uph.git/baselines, branch master

Fix two bugs: PEFT cleanup model corruption and K=16 OOM

2026-04-10T19:50:22+00:00

Bug 1: PEFTBaseline.cleanup() corrupted wrapper.model after LoRA unload,
causing 'Qwen2Model has no attribute prepare_inputs_for_generation' for
subsequent methods. Fix: save reference to original model before wrapping,
restore it directly in cleanup() instead of relying on unload().

Bug 2: fit_theta OOM at K=16 due to large logit chunks (128 × 151936 vocab).
Fix: reduce CHUNK_SIZE from 128 to 32 (~4x less memory per chunk).

Co-Authored-By: Claude Opus 4.6 (1M context)

Fix PromptTuning/PrefixTuning cleanup crash and tune learning rates

2026-04-05T22:41:30+00:00

- peft_baseline.py: Fix cleanup() to handle PromptTuning/PrefixTuning
  which don't support unload(). Falls back to base_model access.
- run_all_methods.py: Reduce lr from 0.3 to 0.01 for PromptTuning,
  0.01 to 0.001 for PrefixTuning. Previous lr caused R-L=0.03 (broken).

Co-Authored-By: Claude Opus 4.6 (1M context)

Add Prompt Tuning and Prefix Tuning baselines

2026-04-05T21:20:20+00:00

- peft_baseline.py: Add PromptTuningConfig (L=5,10,20) and PrefixTuningConfig (L=5,10)
- run_all_methods.py: Add 5 new methods to dispatch (prompt_tuning_5/10/20, prefix_tuning_5/10)
  with per-method directory output structure

Prompt Tuning: params = L * H (e.g. 10*1536 = 15360 params = 30KB)
Prefix Tuning: params = L * num_layers * 2 * H (much larger)

Co-Authored-By: Claude Opus 4.6 (1M context)

Add PEFT baselines, ICL baselines, profile-based, and unified pipeline

2026-04-05T15:31:36+00:00

New baselines:
- baselines/peft_baseline.py: LoRA, Tiny LoRA, VeRA (per-user PEFT adaptation)
- baselines/dense_retrieval.py: Dense retrieval ICL (sentence-transformers)
- baselines/profile_based.py: LLM-generated user profile conditioned generation

New scripts:
- scripts/run_all_methods.py: Unified pipeline running all 9 methods with
  per-method directory output structure (method/per_user.json)
- scripts/run_peft_baselines.py: PEFT-only evaluation (legacy)
- scripts/run_significance.py: Significance tests (UPH+Base per-user)
- scripts/run_uph_base_per_user.py: UPH+Base with full per-user data
- scripts/compute_bertscore.py: BERTScore from saved predictions
- scripts/significance_test.py: Standalone significance test framework

Updated .gitignore to exclude outputs/ directory.

Co-Authored-By: Claude Opus 4.6 (1M context)

Initial commit: UPH project codebase and experiment results

2026-04-03T20:12:34+00:00

Includes model code, evaluation scripts, configs, analysis outputs,
and experiment results for the User Prior Head personalization method.

Co-Authored-By: Claude Opus 4.6 (1M context)