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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 4 new columns ({'has_base_model', 'has_model_card', 'id', 'downloads'}) and 19 missing columns ({'base_id', 'timestamp', 'aratio', 'ztimestamp', 'tratio', 'zbytes_out', 'pred_ratio', 'ratio', 'tbytes_out', 'bytes_out', 'shape', 'atimestamp', 'bytes_in', 'zratio', 'dist', 'ttimestamp', 'abytes_out', 'param_name', 'target_id'}).
This happened while the csv dataset builder was generating data using
hf://datasets/tensordex/tensordex-ae-cache/model_hub_crawl/model_snapshot_merged_last_month.csv (at revision fcefa9e4025810518875dc7b3607c25012e923ca), ['hf://datasets/tensordex/tensordex-ae-cache@fcefa9e4025810518875dc7b3607c25012e923ca/compression_data/real_compression_all_models.csv', 'hf://datasets/tensordex/tensordex-ae-cache@fcefa9e4025810518875dc7b3607c25012e923ca/model_hub_crawl/model_snapshot_merged_last_month.csv', 'hf://datasets/tensordex/tensordex-ae-cache@fcefa9e4025810518875dc7b3607c25012e923ca/model_level_reduction/trace_global_flexsplit.csv', 'hf://datasets/tensordex/tensordex-ae-cache@fcefa9e4025810518875dc7b3607c25012e923ca/model_level_reduction/trace_global_zipllm.csv', 'hf://datasets/tensordex/tensordex-ae-cache@fcefa9e4025810518875dc7b3607c25012e923ca/sample_pairs.tsv', 'hf://datasets/tensordex/tensordex-ae-cache@fcefa9e4025810518875dc7b3607c25012e923ca/tests/output/algo_benchmark/logs/bench_cached_facility_parsed.csv', 'hf://datasets/tensordex/tensordex-ae-cache@fcefa9e4025810518875dc7b3607c25012e923ca/tests/output/compare_methods/zipllm_all_models.csv', 'hf://datasets/tensordex/tensordex-ae-cache@fcefa9e4025810518875dc7b3607c25012e923ca/tests/output/openzl_benchmark.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._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
id: string
has_model_card: string
has_base_model: string
downloads: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 760
to
{'target_id': Value('string'), 'base_id': Value('string'), 'param_name': Value('string'), 'shape': Value('string'), 'bytes_in': Value('int64'), 'dist': Value('float64'), 'pred_ratio': Value('float64'), 'bytes_out': Value('float64'), 'ratio': Value('float64'), 'timestamp': Value('string'), 'zbytes_out': Value('float64'), 'zratio': Value('float64'), 'ztimestamp': Value('string'), 'abytes_out': Value('int64'), 'aratio': Value('float64'), 'atimestamp': Value('string'), 'tbytes_out': Value('float64'), 'tratio': Value('float64'), 'ttimestamp': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 4 new columns ({'has_base_model', 'has_model_card', 'id', 'downloads'}) and 19 missing columns ({'base_id', 'timestamp', 'aratio', 'ztimestamp', 'tratio', 'zbytes_out', 'pred_ratio', 'ratio', 'tbytes_out', 'bytes_out', 'shape', 'atimestamp', 'bytes_in', 'zratio', 'dist', 'ttimestamp', 'abytes_out', 'param_name', 'target_id'}).
