Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
tag: string
name: string
levels: string
K: int64
d: int64
ckpt: string
best_val: double
test_acc: double
logged_test_acc: double
vs
tag: string
name: string
K: int64
d: int64
n_active: int64
usage_frac: double
eff_perplexity: double
traj_div: double
q_error: double
recon_err: double
n_collapsed_dims: int64
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 247, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 4376, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2658, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2836, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2374, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 580, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              tag: string
              name: string
              levels: string
              K: int64
              d: int64
              ckpt: string
              best_val: double
              test_acc: double
              logged_test_acc: double
              vs
              tag: string
              name: string
              K: int64
              d: int64
              n_active: int64
              usage_frac: double
              eff_perplexity: double
              traj_div: double
              q_error: double
              recon_err: double
              n_collapsed_dims: int64

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

COCONUT-FSQ bottleneck ablation — cached artifacts

Companion caches for Part I of pau_amargantialvarez_408221.ipynb (Pau Amargant Alvarez, MNLP). FSQ-COCONUT fine-tuned from plain COCONUT (gsm-coconut/checkpoint_25), varying only fsq_levels. Each run taken at its best-validation checkpoint and evaluated on the GSM8K test split (N=1319), through the same harness as the interpretability part.

config fsq_levels K d best val test acc active codes eff. ppl collapsed dims
FSQ K=1000 (orig) [8,5,5,5] 1000 4 0.248 849 507 0/4
dim6 [4,4,4,4,4,4] 4096 6 0.274 0.240 2097 975 0/6
dim4 [8,8,8,8] 4096 4 0.282 0.262 2197 1324 0/4
k65k [7,5,5,5,5,5,3] 65625 7 0.304 0.288 4280 2681 0/7

Plain COCONUT baseline (continuous latents): 0.344. All FSQ variants underperform it; the largest codebook (k65k) is best and no codebook collapses — the bottleneck is the encoder squeeze, not the grid.

Files

  • latents_fsq_{dim4,dim6,k65k}.pt — per-slot recomputed dumps: pre_vq/z/quant/recon (N,6,·) + code_id (N,6), with recomputed exact-match.
  • fsq_ablation.csv — accuracy table (best-val checkpoint, recomputed + logged test acc).
  • fsq_codeusage.csv — active codes, effective perplexity, trajectory diversity, q-error, recon-error, collapsed dims.
  • fsq_zstd.csv — per-dimension encoder z_std (collapse check).
  • fsq_ablation_summary.json — machine-readable summary.
Downloads last month
51