Summary of Fully Open Source Moxin-7b Technical Report, by Pu Zhao et al.
Fully Open Source Moxin-7B Technical Report
by Pu Zhao, Xuan Shen, Zhenglun Kong, Yixin Shen, Sung-En Chang, Timothy Rupprecht, Lei Lu, Enfu Nan, Changdi Yang, Yumei He, Xingchen Xu, Yu Huang, Wei Wang, Yue Chen, Yong He, Yanzhi Wang
First submitted to arxiv on: 8 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a significant development in the field of Large Language Models (LLMs), which have gained popularity for their impressive performance and versatility. Proprietary LLMs like GPT-4 and GPT-o1 have received attention, while open-source LLMs such as LLaMA and Mistral offer customization options across diverse applications. However, concerns arise from the commercialization of LLMs regarding transparency, reproducibility, and safety. The authors introduce Moxin 7B, a fully open-source LLM that adheres to the Model Openness Framework (MOF), achieving the highest MOF classification level of “open science” through code, dataset, and checkpoint releases. Experiments demonstrate superior performance in zero-shot evaluation and competitiveness in few-shot evaluation compared with popular 7B models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making Large Language Models more open and transparent. Right now, some people are developing these powerful tools but keeping the details secret. The authors of this paper want to change that by creating a fully open-source model called Moxin 7B. This means they’re sharing all the code, data, and steps they took to train the model. They did this to follow principles of openness in science, so others can build on their work and make even better models. |
Keywords
» Artificial intelligence » Attention » Classification » Few shot » Gpt » Llama » Zero shot
