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https://github.com/nebuly-ai/nebuly
A collection of libraries to optimise AI model performances
https://github.com/nebuly-ai/nebuly
ai analytics artificial-intelligence deeplearning large-language-models llm
Last synced: 3 months ago
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A collection of libraries to optimise AI model performances
- Host: GitHub
- URL: https://github.com/nebuly-ai/nebuly
- 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
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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This repository is now in a legacy phase and is no longer actively maintained.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