Integrate Fusedbun Optimizer into Algorithmic Efficiency Submission
Modifies the standard algorithmic-efficiency submission file to use the custom Fusedbun optimizer instead of AdamW, correctly mapping hyperparameters and fixing the learning rate scheduler to handle missing warmup factors.
npx skills add ECNU-ICALK/AutoSkill --skill integrate-fusedbun-optimizer-into-algorithmic-efficiency-submiss --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Integrate Fusedbun Optimizer into Algorithmic Efficiency Submission Modifies the standard algorithmic-efficiency submission file to use the custom Fusedbun optimizer instead of AdamW, correctly mapping hyperparameters and fixing the learning rate scheduler to handle missing warmup factors. ## Prompt # Role & Objective You are an MLPerf/Algorithmic Efficiency submission developer. Your task is to modify the standard `submission.py` file to integrate the custom `Fusedbun` optimizer, replacing the default AdamW optimizer. # Communication & Style Preferences - Write clean, error-free Python code with proper indentation. - Ensure all necessary imports are included. # Operational Rules & Constraints 1. **Optimizer Integration**: - Import `Fusedbun` from `optim`. - In `init_optimizer_state`, instantiate `Fusedbun` instead of `torch.optim.AdamW`. - Map the following hyperparameters from the input `hyperparameters` object to the `Fusedbun` constructor: - `lr`: `hyperparameters.learning_rate` - `beta_decay`: `hyperparameters.beta_decay` - `Lambda`: `hyperparameters.Lambda` - `momentum_beta`: `hyperparameters.momentum_beta` - Set `centralize=True` and `use_rms=True` as defaults. 2. **Schedu
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What does the Integrate Fusedbun Optimizer into Algorithmic Efficiency Submission skill do?
Modifies the standard algorithmic-efficiency submission file to use the custom Fusedbun optimizer instead of AdamW, correctly mapping hyperparameters and fixing the learning rate scheduler to handle missing warmup factors.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill integrate-fusedbun-optimizer-into-algorithmic-efficiency-submiss --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.