Add Apriel2 conversion documentation and supernet pruning examples #446
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- Add README.md documenting the algebraic structure of the conversion system (surgery monoid, action law, plan composition, total vs partial operations) - Add prune_supernet_step1.yaml and prune_supernet_step2.yaml examples demonstrating the two-step workflow for pruning a homogeneous supernet to a heterogeneous network with different mixer types per layer Co-Authored-By: Claude Opus 4.5 <[email protected]>
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Pull request overview
This PR adds comprehensive documentation and practical examples for the Apriel2 conversion system, focusing on the algebraic structure of the surgery/conversion framework and demonstrating a two-step workflow for pruning supernets.
Changes:
- Added a detailed README documenting the algebraic structure of the conversion system (monoid action, plan composition, total vs partial operations)
- Added two example YAML files demonstrating supernet pruning through a two-step surgery workflow
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated no comments.
| File | Description |
|---|---|
| fast_llm_external_models/apriel2/conversion/README.md | Comprehensive documentation of the algebraic structure, including surgery monoid, action laws, plan composition, functoriality, and practical examples for supernet creation and pruning |
| fast_llm_external_models/apriel2/examples/prune_supernet_step1.yaml | First step of supernet pruning: converts fixed to pattern decoder and sets main_mixer_name per block type |
| fast_llm_external_models/apriel2/examples/prune_supernet_step2.yaml | Second step of supernet pruning: unwraps stochastic mixers to non-stochastic types using weights from the designated sub-mixers |
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Summary
Test plan
🤖 Generated with Claude Code