| --- |
| license: other |
| license_name: hippocratic-license |
| license_link: >- |
| https://firstdonoharm.dev/version/3/0/cl-eco-extr-ffd-law-media-mil-my-soc-sv-tal-usta.html |
| datasets: |
| - BASH-Lab/OpenSQA |
| language: |
| - en |
| base_model: |
| - lmsys/vicuna-7b-v1.5 |
| pipeline_tag: question-answering |
| --- |
| |
| # LLaSA-7B |
|
|
| LLaSA-7B is a large language and sensor assistant that can interpret IMU data for human activities. |
|
|
| ## Abstract |
|
|
| Wearable systems can recognize activities from IMU data but often fail to explain their underlying causes or contextual significance. To address this limitation, we introduce two large-scale resources: SensorCap, comprising 35,960 IMU--caption pairs, and OpenSQA, with 199,701 question--answer pairs designed for causal and explanatory reasoning. OpenSQA includes a curated tuning split (Tune-OpenSQA) optimized for scientific accuracy, narrative clarity, and diagnostic insight. Leveraging these datasets, we develop LLaSA (Large Language and Sensor Assistant), a family of compact sensor-aware language models (7B and 13B) that generate interpretable, context-rich responses to open-ended questions grounded in raw IMU data. LLaSA outperforms commercial LLMs, including GPT-3.5 and GPT-4o-mini, on benchmark and real-world tasks, demonstrating the effectiveness of domain supervision and model alignment for sensor reasoning. |
|
|
| ### Model Summary |
|
|
|
|
|
|
| - **Developed by:** BASH Lab, WPI |
| - **Model type:** sensor-text-to-text |
| - **Language(s) (NLP):** English |
| - **Finetuned from model:** lmsys/vicuna-7b-v1.5 |
|
|
| ### Model Sources |
|
|
| - **Repository:** https://github.com/BASHLab/LLaSA |
| - **Paper:** https://arxiv.org/abs/2406.14498 |
| - **Project Website:** https://bashlab.github.io/llasa_project/ |
| |
| ### Usage |
| |
| ```bash |
| git clone https://github.com/BASHLab/LLaSA.git |
| cd LLaSA/LLaSA |
| pip install -e . |
| hf download BASH-Lab/LLaSA-7B |
| ``` |
| |
| You can run any of the inference scripts (zero-shot classification or question-answering) following the scripts in the eval subdirectory of the LLaSA GitHub repository, or you can run one sample as follows. |
| ```Python |
| from llava.eval.run_llava import eval_model |
| from llava.mm_utils import get_model_name_from_path |
|
|
|
|
| sensor_reading = "imu.npy" # 20Hz, 2 sec (shape: (120,6)) |
| prompt = "Narrate this activity by analyzing the data." |
| model_path = "LLaSA-7B" |
| args = type('Args', (), { |
| "model_path": model_path, |
| "model_base": None, |
| "model_name": get_model_name_from_path(model_path), |
| "query": prompt, |
| "conv_mode": None, |
| "image_file": sensor_reading, |
| "sep": ",", |
| "temperature": 0, |
| "top_p": None, |
| "num_beams": 1, |
| "max_new_tokens": 300 |
| })() |
| llasa_answer = eval_model(args) |
| print(llasa_answer) |
| ``` |
| |
| ## Citation |
|
|
| <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
|
|
| **BibTeX:** |
|
|
| ``` |
| @article{imran2024llasa, |
| title={LLaSA: A Sensor-Aware LLM for Natural Language Reasoning of Human Activity from IMU Data}, |
| author={Imran, Sheikh Asif and Khan, Mohammad Nur Hossain and Biswas, Subrata and Islam, Bashima}, |
| journal={arXiv preprint arXiv:2406.14498}, |
| year={2024} |
| } |
| ``` |
