optimum VS safetensors

Shout out to Huggingface's Optimum – which made it easier to quantize models.

Optimum to accelerate inference of transformers with hardware optimization

Yes Optimum lib's documentation is unfortunately not yet in best shape. I would be really thankful if you fill an issue detailing where the doc can be improved: https://github.com/huggingface/optimum/issues . Also, if you have features request, such as having a more flexible API, we are eager for community contributions or suggestions!

In order to support BetterTransformer with the canonical Transformer models from Transformers library, an integration was done with the open-source library Optimum as a one-liner:

It’s Lewis here from the open-source team at Hugging Face 🤗. I'm excited to share the latest release of our Optimum library, which provides a suite of performance optimization tools to make Transformers run fast on accelerated hardware!

Create libraries to optimize ML models during training and inference for specific hardware https://github.com/huggingface/optimum

Have you seen this article from HF https://huggingface.co/blog/bert-cpu-scaling-part-2 , there is also a lib https://github.com/huggingface/optimum? is the gain worth the tweaking? is OneDNN stuff easy to deploy on Triton?

The ML field is doing work in that area: https://github.com/huggingface/safetensors

FYI the file format, safetensors, was proposed, developed and maintained by HF, and involved people from groups such as Eleuther and Stability for external security audits.

https://github.com/huggingface/safetensors https://huggingface.co/blog/safetensors-security-audit

Thank you for note on this. I had not heard there were already trojan horse malware being slipped into tensor files as python scripts. Apparently torch pickle uses eval on the tensor file with no filter.

Heard surprisingly little commentary on this topic. The full explanation of how Safetensors are "Safe" can be found from the developer at: https://github.com/huggingface/safetensors/discussions/111

Here the developer goes into some detail about what kinds of protections .safetensor files have : https://github.com/huggingface/safetensors/discussions/111

Use the safetensors format, which allows safe persistence and loading of models for common libraries - TensorFlow, PyTorch, JAX, etc. We went through external audits in the last few months (blog post). The current direction will be to have this as the default format.

I've been diving in lately and while it's not efficient, the only way to do manage is to create a new conda/mamba environment, or a custom Docker image for all the conflicting packages.

For safety and speed, you should prefer the safetensor format: https://huggingface.co/docs/safetensors/speed

If you know what you are doing you can do your own conversions: https://github.com/huggingface/safetensors or for safety, https://huggingface.co/spaces/diffusers/convert

GitHub - huggingface/safetensors: Simple, safe way to store and distribute tensors