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setup-gpnstar-remote-tool

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Set up, launch, validate, and troubleshoot the GPN-Star ToolUniverse remote tool (single-nucleotide variant effect scores from a multi-species-alignment language model) and optionally relay it through ToolUniverse Connect. Use when deploying or auditing this implementation.

skills/setup-gpnstar-remote-tool/SKILL.md

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# Set up GPN-Star as a remote tool

> Validation status (2026-10-02): GPN-Star `v100-200m` (gpn 0.9.0, optimization kit `exact` mode) ran on the NVIDIA GB10 (aarch64, cc 12.1) under torch 2.13.0+cu130, so the GPU is open here. **What one request is:** a list of single-nucleotide variants (`chrom`, 1-based `pos`, `ref`, `alt`) scored against a multiple-species ALIGNMENT STORE the host holds; each score is `logit(alt) - logit(ref)` at the masked position, averaged over the forward and reverse-complement windows. GPN-Star is a language model over an alignment window, not over bare DNA. **The alignment store is an input, not a weight, and was NOT downloaded for this validation**: the real hg38 100-way store is a 42 GB zip that unpacks to ~64 GB (`songlab/multiz100way`). Everything above was measured against the kit's 0.14 MiB sparse example store (eight windows on chromosomes 1, 2, 3 and 5) and against a synthetic store of the same shape for worst-case content; any other position is refused with "the alignment holds no data". Verified on FOUR CONSECUTIVE IDENTICAL REQUESTS, a mixed-batch-shape sequence, and a window / batch / single-variant / refusal sequence in which the same request returned a byte-identical `scores_sha256` five times. Peak reserved 1.01 GiB at 2 variants rising to 5.79 GiB at the 8192-column ceiling (batch 64 x window 128); declared budget 7.72 GiB, the stock-projection bound at that row. The kit's CUDA-graph lever is declined: with it on, 29 distinct batch shapes grew the reserve to 24.3 GiB. Wall times were taken on a contended card (load average ~65, five other GPU processes); peaks reproduce under contention and times do not. Public publication, cross-user isolation, concurrency, recovery and scientific validity of the scores against a full store are untested; keep this deployment private.

## Prerequisites

- Run from the ToolUniverse repository root on Linux with Python 3.12.3.
- A CUDA GPU is required (driver 580 or newer for the pinned CUDA 13.0 stack). This provider holds its model resident between requests; CPU is not a target.
- Keep provider data, weights, alignment stores, caches, and credentials outside Git.
- Bind to loopback. A non-loopback bind requires TOOLUNIVERSE_API_TOKEN; never put it in arguments or results.

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