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Point at NiLabs-Org after the org rename
ci
#45:
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docs: report the runs that have actually happened
ci
#44:
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Record the first base-vs-tuned eval as inconclusive
ci
#43:
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Keep a sample completion when no code is found
ci
#42:
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Always re-clone in the Colab notebook
ci
#41:
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Point the Colab notebook at the div2 A/B problem set
ci
#40:
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Allow evaluation to be restricted by problem index
ci
#39:
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Fall back to a prompt when the Colab secret is missing
ci
#38:
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Stop hiding pip output in the Colab notebook
ci
#37:
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Make evaluation resumable, and add a Colab notebook to run it
ci
#36:
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b0e04b1
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Record the first trained adapter, and check quota before pushing
ci
#35:
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Actually redirect the library caches
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#34:
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Move mount discovery into the package and test it
ci
#33:
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Print the slug actually pushed to
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#32:
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b23c77a
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Split training and evaluation into separate Kaggle sessions
ci
#31:
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Build SFTConfig directly instead of converting TrainingArguments
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#30:
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Search for the prepared data instead of assuming its mount path
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#29:
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Clone the repo outside /kaggle/working
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#28:
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Add a smoke mode that runs the real kernel on real data
ci
#27:
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Request a T4, and rewrite the trainer for the current trl API
ci
#26:
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Attach the prepared dataset to the training kernel
ci
#25:
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fe99b63
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Record the failed first run
ci
#24:
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Set the length bound from the measured distribution, not a biased sample
ci
#23:
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Record training throughput instead of estimating it (#8)
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#22:
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Move data prep out of the GPU session
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#21:
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