amd-gaia
- Ebuilds: 3, Testing: 0.19.0 Description:
GAIA is AMD's open-source agent framework for local AI agents on
Ryzen AI hardware (NPU + iGPU). It orchestrates LLM-driven workflows
over any OpenAI-compatible inference endpoint, with built-in
integrations for Docker, Jira, code-search, RAG, MCP servers, and
Whisper / Kokoro voice pipelines. The reference local backend is
Lemonade Server (sci-ml/lemonade); GAIA itself is hardware-agnostic
so long as the upstream LLM API is OpenAI-compatible.
Homepage:https://github.com/amd/gaia License: MIT
fastflowlm
- Ebuilds: 5, Testing: 0.9.43-r1, Snapshot: 9999 Description:
FastFlowLM (FLM) is a lightweight LLM inference runtime purpose-built
for AMD Ryzen AI NPUs (XDNA2 architecture). It provides an Ollama-style
CLI and OpenAI-compatible server API for running language models entirely
on the NPU with no GPU or CPU compute required.
Supported hardware: Ryzen AI 300-series (Strix Point, Strix Halo),
400-series (Gorgon Point), and Z2 Extreme. XDNA1 (Ryzen AI 7000/8000)
is NOT supported.
The orchestration code and CLI are MIT-licensed. NPU compute kernels
(xclbins) are proprietary binaries, free for commercial use under
$10M annual company revenue.
Homepage:
https://fastflowlm.com/
https://github.com/FastFlowLM/FastFlowLM
License: MIT FastFlowLM-Binary
kokoros
- Ebuilds: 1, Snapshot: 9999 Description:
Kokoros is a Rust implementation of the Kokoro-82M text-to-speech
model. Provides the `koko` CLI and an OpenAI-compatible HTTP server
used as the kokoro:cpu backend by sci-ml/lemonade.
Tracks upstream lucasjinreal/Kokoros directly. The lemonade-sdk
fork only diverges in CI infrastructure plus a bundled espeak-ng-data
copy that ::gentoo already provides via app-accessibility/espeak-ng,
so source-build users get the same binary either way.
Runtime model files (kokoro-v1.0.onnx + voices-v1.0.bin) are not
bundled — see pkg_postinst for a quick fetch recipe.
Homepage:https://github.com/lucasjinreal/Kokoros License: Apache-2.0
lemonade
- Ebuilds: 4, Testing: 10.6.0, Snapshot: 9999 Description:
Lemonade is a local AI server that exposes optimized LLMs through
OpenAI / Anthropic / Ollama compatible APIs, running inference on
AMD NPU and GPU. The C++ server core (lemond, lemonade-server) is
packaged here without the Tauri desktop wrapper or the bundled
web frontend; both are CMake-toggleable and can be added behind
USE flags later if needed.
Pairs with sci-ml/fastflowlm to drive the AMD Ryzen AI XDNA2 NPU
backend.
Homepage:
https://lemonade-server.ai/
https://github.com/lemonade-sdk/lemonade
License: Apache-2.0
pyannote-audio
- Ebuilds: 1, Testing: 4.0.4 Description:
pyannote.audio is a deep-learning toolkit for speaker diarization,
voice activity detection, overlapped speech detection, and speaker
embedding. The Python package alone does not include any pretrained
models; running pretrained pipelines such as
pyannote/speaker-diarization-3.1 requires accepting the model terms
on HuggingFace and authenticating with a HuggingFace token.
Homepage:
https://github.com/pyannote/pyannote-audio
https://pyannote.github.io/
https://pypi.org/project/pyannote-audio/
License: MIT
sentence-transformers
- Ebuilds: 2, Testing: 5.5.1 Description:
Sentence-Transformers (SBERT) provides sentence, image, and code
embeddings via Transformer networks for semantic search, clustering,
semantic textual similarity, and retrieval-augmented generation.
Bundled training utilities support fine-tuning on contrastive
losses; bundled inference utilities cover bi-encoders, cross-
encoders (rerankers), and sparse encoders. Embedding-based RAG
pipelines downstream — including sci-ml/amd-gaia's ui module and
most LLM-orchestration frameworks — depend on this library.
Homepage:
https://www.SBERT.net
https://github.com/huggingface/sentence-transformers
https://pypi.org/project/sentence-transformers/
License: Apache-2.0
sherpa-onnx
- Ebuilds: 1, Testing: 1.13.2 Description:
sherpa-onnx is a speech-stack toolkit from the k2-fsa project:
speech-to-text, text-to-speech, speaker diarization, voice activity
detection, source separation, and keyword spotting, all running on
ONNX Runtime (no PyTorch dependency).
Source build against system sci-libs/onnxruntime. For the prebuilt
-bin alternative (faster install, ships upstream's manylinux wheels)
see sci-ml/sherpa-onnx-bin.
The CMake build vendors a dozen small deps (eigen, asio, cargs, json,
kaldi-{decoder,native-fbank,fst}, openfst, kissfft, simple-sentencepiece,
hclust-cpp, optionally espeak-ng + piper-phonemize + portaudio +
websocketpp + pybind11) via FetchContent. The ebuild pre-fetches them
all via SRC_URI and stages into ${S} for the cmake fallback paths;
no network access during build.
Runtime model files for each task (ASR, diarization, TTS, etc.) live
upstream — see https://k2-fsa.github.io/sherpa/onnx/pretrained_models/
Homepage:
https://k2-fsa.github.io/sherpa/onnx/
https://github.com/k2-fsa/sherpa-onnx
License: Apache-2.0
sherpa-onnx-bin
- Ebuilds: 1, Testing: 1.13.2 Description:
sherpa-onnx is a speech-stack toolkit from the k2-fsa project:
speech-to-text, text-to-speech, speaker diarization, voice activity
detection, source separation, and keyword spotting, all running on
ONNX Runtime (no PyTorch dependency). Suited to CPU-only deployment
and embedded targets.
This -bin ebuild ships upstream's manylinux wheels (sherpa-onnx-core
for the C++ shared libraries plus a per-CPython-ABI wheel for the
Python bindings). Runtime model files are not bundled — see the
post-install message for download pointers.
Homepage:
https://k2-fsa.github.io/sherpa/onnx/
https://github.com/k2-fsa/sherpa-onnx
https://pypi.org/project/sherpa-onnx/
License: Apache-2.0