Build A Large Language Model -from Scratch- Pdf -2021 Now

import torch
from torch.utils.data import Dataset, DataLoader
class TextDataset(Dataset):
def init(self, text, tokenizer, seq_len):
self.tokens = tokenizer.encode(text)
self.seq_len = seq_len
def __len__(self):
    return len(self.tokens) - self.seq_len
def __getitem__(self, idx):
    x = self.tokens[idx:idx+self.seq_len]
    y = self.tokens[idx+1:idx+self.seq_len+1]
    return torch.tensor(x), torch.tensor(y)

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Training a 1.5B parameter model from scratch in 2021 required significant compute: Build A Large Language Model -from Scratch- Pdf -2021

A 2021 "from scratch" training run for a 125M model on 50B tokens might take 5–10 days on 8×V100 GPUs. import torch from torch

By the end of the PDF, you have a model that costs ~$5k in cloud compute to train for one week. How do you know it works? Training a 1

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