? Learn more about tokenization techniques ? Let me know what you'd like to dive into next! Share public link
What do you have access to for training (e.g., local consumer GPUs, cloud clusters)?
When you build an LLM from scratch, you are not building ChatGPT. You are building a You are building a statistical machine that reads a sequence of numbers and guesses the most probable next number. build a large language model %28from scratch%29 pdf
If you are interested in starting this process, I can recommend the most up-to-date Python libraries or point you toward the most cost-effective cloud GPU providers to get your training started. Vaswani, A., et al. (2017). Attention is All You Need.
Fine-tuning & instruction tuning
Utilizes Brain Floating Point 16-bit precision to cut memory usage in half and accelerate tensor core calculations while preventing underflow/overflow issues common in FP16. 4. Instruction Tuning and Alignment
Replace standard ReLU activations in the Feed-Forward Network (FFN) with SwiGLU (Swish Gated Linear Unit), which offers smoother gradient flow and superior empirical performance. Share public link What do you have access
Modern LLMs are predominantly based on the Transformer architecture, specifically the decoder-only variant popularized by the GPT series. Unlike encoder-decoder models (like T5), decoder-only models are highly optimized for autoregressive next-token prediction. Tokenization Strategy
import torch import torch.nn as nn import torch.nn.functional as F class RMSNorm(nn.Module): def __init__(self, dim, eps=1e-6): super().__init__() self.eps = eps self.weight = nn.Parameter(torch.ones(dim)) def forward(self, x): variance = x.pow(2).mean(-1, keepdim=True) return x * torch.rsqrt(variance + self.eps) * self.weight class FeedForward(nn.Module): def __init__(self, dim, hidden_dim): super().__init__() # SwiGLU variant implementation self.w1 = nn.Linear(dim, hidden_dim, bias=False) self.w2 = nn.Linear(hidden_dim, dim, bias=False) self.w3 = nn.Linear(dim, hidden_dim, bias=False) def forward(self, x): return self.w2(F.silu(self.w1(x)) * self.w3(x)) class TransformerBlock(nn.Module): def __init__(self, dim, num_heads, hidden_dim): super().__init__() self.attention_norm = RMSNorm(dim) self.ffn_norm = RMSNorm(dim) # Core layers self.attention = nn.MultiheadAttention(embed_dim=dim, num_heads=num_heads, batch_first=True) self.feed_forward = FeedForward(dim, hidden_dim) def forward(self, x, causal_mask): # Pre-LN Residual Connections h = x + self.attention_forward(self.attention_norm(x), causal_mask) out = h + self.feed_forward(self.ffn_norm(h)) return out def attention_forward(self, x, mask): # Simplified wrapper for causal multi-head attention attn_output, _ = self.attention(x, x, x, attn_mask=mask, need_weights=False) return attn_output Use code with caution. 4. The Two-Stage Training Process If you are interested in starting this process,
Implementing attention mechanisms and a GPT model to generate text.