Large Language Models (LLMs)
GPT-4
GPT-4, OpenAI’s latest generative AI, surpasses GPT-3.5 with advanced language and multimedia processing. Trained on a trillion parameters, it excels in text generation, image and video understanding, significantly enhancing website content creation, SEO optimization, and interactive marketing strategies.
LlaMA
LlaMA, Meta AI’s open-source LLM, excels in query resolution, language comprehension, and reading. Its design targets educational applications, making it a prime AI assistant for Edtech platforms, enhancing learning experiences with its advanced language learning capabilities.
Falcon
Falcon, by the Technology Innovation Institute, is an open-source, autoregressive language model surpassing Llama in performance. With a diverse text and code dataset, advanced architecture, and efficient data processing, it excels using fewer parameters (40 billion) than top NLP models.
Cohere
Cohere, developed by a Canadian startup, is an open-source, multi-lingual LLM trained on an inclusive dataset. Its effectiveness across various languages and accents stems from training on a vast, diverse text corpus, enabling versatility in a wide range of tasks.
PaLM
Google AI’s PaLM, a burgeoning LLM, leverages Google’s extensive dataset for advanced language understanding, response generation, machine translation, and creative tasks. Emphasizing privacy and security, it’s ideal for secure eCommerce and handling sensitive information, showcasing breakthroughs in responsible AI.
Small Language Models (SLMs)
DistilBERT
DistilBERT, by Hugging Face, is a compact Transformer model, 40% smaller and 60% faster than BERT, with robust performance. It’s ideal for chatbots, content moderation, and mobile app integration.
Orca 2
Orca 2, Microsoft’s compact model in 7 and 13 billion parameter variants, excels in reasoning and outperforms larger models. It’s used for data analysis, comprehension, math solving, and summarization.
T5-Small
T5-Small efficiently manages text summarization, classification, and translation, ideal for moderate-resource settings like small servers and cloud apps, offering robust NLP without high computational demands.
RoBERTa
RoBERTa, a BERT improvement, excels with advanced training and more data. It’s used for in-depth language understanding, moderating content, and analyzing large datasets effectively.
Phi 2
Microsoft’s Phi 2 is a versatile, transformer-based Small Language Model, optimized for both cloud and edge computing. It achieves leading performance in mathematical reasoning, common sense judgment, language comprehension, and logical thinking, showcasing its efficiency and adaptability.
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