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#107 in Machine learning
2,412 downloads per month
Used in rusty-ggml
Bleeding edge Rust bindings for GGML.
This repo is set up with a workflow to automatically check for the latest GGML release several times per day. The workflow currently just builds for Linux x86: if that build succeeds, then a new release and package will be published.
Note that the GGML project is undergoing very rapid development. Other than being able to generate the binding and build the package (on x86 Linux at least) you really can't make any assumptions about a release of this crate.
Releases will be in the format
sourcerepo is going to be
llamacpp (from the
llama.cpp repo) but at
some point it may change to point to the
ggml repo instead (currently
to get the features first). Build metadata after the
+ is informational only.
You can find the crate published here: https://crates.io/crates/ggml-sys-bleedingedge
Automatically generated documentation: https://docs.rs/ggml-sys-bleedingedge/
There is now experimental support for compiling with BLAS.
no_k_quants- Disables building with k_quant quantizations (i.e. Q4_K)
no_accelerate- Only relevant on Mac, disables building with Accelerate.
use_cmake- Builds and links against
cublas- Nvidia's CUDA BLAS implementation.
clblast- OpenCL BLAS.
hipblas- AMD's ROCM/HIP BLAS implementation. Set the
ROCM_PATHenvironment variable to point at your ROCM installation. It defaults to
/opt/rocm. Note: Unless your GPU is natively supported by ROCM it's very likely you'll need to set the
HSA_OVERRIDE_GFX_VERSIONenvironment variable otherwise your app will immediately crash when initializing ROCM. For example on an RX 6600
metal- Metal support, only available on Mac.
llamacpp_api- Include the
llama.cppC++ API in bindings.
Enabling any of the BLAS features or
use_cmake. You will need a working C++ compiler and cmake set up to build with this feature. Due to limitations in the llama.cpp cmake build system currently, it's necessary to build and link against
libllama (which pulls in stuff like
libstdc++) even though we only need GGML. Also, although we can build the library using cmake there's no simple way to know the necessary library search paths and libraries: we try to make a reasonable choice here but if you have libraries in unusual locations or multiple versions then weird stuff may happen.
The project has a slow, irresponsible person like me maintaining it. This is not an ideal situation.
The files under
ggml-src/ are distributed under the terms of the MIT license. As they are simply copied from the source repo (see below) refer to that for definitive information on the license or credits.
Credit goes to the original authors: Copyright (c) 2023 Georgi Gerganov
Currently automatically generated from the llama.cpp project.
Initially derived from the build script and bindings generation in the Rustformers llm project.