ai21-jamba-1-5-large
The AI21 Jamba 1.5 Large model is a state-of-the-art, hybrid SSM-Transformer large language model (LLM) developed by AI21 Labs. This model brings together the best of Transformer and Mamba (Structured State Space) architectures, allowing it to efficiently manage long context windows. Unparalleled Architecture Jamba 1.5 Large boasts an
ai21-labs
The AI21 Labs' Jamba 1.5 Large model is redefining the landscape of large language models (LLMs) with state-of-the-art features and unmatched performance. Part of the Jamba 1.5 family, this model is designed to handle complex tasks with exceptional efficiency and speed. Innovative Architecture The Jamba 1.5
ai21
AI21 Labs has unveiled the AI21/Jamba 1.5 Mini, a groundbreaking advancement in the realm of large language models (LLMs). This model introduces several state-of-the-art features and improvements that set it apart from its predecessors and competitors. Innovative Architecture The Jamba 1.5 Mini model integrates the traditional Transformer
vertex-ai
Google Cloud's Vertex AI has consistently been at the forefront of machine learning and artificial intelligence advancements. The latest addition to its suite of tools, Imagen-3.0-Generate-001, continues this trend by offering enhanced capabilities for developers and data scientists. In this post, we will dive into what Imagen-3.
databricks
We are excited to announce the release of Databricks-Meta-Llama-3-1-405b-Instruct, a groundbreaking advancement in the realm of open-source large language models (LLMs). This model, boasting a staggering 405 billion parameters, sets a new standard for performance and capability in AI. Key Features of Llama 3.1 Model Size and Parameters: The
perplexity-ai
In July 2024, Perplexity AI introduced one of its latest models, LLama-3.1-Sonar-Small-128K-Online. Part of the new and improved Sonar model series, this model is designed for specific tasks and comes in various configurations, including small and large models. Model Specifications The LLama-3.1-Sonar-Small-128K-Online model is tailored to meet diverse
meta
Meta's latest addition to its family of large language models, Llama 3.1, marks a significant advancement in AI technology. Among its various versions, the Llama 3.1-8B-Instruct stands out with its impressive capabilities and fine-tuning tailored for human instructions. Model Overview Llama 3.1 follows in the
perplexity-labs
Perplexity Labs has unveiled its latest innovation in the realm of conversational AI with the launch of LLama-3.1-Sonar-Small-128K-Chat. This model, a proud member of the Llama 3.1 Sonar family, heralds a new era of cost-efficiency, speed, and high performance. Model Specifications The llama-3.1-sonar-small-128k-chat model is distinguished by
azure-ai
Azure AI's Meta-Llama-3.1-8B-Instruct model is a game-changer in the realm of large language models (LLMs). This latest advancement in the Llama series offers a blend of efficiency, performance, and multilingual capabilities that cater to a variety of AI applications. Model Details The Meta-Llama-3.1 series includes models
llm
In the ever-evolving landscape of large language models (LLMs), Perplexity has introduced an impressive addition with the LLama-3.1-Sonar-Huge-128K-Online. This model is a fine-tuned version of Meta's Llama 3.1 405B, boasting significant enhancements in cost-efficiency, speed, and overall performance. Key Features: * Base Model: The foundation of this
perplexity-ai
Perplexity AI has recently launched its new and improved Sonar models, designed to enhance performance and efficiency. Among these models is the Llama-3.1-Sonar-Small-128K-Online, a cutting-edge online language model optimized for real-time interactions. The Llama-3.1-Sonar-Small-128K-Online model boasts several impressive specifications: * Model Name: Llama-3.1-Sonar-Small-128K-Online * Model Type: Online, optimized for
cohere
The Cohere Embed-English-Light-v3.0 model is a recent addition to Cohere's suite of embedding models, engineered for efficient text embeddings. It's a leaner, faster option tailored for English text, making it an ideal choice for applications where speed and lower-dimensional embeddings are paramount. Dimensions and Performance