Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • Vertex AI Reviews & Ratings
    783 Ratings
    Company Website
  • LM-Kit.NET Reviews & Ratings
    23 Ratings
    Company Website
  • Semrush Reviews & Ratings
    6,303 Ratings
    Company Website
  • Podium Reviews & Ratings
    2,075 Ratings
  • Google AI Studio Reviews & Ratings
    11 Ratings
    Company Website
  • Buildium Reviews & Ratings
    2,455 Ratings
    Company Website
  • Resco Mobile App Development Toolkit Reviews & Ratings
    2 Ratings
    Company Website
  • LogicalDOC Reviews & Ratings
    124 Ratings
    Company Website
  • Referral Factory Reviews & Ratings
    352 Ratings
    Company Website
  • SiteMinder Reviews & Ratings
    256 Ratings
    Company Website

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.

What is NVIDIA TensorRT?

NVIDIA TensorRT is a powerful collection of APIs focused on optimizing deep learning inference, providing a runtime for efficient model execution and offering tools that minimize latency while maximizing throughput in real-world applications. By harnessing the capabilities of the CUDA parallel programming model, TensorRT improves neural network architectures from major frameworks, optimizing them for lower precision without sacrificing accuracy, and enabling their use across diverse environments such as hyperscale data centers, workstations, laptops, and edge devices. It employs sophisticated methods like quantization, layer and tensor fusion, and meticulous kernel tuning, which are compatible with all NVIDIA GPU models, from compact edge devices to high-performance data centers. Furthermore, the TensorRT ecosystem includes TensorRT-LLM, an open-source initiative aimed at enhancing the inference performance of state-of-the-art large language models on the NVIDIA AI platform, which empowers developers to experiment and adapt new LLMs seamlessly through an intuitive Python API. This cutting-edge strategy not only boosts overall efficiency but also fosters rapid innovation and flexibility in the fast-changing field of AI technologies. Moreover, the integration of these tools into various workflows allows developers to streamline their processes, ultimately driving advancements in machine learning applications.

Media

Media

Integrations Supported

Python
RankGPT
CUDA
Dataoorts GPU Cloud
Gemini
Gemini Enterprise
Hugging Face
MATLAB
NVIDIA AI Enterprise
NVIDIA Clara
NVIDIA DRIVE
NVIDIA DeepStream SDK
NVIDIA Merlin
NVIDIA Riva Studio
NVIDIA TensorRT
NVIDIA virtual GPU
OpenAI
PyTorch
RankLLM
Rosepetal AI

Integrations Supported

Python
RankGPT
CUDA
Dataoorts GPU Cloud
Gemini
Gemini Enterprise
Hugging Face
MATLAB
NVIDIA AI Enterprise
NVIDIA Clara
NVIDIA DRIVE
NVIDIA DeepStream SDK
NVIDIA Merlin
NVIDIA Riva Studio
NVIDIA TensorRT
NVIDIA virtual GPU
OpenAI
PyTorch
RankLLM
Rosepetal AI

API Availability

Has API

API Availability

Has API

Pricing Information

Free
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

Castorini

Company Location

Canada

Company Website

github.com/castorini/rank_llm/

Company Facts

Organization Name

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

developer.nvidia.com/tensorrt

Categories and Features

Categories and Features

Popular Alternatives

RankGPT Reviews & Ratings

RankGPT

Weiwei Sun

Popular Alternatives

OpenVINO Reviews & Ratings

OpenVINO

Intel
ColBERT Reviews & Ratings

ColBERT

Future Data Systems