Alpamayo R1

SkillAI & models

Router for Alpamayo R1 multimodal driving inference and trajectory sampling.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Alpamayo R1 skill

What this skill tells your AI

The instructions your AI receives, as published by vectorspacelab/arex-skill in skills/repositories/repo-skills/alpamayo-r1/SKILL.md and read by ahel’s review.

Use this skill for Alpamayo R1 inference on PhysicalAI-AV clips: load one clip, build the multimodal prompt, run the model on CUDA, inspect reasoning traces, and compare predicted trajectories to ground truth.

When to use this skill

  • The user wants to run Alpamayo R1 on a PhysicalAI-AV clip.
  • The user asks how to load the dataset, build the chat template, or sample future trajectories.
  • The user wants Chain-of-Causation text, minADE comparison, or notebook-style visualization.
  • The user needs help with gated HF resources, CUDA setup, or flash-attn / SDPA fallback behavior.

Install and verify

Use a Python 3.12 CUDA environment with the package dependencies installed, then perform a minimal import check from outside the checkout:

python -m pip install -e <repo-checkout>
python -I -c "import alpamayo_r1, torch; print(alpamayo_r1.__name__); print(torch.cuda.is_available())"

If the editable install or import fails, open references/troubleshooting.md before changing the workflow.

Route map

  • sub-skills/inference/SKILL.md — the end-to-end driving inference workflow, including dataset loading, prompt creation, model sampling, output interpretation, and the bundled smoke script.
  • references/repo-provenance.md — source commit, version, and evidence paths used to build this skill.
  • references/repo-routing-metadata.json — structured router metadata consumed during repo-skill import.
  • references/troubleshooting.md — cross-cutting install/import/backend guidance.

Shared notes

  • This repository exposes no public CLI entry point; use the Python API or the bundled smoke script from the inference sub-skill.
  • The primary workflow is CUDA-first. Flash-attn is the default attention path, but the inference sub-skill documents the SDPA fallback for compatibility issues.
  • Training, SFT, and RL post-training are out of scope for this skill.

Fastest path

  1. Read sub-skills/inference/SKILL.md.
  2. Use sub-skills/inference/scripts/run_inference_smoke.py for a runnable sample.
  3. If anything fails before the first clip loads, check references/troubleshooting.md.

Signals

GitHub stars
266
Forks
21
Last commit
Sep 2026

ahel review

  • K1binfo
    installs-packages

Automated review, not a security audit. Ruleset v1+k2.

Advanced
Catalog kind
skill
Gateway key
alpamayo-r1
Source
github.com/vectorspacelab/arex-skill