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Zest v0.2.0

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@github-actions github-actions released this 22 Oct 12:03
· 7 commits to main since this release
v0.2.0
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Learn about vigilant mode.

Zest accelerates long prompts for many GGUF models through Lemonade's
llama.cpp backend, using an AMD Ryzen AI NPU. The GPU handles attention and
writes the reply. No ONNX or Q4NX export or per-model NPU kernel compilation
is required. Coverage follows the bundled build and supported operations;
speedups vary by model.

This is an independent community add-on, tested with Lemonade 2026.43.3
and the bundled b10944 backend.

Windows and Linux packages include both zest and npu. Either command runs
the same launcher with the same options.

Use it with Lemonade on Windows

  1. Download the Windows ZIP below and extract it. Keep the files together.
  2. Quit your existing Lemonade tray server and double-click Start-Lemonade.cmd.
    Choose Lemonade's server executable if asked, then your local GGUF model.
  3. In Lemonade App, set Settings > Base URL to http://127.0.0.1:13315
    and save. Select the imported model and send a long prompt.

The launcher keeps its configuration separate and uses the GPU if the NPU
can't start. It doesn't include Lemonade, model weights, or AMD drivers.
The ZIP has an internal checksum manifest; its .sha256 file is attached.

Website
· Lemonade setup and troubleshooting
· Test report

Before you use it

We tested Windows 11 on a Ryzen AI 9 HX PRO 370 with Radeon 890M graphics.
Other Ryzen AI hardware, including Strix Halo, hasn't been verified by this
project. Linux builds and host tests pass; Linux NPU hardware and Lemonade
integration remain unverified.

The NPU keeps extra converted weight copies in RAM and changes numerical
results through block rounding. Small prompt chunks stay on the GPU.
Speed depends on the model. If the NPU fails during a request, failed work
is retried on the GPU and later requests use the GPU. The CPU reference
remains available if the GPU cannot run a failed piece. This build includes
the GPU recovery fix already merged into main.

Direct llama.cpp use

On Windows, run npu llama-server -m model.gguf from the extracted ZIP.
On Linux, unpack the Ubuntu archive, enter its directory, and run
./npu llama-server -m model.gguf. You can also use zest in either command.
See the technical guide
for commands, build instructions, accuracy checks, and standalone benchmarks.

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