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self-attention-implementation

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This repository provides a basic implementation of self-attention. The code demonstrates how attention mechanisms work in predicting the next word in a sequence. It's a basic implementation that demonstrates the core concept of attention but lacks the complexity of more advanced models like Transformers.

  • Updated May 21, 2026
  • Python

SeedGPT is a minimal decoder-only LLM from scratch, inspired by andrej karpathy's tutorials, generating text token-by-token based on input word prompts.

  • Updated Aug 15, 2025
  • Python

A from-scratch implementation of a small language model, built part by part, from a text file to a system capable of generation, classification, instruction following, parameter-efficient adaptation, and reasoning-style preference training.

  • Updated Sep 3, 2026
  • Python

An educational GPT-style Transformer Language Model built from scratch in Python and PyTorch featuring custom tokenization, embeddings, positional encoding, causal masked self-attention, multi-head attention, autoregressive text generation, checkpointing, and temperature/top-k sampling.

  • Updated Jul 25, 2026
  • TypeScript

Proiect Natural Language Processing (NLP), Anul 3, Semestrul 2, Facultatea de Matematica si Informatica, Universitatea din Bucuresti

  • Updated Oct 8, 2025
  • Jupyter Notebook

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