This happened while the csv dataset builder was generating data using
hf://datasets/tensordex/tensordex-ae-cache/model_hub_crawl/model_snapshot_merged_last_month.csv (at revision fcefa9e4025810518875dc7b3607c25012e923ca), ['hf://datasets/tensordex/tensordex-ae-cache@fcefa9e4025810518875dc7b3607c25012e923ca/compression_data/real_compression_all_models.csv', 'hf://datasets/tensordex/tensordex-ae-cache@fcefa9e4025810518875dc7b3607c25012e923ca/model_hub_crawl/model_snapshot_merged_last_month.csv', 'hf://datasets/tensordex/tensordex-ae-cache@fcefa9e4025810518875dc7b3607c25012e923ca/model_level_reduction/trace_global_flexsplit.csv', 'hf://datasets/tensordex/tensordex-ae-cache@fcefa9e4025810518875dc7b3607c25012e923ca/model_level_reduction/trace_global_zipllm.csv', 'hf://datasets/tensordex/tensordex-ae-cache@fcefa9e4025810518875dc7b3607c25012e923ca/sample_pairs.tsv', 'hf://datasets/tensordex/tensordex-ae-cache@fcefa9e4025810518875dc7b3607c25012e923ca/tests/output/algo_benchmark/logs/bench_cached_facility_parsed.csv', 'hf://datasets/tensordex/tensordex-ae-cache@fcefa9e4025810518875dc7b3607c25012e923ca/tests/output/compare_methods/zipllm_all_models.csv', 'hf://datasets/tensordex/tensordex-ae-cache@fcefa9e4025810518875dc7b3607c25012e923ca/tests/output/openzl_benchmark.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)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.
target_id string | base_id string | param_name string | shape string | bytes_in int64 | dist float64 | pred_ratio float64 | bytes_out float64 | ratio float64 | timestamp string | zbytes_out float64 | zratio float64 | ztimestamp string | abytes_out int64 | aratio float64 | atimestamp string | tbytes_out float64 | tratio float64 | ttimestamp string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
3248a0f161d6937f8c3f07db208b24de | 194960234f8ebfb7fc7ae25eb31ee1bb | model.embed_tokens.weight | 32000x4096 | 262,144,000 | 0.004926 | 0.650411 | 211,927,731 | 0.80844 | 2025-12-04T21:05:03.483697 | 195,096,504 | 0.744234 | 2026-02-19T16:56:07.689881 | 186,294,406 | 0.710657 | 2026-03-04T05:28:48.850238 | 185,553,846 | 0.707832 | 2026-03-28T08:07:53.354419 |
5ea4a1ab8b0a1aaae65c1ca2a85497f6 | c8779cf20be6da057db20875ab9c6774 | model.layers.16.post_attention_layernorm.weight | (4096,) | 8,192 | null | 0.679419 | null | null | null | 2,496 | 0.304688 | 2026-03-02T02:12:54.814017 | 3,424 | 0.417969 | 2026-03-04T05:29:01.595867 | 4,234 | 0.516846 | 2026-03-28T08:08:09.619902 |
443a2d256a8a7f3645dc647951a850fe | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.346427 | 4,286 | 0.523193 | 2026-03-03T19:35:38.161811 | 4,162 | 0.508057 | 2026-03-02T02:12:39.548724 | 4,334 | 0.529053 | 2026-03-04T05:28:48.850238 | 4,123 | 0.503296 | 2026-03-28T08:07:53.354419 |
4a6e08467dffa729ee9428429cc35a8e | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.379075 | 4,683 | 0.571655 | 2026-03-03T19:35:38.161811 | 4,437 | 0.541626 | 2026-03-02T02:12:39.548724 | 4,722 | 0.576416 | 2026-03-04T05:28:48.850238 | 4,560 | 0.556641 | 2026-03-28T08:07:53.354419 |
