RankLLM

RankLLM

Castorini
+
+

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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.

About

oMLX is a macOS-native MLX server designed to make local AI faster and more practical on Apple Silicon. Built for the way coding agents actually work, it uses paged SSD KV caching to persist cache blocks to disk, allowing previously seen prefixes to be restored across requests and server restarts instead of being recomputed from scratch. This can reduce time to first token on long contexts from 30–90 seconds to under five seconds after the first turn. Continuous batching handles concurrent requests through mlx-lm’s BatchGenerator, improving generation throughput without forcing requests to wait behind a single job. oMLX can serve LLMs, vision-language models, embedding models, and rerankers simultaneously, using LRU eviction when memory runs low. It supports any MLX-format model from Hugging Face, including Qwen, LLaMA, Mistral, Gemma, DeepSeek, MiniMax, and GLM, and can reuse models already stored in the standard Hugging Face cache, LM Studio folders, or custom directories.

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

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

Audience

Developers and AI power users needing to run fast local LLM inference and agentic coding workflows on Apple Silicon

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

No information available.
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

Castorini
Canada
github.com/castorini/rank_llm/

Company Information

oMLX
United States
omlx.ai/

Alternatives

RankGPT

RankGPT

Weiwei Sun

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ColBERT

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Macyou

Macyou

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BaseRT

BaseRT

Base Compute

Categories

Categories

Integrations

Llama
Mistral AI
OpenAI
Python
Qwen
Anthropic
Cursor
DeepSeek
Gemini
Gemini Enterprise
Gemma
GitHub
Hugging Face
JSON
LM Studio
MiniMax
Model Context Protocol (MCP)
NVIDIA TensorRT
OpenClaw
RankGPT

Integrations

Llama
Mistral AI
OpenAI
Python
Qwen
Anthropic
Cursor
DeepSeek
Gemini
Gemini Enterprise
Gemma
GitHub
Hugging Face
JSON
LM Studio
MiniMax
Model Context Protocol (MCP)
NVIDIA TensorRT
OpenClaw
RankGPT
Claim RankLLM and update features and information
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