serve VS swiss_army_llama

What's wrong with just using Torchserve[1]? We've been using it to serve embedding models in production.

[1] https://pytorch.org/serve/

Looks like you are already up to speed loading LLaMa models which is great. Assuming this is Hugging Face PyTorch checkpoint, I think it should be possible to spin up a TorchServe instance which has in-built support for API access and HF Transformers. Since scale and latency aren’t a big concern for you, this should be good enough start.

You could split the service in two, flask for the web part and a service to serve the model, I haven't used it but there is https://pytorch.org/serve/

I'm attempting to set up inference for a torchserve container, and it's really tough to figure out what's not allowing me to connect to my network with the ports that I'm trying to expose. I'm using Google Kubernetes Engine and Helm via tweaking one of the tutorials at [torchserve](github.com/pytorch/serve). Specifically, it's the GKE tutorial [here](https://github.com/pytorch/serve/tree/master/kubernetes).

I did a Space to showcase a bit the speedups we can have in a end-to-end case with TorchServe to deploy the model on a cloud instance (AWS EC2 g4dn, using one T4 GPU): https://huggingface.co/spaces/fxmarty/bettertransformer-demo

For 2), I am aware of a few options. Triton inference server is an obvious one as is the ‘transformer-deploy’ version from LDS. My only reservation here is that they require the model compilation or are architecture specific. I am aware of others like Bento, Ray serving and TorchServe. Ideally I would have something that allows any (PyTorch model) to be used without the extra compilation effort (or at least optionally) and has some convenience things like ease of use, easy to deploy, easy to host multiple models and can perform some dynamic batching. Anyway, I am really interested to hear people's experience here as I know there are now quite a few options! Any help is appreciated! Disclaimer - I have no affiliation or are connected in any way with the libraries or companies listed here. These are just the ones I know of. Thanks in advance.

If you built the model using pytorch or tensorflow, I'd suggest using torchserve or TF serving to serve the model as its own "microservice," then query it from your django app. Among other things, it will make retraining and updating your model a lot easier.

GitHub

Depends what you mean by "local". If you mean in your own home, then there isn't a particularly cheap way unless you have a decent spare machine. If you mean "I get to control everything myself" then you can rent a cheap VPS on a value host like Contabo (you can get 8cores, 30gb of ram, and 1tb SSD on Ubuntu 22.04 for something like $35/month-- just stick the to US data centers).

Then if you want something that is extremely quick and easy to set up and provides a convenient REST api for completions/embeddings with some other nice features, you might want to check out my project here:

https://github.com/Dicklesworthstone/swiss_army_llama

Especially if you use Docker to set it up, you can go from a brand new box to a working setup in under 20 minutes and then access it via the Swagger page from any browser.

I found all of them to be quite bloated and annoying to use directly, which is why I made my own FastAPI based one, Swiss Army Llama. I’m obviously biased, but I far prefer it:

https://github.com/Dicklesworthstone/swiss_army_llama

I just added a very cool feature that lets you supply a sample JSON file and it will automatically generate a BNF grammar for it. You can also supply a pydantic data model description and it will generate the corresponding JSON BNF for you:

https://github.com/Dicklesworthstone/swiss_army_llama/blob/m...

And then you can add that grammar file and it will validate it with this:

https://github.com/Dicklesworthstone/swiss_army_llama/blob/5...

Cool, I also made a similar kind of tool recently that I also shared on HN a couple weeks ago. You might find it useful for generating and managing LLM embeddings locally:

https://github.com/Dicklesworthstone/llama_embeddings_fastap...

Thanks for pointing out those models. I see from a quick Huggingface search that the bge model is available in GGML format. You can trivially add new GGML format models to the code by simply adding the direct download link to this line:

https://github.com/Dicklesworthstone/llama_embeddings_fastap...

So to add the base bge model, you could just add this URL to the list:

https://huggingface.co/maikaarda/bge-base-en-ggml/resolve/ma...

I will add that as an additional default.