Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

no code implementations • 22 Apr 2024 • Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Jianmin Bao, Harkirat Behl, Alon Benhaim, Misha Bilenko, Johan Bjorck, Sébastien Bubeck, Qin Cai, Martin Cai, Caio César Teodoro Mendes, Weizhu Chen, Vishrav Chaudhary, Dong Chen, Dongdong Chen, Yen-Chun Chen, Yi-Ling Chen, Parul Chopra, Xiyang Dai, Allie Del Giorno, Gustavo de Rosa, Matthew Dixon, Ronen Eldan, Victor Fragoso, Dan Iter, Mei Gao, Min Gao, Jianfeng Gao, Amit Garg, Abhishek Goswami, Suriya Gunasekar, Emman Haider, Junheng Hao, Russell J. Hewett, Jamie Huynh, Mojan Javaheripi, Xin Jin, Piero Kauffmann, Nikos Karampatziakis, Dongwoo Kim, Mahoud Khademi, Lev Kurilenko, James R. Lee, Yin Tat Lee, Yuanzhi Li, Yunsheng Li, Chen Liang, Lars Liden, Ce Liu, Mengchen Liu, Weishung Liu, Eric Lin, Zeqi Lin, Chong Luo, Piyush Madan, Matt Mazzola, Arindam Mitra, Hardik Modi, Anh Nguyen, Brandon Norick, Barun Patra, Daniel Perez-Becker, Thomas Portet, Reid Pryzant, Heyang Qin, Marko Radmilac, Corby Rosset, Sambudha Roy, Olatunji Ruwase, Olli Saarikivi, Amin Saied, Adil Salim, Michael Santacroce, Shital Shah, Ning Shang, Hiteshi Sharma, Swadheen Shukla, Xia Song, Masahiro Tanaka, Andrea Tupini, Xin Wang, Lijuan Wang, Chunyu Wang, Yu Wang, Rachel Ward, Guanhua Wang, Philipp Witte, Haiping Wu, Michael Wyatt, Bin Xiao, Can Xu, Jiahang Xu, Weijian Xu, Sonali Yadav, Fan Yang, Jianwei Yang, ZiYi Yang, Yifan Yang, Donghan Yu, Lu Yuan, Chengruidong Zhang, Cyril Zhang, Jianwen Zhang, Li Lyna Zhang, Yi Zhang, Yue Zhang, Yunan Zhang, Xiren Zhou

We introduce phi-3-mini, a 3. 8 billion parameter language model trained on 3. 3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3. 5 (e. g., phi-3-mini achieves 69% on MMLU and 8. 38 on MT-bench), despite being small enough to be deployed on a phone.

Language Modelling

Disease Progression Modeling Workbench 360

no code implementations • 24 Jun 2021 • Parthasarathy Suryanarayanan, Prithwish Chakraborty, Piyush Madan, Kibichii Bore, William Ogallo, Rachita Chandra, Mohamed Ghalwash, Italo Buleje, Sekou Remy, Shilpa Mahatma, Pablo Meyer, Jianying Hu

In this work we introduce Disease Progression Modeling workbench 360 (DPM360) opensource clinical informatics framework for collaborative research and delivery of healthcare AI.

BIG-bench Machine Learning

Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge

no code implementations • 9 Apr 2021 • Prithwish Chakraborty, James Codella, Piyush Madan, Ying Li, Hu Huang, Yoonyoung Park, Chao Yan, Ziqi Zhang, Cheng Gao, Steve Nyemba, Xu Min, Sanjib Basak, Mohamed Ghalwash, Zach Shahn, Parthasararathy Suryanarayanan, Italo Buleje, Shannon Harrer, Sarah Miller, Amol Rajmane, Colin Walsh, Jonathan Wanderer, Gigi Yuen Reed, Kenney Ng, Daby Sow, Bradley A. Malin

Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains.

BIG-bench Machine Learning

WNTRAC: AI Assisted Tracking of Non-pharmaceutical Interventions Implemented Worldwide for COVID-19

no code implementations • 2 Sep 2020 • Parthasarathy Suryanarayanan, Ching-Huei Tsou, Ananya Poddar, Diwakar Mahajan, Bharath Dandala, Piyush Madan, Anshul Agrawal, Charles Wachira, Osebe Mogaka Samuel, Osnat Bar-Shira, Clifton Kipchirchir, Sharon Okwako, William Ogallo, Fred Otieno, Timothy Nyota, Fiona Matu, Vesna Resende Barros, Daniel Shats, Oren Kagan, Sekou Remy, Oliver Bent, Pooja Guhan, Shilpa Mahatma, Aisha Walcott-Bryant, Divya Pathak, Michal Rosen-Zvi

We present the Worldwide Non-pharmaceutical Interventions Tracker for COVID-19 (WNTRAC), a comprehensive dataset consisting of over 6, 000 NPIs implemented worldwide since the start of the pandemic.

A Canonical Architecture For Predictive Analytics on Longitudinal Patient Records

no code implementations • 24 Jul 2020 • Parthasarathy Suryanarayanan, Bhavani Iyer, Prithwish Chakraborty, Bibo Hao, Italo Buleje, Piyush Madan, James Codella, Antonio Foncubierta, Divya Pathak, Sarah Miller, Amol Rajmane, Shannon Harrer, Gigi Yuan-Reed, Daby Sow

Many institutions within the healthcare ecosystem are making significant investments in AI technologies to optimize their business operations at lower cost with improved patient outcomes.

Fairness Management

Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration

no code implementations • 21 Sep 2018 • Ritesh Noothigattu, Djallel Bouneffouf, Nicholas Mattei, Rachita Chandra, Piyush Madan, Kush Varshney, Murray Campbell, Moninder Singh, Francesca Rossi

To ensure that agents behave in ways aligned with the values of the societies in which they operate, we must develop techniques that allow these agents to not only maximize their reward in an environment, but also to learn and follow the implicit constraints of society.

Multi-Objective Reinforcement Learning reinforcement-learning

Effect of secular trend in drug effectiveness study in real world data

no code implementations • 18 Aug 2018 • Sharon Hensley Alford, Piyush Madan, Shilpa Mahatma, Italo Buleje, Yanyan Han, Fang Lu

Second, we included a covariate in the model for the number of days between the date of approval of the new drug and the treatment (new or standard) start date.

Attribute Causal Inference