35aae388080d850874eb51657eee4371 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.296857 | 3,957 | 0.483032 | 2026-03-03T19:35:38.161811 | 3,775 | 0.460815 | 2026-03-02T02:12:39.548724 | 3,945 | 0.481567 | 2026-03-04T05:28:48.850238 | 3,784 | 0.461914 | 2026-03-28T08:07:53.354419 |
6ddf3bcc186c47904ea606eb28c17ee1 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.356536 | 4,446 | 0.542725 | 2026-03-03T19:35:38.161811 | 4,345 | 0.530396 | 2026-03-02T02:12:39.548724 | 4,553 | 0.555786 | 2026-03-04T05:28:48.850238 | 4,336 | 0.529297 | 2026-03-28T08:07:53.354419 |
362ed56a7e25b4435f4de07b2c8694f4 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.461052 | 5,285 | 0.645142 | 2026-03-03T19:35:38.161811 | 5,126 | 0.625732 | 2026-03-02T02:12:39.548724 | 5,505 | 0.671997 | 2026-03-04T05:28:48.850238 | 5,188 | 0.633301 | 2026-03-28T08:07:53.354419 |
47e06499bb3c4cf330fd602ad7a63f08 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.381408 | 4,455 | 0.543823 | 2026-03-03T19:35:38.161811 | 4,487 | 0.547729 | 2026-03-02T02:12:39.548724 | 4,595 | 0.560913 | 2026-03-04T05:28:48.850238 | 4,308 | 0.525879 | 2026-03-28T08:07:53.354419 |
e6baa62b488d6439818ec4307f67185e | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.347534 | 4,432 | 0.541016 | 2026-03-03T19:35:38.161811 | 4,446 | 0.542725 | 2026-03-02T02:12:39.548724 | 4,572 | 0.558105 | 2026-03-04T05:28:48.850238 | 4,285 | 0.523071 | 2026-03-28T08:07:53.354419 |
f99dc1fc55018b117e26c080bf182048 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.418577 | 4,753 | 0.5802 | 2026-03-03T19:35:38.161811 | 4,568 | 0.557617 | 2026-03-02T02:12:39.548724 | 4,873 | 0.594849 | 2026-03-04T05:28:48.850238 | 4,673 | 0.570435 | 2026-03-28T08:07:53.354419 |
b18b26dd32e5a90bec78d7ae4bae60be | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.347073 | 4,368 | 0.533203 | 2026-03-03T19:35:38.161811 | 4,367 | 0.533081 | 2026-03-02T02:12:39.548724 | 4,491 | 0.548218 | 2026-03-04T05:28:48.850238 | 4,252 | 0.519043 | 2026-03-28T08:07:53.354419 |
814f4ecb2c3d12deb5530c9895dde408 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.323864 | 4,206 | 0.513428 | 2026-03-03T19:35:38.161811 | 4,121 | 0.503052 | 2026-03-02T02:12:39.548724 | 4,278 | 0.522217 | 2026-03-04T05:28:48.850238 | 4,046 | 0.493896 | 2026-03-28T08:07:53.354419 |
d74cbedcfabccc8c6ba2a3009acc8603 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.329859 | 4,124 | 0.503418 | 2026-03-03T19:35:38.161811 | 3,994 | 0.487549 | 2026-03-02T02:12:39.548724 | 4,147 | 0.506226 | 2026-03-04T05:28:48.850238 | 3,958 | 0.483154 | 2026-03-28T08:07:53.354419 |
a0b1c2d53a5c70d2f24718fce2f55694 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.371927 | 4,419 | 0.539429 | 2026-03-03T19:35:38.161811 | 4,333 | 0.528931 | 2026-03-02T02:12:39.548724 | 4,496 | 0.548828 | 2026-03-04T05:28:48.850238 | 4,288 | 0.523438 | 2026-03-28T08:07:53.354419 |
fe6328b965b858e9744512626fd651eb | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.466475 | 4,915 | 0.599976 | 2026-03-03T19:35:38.161811 | 4,709 | 0.574829 | 2026-03-02T02:12:39.548724 | 4,958 | 0.605225 | 2026-03-04T05:28:48.850238 | 4,797 | 0.585571 | 2026-03-28T08:07:53.354419 |
