---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb
language:
- en
library_name: transformers
tags:
- IoT
- sensor
- embedded
---

# TinyLLM

## Overview

This repository hosts a small language model developed as part of the TinyLLM framework ([arxiv link]). These models are specifically designed and fine-tuned with sensor data to support embedded sensing applications. They enable locally hosted language models on low-computing-power devices, such as single-board computers. The models, based on the GPT-2 architecture, are trained using Nvidia's H100 GPUs. This repo provides base models that can be further fine-tuned for specific downstream tasks related to embedded sensing.
## Model Information

- **Parameters:** 124M (Hidden Size = 768)
- **Architecture:** Decoder-only transformer
- **Training Data:** Up to 10B tokens from the [SHL](http://www.shl-dataset.org/) and [Fineweb](https://huggingface.co/datasets/HuggingFaceFW/fineweb) datasets, combined in a 0:1 ratio
- **Input and Output Modality:** Text
- **Context Length:** 1024

## Acknowledgements

We would like to acknowledge the open-source frameworks [llm.c](https://github.com/karpathy/llm.c) and [llama.cpp](https://github.com/ggerganov/llama.cpp), which were instrumental in training and testing these models.

## Usage

The model can be used in two primary ways:
1. **With Hugging Face’s Transformers Library**
2. **With llama.cpp**

## Disclaimer

This model is intended solely for research purposes.