Root Mean Square Layer Normalization

Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Efficient kernel for RMS normalization with fused operations, includes both forward and backward passes, compatibility with PyTorch.

Simple and easy to understand PyTorch implementation of Large Language Model (LLM) GPT and LLAMA from scratch with detailed steps. Implemented: Byte-Pair Tokenizer, Rotational Positional Embedding (RoPe), SwishGLU, RMSNorm, Mixture of Experts (MOE). Tested on Taylor Swift song lyrics dataset.

Simple character level Transformer

Optimized Fused RMSNorm implementation with CUDA. Features vectorized memory access (float4), warp-level reductions, and efficient backward pass for LLM training

Generative models nano version for fun. No STOA here, nano first.

Build an LLM in PyTorch: BPE tokenizer, GPT-1/2 + LLaMA, end-to-end train/infer

A from-scratch PyTorch LLM implementing Sparse Mixture-of-Experts (MoE) with Top-2 gating. Integrates modern Llama-3 components (RMSNorm, SwiGLU, RoPE, GQA) and a custom-coded Byte-Level BPE tokenizer. Pre-trained on a curated corpus of existential & dark philosophical literature.

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