ch32v003fun VS onnxruntime

I agree with your sentiment, but I am more optimistic about the future of RISC-V. It considerably lowered the barrier of entry for vendors, and so there are more of them! Higher competition usually means that users win: they get lower prices, open-source toolchains and firmware, etc.

For one, I am very excited about the tiny CH32V003 ([1], [2]) that costs ~$0.10 and can be programmed with completely open-source tools, see [3] and [4].

1. https://www.youtube.com/watch?v=L9Wrv7nW-S8

2. http://www.wch-ic.com/products/CH32V003.html

3. https://github.com/cnlohr/ch32v003fun

4. https://github.com/aappleby/PicoRVD

Commodity RISCV chips. Some of these have just entered mass production, such as the CH32V003 (10 cents each in 1k quantities).

https://github.com/cnlohr/ch32v003fun

Fully open source stack!

Grab the below eval board and peck around:

ONNX Runtime: ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

Embedding generation uses the ONNX runtime under the hood. This is a cross-platform inferencing library that supports multiple execution providers from CPU to specialized GPUs.

tfjs is dead, looking at the commit history. The standard now is to convert PyTorch to onnx, then use onnxruntime (https://github.com/microsoft/onnxruntime/tree/main/js/web) to run the model on the browsdr.

- slang[2] has the potential, but the meta programming part is not as strong as C++, existing libraries cannot be used.

The above conclusion is drawn from my work https://github.com/microsoft/onnxruntime/tree/dev/opencl, purely nightmare to work with thoes drivers and jit compilers. Hopefully Vcc can take compute shader more seriously.

[1]: https://www.circle-lang.org/

I did. It depends what you want, for an overview of how ONNX Runtime works then Microsoft have a bunch of things on https://onnxruntime.ai, but the Java content is a bit lacking on there as I've not had time to write much. Eventually I'll probably write something similar to the C# SD tutorial they have on there but for the Java API.

For writing ONNX models from Java we added an ONNX export system to Tribuo in 2022 which can be used by anything on the JVM to export ONNX models in an easier way than writing a protobuf directly. Tribuo doesn't have full coverage of the ONNX spec, but we're happy to accept PRs to expand it, otherwise it'll fill out as we need it.

What about models besides GPT? Most of the popular vector encoding models aren't using this architecture.

If you really didn't want PyTorch/Transformers, you could consider exporting your models to ONNX (https://github.com/microsoft/onnxruntime).