Build A Large Language Model From Scratch Pdf - Repack

After following the 300-page PDF for two weeks, you will have a model that:

Most people use the Hugging Face transformers library and call it a day. But building from scratch means: build a large language model from scratch pdf

import torch import torch.nn as nn import torch.nn.functional as F class MultiHeadAttention(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.n_heads = n_heads self.d_model = d_model self.head_dim = d_model // n_heads self.q_linear = nn.Linear(d_model, d_model) self.k_linear = nn.Linear(d_model, d_model) self.v_linear = nn.Linear(d_model, d_model) self.out_linear = nn.Linear(d_model, d_model) def forward(self, x, mask=None): B, T, C = x.shape # Split into heads: (B, T, n_heads, head_dim) -> Transpose to (B, n_heads, T, head_dim) q = self.q_linear(x).view(B, T, self.n_heads, self.head_dim).transpose(1, 2) k = self.k_linear(x).view(B, T, self.n_heads, self.head_dim).transpose(1, 2) v = self.v_linear(x).view(B, T, self.n_heads, self.head_dim).transpose(1, 2) # Scaled Dot-Product Attention scores = (q @ k.transpose(-2, -1)) / (self.head_dim ** 0.5) if mask is not None: scores = scores.masked_fill(mask == 0, float('-inf')) attention = F.softmax(scores, dim=-1) out = attention @ v # Concatenate heads and project back out = out.transpose(1, 2).contiguous().view(B, T, C) return self.out_linear(out) class TransformerBlock(nn.Module): def __init__(self, d_model, n_heads): super().__init__() self.ln1 = nn.LayerNorm(d_model) self.attn = MultiHeadAttention(d_model, n_heads) self.ln2 = nn.LayerNorm(d_model) self.ffn = nn.Sequential( nn.Linear(d_model, 4 * d_model), nn.GELU(), nn.Linear(4 * d_model, d_model) ) def forward(self, x, mask=None): # Pre-LN architecture for training stability x = x + self.attn(self.ln1(x), mask=mask) x = x + self.ffn(self.ln2(x)) return x Use code with caution. The Causal Mask After following the 300-page PDF for two weeks,

When a model cannot fit into the memory of a single GPU, you must implement parallel execution frameworks: Description Best Used For Copies the model across all GPUs; splits the batch size. Models that fit entirely on a single GPU. Tensor Parallelism (TP) Models that fit entirely on a single GPU