https://github.com/nebuly-ai/optimate

A collection of libraries to optimise AI model performances
https://github.com/nebuly-ai/optimate

ai analytics artificial-intelligence deeplearning large-language-models llm

Last synced: 2 days ago
JSON representation

A collection of libraries to optimise AI model performances

• Host: GitHub
• URL: https://github.com/nebuly-ai/optimate
• Owner: nebuly-ai
• License: apache-2.0
• Created: 2022-02-12T17:17:14.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-07-22T02:07:03.000Z (4 months ago)
• Last Synced: 2024-07-22T10:41:29.042Z (4 months ago)
• Topics: ai, analytics, artificial-intelligence, deeplearning, large-language-models, llm
• Language: Python
• Homepage: https://www.nebuly.com/
• Size: 4.32 MB
• Stars: 8,365
• Watchers: 94
• Forks: 644
• Open Issues: 110
• Metadata Files:
  • Readme: README.md
  • Code of conduct: CODE_OF_CONDUCT.md
  • Citation: CITATION.cff

Awesome Lists containing this project

README

        
# OptiMate

**[Legacy]**

This repository is now in a legacy phase and is no longer actively maintained. Although the source code is still available in the Git history, there will be no additional updates or official support.

**[About Nebuly]**

Our team is fully committed on creating the best user-experience platform for LLMs so that companies can understand user behavior at scale when interacting with their LLM-based products. 

- To learn more on how to get started, visit our [official documentation](https://docs.nebuly.com/welcome/overview)

- If you need enterprise support, please contact us [here](https://www.nebuly.com/nebuly-book-a-demo)

**[About optimate]**

We have open-sourced a couple of internal projects to the community, but we are not currently maintaining them. Optimate is a collection of libraries designed to help you optimize your AI models. It is an open-source project developed by Nebuly AI but is **not actively maintained**.

The tools available to assist you in your optimization are:

✅ [Speedster](https://github.com/nebuly-ai/optimate/tree/main/optimization/speedster): reduce inference costs by leveraging SOTA optimization techniques that best couple your AI models with the underlying hardware (GPUs and CPUs)

✅ [Nos](https://github.com/nebuly-ai/nos): reduce infrastructure costs by leveraging real-time dynamic partitioning and elastic quotas to maximize the utilization of your Kubernetes GPU cluster

✅ [ChatLLaMA](https://github.com/nebuly-ai/optimate/tree/main/optimization/chatllama): reduce hardware and data costs by leveraging fine-tuning optimization techniques and RLHF alignment