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---
library_name: transformers
tags:
- indonesia
license: mit
language:
- id
inference: true
---
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<h1>First steps to descale low-resource language models</h1>
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<img src="https://i.imgur.com/z9ey830.png" alt="Sasando" width="500" height="250">
<p><em>Sasando-1 is a tiny, highly experimental short-sequence text generator built using the Phi-3 architecture.</em></p>
<p><strong><a href="https://huggingface.co/spaces/afrizalha/Sasando-1" style="color: blue; font-family: Tahoma;">❕Go straight to the gradio demo❕</a></strong></p>
<p><em style="color: black; font-weight: bold;">This repo contains the 25M version</em></p>
<p><em style="color: black; font-weight: bold;">Preliminary research preview</em></p>
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## 🎻 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
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<img src="https://imgur.com/32NFAKm.png" alt="Training log" width="500" height="250">
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