Stack Overflow’s Post

In the rapidly evolving landscape of large language models (LLMs), ensuring their reliability and robustness is a paramount challenge. The quest for comprehensive evaluation tools to navigate these models through adversarial scenarios while maintaining precision in assessment has been a persistent endeavour for researchers and developers. To tackle the problem, researchers recently introduced “PromptBench”, an innovative framework that offers a unified platform to meticulously assess these models' responses and resilience against malicious inputs.🌐 Know more on the link below:🔗👇 https://lnkd.in/gHrBi6GM #LLMs #PromptBench #LanguageModelEvaluation #RobustnessTesting

Open-Source and Community-Focused: Mixtral is released under the Apache 2.0 license, making it an open-weight model. Its open-source nature encourages community involvement, fostering innovation and allowing developers to customize the model for specific needs. Pre-training and Fine-Tuning: The model is pre-trained on data extracted from the open web, with experts and routers trained simultaneously. Mixtral can also be fine-tuned for specific tasks, such as instruction following, where it achieves impressive performance. In summary, Mixtral represents a significant advancement in AI model development. It combines a novel architecture with high efficiency, multilingual support, and a strong focus on community and open-source principles. Llama 2 An AI model developed by Meta AI designed to enhance the capabilities and safety of language models. Here are the key features and aspects of Llama 2 based on the context documents: Updated Architecture and Increased Size: Llama 2 is an updated version of its predecessor, Llama 1, featuring improvements such as a 40% increase in the size of the pretraining corpus, doubled context length, and the adoption of grouped-query attention. The model is available in variants with 7B, 13B, and 70B parameters, although the 34B variant is mentioned but not released. Focus on Safety and Responsible Use: Llama 2 has undergone extensive safety testing and tuning, including the use of techniques like Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) to improve safety alignment. The model has been evaluated for safety across various benchmarks, demonstrating improvements in truthfulness, toxicity reduction, and bias mitigation compared to its predecessor. Pretraining and Fine-Tuning Methodologies: The model was pretrained on a diverse mix of publicly available data, following Meta’s standard privacy and legal review processes. It was also fine-tuned for specific applications, such as dialogue use cases, to optimize its performance and safety for those scenarios. Open Release for Research and Commercial Use: Llama 2 and its fine-tuned variant, Llama 2-Chat, have been released to the public for both research and commercial applications. This open release aims to benefit society by enabling broader access to advanced language model technology while emphasizing the importance of responsible deployment. In summary, Llama 2 represents a significant advancement in language model technology, focusing on improved architecture, safety, and accessibility for a wide range of applications. Comparing the Models While Gemma, Mixtral, and Llama 2 all aim to propel AI forward, their distinct paths reflect the diverse priorities and challenges in the field:

Unlocking the future of human-computer interaction with GPT-4o! A new era of seamless communication is here.🚀💻

Are you intrigued by the magic of LLMOps? Let's explore its power and find out how it is revolutionizing businesses in today’s fast-paced world. LLMOps, or Large Language Model Operations, is the powerhouse behind the scenes that propels our language models to new heights. It's the key to handling vast datasets and unlocking unparalleled linguistic sophistication! In an era where advanced technology shapes our every interaction, LLMOps stands out as a pivotal force. From enhancing natural language understanding to supercharging scalability, LLMOps ensures our language models perform at their best, delivering results that go beyond expectations. Ready to elevate your language AI game? Let's embark on the LLMOps adventure together! #Mobiz #LLMOps #LanguageAI #BusinessInnovation #BusinessTransformation #AIInnovation

GenAI has quickly gone from being a tech buzzword to a mainstream technology - used by individual consumers and corporate enterprises. And at the heart of this power is the large language model - or LLM for short. Check out our latest blog, The Breakdown, where we discuss what LLMs are and how they work, on the Outshift Blog. http://cs.co/6044dt6fu #OutshiftBlog #GenAI #LLM

🌟 Exciting News! 🌟 We're diving deep into the world of AI with our latest YouTube video: "What is Meta LLaMA 3 - The Most Capable Large Language Model." Join us at ValueCoders as we explore the incredible capabilities and potential of Meta LLaMA 3. Don't miss out on understanding the future of AI technology! 📺 Watch now: https://lnkd.in/gY2Ke3cu #MetaLLaMA3 #AI #ArtificialIntelligence #ValueCoders #TechInnovation #FutureOfAI #MachineLearning #TechTalk #AITech #Innovation 🔔 Don't forget to like, comment, and subscribe for more tech insights!

This is a huge jump to scaling the value of GenAI and LLMs.

Announcing our Vol. 2 Product Pub AI Product Management event that will be held on Mar 15! In this session, we will explore the fusion of Large Language Models (LLMs) with fresh data, vital for building GenAI applications. Come join us to discover how the Retrieval Augmented Generation (RAG) approach meets this need, and delve into the impact of recent long-context LLM releases such as Gemini 1.5 Pro on product design. #genai #aiproductmanagement #productinnovation #rag #llm #genaitrends

Sharing an exciting throwback from before the LLM era. 👇 Back in 2016, before Large Language Models were even a thing, we joined forces with KITT.AI visionary crew, PhDs Xuchen Yao and Guoguo Chen. Our mission? To craft an innovative chatbot-building platform that put human conversation at the center of AI bоt development. 🤖 ChatFlow focused more on conversational flows than on programming complexities. A radical idea back then, right? The solution expanded to function across multiple popular applications and was ultimately acquired by Baidu. 🚀 The impact? ChatFlow attracted thousands of users, proving its power and ease-of-use. A true testament to our ability to engineer market-transforming products. Together, we faced the fascinating challenge of humanizing bot conversations. We successfully made the bot-making process low-code for the end clients, with an intuitive interface and robust architecture. Here's to challenging the norms, revolutionizing industries, and creating transformative solutions that shape the future. 💡 Read the full story 👉https://lnkd.in/eC9zuMBd

Wow that's 7 years ago! We went through actually 3 generations of chatbot in the past 7 years: In 2016, ChatFlow was a no-code/low-code platform. Similar products still exists today, such as DialogFlow. But they all had their prime passed after the home automation era. Then in 2020 we developed a full-code platform ("ngChat", next-gen chat) that has deep integration with Microsoft's VS Code. This is response to realizing that *no-code can do only so little*. It's heavily influenced by Adam Cheyer's work and the Samsung Bixby platform (which never took off). Now we are headed to instruction based platform (with LLM). This makes multi-turn dialog possible for the first time in history. This is so amazing that any chatbot today would easily win the Leobner Prize or the Alexa Prize. We haven't publicly announced the platform yet, because existing users kept everyone so busy that everyone's working during the holidays: https://chat.seasalt.ai/ In January we'll have another big announcement that'll bring natural language interaction to a whole new level!