llm-dot-py 0.1.0

A modular, block-by-block LLM building library

llm-py

Build LLMs block by block.

llm-py is a modular, educational, and practical library for building Large Language Models (LLMs) from scratch. It provides implementations of modern components like Rotary Positional Embeddings (RoPE), SwiGLU, RMSNorm, and various attention mechanisms.

Features

• Modular Design: Plug-and-play components (Component based architecture).
• Modern Components:
  • Positional Embeddings: Rotary (RoPE), Alibi, Sinusoidal, Learned.
  • Attention: Multi-Head, Multi-Query (MQA), Grouped-Query (GQA).
  • Activations & Norms: SwiGLU, RMSNorm, LayerNorm.
• Configurable: Easy-to-use configuration system for different model sizes.

Installation

pip install llm-py

Note: You may need to install PyTorch separately depending on your CUDA version.

Usage

Here is a simple example of how to build a model:

from llm_py import (
    Model, small_config,
    Embedding, RotaryPE, SelfAttention, FeedForward, LMHead
)

# Initialize configuration
cfg = small_config(vocab_size=10000)

# Build the model block by block
model = (
    Model(cfg)
        .add(Embedding())
        .add(RotaryPE())
        .repeat(SelfAttention, 4, dropout=0.1)
        .add(FeedForward())
        .add(LMHead(tie_weights=True))
)

# Validate and print summary
model.validate()
model.summary()

# Run a forward pass
import torch
x = torch.randint(0, cfg.vocab_size, (2, 32)) 
output = model(x)
print(f"Output shape: {output.shape}")

Architecture

The library revolves around the Model class, which acts as a container for sequential Components.

• Component: Base class for all layers. Implementation of specific logic (e.g., RotaryPE) resides here.
• Config: Dataclass holding hyperparameters (dimension, heads, layers, etc.).

License

MIT