docs/lora_results.md

LoRA Fold 0 Results

This page records the completed fold 0 LoRA experiments and the checkpoint to use.

Recommendation

Use the original all-task fold 0 checkpoint:

• Local checkpoint path: artifacts/lora/fold0_best.pt
• Durable release asset: fold0_best_all_task.pt
• GitHub release: https://github.com/miyu-horiuchi/microbe-model/releases/tag/lora-fold0-20260518

The all-task checkpoint is the best current fold 0 LoRA result. Oxygen-only training and the anaerobe-weighted run were useful checks, but neither improved the clean validation comparison enough to replace the original checkpoint.

Experiments

All runs used fold 0, ESM-2 t12, LoRA r=8, one epoch, batch size 2, gradient accumulation 8, and Lambda A100 SXM4 GPU training.

Run	Local result file	Oxygen macro F1	Oxygen n	Use?
All-task LoRA	artifacts/lora/fold0_results.json	0.944823	2266	Yes
Oxygen-only LoRA	artifacts/lora/fold0_results_oxygen.json	0.916836	2214	No
Anaerobe-weighted all-task LoRA	artifacts/lora_weighted_anaerobe/fold0_results.json	0.944776	2266	No

The anaerobe-weighted run used oxygen class weights:

aerobe=1.0, anaerobe=1.5, facultative_anaerobe=1.0, microaerobe=1.0

It slightly improved anaerobe recall in the detailed diagnostic, but its fold 0 training-validation oxygen macro F1 was fractionally lower than the all-task run.

Checkpoint Assets

The .pt files are not committed to git. They are stored as GitHub Release assets:

Asset	SHA256
fold0_best_all_task.pt	8a73ee20252b1aa710b0480abd307ffbc38b788b1a152a7e63298c525a04be23
fold0_best_oxygen_only.pt	fd10d4a2a7cba5d564fb9ba1f730cace07a0a2173d3622f1f572cfd29306fc95
fold0_best_weighted_anaerobe.pt	c8d34999f570663e020e5644a994f821bf9539a6fcc3e029d5942b8dc7709826

Loading The Best Checkpoint

The checkpoint is a PyTorch dictionary with these keys:

• epoch
• model_cfg
• train_cfg
• state_dict

Minimal load pattern:

import torch

from microbe_model.train.lora_model import LoraModelConfig, PhenoLoRAModel

checkpoint = torch.load("artifacts/lora/fold0_best.pt", map_location="cpu")
model_cfg = LoraModelConfig(**checkpoint["model_cfg"])
model = PhenoLoRAModel(model_cfg)
model.load_state_dict(checkpoint["state_dict"], strict=False)
model.eval()

To regenerate oxygen diagnostics:

PYTHONPATH=src uv run --python 3.11 --extra dev python scripts/38_eval_lora_checkpoint.py \
  --checkpoint artifacts/lora/fold0_best.pt \
  --output-json artifacts/lora/fold0_oxygen_diagnostics.json \
  --output-md artifacts/lora/fold0_oxygen_diagnostics.md \
  --batch-size 2

Next GPU Work

Do not spend more GPU on fold 0 variants unless there is a new hypothesis. The next meaningful validation step is to run the selected all-task LoRA setup across folds 1-4 and report the mean and variance across all five folds. That is a stronger scientific result, but it is also the next major GPU-cost item.