character-level

Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch. Based on code of https://github.com/karpathy/char-rnn. Support Chinese and other things.

Code accompanying Incorporating Chinese Characters of Words for Lexical Sememe Prediction (ACL2018) https://arxiv.org/abs/1806.06349

Multi lingual character based named entity recognizer

Implementation of the Character-level Intra Attention Network (CIAN) for Natural Language Inference (NLI) upon SNLI and MultiNLI corpus

AI name parsing. Predict first or last name using a DL model.

a 5M parameter solution to a problem you could solve by counting on your fingers

LittleLM: A tiny character-level n-gram language model for local corpus building and collaborative experimentation.

Character-level vanilla RNN in PyTorch that predicts a name's language of origin, letter by letter. Includes training, confusion matrix evaluation, and inference — plus a real example of dataset distribution shift when tested on modern names.

🤖 From-scratch GPT in PyTorch — bigram, single-head & multi-head attention • FastAPI /generate endpoint • React + Vite frontend • Character-level tokenizer • CUDA support

generative model for names

In this project, I worked with a small corpus consisting of simple sentences. I tokenized the words using n-grams from the NLTK library and performed word-level and character-level one-hot encoding. Additionally, I utilized the Keras Tokenizer to tokenize the sentences and implemented word embedding using the Embedding layer. For sentiment analysis

A small experimental model based on SP-JEPA. Character-level text prediction in vector space.

Um GPT em nível de caractere escrito do zero em Java puro, sem bibliotecas de machine learning. Três níveis: bigrama por contagem, MLP com backpropagation manual e um Transformer com atenção causal.

A tiny GPT built from scratch in PyTorch: character-level training with resume + cosine LR, evaluation (loss/perplexity), and top-k/top-p streaming generation.

Small character level GPT built from scratch in PyTorch and trained on Tiny Shakespeare text. It has 10.8M params, measured val loss 1.48 vs 2.48 for a bigram model

build your own GPT, one letter at a time - a character-level GPT trained from scratch on public-domain books: trainer, corpus pipeline, benchmark harness, and 5 trained models

Learning experiment: training a tiny character-level GPT on Shakespeare, following Karpathy’s nanoGPT. My notes and runs.

TapeLM: facts as fingerprints on character ink — not token-id memory, not chunk RAG. One frozen curve encoder for generation and structured slot memory (write, bind, hop, resolve). Noisy recall, lexicon calibration, one-shot edits, clean unlearning — vs fair GPT/RAG; reproducible JSON benchmarks.

TinyBard: a 2.8M-parameter, character-level language model built from scratch in PyTorch with modern Llama-style components (RMSNorm, RoPE, grouped-query attention, SwiGLU), trained on Tiny Shakespeare.

An implementation of character level text generation with LSTM.