Install this version:
emerge -a =dev-python/humming-kernels-0.1.2If this version is masked, you can unmask it using the autounmask tool or standard emerge options:
autounmask =dev-python/humming-kernels-0.1.2Or alternatively:
emerge --autounmask-write -a =dev-python/humming-kernels-0.1.2| Version | EAPI | Keywords | Slot |
|---|---|---|---|
| 0.1.2 | 8 | ~amd64 | 0 |
# Copyright 1999-2026 Gentoo Authors
# Distributed under the terms of the GNU General Public License v2
EAPI=8
DISTUTILS_USE_PEP517=setuptools
PYTHON_COMPAT=( python3_{12..14} )
DISTUTILS_SINGLE_IMPL=1
inherit distutils-r1 pypi
DESCRIPTION="JIT-compiled quantization GEMM kernel library (vLLM humming backend)"
HOMEPAGE="
https://github.com/inclusionAI/humming
https://pypi.org/project/humming-kernels/
"
S="${WORKDIR}/humming_kernels-${PV}"
LICENSE="Apache-2.0"
SLOT="0"
KEYWORDS="~amd64"
# Bundled tests need a CUDA device + nvcc JIT (SM75+); unrunnable in the
# build sandbox. # 2026-06-14
RESTRICT="test"
# Pure-Python wheel; the GEMM kernels ship as bundled CUDA sources
# (.cu/.cuh/.cpp) and are JIT-compiled at first use via the system nvcc.
# Upstream's install target is humming-kernels[cu13]; its
# nvidia-cuda-nvcc/nvrtc/runtime wheels are satisfied here by
# dev-util/nvidia-cuda-toolkit, which the only consumer
# (dev-python/vllm[cuda]) already pulls -- no USE flag or pip cuda wheels
# needed. cuda-only by nature: vllm imports this module only under
# `if current_platform.is_cuda():`. # added 2026-06-14
RDEPEND="
sci-ml/pytorch[${PYTHON_SINGLE_USEDEP}]
$(python_gen_cond_dep '
dev-python/triton-bin[${PYTHON_USEDEP}]
dev-python/numpy[${PYTHON_USEDEP}]
sci-ml/safetensors[${PYTHON_USEDEP}]
dev-python/jinja2[${PYTHON_USEDEP}]
dev-python/pyelftools[${PYTHON_USEDEP}]
dev-python/nvidia-ml-py[${PYTHON_USEDEP}]
dev-python/cuda-bindings[${PYTHON_USEDEP}]
dev-python/tqdm[${PYTHON_USEDEP}]
dev-python/tabulate[${PYTHON_USEDEP}]
')
"
sci-ml/pytorch[${PYTHON_SINGLE_USEDEP}] $(python_gen_cond_dep ' dev-python/triton-bin[${PYTHON_USEDEP}] dev-python/numpy[${PYTHON_USEDEP}] sci-ml/safetensors[${PYTHON_USEDEP}] dev-python/jinja2[${PYTHON_USEDEP}] dev-python/pyelftools[${PYTHON_USEDEP}] dev-python/nvidia-ml-py[${PYTHON_USEDEP}] dev-python/cuda-bindings[${PYTHON_USEDEP}] dev-python/tqdm[${PYTHON_USEDEP}] dev-python/tabulate[${PYTHON_USEDEP}] ')