sci-ml/llama-cpp (bentoo)

Search

Install

Install this package:

emerge -a sci-ml/llama-cpp

Package Information

Description:
llama.cpp is an inference engine for large language models written in plain C/C++, with no external runtime dependencies. It loads quantized models in the GGUF format and runs them on CPU, or with optional acceleration through CUDA, ROCm/HIP, Vulkan or OpenCL backends. The package provides llama-cli (interactive and one-shot inference), llama-server (an HTTP server exposing an OpenAI-compatible API, optionally with an embedded WebUI), llama-quantize, llama-bench and the ggml libraries used by many downstream projects. Models are not shipped with this package and must be downloaded separately, typically from Hugging Face. The CPU backend is selected through CPU_FLAGS_X86, so enabling the AVX-512 and AMX flags supported by the host processor has a large impact on inference throughput. Note that ebuild is ported from the gentoo-zh overlay (sci-ml/llama-cpp).
Homepage:
https://github.com/ggml-org/llama.cpp
License:
MIT

Versions

Version EAPI Keywords Slot
0_pre9967 8 ~amd64 0

Metadata

Description

Maintainers

Upstream

Raw Metadata XML
<pkgmetadata>
	<maintainer type="person">
		<email>lucascs@proton.me</email>
		<name>Lucas C.S.</name>
	</maintainer>
	<longdescription lang="en">
		llama.cpp is an inference engine for large language models written in plain
		C/C++, with no external runtime dependencies. It loads quantized models in
		the GGUF format and runs them on CPU, or with optional acceleration through
		CUDA, ROCm/HIP, Vulkan or OpenCL backends.

		The package provides llama-cli (interactive and one-shot inference),
		llama-server (an HTTP server exposing an OpenAI-compatible API, optionally
		with an embedded WebUI), llama-quantize, llama-bench and the ggml libraries
		used by many downstream projects.

		Models are not shipped with this package and must be downloaded separately,
		typically from Hugging Face. The CPU backend is selected through
		CPU_FLAGS_X86, so enabling the AVX-512 and AMX flags supported by the host
		processor has a large impact on inference throughput.

		Note that ebuild is ported from the gentoo-zh overlay (sci-ml/llama-cpp).
	</longdescription>
	<use>
		<flag name="blis">Build a BLIS backend</flag>
		<flag name="flexiblas">Build a FlexiBLAS backend</flag>
		<flag name="openblas">Build an OpenBLAS backend</flag>
		<flag name="rocm">Build a HIP (ROCm) backend</flag>
		<flag name="wmma">Use rocWMMA to enhance flash attention performance</flag>
		<flag name="opencl">Build an OpenCL backend, so far only works on Adreno and Intel GPUs</flag>
		<flag name="rpc">Build with rpc-server</flag>
		<flag name="server">Build with example server</flag>
		<flag name="webui">Build server with embedded WebUI</flag>
	</use>
	<upstream>
		<changelog>https://github.com/ggml-org/llama.cpp/releases</changelog>
		<bugs-to>https://github.com/ggml-org/llama.cpp/issues</bugs-to>
		<remote-id type="github">ggml-org/llama.cpp</remote-id>
	</upstream>
</pkgmetadata>

Lint Warnings

USE Flags

Manage flags for this package: euse -i <flag> -p sci-ml/llama-cpp | euse -E <flag> -p sci-ml/llama-cpp | euse -D <flag> -p sci-ml/llama-cpp

Flag Description 0_pre9967
"( ⚠️
( ⚠️
) ⚠️
)" ⚠️
amx_bf16 ⚠️
amx_int8 ⚠️
amx_tile ⚠️
avx ⚠️
avx2 ⚠️
avx512_bf16 ⚠️
avx512_vnni ⚠️
avx512bw ⚠️
avx512cd ⚠️
avx512dq ⚠️
avx512f ⚠️
avx512vbmi ⚠️
avx512vl ⚠️
avx_vnni ⚠️
blis Build a BLIS backend
bmi2 ⚠️
cuda Build cycles renderer with nVidia CUDA support. ⚠️
examples Example ufw config files ⚠️
f16c ⚠️
flexiblas Build a FlexiBLAS backend
fma3 ⚠️
openblas Build an OpenBLAS backend
opencl Build an OpenCL backend, so far only works on Adreno and Intel GPUs
openmp ⚠️
rocm Build a HIP (ROCm) backend
rpc Build with rpc-server
server Build with example server
sse4_2 ⚠️
vulkan Add support for the Vulkan viewport backend ⚠️
webui Build server with embedded WebUI
wmma Use rocWMMA to enhance flash attention performance

Manifest

Type File Size Versions
DIST llama-cpp-0_pre9967-ui.tar.gz 2739768 bytes 0_pre9967
DIST llama-cpp-0_pre9967.tar.gz 35681892 bytes 0_pre9967
Unmatched Entries
Type File Size
DIST ggml-org_models_tinyllamas_stories15M-q4_0-99dd1a73db5a37100bd4ae633f4cfce6560e1567.gguf 19077344 bytes