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    Home»Development»WizardLM-2: An Open-Source AI Model that Claims to Outperform GPT-4 in the MT-Bench Benchmark

    WizardLM-2: An Open-Source AI Model that Claims to Outperform GPT-4 in the MT-Bench Benchmark

    April 16, 2024

    A team of AI researchers has introduced a new series of open-source large language models named WizardLM-2. This development is a significant breakthrough in the world of artificial intelligence. The series consists of three models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B. Each of these models is designed for different complex tasks and aims to push the boundaries of machine learning capabilities.

    Advancements and Innovations

    The WizardLM-2 signifies a significant milestone in the field of AI, which is the result of a year of extensive research and development by the team. They have worked on enhancing the model’s ability to comprehend complex instructions, and the new models demonstrate outstanding performance in chat, multilingual processing, reasoning, and serving as an agent. They are on par with the best proprietary large language models (LLMs) currently available.

    The flagship model, WizardLM-2 8x22B, has been assessed by the team and has been identified as the most advanced open-source LLM for handling complex tasks. The WizardLM-2 70B is particularly proficient in reasoning, making it an excellent choice for tasks that require deep cognitive processes. Meanwhile, the smaller WizardLM-2 7B is highly competitive, despite its size, delivering rapid response times and impressive performance that rivals models ten times its size. All three models have unique strengths that make them ideal for different applications.

    Methodology and Training Techniques

    WizardLM-2 was developed using advanced techniques, including a fully AI-powered synthetic training system that utilized progressive learning. This approach improved the model’s abilities while reducing the amount of data required for effective training.

    The “AI Align AI” (AAA) framework is utilized to foster a collaborative and mutually supportive learning environment among various cutting-edge LLMs, including previous iterations of Wizard models. Through simulated interactions and peer learning, these models are able to enhance each other’s capabilities.

    Performance Evaluations

    WizardLM-2 underwent rigorous evaluations, including human and automatic assessments, compared to other leading models. The results showed that WizardLM-2 closely matched or exceeded the capabilities of leading models like GPT-4.

    Key Takeaways and Future Directions

    The introduction of WizardLM-2 is a milestone for the open-source community, offering advanced tools that were previously available only through proprietary models. The key takeaways from the development and evaluation of WizardLM-2 include:

    WizardLM-2’s models demonstrate high performance in complex AI tasks, with capabilities that challenge and even exceed those of proprietary counterparts.

    The progressive learning and AI co-teaching methods (AAA) signify a breakthrough in training methodologies, promising more efficient and effective model training.

    The open-sourcing of WizardLM-2 encourages transparency and collaboration in the AI community, fostering further innovation and application across various fields.

    Disclaimer: The project page and detailed information for WizardLM-2 are currently being finalized by the development team. Availability is expected soon. Please check back periodically for updates and access to full documentation and resources.

    We can do it!  First open LLM outperforms @OpenAI GPT-4 (March) on MT-Bench. WizardLM 2 is a fine-tuned and preferences-trained Mixtral 8x22B!

    TL;DR;
     Mixtral 8x22B based (141B-A40 MoE)
     Apache 2.0 license
     First > 9.00 on MT-Bench with an open LLM
     Used multi-step… pic.twitter.com/XcixP226Cz

    — Philipp Schmid (@_philschmid) April 15, 2024

    The post WizardLM-2: An Open-Source AI Model that Claims to Outperform GPT-4 in the MT-Bench Benchmark appeared first on MarkTechPost.

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