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Description
Mercury 2.5 represents the pinnacle of production models from Inception, demonstrating a remarkable enhancement in quality compared to its predecessor, Mercury 2, all while upholding an impressive low-latency serving profile. It stands out as the most advanced diffusion language model currently available and is touted by Inception as the largest diffusion LLM ever developed. With a 40% boost in intelligence over Mercury 2, its performance aligns closely with that of cost-efficient frontier models like GPT-5.6 Luna (Low), Gemini 3.5 Flash-Lite, and Claude Haiku 4.5. The model boasts a generation speed of 1,107 tokens per second on commonly accessible NVIDIA GPUs and accommodates a generous 260K-token context window. Among its features are adjustable reasoning capabilities, simultaneous tool calls, and JSON that aligns with schemas. Specifically engineered for latency-sensitive tasks, it is well-suited for scenarios involving numerous model calls during a single interaction. In applications such as search agents and RAG pipelines, Mercury 2.5 excels in functions like planning, query rewriting, re-ranking, fact structuring, source summarization, and answer verification, all while ensuring rapid response times, making it an essential tool for developers seeking efficiency in their workflows.
Description
RankLLM is a comprehensive Python toolkit designed to enhance reproducibility in information retrieval research, particularly focusing on listwise reranking techniques. This toolkit provides an extensive array of rerankers, including pointwise models such as MonoT5, pairwise models like DuoT5, and listwise models that work seamlessly with platforms like vLLM, SGLang, or TensorRT-LLM. Furthermore, it features specialized variants like RankGPT and RankGemini, which are proprietary listwise rerankers tailored for enhanced performance. The toolkit comprises essential modules for retrieval, reranking, evaluation, and response analysis, thereby enabling streamlined end-to-end workflows. RankLLM's integration with Pyserini allows for efficient retrieval processes and ensures integrated evaluation for complex multi-stage pipelines. Additionally, it offers a dedicated module for in-depth analysis of input prompts and LLM responses, which mitigates reliability issues associated with LLM APIs and the unpredictable nature of Mixture-of-Experts (MoE) models. Supporting a variety of backends, including SGLang and TensorRT-LLM, it ensures compatibility with an extensive range of LLMs, making it a versatile choice for researchers in the field. This flexibility allows researchers to experiment with different model configurations and methodologies, ultimately advancing the capabilities of information retrieval systems.
API Access
Has API
API Access
Has API
Integrations
Gemini
Gemini Enterprise
JSON
Llama
Mistral AI
NVIDIA TensorRT
OpenAI
Python
Qwen
RankGPT
Integrations
Gemini
Gemini Enterprise
JSON
Llama
Mistral AI
NVIDIA TensorRT
OpenAI
Python
Qwen
RankGPT
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Inception
Country
United States
Website
www.inceptionlabs.ai/blog/introducing-mercury-2-5
Vendor Details
Company Name
Castorini
Country
Canada
Website
github.com/castorini/rank_llm/