karpathy's llama2.c is like llama.cpp but it is written in C and the python training code is available in that same repo. llama2.c's goal is to be a elegant single file C implementation of the inference and an elegant python implementation for training.
His goal is for people to understand how llama 2 and LLM's work, so he keeps it simple and sweet. As the project progresses, so will features and performance improvements added.
Currently it can infer baby (small) Story models trained by Karpathy at a fast pace. It can also infer Meta LLAMA 2 7b models, but at a very slow rate such as 1 token per second.
So currently this can be used for learning or as a tech preview.
Our friendly fork tries to make it portable, performant and more usable (bells and whistles) over time. Since we mirror upstream closely, the inference capabilities of our fork is similar but slightly faster if compiled with acceleration. What we try to do different is that we try to make this bootable (not there yet) and portable. Right now you can get binary portablity - use the same run.com on any x86_64 machine running on any OS, it will work (possible due to cosmopolitan toolchain). The other part that works is unikernels - boot this as unikernel in VM's (possible due unikraft unikernel & toolchain).
See our fork currently as a release early and release often toy tech demo. We plan to build it out into a useful product.
It got some nice attention here: - https://github.com/karpathy/llama2.c
I think there may be some applications in this limited space that are worth looking into. You won’t replicate GPT-anything but it may be possible to solve some nice problems very much more efficiently that one would expect at first.
Found the repo, couldn't easily find the HN thread.