# Attention mechanism att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf')) att = torch.nn.functional.softmax(att, dim=-1)
llm-from-scratch/ │ ├── data/ # Data handling modules │ ├── __init__.py │ ├── dataset.py # PyTorch Dataset class for text chunking │ └── tokenizer.py # BPE or Character-level tokenizer implementation │ ├── model/ # Core architecture │ ├── __init__.py │ ├── attention.py # Multi-head attention and Causal masking │ ├── feed_forward.py # FFN layers │ ├── transformer_block.py # Single Transformer Block composition │ └── gpt.py # The main GPT Model class (nn.Module) │ ├── config/ # Configuration files │ └── config.yaml # Hyperparameters (n_layer, n_head, lr, batch_size) │ ├── engine/ # Training and Inference logic │ ├── __init__.py │ ├── trainer.py # Training loop, optimization, checkpointing │ └── generator.py # Text generation and sampling strategies │ ├── scripts/ # Entry points │ ├── train.py # CLI script to start training │ └── inference.py # CLI script to generate text │ ├── requirements.txt # Dependencies (torch, numpy, tiktoken, etc.) └── README.md # Project documentation build a large language model from scratch github
Building from scratch on consumer hardware requires efficiency techniques: # Attention mechanism att = (q @ k
I'll help you create a conceptual guide and code structure for building a large language model from scratch, as if it were a GitHub repository README. This is educational—actual training requires massive compute. :T] == 0
# Reshape for Multi-Head Attention k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)