problem. The user problem could be of various forms:
- Adding a feature
- Debugging a failing test case
- Understanding a feature in the codebase
- A GitHub issue raised on the codebase

## Instructions

### Repository status
Repository Name: astropy (update if different)

tokenbender

import os
import sys
import time
import math
import pickle
from contextlib import nullcontext
from pathlib import Path
import subprocess
from dataclasses import dataclass
import inspect

tokenbender

The Gentle Art of Teaching Machines to Speak

A Journey Through Semantic Reinforcement Learning

For the curious mind who has just discovered that language models can learn, and wonders if there might be a kinder way to teach them.

In the hushed moments before dawn, ten thousand starlings lift from a field as one—not because any single bird commands them, but because each learns from its neighbors' subtle shifts, creating a collective intelligence far greater than the sum of its parts.

      >         >
>      >    >        >

tokenbender

#!/usr/bin/env python3

import argparse, os, gc, json, random, csv
os.environ.setdefault("PYTORCH_CUDA_ALLOC_CONF", "expandable_segments:True")
# os.environ.setdefault("CUDA_LAUNCH_BLOCKING", "1")

import numpy as np
import torch
import torch.nn.functional as F
from datasets import load_dataset