Llama 3.3
Llama 3.3 is the latest iteration in the Llama series of language models, developed to push the boundaries of AI-powered understanding and communication. With enhanced contextual reasoning, improved language generation, and advanced fine-tuning capabilities, Llama 3.3 is designed to deliver highly accurate, human-like responses across diverse applications. This version features a larger training dataset, refined algorithms for nuanced comprehension, and reduced biases compared to its predecessors. Llama 3.3 excels in tasks such as natural language understanding, creative writing, technical explanation, and multilingual communication, making it an indispensable tool for businesses, developers, and researchers. Its modular architecture allows for customizable deployment in specialized domains, ensuring versatility and performance at scale.
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Azure AI Search
Deliver high-quality responses with a vector database built for advanced retrieval augmented generation (RAG) and modern search. Focus on exponential growth with an enterprise-ready vector database that comes with security, compliance, and responsible AI practices built in. Build better applications with sophisticated retrieval strategies backed by decades of research and customer validation. Quickly deploy your generative AI app with seamless platform and data integrations for data sources, AI models, and frameworks. Automatically upload data from a wide range of supported Azure and third-party sources. Streamline vector data processing with built-in extraction, chunking, enrichment, and vectorization, all in one flow. Support for multivector, hybrid, multilingual, and metadata filtering. Move beyond vector-only search with keyword match scoring, reranking, geospatial search, and autocomplete.
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BGE
BGE (BAAI General Embedding) is a comprehensive retrieval toolkit designed for search and Retrieval-Augmented Generation (RAG) applications. It offers inference, evaluation, and fine-tuning capabilities for embedding models and rerankers, facilitating the development of advanced information retrieval systems. The toolkit includes components such as embedders and rerankers, which can be integrated into RAG pipelines to enhance search relevance and accuracy. BGE supports various retrieval methods, including dense retrieval, multi-vector retrieval, and sparse retrieval, providing flexibility to handle different data types and retrieval scenarios. The models are available through platforms like Hugging Face, and the toolkit provides tutorials and APIs to assist users in implementing and customizing their retrieval systems. By leveraging BGE, developers can build robust and efficient search solutions tailored to their specific needs.
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Entry Point AI
Entry Point AI is the modern AI optimization platform for proprietary and open source language models. Manage prompts, fine-tunes, and evals all in one place. When you reach the limits of prompt engineering, it’s time to fine-tune a model, and we make it easy. Fine-tuning is showing a model how to behave, not telling. It works together with prompt engineering and retrieval-augmented generation (RAG) to leverage the full potential of AI models. Fine-tuning can help you to get better quality from your prompts. Think of it like an upgrade to few-shot learning that bakes the examples into the model itself. For simpler tasks, you can train a lighter model to perform at or above the level of a higher-quality model, greatly reducing latency and cost. Train your model not to respond in certain ways to users, for safety, to protect your brand, and to get the formatting right. Cover edge cases and steer model behavior by adding examples to your dataset.
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