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.
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Oct 19, 2016 - Lua
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
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.
🤖 From-scratch GPT in PyTorch — bigram, single-head & multi-head attention • FastAPI /generate endpoint • React + Vite frontend • Character-level tokenizer • CUDA support
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
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.
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
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.
TinyTalker is a small GPT model based on a character level tokenization system built from scratch. You can prompt it or train it on your own data super easily. It is also super small and can be ran on laptops.
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.
A decoder-only GPT built from scratch in pure PyTorch — no nn.Transformer, no HuggingFace. Character-level, writes pseudo-Shakespeare in ~20 min on a laptop CPU. Streaming web playground, 31 tests, every design choice motivated inline.
Character-level GPT with six-part attention head analysis — ablation, head patching, residual patching, redundancy testing, seed robustness, and logit lens — converging on one load-bearing head.
An implementation of character level text generation with LSTM.
Optimized LSTM-based character-level text generator trained on Shakespeare, achieving 3.5x faster training with mixed precision.
Bit-exact MicroGPT inference in pure SQLite SQL—not text-to-SQL. Integer-only Q16.48 transformer math and deterministic sampling, with no UDFs or extensions.
Character-level fork of Fairseq for sequence-to-sequence learning
Character-level language model series from bigram to WaveNet, with probing analysis of learned phonological representations.
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