pyatv VS exllamav2

It's definitely not impossible at least.

Someone is doing it in python here:

https://pyatv.dev/

GPT-4 actually sent me here:

"Here is an example of a C# library that implements the HAP: CSharp.HomeKit (https://github.com/brutella/hkhomekit). You can use this library as a reference or directly use it in your project."

Which, to no surprise based on my experiences with LLMs for programming does not exist and doesn't seem to have ever existed.

I get that they aren't magic, but I guess I am just bad at trying to use LLMs to help in my programming. Apparently all I do are obscure things or something. Or I am just not good enough at prompting. But I feel like that's also a reflection of the weakness of an LLM in that it needs such perfect and specific prompting to get good answers.

-> https://github.com/postlund/pyatv/issues/1931

I love Python, but I really wish I didn't have to use it here. The reason I do is that tvOS changed the way network remote control works, and the only library that could do that was / is a fantastic Python library called pyatv: https://pyatv.dev/.

1) Install pyatv via command line. The command pip install pyatv is one way to achieve this. The important thing is that path needs to be accessible to homebridge. For me, that was /usr/local/bin Before proceeding, I recommend reading more about the API as this will be helpful if you want to build additional switches that track power status or other attributes. https://pyatv.dev

You can use pyatv to do this and if MQTT is your thing you can use this plugin.

The video above is just a silly experiment, but the library that's driving it (pyatv) is super useful. For example, on my Mac I can press Cmd+Shift+R to toggle an Apple TV remote control. Really useful for pausing a video or jumping back a few seconds. The cool thing is that it also works on Windows, Linux, etc. People are investigating adding regular text input to the library as well.

There's not an API provided by Apple. There are 3 protocols that have been reverse engineered by different people. The main one I've found is a python library called pyatv (https://pyatv.dev/) that handles pairing and remote control commands.

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.