sci-ml/colibri (bennypowers)

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Install

Install this package:

emerge -a sci-ml/colibri

Package Information

Description:
Colibri runs GLM-5.2 (744B-parameter MoE) on consumer hardware with as little as 25GB RAM. Pure C engine with zero runtime dependencies, streaming experts from disk with an LRU cache. Supports MLA attention, MTP speculative decoding, multiple conversations per server, and optional CUDA/ROCm or Vulkan GPU offloading.
Homepage:
https://github.com/JustVugg/colibri
License:
Apache-2.0

Versions

Version EAPI Keywords Slot
2.1.0 8 ~amd64 0
2.0.0 8 ~amd64 0

Metadata

Description

Maintainers

Upstream

Raw Metadata XML
<pkgmetadata>
	<maintainer type="person">
		<email>web@bennypowers.com</email>
		<name>Benny Powers</name>
	</maintainer>
	<longdescription lang="en">
		Colibri runs GLM-5.2 (744B-parameter MoE) on consumer hardware with
		as little as 25GB RAM. Pure C engine with zero runtime dependencies,
		streaming experts from disk with an LRU cache. Supports MLA attention,
		MTP speculative decoding, multiple conversations per server, and
		optional CUDA/ROCm or Vulkan GPU offloading.
	</longdescription>
	<use>
		<flag name="bench">Install dependencies for quality benchmarks
			(MMLU, HellaSwag, ARC)</flag>
		<flag name="cuda">Build with NVIDIA CUDA GPU offloading for
			resident expert tensors</flag>
		<flag name="python">Install the coli Python CLI for model management,
			chat, serving, and weight conversion</flag>
		<flag name="rocm">Build with AMD ROCm/HIP GPU offloading for
			resident expert tensors</flag>
		<flag name="vulkan">Build the Vulkan compute backend for the
			shared routed-expert tier and dense chains (any Vulkan 1.2
			driver; independent of cuda/rocm)</flag>
	</use>
	<upstream>
		<remote-id type="github">JustVugg/colibri</remote-id>
	</upstream>
</pkgmetadata>

Lint Warnings

USE Flags

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

Flag Description 2.1.0 2.0.0
bench Install dependencies for quality benchmarks (MMLU, HellaSwag, ARC) ✓ ✓
cuda Build with NVIDIA CUDA GPU offloading for resident expert tensors ✓ ✓
python Install the coli Python CLI for model management, chat, serving, and weight conversion ⊕ ⊕
rocm Build with AMD ROCm/HIP GPU offloading for resident expert tensors ✓ ✓
vulkan Build the Vulkan compute backend for the shared routed-expert tier and dense chains (any Vulkan 1.2 driver; independent of cuda/rocm) ✓ ✓

Manifest

Type File Size Versions
DIST colibri-2.0.0.tar.gz 11408963 bytes 2.0.0
DIST colibri-2.1.0.tar.gz 15310599 bytes 2.1.0
Unmatched Entries
Type File Size