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Description
RankLLM is a comprehensive Python toolkit designed to enhance reproducibility in information retrieval research, particularly focusing on listwise reranking techniques. This toolkit provides an extensive array of rerankers, including pointwise models such as MonoT5, pairwise models like DuoT5, and listwise models that work seamlessly with platforms like vLLM, SGLang, or TensorRT-LLM. Furthermore, it features specialized variants like RankGPT and RankGemini, which are proprietary listwise rerankers tailored for enhanced performance. The toolkit comprises essential modules for retrieval, reranking, evaluation, and response analysis, thereby enabling streamlined end-to-end workflows. RankLLM's integration with Pyserini allows for efficient retrieval processes and ensures integrated evaluation for complex multi-stage pipelines. Additionally, it offers a dedicated module for in-depth analysis of input prompts and LLM responses, which mitigates reliability issues associated with LLM APIs and the unpredictable nature of Mixture-of-Experts (MoE) models. Supporting a variety of backends, including SGLang and TensorRT-LLM, it ensures compatibility with an extensive range of LLMs, making it a versatile choice for researchers in the field. This flexibility allows researchers to experiment with different model configurations and methodologies, ultimately advancing the capabilities of information retrieval systems.
Description
Recursive Flint is an advanced language model featuring 40 billion parameters, designed as a mixture-of-experts specifically for coding tasks executed locally. It engages around 2.5 billion parameters for each token processed, enabling sophisticated reasoning, multi-turn tool utilization, and accommodating context windows as extensive as 512,000 tokens. Developers can leverage Flint within coding agents to analyze codebases, rectify bugs, develop new features, and create tests while executing inference directly on their own hardware. The model's weights are made available under the Apache 2.0 license, promoting accessibility. Flint is compatible with various platforms such as llama.cpp, Ollama, Unsloth, LM Studio, vLLM, and SGLang; additionally, Recursive offers rec, an inference engine that is specifically optimized for use with Apple Silicon M4 Pro and M5 Pro/Max chips. Comprehensive setup documentation and practical examples are provided to assist developers in executing the model on local machines. Flint serves as a valuable tool for developers and technical teams aiming to effectively run and customize a code-centric model tailored to their specific system requirements. This innovative approach empowers users to enhance their coding capabilities and streamline development processes more efficiently than ever before.
API Access
Has API
Yes
API Access
Has API
Yes
Screenshots View All
No images available
Integrations
Gemini
Yes
Gemini Enterprise
Yes
LM Studio
No
Llama
Yes
Mistral AI
Yes
NVIDIA TensorRT
Yes
Ollama
No
OpenAI
Yes
OpenRouter
No
Python
Yes
Integrations
Gemini
No
Gemini Enterprise
No
LM Studio
Yes
Llama
No
Mistral AI
No
NVIDIA TensorRT
No
Ollama
Yes
OpenAI
No
OpenRouter
Yes
Python
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
Yes
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Castorini
Country
Canada
Website
github.com/castorini/rank_llm/
Vendor Details
Company Name
Recursive
Founded
2026
Country
United States
Website
recursive.com/updates/introducing-recursive-flint