sharpenb (Bertrand Charpentier)

reacted to their post with 🚀 25 days ago

Post

3156

How to learn about efficient AI? - Happy to announce the Awesome AI Efficiency repo that gathers a curated list of 100+ materials to understand the challenges and solutions in making AI faster, smaller, cheaper, greener.

🚀 It is designed for a **large audience** including beginners, decision-makers, engineers, and researchers.
📚 It contains **diverse materials** with newspaper articles, blogs, tools, tech reports, research papers, books, and lectures.

This is an ongoing project. Do not hesitate to share your feedback/suggestions and star the repo! 🌟

https://github.com/PrunaAI/awesome-ai-efficiency

2 replies

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posted an update 26 days ago

Post

3156

How to learn about efficient AI? - Happy to announce the Awesome AI Efficiency repo that gathers a curated list of 100+ materials to understand the challenges and solutions in making AI faster, smaller, cheaper, greener.

🚀 It is designed for a **large audience** including beginners, decision-makers, engineers, and researchers.
📚 It contains **diverse materials** with newspaper articles, blogs, tools, tech reports, research papers, books, and lectures.

This is an ongoing project. Do not hesitate to share your feedback/suggestions and star the repo! 🌟

https://github.com/PrunaAI/awesome-ai-efficiency

2 replies

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reacted to their post with 🚀 2 months ago

Post

3107

We open-sourced the pruna package that can be easily installed with pip install pruna :) It allows to easily ccompress and evaluate AI models including transformers and diffusers.

- Github repo: https://github.com/PrunaAI/pruna
- Documentation: https://docs.pruna.ai/en/stable/index.html

With open-sourcing, people can now inspect and contribute to the open code. Beyond the code, we provide detailed readme, tutorials, benchmarks, and documentation to make transparent compression, evaluation, and saving/loading/serving of AI models.

Happy to share it with you and always interested in collecting your feedback :)

2 replies

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posted an update 2 months ago

Post

3107

We open-sourced the pruna package that can be easily installed with pip install pruna :) It allows to easily ccompress and evaluate AI models including transformers and diffusers.

- Github repo: https://github.com/PrunaAI/pruna
- Documentation: https://docs.pruna.ai/en/stable/index.html

With open-sourcing, people can now inspect and contribute to the open code. Beyond the code, we provide detailed readme, tutorials, benchmarks, and documentation to make transparent compression, evaluation, and saving/loading/serving of AI models.

Happy to share it with you and always interested in collecting your feedback :)

2 replies

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posted an update 4 months ago

Post

534

How to deploy compressed ML models in your pipeline?

We wrote a series of blogs on this topics. Hope that it can be helpful to people:
- Standard Model Compression in ML Pipeline: https://www.pruna.ai/blog/standard-model-compression-ml-pipeline
- Boost Your Replicate Models with Pruna AI: A Step-by-Step Guide: https://www.pruna.ai/blog/guide-replicate-pruna-ai
- Pruna + Triton: A Winning Combination for High-Performance AI Deployments: https://www.pruna.ai/blog/pruna-triton-combination

Feel free to join our discord (https://discord.com/invite/rskEr4BZJx) if you have questions ;)

replied to their post 4 months ago

Thanks for the notification! Indeed we support only Linux for now. We would be happy to work on the token problem. If you could share your colab config and the trace here or on our discord, it would help us :)

posted an update 4 months ago

Post

1916

We compressed SmolLMs to make 135 variations of them (see

PrunaAI ) with different quantization configurations with pruna (https://docs.pruna.ai/en/latest/).

We made a blog to summarize our findings (see https://www.pruna.ai/blog/smollm2-smaller-faster) and small LM can be made smaller! :)

3 replies

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Bertrand Charpentier

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