myyycroft/Gemma-4-E2B-AmbigQA-full-member-0

myyycroft/Gemma-4-E2B-AmbigQA-full-member-0

Ensemble member 0 (seed 42) from run gemma4_e2b_full_small_lr.

Fine-tuned from `google/gemma-4-E2B-it` on AmbigQA.

Evaluation note

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):

model:
  name: google/gemma-4-E2B-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: full
training:
  num_train_epochs: 3.0
  max_steps: -1
  learning_rate: 2.0e-05
  weight_decay: 0.1
  warmup_ratio: 0.03
  per_device_train_batch_size: 4
  per_device_eval_batch_size: 16
  gradient_accumulation_steps: 8
  max_seq_length: 512
  logging_steps: 10
  save_every_steps: null
  save_total_limit: null
  gradient_checkpointing: true
  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: 0.0922 ± 0.0116
• —AmbigQA (128) AlignScore: 0.1887 ± 0.0125
• —IFEval (64) prompt_level_strict_accuracy: 0.6250 ± 0.0442
• —MMLU (228) accuracy: 0.5596 ± 0.0200
• —train_loss: 1.5896 ± 0.0063
• —steps: 1620
• —epochs: 3

Per-member (final step)

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