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What is Tensor?

Tensor aspires to be the leading trading platform tailored for professional NFT traders. The platform was born from our personal experiences of frequently flipping NFTs, during which we identified significant gaps in the existing tools. Our quest for improved speed, expanded coverage, more detailed data, and advanced order types inspired the development of Tensor. When users access Tensor, they will find a user-friendly decentralized application (dApp) that operates seamlessly with various elements working together in the background. With our innovative bonding-curve-based orders, available in both linear and exponential formats, traders can easily execute dollar-cost averaging strategies for their NFT investments. We also recognize the importance of promptly listing new collections, catering to traders eager to explore the latest assets. Users can earn trading fees and liquidity provider rewards by supplying liquidity and aiding in the creation of markets for their preferred NFT collections on TensorSwap. Furthermore, market makers are essential in boosting market liquidity, allowing other traders to enter and exit positions at more favorable prices, which ultimately contributes to a lively trading atmosphere. These features combine to make Tensor an essential resource for NFT aficionados aiming to refine their trading approaches, ensuring they have the tools necessary to thrive in a competitive landscape.

What is RankLLM?

RankLLM is an advanced Python framework aimed at improving reproducibility within the realm of information retrieval research, with a specific emphasis on listwise reranking methods. The toolkit boasts a wide selection of rerankers, such as pointwise models exemplified by MonoT5, pairwise models like DuoT5, and efficient listwise models that are compatible with systems including vLLM, SGLang, or TensorRT-LLM. Additionally, it includes specialized iterations like RankGPT and RankGemini, which are proprietary listwise rerankers engineered for superior performance. The toolkit is equipped with vital components for retrieval processes, reranking activities, evaluation measures, and response analysis, facilitating smooth end-to-end workflows for users. Moreover, RankLLM's synergy with Pyserini enhances retrieval efficiency and guarantees integrated evaluation for intricate multi-stage pipelines, making the research process more cohesive. It also features a dedicated module designed for thorough analysis of input prompts and LLM outputs, addressing reliability challenges that can arise with LLM APIs and the variable behavior of Mixture-of-Experts (MoE) models. The versatility of RankLLM is further highlighted by its support for various backends, including SGLang and TensorRT-LLM, ensuring it works seamlessly with a broad spectrum of LLMs, which makes it an adaptable option for researchers in this domain. This adaptability empowers researchers to explore diverse model setups and strategies, ultimately pushing the boundaries of what information retrieval systems can achieve while encouraging innovative solutions to emerging challenges.

Media

Media

Integrations Supported

Base App
Brave Browser
Brave Wallet
Dataoorts GPU Cloud
Gemini
Gemini Enterprise
Glow Wallet
Ledger
Mistral AI
NVIDIA TensorRT
OpenAI
Phantom
Python
Rally
RankGPT
Slope
Solana
Solflare
Torus

Integrations Supported

Base App
Brave Browser
Brave Wallet
Dataoorts GPU Cloud
Gemini
Gemini Enterprise
Glow Wallet
Ledger
Mistral AI
NVIDIA TensorRT
OpenAI
Phantom
Python
Rally
RankGPT
Slope
Solana
Solflare
Torus

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Tensor

Company Website

www.tensor.trade/

Company Facts

Organization Name

Castorini

Company Location

Canada

Company Website

github.com/castorini/rank_llm/

Categories and Features

Categories and Features

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