1 citations · 1 across the 4 of their papers we have counts for
4 papers
But How Would AI Agents Run a Town's Economy?
Sajal Regmi, Siddhartha Pudasaini, Chetan Phakami Pun
We placed 100 memory-equipped large language model (LLM) agents in charge of a closed, money-conserving spatial economy on real Pokhara Lakeside geography (earning wages, running b…
Arkios: An Open Bilingual English-Nepali Language Model Trained From Scratch, with a Devanagari-Aware Tokenizer
Sajal Regmi, Siddhartha Pudasaini, Chetan Phakami Pun
We present Arkios, a 1.04B-parameter dense transformer pretrained from scratch on 150B tokens of bilingual English-Nepali text, using a custom single-file C/CUDA training stack and…
Vowel Signs Are Not Letters: A Pre-tokenization Ceiling on Multilingual Tokenizer Fertility
Sajal Regmi, Siddhartha Pudasaini, Chetan Phakami Pun
Byte-level BPE tokenizers that use the HuggingFace ByteLevel pre-tokenizer inherit GPT-2's word regex, where a word is defined as \p{L}+, one or more Unicode letters. In abugida sc…
GPT Semantic Cache: Reducing LLM Costs and Latency via Semantic Embedding Caching
Sajal Regmi, Chetan Phakami Pun
Large Language Models (LLMs), such as GPT, have revolutionized artificial intelligence by enabling nuanced understanding and generation of human-like text across a wide range of ap…