Creating your own Large Language Model

Noaman Rashid

Large Language Models (LLMs) are neural networks trained on vast amounts of text data to understand and generate human-like text. Their core architecture relies on 

class TransformerBlock(nn.Module):
    def __init__(self, d_model, n_heads):
        super().__init__()
        self.attention = MultiHeadAttention(d_model, n_heads)
        self.norm1 = nn.LayerNorm(d_model)
        self.ffn = PositionwiseFFN(d_model)
        self.norm2 = nn.LayerNorm(d_model)

    def forward(self, x):
        x = x + self.attention(self.norm1(x))
        x = x + self.ffn(self.norm2(x))
        return x


Training LLMs like GPT-3 (175B parameters) can cost millions of dollars and require substantial computational power.

Reinforcement Learning from Human Feedback (RLHF)

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