llama-cpp-python VS mlc-llm

I'll share the core bit that took a while to figure out the right format, my main script is a hot mess using embeddings with SentenceTransformer, so I won't share that yet. E.g: last night I did a PR for llama-cpp-python that shows how Phi might be used with JSON only for the author to write almost exactly the same code at pretty much the same time. https://github.com/abetlen/llama-cpp-python/pull/1184

Python Bindings for llama.cpp

If the model could be made to work with llama.cpp, then https://github.com/abetlen/llama-cpp-python might be more compact. llama.cpp only supports a limited list of model types though.

https://github.com/abetlen/llama-cpp-python has a web server mode that replicates openai's API iirc and the readme shows it has docker builds already.

LocalAI https://localai.io/ and LMStudio https://lmstudio.ai/ both have fairly complete OpenAI compatibility layers. llama-cpp-python has a FastAPI server as well: https://github.com/abetlen/llama-cpp-python/blob/main/llama_... (as of this moment it hasn't merged GGUF update yet though)

I went with Python, llama-cpp-python, since my goal is just to get a small project up and running locally.

I see you’re using gpt4all; do you have a supported way to change the model being used for local inference?

A number of apps that are designed for OpenAI’s completion/chat APIs can simply point to the endpoints served by llama-cpp-python [0], and function in (largely) the same way, while supporting the various models and quants supported by llama.cpp. That would allow folks to run larger models on the hardware of their choice (including Apple Silicon with Metal acceleration) or using other proxies like openrouter.io.

[0]: https://github.com/abetlen/llama-cpp-python

This one uses gpu, it doesn't support Mistral yet: https://github.com/mlc-ai/mlc-llm

I have tried running mistral 7B with MLC on my m1 metal. And it kept crushing (git issue with description). Memory inefficiency problems.

Project: https://github.com/mlc-ai/mlc-llm

For LLM inference, a shoutout to MLC LLM, which runs LLM models on basically any API that's widely available: https://github.com/mlc-ai/mlc-llm

One of your problems might be that gfx1032 is not supported by AMD's ROCm packages, which has a laughably short list of supported hardware: https://rocm.docs.amd.com/en/latest/release/gpu_os_support.h...

The normal workaround is to assign the closest architecture, eg gfx1030, so `HSA_OVERRIDE_GFX_VERSION=10.3.0` might help

Also, it looks like some of your tested projects are OpenCL? For me, I do something like: `yay -S rocm-hip-sdk rocm-ml-sdk rocm-opencl-sdk` to cover all the bases.

My recent interest has been LLMs and this is my general step by step for those (llama.cpp, exllama) for those interested: https://llm-tracker.info/books/howto-guides/page/amd-gpus

I didn't port the docs back in, but also here's a step-by-step w/ my adventures getting TVM/MLC working w/ an APU: https://github.com/mlc-ai/mlc-llm/issues/787

From my experience, ROCm is improving, but there's a good reason that Nvidia has 90% market share even at big price premiums.

Maybe they're talking about https://github.com/mlc-ai/mlc-llm which is used for web-llm (https://github.com/mlc-ai/web-llm)? Seems to be using TVM.

you already have TVM for the cross platform stuff

see https://tvm.apache.org/docs/how_to/deploy/android.html

or https://octoml.ai/blog/using-swift-and-apache-tvm-to-develop...

or https://github.com/mlc-ai/mlc-llm