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Productivité · SKILL.md

model-assets-and-runtime

model-assets-and-runtime

Set up and inspect min(DALL·E) model assets, tokenizer, model variants, runtime device/dtype decisions, and internal shapes without unnecessary downloads.

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Documentation de la Skill

GitHub

name: model-assets-and-runtime description: "Set up and inspect min(DALL·E) model assets, tokenizer, model variants, runtime device/dtype decisions, and internal shapes without unnecessary downloads." disable-model-invocation: true metadata: disco-role: operating license: MIT

model-assets-and-runtime

Use this sub-skill when the task is about min(DALL·E) model weight caches, tokenizer assets, model variant sizing, device/dtype choices, memory/runtime setup, or internal tensor shapes. The goal is to inspect and prepare safely before running expensive generation.

Fast routing

  • For cache layout, download side effects, Hugging Face asset names, is_mega constants, dtype/device choices, and no-download preflight checks, read references/runtime-and-assets.md.
  • For tokenizer normalization, BPE behavior, special tokens, 64-token text limit, 256 image-token loop, decoder sampling, and VQGAN detokenizer shapes, read references/tokenizer-and-model-internals.md.
  • For failed downloads, corrupt cache files, CUDA/CPU dtype surprises, OOM, SSL/proxy problems, and tokenizer surprises, read references/troubleshooting.md.
  • For a deterministic no-network tokenizer check, run scripts/tokenizer_smoke.py. It uses a synthetic vocabulary and does not instantiate MinDalle or load model weights.

Route out of this sub-skill

  • User-facing text-to-image generation recipes, MinDalle.generate_image* usage, stream consumption, seed/top-k/temperature recipe choices, and saving generated PIL/tensor outputs belong in ../text-to-image-generation/SKILL.md.
  • Command-line usage, Replicate/Cog, Tkinter, Colab/notebook, and public interface behavior belong in ../deployment-and-interfaces/SKILL.md.
  • Architecture modification, training, conversion from upstream Flax/JAX weights, and benchmarking beyond lightweight preflight are out of scope for this generated repo skill.

Operating rules

  1. Prefer no-download inspection first: examine the chosen models_root layout and run the tokenizer smoke script before constructing a full MinDalle object.
  2. Treat MinDalle(...) construction as potentially network- and memory-active. Even tokenizer initialization can contact the Hugging Face asset endpoint; is_reusable=True can download and load all major .pt weights.
  3. Do not load .pt files just to prove that a cache path exists. Check existence, non-zero size, and obvious partial-download symptoms first; only load weights when the user approved full runtime setup.
  4. Choose device, dtype, is_mega, and is_reusable from the user's hardware and budget rather than from examples copied blindly.
  5. Keep runtime instructions self-contained in this sub-skill; the original repository checkout is not required for routine setup or debugging.
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