koboldcpp VS exllamav2

FYI this is the easiest way to host on the horde: https://github.com/LostRuins/koboldcpp

For more information of Koboldcpp look this guide: https://github.com/LostRuins/koboldcpp/wiki

Out of curiosity, is there a specific reason for this? The most popular fork KoboldCpp is in active development, and was the first to adopt the Min P sampler, and even distincts itself with the context shift feature. Just wondering what this means for the future. Thanks!

There are already some implementations out there which attempt to accomplish this!

Here's an example: https://github.com/silphendio/sliced_llama

A gist pertaining to said example: https://gist.github.com/silphendio/535cd9c1821aa1290aa10d587...

Here's a discussion about integrating this capability with ExLlama: https://github.com/turboderp/exllamav2/pull/275

And same as above but for llama.cpp: https://github.com/ggerganov/llama.cpp/issues/4718#issuecomm...

Sorry, I'm somewhat familiar with this term (I've seen it as a model loader in Oobabooga), but still not following the correlation here. Are you saying I should instead be using this project in lieu of llama.cpp? Or are you saying that there is, perhaps, an exllamav2 "extension" or similar within llama.cpp that I can use?

EDIT: I reported the bug to the exllamav2 Github. It's actually already fixed, just not on any current built release.

install exllamav2 from git with pip install git+https://github.com/turboderp/exllamav2.git. Make sure you have flash attention 2 as well.

Recent releases for exllamav2 brings working fp8 cache support, which I've been very excited to test. This feature doubles the maximum context length you can run with your model, without any visible downsides.

Without batching, I was actually thinking that's kind of modest.

ExllamaV2 will get 48 tokens/s on a 4090, which is much slower/cheaper than an H100:

https://github.com/turboderp/exllamav2#performance

I didn't test codellama, but the 3090 TI figures are in the ballpark of my generation speed on a 3090.

First, you need the convert.py script from turboderp's Exllama2 repo. You can read all about the convert.py arguments here.

> brute aggressive quantization

Cutting edge quantization like ExLlama's EX2 is far from brute force: https://github.com/turboderp/exllamav2#exl2-quantization

> The format allows for mixing quantization levels within a model to achieve any average bitrate between 2 and 8 bits per weight. Moreover, it's possible to apply multiple quantization levels to each linear layer, producing something akin to sparse quantization wherein more important weights (columns) are quantized with more bits. The same remapping trick that lets ExLlama work efficiently with act-order models allows this mixing of formats to happen with little to no impact on performance. Parameter selection is done automatically by quantizing each matrix multiple times, measuring the quantization error (with respect to the chosen calibration data) for each of a number of possible settings, per layer. Finally, a combination is chosen that minimizes the maximum quantization error over the entire model while meeting a target average bitrate.

Llama.cpp is also working on a feature that let's a small model "guess" the output of a big model which then "checks" it for correctness. This is more of a performance feature, but you could also arrange it to accelerate a big model on a small GPU.