--- 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:** 82M (Hidden Size = 640) - **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 4:6 ratio - **Input and Output Modality:** Text - **Context Length:** 1024 ## Acknowledgements We want to acknowledge the open-source frameworks [llm.c](https://github.com/karpathy/llm.c) and [llama.cpp](https://github.com/ggerganov/llama.cpp) and the sensor dataset provided by SHL, 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** ```python from transformers import pipeline import torch path = "tinyllm/82M-0.4" prompt = "The sea is blue but it's his red sea" generator = pipeline("text-generation", model=path,max_new_tokens = 30, repetition_penalty=1.3, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto") print(generator(prompt)[0]['generated_text']) ``` 2. **With llama.cpp** Generate a GGUF model file using this [tool](https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py) and use the generated GGUF file for inferencing. ```python python3 convert_hf_to_gguf.py models/mymodel/ ``` ## Disclaimer This model is intended solely for research purposes.