Metrics below are computed on small fixed subsets, not full benchmarks. Exact subset sizes: AmbigQA (128), IFEval (64), MMLU (228). Reported values are from the final training step (step=1620, epoch=3).

Dataset

Hub dataset: sewon/ambig_qa (config_name=light)
Revision: 6e667596df70f17ba3c8e7be4b7361f6be8b60f8
Splits: train=train, validation=validation
Dev holdout: dev_fraction=0.1, split_seed=1729
Bootstrap resampling: False

System prompt

No system prompt (dataset.system_prompt: null).

Hyperparameters

Exact resolved settings used for this run (also attached as resolved_config.yaml):

yaml

model:
  name: google/gemma-4-E4B-it
  revision: main
  dtype: bfloat16
  enable_thinking: false
dataset:
  name: sewon/ambig_qa
  config_name: light
  revision: 6e667596df70f17ba3c8e7be4b7361f6be8b60f8
  train_split: train
  validation_split: validation
  dev_fraction: 0.1
  split_seed: 1729
  bootstrap: false
  max_train_examples: null
  max_dev_examples: null
  system_prompt_set: false
adaptation:
  method: oft
  oft:
    target_modules: all-linear
    r: null
    block_size: 32
    module_dropout: 0.05
    use_cayley_neumann: true
    num_cayley_neumann_terms: 5
    coft: false
    eps: 6.0e-05
    block_share: false
    bias: none
training:
  num_train_epochs: 3.0
  max_steps: -1
  learning_rate: 5.0e-05
  weight_decay: 0.01
  warmup_ratio: 0.03
  per_device_train_batch_size: 16
  per_device_eval_batch_size: 32
  gradient_accumulation_steps: 2
  max_seq_length: 512
  logging_steps: 10
  save_every_steps: 150
  save_total_limit: 3
  gradient_checkpointing: false
  bf16: true
  tf32: true
  max_grad_norm: 1.0
  dataloader_num_workers: 2
  adam_beta1: 0.9
  adam_beta2: 0.999
  adam_epsilon: 1.0e-08
ensemble:
  size: 5
  base_seed: 42
  this_member_index: 0
  this_member_seed: 42
evaluation_subsets:
  ambigqa:
    subset_size: 128
    seed: 1001
  ifeval:
    subset_size: 64
    seed: 1002
  mmlu:
    subset_size: 228
    seed: 1004

Metrics breakdown

The metrics in this section use small fixed subsets, not full benchmarks: AmbigQA (128), IFEval (64), MMLU (228).

Final-step ensemble mean ± std (n=5)

AmbigQA (128) accuracy: 0.1469 ± 0.0065
AmbigQA (128) AlignScore: 0.2175 ± 0.0085
IFEval (64) prompt_level_strict_accuracy: 0.8562 ± 0.0204
MMLU (228) accuracy: 0.7281 ± 0.0044
train_loss: 1.4435 ± 0.0029
steps: 1620
epochs: 3

Per-member (final step)

member	seed	steps	epochs	train_loss	AmbigQA (128) acc	AmbigQA (128) AlignScore	IFEval (64) strict	MMLU (228) acc
0 (this repo)	42	1620	3	1.4437	0.1406	0.2170	0.8438	0.7237
1	1051	1620	3	1.4469	0.1562	0.2172	0.8750	0.7281
2	2060	1620	3	1.4425	0.1484	0.2299	0.8750	0.7237
3	3069	1620	3	1.4393	0.1406	0.2059	0.8594	0.7325
4	4078	1620	3	1.4453	0.1484	0.2176	0.8281	0.7325

Files

Model weights / adapter files from members/member_000/final/
resolved_config.yaml — full resolved training config
ensemble_metrics.png — train/eval curves for the whole ensemble
run_artifacts/ — ensemble-level manifests, status, and captured environment
run_artifacts/members/member_000/ — this member's manifests, metadata, and status
run_artifacts/members/member_000/predictions/ — per-dataset JSONL predictions from every intermediate evaluation step