Transforming Workspaces with LLMs and Customizable Models for Enterprises
It seems like every week there are new Large Language Models (LLMs) being released, each promising to revolutionize how companies manage knowledge. Sometimes they come out so fast that it can be tough to keep up.
This post will help you get an overview by offering a snapshot of the latest LLMs as of May 2024, including:
ChatGPT 4 by OpenAI Llama3 by MetaAI Phi-3 by Microsoft Mixtral 8x22 by MistralWe’ll delve into their capabilities and how they stand out, providing you with the insights you need to choose the best model for you.
Why you should use ChatGPT 4:
GPT-4 can handle inputs up to 25,000 words, significantly expanding the scope of conversations, document analysis, and more complex queries. This version makes fewer mistakes, notably reducing instances of ‘hallucinations’ — where the model might previously offer nonsensical answers or incorrect information. GPT-4 shows improved capabilities in creative tasks, such as playing with language in unique ways and writing poetry or creative prose. GPT-4 can handle tasks like website creation from sketches, tax return completion, and legal information processing.Challenges with ChatGPT 4:
As GPT-4 introduces more advanced features, users may face a learning curve to fully leverage these new capabilities effectively. Despite reductions in errors, there’s an implicit challenge in ensuring the model’s outputs are accurate and free from biases present in the source material.Why you should use Llama 3:
Llama 3 includes models with 8 billion and 70 billion parameters, tailored for specific tasks like powering conversational chatbots. Built on a 15 trillion token dataset, about seven times larger than its predecessor’s, enabling more complex understanding and responses. New guards for content safety classification, capable of determining if text inputs or responses are safe or unsafe across various harm categories. Meta offers Llama 3 for free, promoting innovation and allowing developers worldwide to explore and expand its uses. Can be run locally for full data securityChallenges with Llama 3:
Navigating the maze of regional regulations, especially stringent AI and data privacy laws in the EU and the UK, poses a significant challenge. Being open-source, Llama 3 risks being exploited for unethical purposes if accessed by malicious entities. The complexity of Llama 3 demands considerable computational resources, which could escalate operating costs and environmental impact.Why you should use Phi 3:
Outshines similar and larger models in efficiency and performance. Offers a wide selection for diverse generative AI applications. First in its class to support a context window of up to 128K tokens for processing long contexts. Ready to use out-of-the-box with instruction tuning reflecting natural human instructions. Easily deployable on Azure AI and Ollama, optimized for performance in various environments. Supports cross-platform deployment, including GPU, CPU, and mobile, through ONNX Runtime optimization. Well-suited for scenarios with limited resources, requiring fast response times, or operating under budget constraints, with enhanced reasoning for analytical tasks.Challenges with Phi 3:
Struggles with factual knowledge benchmarks due to its smaller model size, limiting its ability to store and recall facts. The heavy focus on high-quality and textbook-exercise-like data could potentially limit its adaptability to real-world coding challenges that deviate from its training data.Why you should use Mixtral 8x22:
Mixtral 8x22 utilizes sparse activation patterns, offering unparalleled cost efficiency with only 39B active parameters out of 141B, surpassing dense 70B models in speed while being more capable. Cost-effective and supports multiple languages. It excels in mathematics and coding tasks compared to other open models, demonstrating strong capabilities in these areas. Natively capable of function calling, enabling application development and tech stack modernization at scale. Released under the Apache 2.0 license, promoting innovation and collaboration by allowing unrestricted use anywhere. Can be run locally for full data security.Challenges with Mixtral 8x22:
While optimized for reasoning and multilingual capabilities, its performance may vary depending on the specific use case and language. Achieving optimal performance may require hardware capable of handling the model’s demands. Although it serves as an excellent base model for fine-tuning, the process may require additional resources and expertise to tailor it to specific applications effectively.There are lots of applications for LLMs — regardless which one you pick.
At Curiosity, we use LLMs primarily for Retrieval-Augmented Generation (RAG) to make it easier to find and use the knowledge you need.
Curiosity Workspaces include:
Private GPT Functionality: A chat interface for translation, summarization, and coding assistance. Information Retrieval: Q&A-style interactions with documents and folders, integrating with company data sources like SharePoint. Flexibility and Customization: Supports easy model switching and various models, including OpenAI-based GPTs and local LLMs. Talk-to-Your-Files: Enables direct file interaction for questions and summaries directly within the document content. Custom AI Assistants: Allows the creation of custom AI assistants for specific tasks, personalizing support and improving workflow efficiency. Choose your preferred LLM and talk to your files inside CuriosityEach LLM has its sweet spot, depending on how it’s built, the data it’s trained on, and what you plan to use it for inside your business.
If you want to keep things local, there are some models to choose that fit your needs. You can train them on your data and ensure they are more compliant. But, you’ll need hardware that can handle it.
Conversely, going cloud-based means fast access to updated models (like the one powering ChatGPT), but your data heads to the cloud for answers, which might raise some privacy concerns. Plus, cloud models can be more restricted in what they can say.
Finding the right balance is key to getting the most out of your LLM journey — and Curiosity is right with you on all of these releases!�
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