The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
revision: string
step: int64
model: string
dtype: string
batch: int64
seqlen: int64
icl_score_mean: double
icl_score_seeds: list<item: double>
child 0, item: double
n_layers: int64
n_heads: int64
heads: list<item: struct<layer: int64, head: int64, induction_mean: double, induction_std: double, prev_tok (... 17 chars omitted)
child 0, item: struct<layer: int64, head: int64, induction_mean: double, induction_std: double, prev_token_mean: do (... 5 chars omitted)
child 0, layer: int64
child 1, head: int64
child 2, induction_mean: double
child 3, induction_std: double
child 4, prev_token_mean: double
random_heads: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
delta_random: double
delta_induction: double
ablate_random: double
baseline_2nd_copy_loss: double
ablate_induction: double
top_heads: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
to
{'revision': Value('string'), 'step': Value('int64'), 'baseline_2nd_copy_loss': Value('float64'), 'ablate_induction': Value('float64'), 'ablate_random': Value('float64'), 'delta_induction': Value('float64'), 'delta_random': Value('float64'), 'top_heads': List(List(Value('int64'))), 'random_heads': List(List(Value('int64')))}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
revision: string
step: int64
model: string
dtype: string
batch: int64
seqlen: int64
icl_score_mean: double
icl_score_seeds: list<item: double>
child 0, item: double
n_layers: int64
n_heads: int64
heads: list<item: struct<layer: int64, head: int64, induction_mean: double, induction_std: double, prev_tok (... 17 chars omitted)
child 0, item: struct<layer: int64, head: int64, induction_mean: double, induction_std: double, prev_token_mean: do (... 5 chars omitted)
child 0, layer: int64
child 1, head: int64
child 2, induction_mean: double
child 3, induction_std: double
child 4, prev_token_mean: double
random_heads: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
delta_random: double
delta_induction: double
ablate_random: double
baseline_2nd_copy_loss: double
ablate_induction: double
top_heads: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
to
{'revision': Value('string'), 'step': Value('int64'), 'baseline_2nd_copy_loss': Value('float64'), 'ablate_induction': Value('float64'), 'ablate_random': Value('float64'), 'delta_induction': Value('float64'), 'delta_random': Value('float64'), 'top_heads': List(List(Value('int64'))), 'random_heads': List(List(Value('int64')))}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Induction-head emergence across Pythia training
Per-head induction, previous-token, and in-context-learning scores across the full training checkpoint sequence of Pythia models — plus the causal ablation control that turns a correlational score into a mechanism.
This is a data layer, not a new finding. Induction-head emergence on Pythia has been studied before (see Prior work); what has never been published is the computed scores themselves, as a tidy downloadable table. Olsson et al. (2022), the origin of the phase-change result, used 34 internally-trained models — no weights, no checkpoints, no per-head data were ever released.
Contents
| file | rows | what |
|---|---|---|
induction_pythia-160m.jsonl |
154 checkpoints | all of step0…step143000, incl. the log-spaced step1–512 region |
induction_pythia-160m-seed{1..9}.jsonl |
11 ckpts × 9 | PolyPythias seed axis |
induction_pythia-{410m,1b,1.4b}.jsonl |
11 ckpts each | scale axis |
induction_pythia-{1b,1.4b}_fp32.jsonl |
11 each | fp32 re-runs (dtype control) |
induction_pythia-160m_bf16.jsonl |
11 | bf16/fp32 comparison |
ablation_pythia-160m.jsonl |
5 ckpts | causal control |
Each row: revision, step, dtype, batch, seqlen, icl_score_mean, icl_score_seeds,
and a heads list of {layer, head, induction_mean, induction_std, prev_token_mean} over
3 stimulus seeds.
What the data shows
A sharp phase change, flat until step 512 then complete by step 1000 (induction 0.035 → 0.963; ICL −0.02 → −9.97). Bounded by Pythia's checkpoint spacing — no checkpoints exist between 512 and 1000, so that interval is the finest timing statement this model permits.
Invariant timing, arbitrary implementation. All 10 seeds and all 4 sizes cross in the same interval. Since Pythia uses identical data order and batch size across sizes, same step = same tokens, so the transition is data-determined. Yet the top induction head is a different (layer, head) in every one of the 10 seeds, spanning layers 4–8.
A visible precursor: at step 512 the previous-token score is 10–17× the induction score in every seed — the prerequisite circuit forms first.
The ablation control tracks the phase change: ablating the top-5 induction heads costs −0.003 at step 512 but +9.48 at step 1000 (+10.1 at step 143000); five random heads ≈ 0 throughout. Before the transition the top-scoring heads have no causal role at all.
Caveats
- dtype matters at the final checkpoint. bf16 and fp32 agree to ±0.001 through the phase change, but at step 143000 on 160m, 22/144 heads shift by >0.05 and the argmax head changes. 1b and 1.4b show no such effect. Use the fp32 files for mature-checkpoint claims.
- A late, gradual ICL improvement (−12.3 → −18.8 from ~step 105000 on 160m) coincides with the tail of Pythia's cosine LR decay — confounded, and it is also dtype-sensitive.
- Induction score is correlational; the ablation file is what makes it causal.
- Yin & Steinhardt argue function-vector heads, not induction heads, drive few-shot ICL.
Prior work (please cite these too)
Olsson et al., In-context Learning and Induction Heads (2022) · Tigges, Hanna, Yu & Biderman, LLM Circuit Analyses Are Consistent Across Training and Scale (NeurIPS 2024) · Yin & Steinhardt, Which Attention Heads Matter for In-Context Learning? · Feucht et al., Dual-Route Model of Induction (nearest prior artifact: 7 of 154 checkpoints, copying scores) · Aoyama, Wilcox & Schneider, Predicting the Emergence of Induction Heads (ICML 2026).
Reproduce
scripts/sweep_induction.py and scripts/ablate_induction.py. Probe is seconds per checkpoint;
the whole sweep is bandwidth-bound and cost under $20.
Part of Controls & Trajectories — publishing the null distributions and developmental trajectories that interpretability papers rely on but rarely ship. Curriculum · Morgan Hough, Orthogonal Research and Education Lab (OREL).
- Downloads last month
- 31