RankLLM

RankLLM

Castorini
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+

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About

NVIDIA TensorRT is an ecosystem of APIs for high-performance deep learning inference, encompassing an inference runtime and model optimizations that deliver low latency and high throughput for production applications. Built on the CUDA parallel programming model, TensorRT optimizes neural network models trained on all major frameworks, calibrating them for lower precision with high accuracy, and deploying them across hyperscale data centers, workstations, laptops, and edge devices. It employs techniques such as quantization, layer and tensor fusion, and kernel tuning on all types of NVIDIA GPUs, from edge devices to PCs to data centers. The ecosystem includes TensorRT-LLM, an open source library that accelerates and optimizes inference performance of recent large language models on the NVIDIA AI platform, enabling developers to experiment with new LLMs for high performance and quick customization through a simplified Python API.

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.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Machine learning engineers and data scientists seeking a tool to optimize their deep learning operations

Audience

Academic researchers and developers seeking a solution offering tools for implementing and evaluating listwise reranking with large language models

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

NVIDIA
Founded: 1993
United States
developer.nvidia.com/tensorrt

Company Information

Castorini
Canada
github.com/castorini/rank_llm/

Alternatives

OpenVINO

OpenVINO

Intel

Alternatives

RankGPT

RankGPT

Weiwei Sun
ColBERT

ColBERT

Future Data Systems

Categories

Categories

Integrations

Python
RankGPT
CUDA
Gemini
Kimi K2
Kimi K2.5
Llama
MATLAB
NVIDIA AI Enterprise
NVIDIA Broadcast
NVIDIA Jetson
NVIDIA Merlin
NVIDIA Morpheus
NVIDIA Riva Studio
NVIDIA TensorRT
OpenAI
PyTorch
Rosepetal AI
TensorFlow
Ultralytics

Integrations

Python
RankGPT
CUDA
Gemini
Kimi K2
Kimi K2.5
Llama
MATLAB
NVIDIA AI Enterprise
NVIDIA Broadcast
NVIDIA Jetson
NVIDIA Merlin
NVIDIA Morpheus
NVIDIA Riva Studio
NVIDIA TensorRT
OpenAI
PyTorch
Rosepetal AI
TensorFlow
Ultralytics
Claim NVIDIA TensorRT and update features and information
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