setfit VS TornadoVM

It did, sorry for the hasty edits! I removed that part b/c I realized that there isn't a compelling-enough reason for me to believe that text similarity is clearly inappropriate. In fact, you can train the Pr(condition | chat) classifier I suggested above using similarity training! Use SetFit for that. In the end you'll get a classifier and a similarity model.

[3] https://github.com/huggingface/setfit

There's also kinda related approaches like SetFit which calculate embeddings from pretrained transformer models then then fit a classifier on top of the embeddings. I've yet to try it but it supposedly works well with very few labelled examples.

You could have a look at setfit.

Found relevant code at https://github.com/huggingface/setfit + all code implementations here

Efficient Few-Shot Learning Without Prompts https://github.com/huggingface/setfit https://arxiv.org/abs/2209.11055v1

You don't need to use C++ to interface with CUDA or even write it.

A while ago NVIDIA and the GraalVM team demoed grCUDA which makes it easy to share memory with CUDA kernels and invoke them from any managed language that runs on GraalVM (which includes JIT compiled Python). Because it's integrated with the compiler the invocation overhead is low:

https://developer.nvidia.com/blog/grcuda-a-polyglot-language...

And TornadoVM lets you write kernels in JVM langs that are compiled through to CUDA:

https://www.tornadovm.org

There are similar technologies for other languages/runtimes too. So I don't think that will cause NVIDIA to lose ground.

Presented by Juan Fumero, PhD & Research Fellow (The University of Manchester, UK) during the JVM Language Summit 2023 (Santa Clara CA).

More information on TornadoVM can be found at https://www.tornadovm.org/

Tags: #Java #JVMLS #GPU #FPGA #OpenJDK #GraalVM #AI