Skip to main content

model-specs

Use when asked about a catalog model's own static characteristics — architecture, dataset/training compatibility, serving runtime requirements — e.g. "what was DreamZero trained on" or "does pi0.5 need a fixed camera count." Not for live status, scaling, or calling a deployed model (see the models skill for that).

Ir para a instalação

Informações da origem

Repositório
redhat-et/physical-ai-skills
Última atividade na origem
4 de agosto de 2026 às 22:21
Idioma detectado do SKILL.md
inglês
Estrelas
0
Forks
0

Opções de instalação

Por padrão, está selecionado o prompt que primeiro revisa a origem. Você pode mudar para um comando direto ou baixar uma cópia local.

Revise os arquivos de origem

Leia o SKILL.md e os arquivos complementares exibidos pelo SkillsMP antes de decidir se vai instalar.

Explorador de arquivos
4 arquivos

Exibindo SKILL.md

SKILL.md
Instruções da origem · Visualização somente leitura
name
model-specs
description
Use when asked about a catalog model's own static characteristics — architecture, dataset/training compatibility, serving runtime requirements — e.g. "what was DreamZero trained on" or "does pi0.5 need a fixed camera count." Not for live status, scaling, or calling a deployed model (see the models skill for that).
MODEL SPECS — this skill is knowledge-only: no cluster calls, no scaling/inference actions. It's the reference for what a catalog model *is*, as opposed to the models skill (what a deployed instance is currently doing). ## Scripts Run via the shell tool: `python3 "$SKILLS_ROOT/model-specs/scripts/get_model_reference.py" --model-name <name>`. Each catalog model with a documented dataset/training profile has a file under `references/<model_name>.md` (e.g. `references/pi05.md`, `references/dreamzero.md`), keyed by the same catalog directory name used elsewhere (platform/base/models/<model_name>/). A model with no file here either has no fine-tuning recipe on this platform or hasn't been documented yet — don't guess its specifics from general knowledge of the base model. pi0.5's reference is a Dimension/Priority checklist table matching the datasets skill's own DATASET COMPATIBILITY CHECKLIST row for row — read them side by side by row number when validating a candidate dataset. DreamZero's is plain training-data provenance prose instead of a table, since it's inference-only on this platform (no recipe to check a dataset against).
Ver no GitHub