Commit History
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Fix hypernetwork input collapse; add serving app + H100 deploy kit
El-Mouden Moncif +1: Claude Opus 5The trained head emitted a near-identical adapter for every repo (pairwise cosine 0.96) and scored worse than random noise on held-out repo text. Cause: ~64% of each 6-view embedding is a constant vector shared by all repos, which dominated the trunk (input cosine 0.73 -> 0.978 after the trunk). - MemoryLoRAHead: standardize the conditioning input via input_mean/input_std buffers fitted on train docs only and stored in the checkpoint, so training and inference apply the same transform. After 40 steps the emitted-adapter cosine dropped 0.96 -> 0.21. - Training: log the no-adapter baseline and delta_vs_baseline every eval, plus diag/adapter_cosine. Tracking only the adapted loss is why the collapse went unnoticed. - app/: Next.js app serving repo-personalized models over OpenAI- and Anthropic-compatible APIs for coding CLIs. - deploy/h100/: provisioning + training scripts for a single-GPU cloud run. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>f17edea
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Memory-LoRA hypernetwork for Gemma-4-E2B: code, curated data, sixview checkpoints
moncefem +1: Claude Opus 4.8Hypernetwork that generates repo-specific LoRA adapters for a frozen google/gemma-4-E2B (zero inference-time token overhead). Includes the memory_lora package, all data/build/train/eval scripts, the aligned 6-view dataset, and the sixview_v1/v2 checkpoints. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CeUvTz4vLw4ni2ZNR6pS1p481fbb6