---
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-13b-v1.5-16k
pipeline_tag: question-answering
---

# LLaSA-13B

LLaSA-13B 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-13b-v1.5-16K

### 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-13B
```

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-13B"
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}
}
```