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How we train saes replication #123
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #123 +/- ##
==========================================
+ Coverage 59.91% 64.40% +4.48%
==========================================
Files 17 17
Lines 1654 1753 +99
Branches 277 289 +12
==========================================
+ Hits 991 1129 +138
+ Misses 602 560 -42
- Partials 61 64 +3 ☔ View full report in Codecov by Sentry. |
tom-pollak
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Oct 22, 2024
* l1 scheduler, clip grad norm * add provisional ability to normalize activations * notebook * change heuristic norm init to constant, report b_e and W_dec norms (fix tests later) * fix mse calculation * add benchmark test * update heuristic init to 0.1 * make tests pass device issue * continue rebase * use better args in benchmark * remove stack in get activations * broken! improve CA runner * get cache activation runner working and add some tests * add training steps to path * avoid ghost grad tensor casting * enable download of full dataset if desired * add benchmark for cache activation runner * add updated tutorial * format --------- Co-authored-by: Johnny Lin <[email protected]>
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Description
Please include a summary of the change and which issue is fixed. Please also include relevant motivation and context. List any dependencies that are required for this change.
Fixes # (issue)
Type of change
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Checklist:
You have tested formatting, typing and unit tests (acceptance tests not currently in use)
make check-ci
to check format and linting. (you can runmake format
to format code if needed.)Performance Check.
If you have implemented a training change, please indicate precisely how performance changes with respect to the following metrics:
Please links to wandb dashboards with a control and test group: https://api.wandb.ai/links/jbloom/syh38mi2