| | --- |
| | 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} |
| | } |
| | ``` |
