ska_sort VS llama.cpp

Hang on, you can't just quote MB/s numbers for an O(n log(n)) sort. What length were these tests run at?

The code size might not end up quite as good, but a branchless merge sort is a contender for a fast and lightweight merge. Just published, tiny-sort-rs[0] cites 632 bytes and looks like ~350MB/s at 1e4 elements on Zen 3. In my tests, my own pisort[1] benches a little over twice as fast as LongSort, but it uses sorting networks as the base case so it's like 5KB. It's roughly based on piposort[2] which has more complicated recursion but a simpler base case.

400 MB/s seems a bit slow for a radix sort on that hardware: I'm hitting those numbers on my i5-6200U, which has less than half the clock rate, with my own radix sort. Recommend checking ska_sort_copy from [3] as it has about the same performance.

[0] https://github.com/Voultapher/tiny-sort-rs

[1] https://github.com/mlochbaum/SingeliSort/blob/master/src/mer...

[2] https://github.com/scandum/piposort

[3] https://github.com/skarupke/ska_sort

Skarupke has a good radix sort implementation https://github.com/skarupke/ska_sort https://probablydance.com/2016/12/27/i-wrote-a-faster-sorting-algorithm/

For anyone interested in exploring this, llama.cpp has an example implementation here:

https://github.com/ggerganov/llama.cpp/tree/master/examples/...

Getting started with LLMs can be intimidating. In this tutorial we will show you how to fine-tune a large language model using LoRA, facilitated by tools like llama.cpp and KitOps.

well https://github.com/ggerganov/llama.cpp/issues/6849

Have just done this recently for local chat with pdf feature in https://recurse.chat. (It's a macOS app that has built-in llama.cpp server and local vector database)

Running an embedding server locally is pretty straightforward:

- Get llama.cpp release binary: https://github.com/ggerganov/llama.cpp/releases