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Octopus v4: Graph of language models Wei Chen∗ Nexa AI {alexchen}@nexa4ai. com Zhiyuan Li Nexa AI {zack}@nexa4ai. com Abstract Language models have been effective in a wide range of applications, yet the most sophisticated models are often proprietary. For example, GPT-4 by Open AI and various models by Anthropic are e... |
1 Introduction The rapid progress in large language models (LLMs) has revolutionized natural language processing, enabling AI systems to understand and generate human-like text with remarkable accuracy. LLMs like GPT-4 [ 29] and Anthropic [ 5], trained on vast datasets, can capture the nuances of language, context, and... |
potential of on-device AI lies in its seamless integration with cloud-based models, giving rise to the concept of cloud-on-device collaboration [48,36]. By harnessing the power of both on-device and cloud-based models, AI systems can achieve unprecedented levels of performance, scalability, and flexibility. This collab... |
functionalities like parallel function calling can be achieved through self-connections and sequential action processing via graph traversal, enhancing their operational efficiency and scalability. LLM scaling law Scaling laws [ 22] for Large Language Models (LLMs) have revolutionized our understanding of the relations... |
q u e r ym a th q u e r yl a w q u e r yh e a l th c a r e q u e r yc o d i n g q u e r yd e s i g n q u e r y Figure 2: The Octopus model is utilized to determine the optimal neighboring node and generate appropriate information for transmission. Consider a scenario where the Octopus model's neighbors are Math GPT [ 2... |
those with less than 10B parameters, attempts to process lengthy function descriptions. Such models struggle to grasp extensive descriptions effectively. Moreover, this method didn't consider the inherent relevance among different function descriptions. To address these challenges, constructing a graph that maps the re... |
-query (str): A detailed prompt that encapsulates a law-related question or issue. Speak in a professional legal manner. Returns:-str: Comprehensive legal analyses, solutions, or information related to the law query. """ Additionally, when we construct the dataset using similar strategy to Octopus v2 paper. Following t... |
q u e r y M a s t e r n o d e l o a d b a l a n c e r Figure 4: Our system design features a graph of language models with a master node deployed on a central device and worker nodes distributed across various devices. We employ Kubernetes (k8s) for serverless deployment of each individual worker language model. For ef... |
Llama-3-8B-Instruct Phi-3-mini-128k-instruct GPT-3. 5 Octopus-v4GPT-4020406080100MMLU(5-shot)68. 4 68. 170. 074. 886. 4Benchmark comparison Figure 5: The comparison of MMLU scores between Octopus v4 and other models. During Octopus v4's inference, only two small language models, each with fewer than 10B parameters, are... |
5. 1 How to train a vertical model To effectively fine-tune a large language model for domain-specific expertise, begin by gathering a substantial corpus of high-quality, domain-relevant data. This collection should include textbooks, research papers, articles, and other pertinent materials that thoroughly address the ... |
References [1]Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Harkirat Behl, et al. Phi-3 technical report: A highly capable language model locally on your phone. ar Xiv preprint ar Xiv:2404. 14219, 2024. [2]Abridge. Powering deeper ... |
[21] Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. Mistral 7b. ar Xiv preprint ar Xiv:2310. 06825, 2023. [22] Jared Kaplan, Sam Mc Candlish, Tom Henighan, Tom B Brown, Benjamin... |
Weng, Matt Wiethoff, Dave Willner, Clemens Winter, Samuel Wolrich, Hannah Wong, Lauren Workman, Sherwin Wu, Jeff Wu, Michael Wu, Kai Xiao, Tao Xu, Sarah Yoo, Kevin Yu, Qiming Yuan, Wojciech Zaremba, Rowan Zellers, Chong Zhang, Marvin Zhang, Shengjia Zhao, Tianhao Zheng, Juntang Zhuang, William Zhuk, and Barret Zoph. Gp... |
[49] Alex Young, Bei Chen, Chao Li, Chengen Huang, Ge Zhang, Guanwei Zhang, Heng Li, Jiangcheng Zhu, Jianqun Chen, Jing Chang, et al. Yi: Open foundation models by 01. ai. ar Xiv preprint ar Xiv:2403. 04652, 2024. [50] Si Zhang, Hanghang Tong, Jiejun Xu, and Ross Maciejewski. Graph convolutional networks: a compre-hens... |
""" This language model is dedicated to providing insights and answers on biology, encompassing high school biology, college biology, human anatomy, and related fields. It is an essential resource for students across educational levels and biology enthusiasts. This model also reformats user queries into professional bi... |
the field. It addresses questions related to fundamental and advanced electrical engineering concepts. This model also reformats user queries into professional electrical engineering language. Parameters:-query (str): A detailed prompt that encapsulates an electrical engineering-related question or problem, fostering p... |
professional law. This model serves law students, practicing lawyers, and professionals in the legal field needing detailed legal explanations or interpretations. This model also reformats user queries into professional legal language. Parameters:-query (str): A detailed prompt that encapsulates a law-related question ... |
macroeconomics, and high school microeconomics. This model assists students, economists, and financial analysts in understanding economic theories and applications. This model also reformats user queries into professional economics language. Parameters:-query (str): A detailed prompt that encapsulates an economics-rela... |
understanding business practices, theories, and market dynamics. This model also reformats user queries into professional business language. Parameters:-query (str): A detailed prompt that encapsulates a business-related question or problem. Speak in a professional business manner. Returns:-str: Detailed business insig... |
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