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Description
Nomic Embed is a comprehensive collection of open-source, high-performance embedding models tailored for a range of uses, such as multilingual text processing, multimodal content integration, and code analysis. Among its offerings, Nomic Embed Text v2 employs a Mixture-of-Experts (MoE) architecture that efficiently supports more than 100 languages with a remarkable 305 million active parameters, ensuring fast inference. Meanwhile, Nomic Embed Text v1.5 introduces flexible embedding dimensions ranging from 64 to 768 via Matryoshka Representation Learning, allowing developers to optimize for both performance and storage requirements. In the realm of multimodal applications, Nomic Embed Vision v1.5 works in conjunction with its text counterparts to create a cohesive latent space for both text and image data, enhancing the capability for seamless multimodal searches. Furthermore, Nomic Embed Code excels in embedding performance across various programming languages, making it an invaluable tool for developers. This versatile suite of models not only streamlines workflows but also empowers developers to tackle a diverse array of challenges in innovative ways.
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
Python
Baseten
Gemini
Go
Java
JavaScript
Llama
Mistral AI
NVIDIA TensorRT
OpenAI
Integrations
Python
Baseten
Gemini
Go
Java
JavaScript
Llama
Mistral AI
NVIDIA TensorRT
OpenAI
Pricing Details
Free
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
Nomic
Country
United States
Website
www.nomic.ai/embed
Vendor Details
Company Name
Castorini
Country
Canada
Website
github.com/castorini/rank_llm/