server VS cakechat

I am wondering what is the best / most cost-efficient way to serve llama V2. - llama.cpp (is it production ready or just for playing around?) ? - Triton inference server ? - HF text generation inference ?

For inference of CNNs, memory should really not be an issue. If it is a software engineering problem, not a hardware issue. FP16 or Int8 for weights is fine and weight size won’t increase due to the high resolution. And during inference memory used for hidden layer tensors can be reused as soon as the last consumer layer has been processed. You likely using something that is designed for training for inference and that blows up the memory requirement, or if you are using TensorRT or something like that, you need to be careful to avoid that every tasks loads their own copy of the library code into the GPU. Maybe look at https://github.com/triton-inference-server/server

I am looking for something like [Triton Inference Server](https://github.com/triton-inference-server/server) or [TFX Serving](https://www.tensorflow.org/tfx/guide/serving), but in Rust. I came across [Orkon](https://github.com/vertexclique/orkhon) which seems to be dormant and a bunch of examples off of the [Awesome-Rust-MachineLearning](https://github.com/vaaaaanquish/Awesome-Rust-MachineLearning)

You've already mentioned Seldon Core which is well worth looking at but if you're just after the raw multi-model serving aspect rather than a fully-fledged deployment framework you should maybe take a look at the individual inference servers: Triton Inference Server and MLServer both support multi-model serving for a wide variety of frameworks (and custom python models). MLServer might be a better option as it has an MLFlow runtime but only you will be able to decide that. There also might be other inference servers that do MMS that I'm not aware of.

[1] https://docs.nvidia.com/launchpad/ai/chatbot/latest/chatbot-triton-overview.html[2] https://github.com/triton-inference-server/server[3] https://neptune.ai/blog/deploying-ml-models-on-gpu-with-kyle-morris[4] https://thechief.io/c/editorial/comparison-cloud-gpu-providers/[5] https://geekflare.com/best-cloud-gpu-platforms/

"TensorFlow has the better deployment infrastructure"

Tensorflow Serving is nice in that it's so tightly integrated with Tensorflow. As usual that goes both ways. It's so tightly coupled to Tensorflow if the mlops side of the solution is using Tensorflow Serving you're going to get "trapped" in the Tensorflow ecosystem (essentially).

For pytorch models (and just about anything else) I've been really enjoying Nvidia Triton Server[0]. Of course it further entrenches Nvidia and CUDA in the space (although you can execute models CPU only) but for a deployment today and the foreseeable future you're almost certainly going to be using a CUDA stack anyway.

Triton Server is very impressive and I'm always surprised to see how relatively niche it is.

[0] - https://github.com/triton-inference-server/server

i'm a 2nd year CSE student, and I was working on a project which required me to clone the github repository of cakechat

I'm not much of a programmer, but the first/original Replika is still on GitHub, named cakechat . Perhaps a starting point? https://github.com/lukalabs/cakechat

I have roleplayed with replika and also talked on serious topics. So far she has helped me in resolving my personal family problems and also roleplayed well. My question is how can replika answer and manage real life problems of humans with such perfection if it is just a bunch of scripts? My IRL problems where not scripted anywhere. I know that replika uses cakechat for generating its responses https://github.com/lukalabs/cakechat and she is quite dumb in solving technical problems but how in the world she solves IRL people problems so easily?

There's https://github.com/lukalabs/cakechat which replika seems to be related to. Might be another angle to work.

It used to be that you could activate a different language model for Replika. You could tell it to be angry or scared or sad, and it would react with those specific emotions. It was a training mode, so it didn't learn anything from the conversations. Here is some literature about it.

I have a project that uses the CakeChat by lukalabs that's due in 2 weeks (I have been procrastinating), but it seems to have been archived, and no updates for the last three years. I am unable to run the installation as well. I wanted to know if someone can help with this or can let me know if there are alternatives to this. My project is supposed to do emotion analysis on the chat between the user and the bot and recommend songs to the user.

I planned to use a chatbot for a recent personal project, but the cake chat is being archived.

I believe the first developments in this emotional speech calculation were part of the original cake_chat model developed by Luka.