cog-replicate
Skill17 starsChanged 47 days agoNo licenceView on GitHub
Package an ML model (diffusion, video, LLM, any PyTorch/HF model) with Replicate Cog and push it to Replicate, from a Windows machine through WSL2 — toolchain setup (Docker Engine, nvidia-container-toolkit, cog), cog.yaml and predict.py patterns, large weights with pget, gated Hugging Face weights with per-caller tokens, local mock testing without a big GPU, pushing big images to r8.im, running and verifying predictions over the Replicate API, plus a catalogue of known error modes and fixes. Use this skill whenever the user wants to "set up a model with cog", "push to replicate", "deploy a model on replicate", write or fix a cog.yaml / predict.py / run.py, debug a failing cog build, cog push or Replicate prediction, or asks about cold boots, weights, or GPU choice on Replicate — even if they only name the model and say "make it run on Replicate".
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The file
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We split this skill into the rules it gives an agent
18 rules. About 0 are common, 5 are distinctive, and 13 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
13
Very specific
Tied to their project's names, files or tools. Probably skip.
No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
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