--- library_name: transformers tags: - indonesia license: mit language: - id inference: true --- Document Title

First steps to descale low-resource language models

Sasando-1 is a tiny, highly experimental short-sequence text generator built using the Phi-3 architecture.

This repo contains the 25M version

Preliminary research preview

## 🎻 Welcome! Sasando-1 is a tiny, highly experimental Indonesian text generator built using the Phi-3 architecture. It comes with two variations of microscopic sizes: 7M and 25M parameters. It is trained on a tightly-controlled Indo4B dataset filtered to only have 18000 unique words. The method is inspired by Microsoft's TinyStories paper which demonstrates that a tiny language model can produce fluent text when trained on tightly-controlled dataset. ## 🇮🇩 Context Indonesia has +700 languages, and many of them are dying at an alarming rate. Language technologies like generative AI can play a massive role in language preservation. However, Indonesia has several contextual issues: - Many languages, including those with millions of speakers, have low-volume digital resources - Running large models can be costly, while Indonesia is a middle-income country with little funding Overcoming these challenges require developers to work with what little data and money that they have. Sasando-1 is a prototypical demonstration that thinly-available resources can potentially still be leveraged to develop generative models with cheap compute. ## ✨ Specs - Comes with 7M and 25M parameters - Based on Phi-3 architecture - Embedding vocab 4096 - Trained on ~257M tokens * 4 epoch ## 🔭 Out-of-Scope Use This is a research preview base model. It is not intruction-tuned and has minimal safety curation. It is not intended for commercial or practical applications. You are also not allowed to use this model without having fun. ## Acknowledgments - **Developed by:** Afrizal Hasbi Azizy - **License:** MIT ## Training log