RankLLMCastorini
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Related Products
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About
RankLLM is a Python toolkit for reproducible information retrieval research using rerankers, with a focus on listwise reranking. It offers a suite of rerankers, pointwise models like MonoT5, pairwise models like DuoT5, and listwise models compatible with vLLM, SGLang, or TensorRT-LLM. Additionally, it supports RankGPT and RankGemini variants, which are proprietary listwise rerankers. It includes modules for retrieval, reranking, evaluation, and response analysis, facilitating end-to-end workflows. RankLLM integrates with Pyserini for retrieval and provides integrated evaluation for multi-stage pipelines. It also includes a module for detailed analysis of input prompts and LLM responses, addressing reliability concerns with LLM APIs and non-deterministic behavior in Mixture-of-Experts (MoE) models. The toolkit supports various backends, including SGLang and TensorRT-LLM, and is compatible with a wide range of LLMs.
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About
TensorBlock is an open source AI infrastructure platform designed to democratize access to large language models through two complementary components. It has a self-hosted, privacy-first API gateway that unifies connections to any LLM provider under a single, OpenAI-compatible endpoint, with encrypted key management, dynamic model routing, usage analytics, and cost-optimized orchestration. TensorBlock Studio delivers a lightweight, developer-friendly multi-LLM interaction workspace featuring a plugin-based UI, extensible prompt workflows, real-time conversation history, and integrated natural-language APIs for seamless prompt engineering and model comparison. Built on a modular, scalable architecture and guided by principles of openness, composability, and fairness, TensorBlock enables organizations to experiment, deploy, and manage AI agents with full control and minimal infrastructure overhead.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Academic researchers and developers seeking a solution offering tools for implementing and evaluating listwise reranking with large language models
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Audience
Developers, AI engineers, and organizations wanting a solution to integrate, manage, and interact with multiple large language models through a consistent API and extensible workspace
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
Free
Free Version
Free Trial
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Pricing
Free
Free Version
Free Trial
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationCastorini
Canada
github.com/castorini/rank_llm/
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Company InformationTensorBlock
United States
tensorblock.co
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Categories |
Categories |
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Integrations
Mistral AI
OpenAI
Claude
Cloudflare
DeepSeek
Gemini
Gemini Enterprise
Gemini Enterprise Agent Platform
Google Cloud Platform
Lambda
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Integrations
Mistral AI
OpenAI
Claude
Cloudflare
DeepSeek
Gemini
Gemini Enterprise
Gemini Enterprise Agent Platform
Google Cloud Platform
Lambda
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