Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
source_artifact: string
metadata: struct<total_steps: int64, pos_encoding: string, best_val_loss: double, final_val_ppl: double, final (... 18 chars omitted)
child 0, total_steps: int64
child 1, pos_encoding: string
child 2, best_val_loss: double
child 3, final_val_ppl: double
child 4, final_val_loss: double
created_at: timestamp[s]
run_id: string
run_name: string
run_path: string
artifact_created_at: timestamp[s]
sha256: string
to
{'source_artifact': Value('string'), 'metadata': {'adapt': Value('string'), 'lora_r': Value('int64'), 'val/ppl': Value('float64'), 'val/loss': Value('float64'), 'base_model': Value('string'), 'lora_alpha': Value('int64'), 'lora_dropout': Value('float64'), 'delta_enabled': Value('bool'), 'delta_hidden_dim': Value('null'), 'val/loss_vs_base': Value('float64'), 'lora_target_modules': List(Value('string'))}, 'sha256': Value('string'), 'run_id': Value('string'), 'run_name': Value('string'), 'run_path': Value('string'), 'artifact_created_at': Value('timestamp[s]')}
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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
source_artifact: string
metadata: struct<total_steps: int64, pos_encoding: string, best_val_loss: double, final_val_ppl: double, final (... 18 chars omitted)
child 0, total_steps: int64
child 1, pos_encoding: string
child 2, best_val_loss: double
child 3, final_val_ppl: double
child 4, final_val_loss: double
created_at: timestamp[s]
run_id: string
run_name: string
run_path: string
artifact_created_at: timestamp[s]
sha256: string
to
{'source_artifact': Value('string'), 'metadata': {'adapt': Value('string'), 'lora_r': Value('int64'), 'val/ppl': Value('float64'), 'val/loss': Value('float64'), 'base_model': Value('string'), 'lora_alpha': Value('int64'), 'lora_dropout': Value('float64'), 'delta_enabled': Value('bool'), 'delta_hidden_dim': Value('null'), 'val/loss_vs_base': Value('float64'), 'lora_target_modules': List(Value('string'))}, 'sha256': Value('string'), 'run_id': Value('string'), 'run_name': Value('string'), 'run_path': Value('string'), 'artifact_created_at': Value('timestamp[s]')}
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.
learned-position-increments-experiment — checkpoints
Model checkpoints for the experiments in
brendanlong/learned-position-increments-experiment
(learned per-token position increments, "selective PE", and grafting them onto
SmolLM2). Training curves are in the wandb projects
byte-pe and
delta-graft.
Layout: <experiment>/<run_name>/<file>.pt, each with a .json metadata file
next to it (wandb run ID and path, the final metrics, and the sha256 of the
.pt). Run names match --wandb-run-name and the run tables in the repo's
RESULTS.md.
selective_pe/ (step_50000.pt: a dict with step, model_state_dict and model_config)
| run | wandb | what |
|---|---|---|
byte_selective_rope_bilingual_256d_8h_6L_lr1e3 |
rt7cv5ww |
the blog post's shared-DeltaMLP model (the post's per-layer model was not saved) |
repro_byte_selective_rope_bilingual_256d_8h_6L_lr1e3 |
q19navjs |
shared-model replica trained from the repo (see REPRODUCTION.md) |
repro_byte_selective_rope_perlayer_bilingual_256d_8h_6L_lr1e3 |
0eqetsep |
per-layer replica |
docmask_{rope_s42,shared_s42,perlayer_s42,perlayer_s1,perlayer_s2} |
see RESULTS.md Phase 13 | Phase 13: document-masked, unbounded deltas |
nodoc_mask_perlayer_unbounded_s42 |
rrzpmhro |
Phase 13 control without the document mask |
big_docmask_{rope_s42,perlayer_s42,perlayer_s1} |
see RESULTS.md | Phase 14: 122M-parameter SmolLM2-135M-shaped model |
delta_graft/ (delta_mlp.pt: only the DeltaMLP plus its config; the
base model HuggingFaceTB/SmolLM2-1.7B is reloaded from the Hub)
| run | wandb | what |
|---|---|---|
SmolLM2-1.7B_lora_delta |
7ppz1qzy |
LoRA graft |
SmolLM2-1.7B_full_delta |
unsxsqhd |
full fine-tune graft |
All files load with torch.load(..., weights_only=True). The repo's scripts
accept hf:<experiment>/<run_name>/<file> wherever they take a checkpoint
path, and download from here automatically, e.g.
hf:selective_pe/repro_byte_selective_rope_bilingual_256d_8h_6L_lr1e3/step_50000.pt.
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