flake
llama.cpp
| flake | llama.cpp | |
|---|---|---|
| 5 | 1 | |
| 593 | 4 | |
| 3.9% | - | |
| 4.4 | 9.4 | |
| 7 days ago | 9 months ago | |
| Nix | C | |
| GNU Affero General Public License v3.0 | MIT License |
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flake
- Running AI Models on NixOS
- Nixified.Ai Release 2
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Llama.cpp: Full CUDA GPU Acceleration
> Ideally, there's Nix (and poetry2nix) that could take care of everything, but only a few folks write Flakes for their projects.
Relevant to "AI, Python, setting up is hard ... nix", there's stuff like:
https://github.com/nixified-ai/flake
-
Can you substitute conda with Nix for Data Science and ML/AI?
However, I would reach out to the Nixified.ai folks about it, because I can see that the invoke.ai build script mentions pytorch and several other hard-to-install packages (albeit not detectron).
- A Nix flake for many AI projects
llama.cpp
-
Llama.cpp: Full CUDA GPU Acceleration
llama.cpp can be run with a speedup for AMD GPUs when compiled with `LLAMA_CLBLAST=1` and there is also a HIPified fork [1] being worked on by a community contributor. The other week I was poking on how hard it would be to get an AMD card running w/ acceleration on Linux and was pleasantly surprised, it wasn't too bad: https://mostlyobvious.org/?link=/Reference%2FSoftware%2FGene...
That being said, it's important to note that ROCm is Linux only. Not only that, but ROCm's GPU support has actually been decreasing over the past few years. The current list: https://rocm.docs.amd.com/en/latest/release/gpu_os_support.h... Previously (2022): https://docs.amd.com/bundle/Hardware_and_Software_Reference_...
The ELI5 is that a few years back, AMD split their graphics (RDNA) and compute (CDNA) architectures, which Nvidia does too, but notably (what Nvidia definitely doesn't do, and a key to their success IMO) AMD also decided they would simply not support any CUDA-parity compute features on Windows or their non "compute" cards. In practice, this means that community/open-source developers will never have, tinker, port, or develop on AMD hardware, while on Nvidia you can start with a GTX/RTX card on your laptop, and use the same code up to an H100 or DGX.
llama.cpp is a super-high profile project, has almost 200 contributiors now, but AFAIK, no contributors from AMD. If AMD doesn't have the manpower, IMO they should simply be sending nsa free hardware to top open source project/library developers (and on the software side, their #1 priority should be making sure every single current GPU they sell is at least "enabled" if not "supported" in ROCm, on Linux and Windows).
[1] https://github.com/SlyEcho/llama.cpp/tree/hipblas
What are some alternatives?
nonguix - Nonguix mirror – pull requests ignored, please use upstream for that
whisper.cpp - Port of OpenAI's Whisper model in C/C++
guix-nonfree - Unofficial collection of packages that are not going to be accepted in to guix
lit-llama - Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.
llama_cpp.rb - llama_cpp provides Ruby bindings for llama.cpp
llama.cpp - LLM inference in C/C++
serving - A flexible, high-performance serving system for machine learning models
TokenHawk - WebGPU LLM inference tuned by hand [Moved to: https://github.com/kayvr/token-hawk]
guix-nonfree