f398f52a2be2012565188aff1b9ea9f5 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.377053 | 4,721 | 0.576294 | 2026-03-03T19:35:38.161811 | 4,719 | 0.57605 | 2026-03-02T02:12:39.548724 | 4,844 | 0.591309 | 2026-03-04T05:28:48.850238 | 4,586 | 0.559814 | 2026-03-28T08:07:53.354419 |
5a4d383f82e8f49d46332b885759d3ce | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.368266 | 4,658 | 0.568604 | 2026-03-03T19:35:38.161811 | 4,633 | 0.565552 | 2026-03-02T02:12:39.548724 | 4,766 | 0.581787 | 2026-03-04T05:28:48.850238 | 4,547 | 0.555054 | 2026-03-28T08:07:53.354419 |
8ba52f2ed5c69e1f6fac645a6298cd96 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.364453 | 4,649 | 0.567505 | 2026-03-03T19:35:38.161811 | 4,675 | 0.570679 | 2026-03-02T02:12:39.548724 | 4,769 | 0.582153 | 2026-03-04T05:28:48.850238 | 4,478 | 0.546631 | 2026-03-28T08:07:53.354419 |
5b66f354f7976a8baacdea5b8deeefc6 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.37289 | 4,648 | 0.567383 | 2026-03-03T19:35:38.161811 | 4,611 | 0.562866 | 2026-03-02T02:12:39.548724 | 4,729 | 0.577271 | 2026-03-04T05:28:48.850238 | 4,452 | 0.543457 | 2026-03-28T08:07:53.354419 |
d8ca594960eabd5ce130450fc6c485b8 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.34932 | 4,478 | 0.546631 | 2026-03-03T19:35:38.161811 | 4,400 | 0.537109 | 2026-03-02T02:12:39.548724 | 4,505 | 0.549927 | 2026-03-04T05:28:48.850238 | 4,291 | 0.523804 | 2026-03-28T08:07:53.354419 |
e453bed5cfea4a67f9883b63c6666797 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.472567 | 5,027 | 0.613647 | 2026-03-03T19:35:38.161811 | 4,811 | 0.58728 | 2026-03-02T02:12:39.548724 | 5,111 | 0.623901 | 2026-03-04T05:28:48.850238 | 4,950 | 0.604248 | 2026-03-28T08:07:53.354419 |
7fd560b6ba10967195f6e95e00e69d0e | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.493496 | 5,047 | 0.616089 | 2026-03-03T19:35:38.161811 | 4,805 | 0.586548 | 2026-03-02T02:12:39.548724 | 5,097 | 0.622192 | 2026-03-04T05:28:48.850238 | 4,949 | 0.604126 | 2026-03-28T08:07:53.354419 |
73db0cff9009184e236a56576c8e29f4 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.476671 | 5,377 | 0.656372 | 2026-03-03T19:35:38.161811 | 5,199 | 0.634644 | 2026-03-02T02:12:39.548724 | 5,565 | 0.679321 | 2026-03-04T05:28:48.850238 | 5,277 | 0.644165 | 2026-03-28T08:07:53.354419 |
dd3788cb43343ac87e5a585411d213e3 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.546266 | 5,794 | 0.707275 | 2026-03-03T19:35:38.161811 | 5,827 | 0.711304 | 2026-03-02T02:12:39.548724 | 6,133 | 0.748657 | 2026-03-04T05:28:48.850238 | 5,802 | 0.708252 | 2026-03-28T08:07:53.354419 |
ac19f75592df40f9a2a8a4e61e61ff03 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.483473 | 5,576 | 0.680664 | 2026-03-03T19:35:38.161811 | 5,491 | 0.670288 | 2026-03-02T02:12:39.548724 | 5,878 | 0.717529 | 2026-03-04T05:28:48.850238 | 5,584 | 0.681641 | 2026-03-28T08:07:53.354419 |
dc65ce228465fa7c4780456fc9cdc6b9 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.537036 | 5,663 | 0.691284 | 2026-03-03T19:35:38.161811 | 5,640 | 0.688477 | 2026-03-02T02:12:39.548724 | 5,995 | 0.731812 | 2026-03-04T05:28:48.850238 | 5,672 | 0.692383 | 2026-03-28T08:07:53.354419 |
cd636c33a2a15f1360ce310243ac3f1d | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.553781 | 5,723 | 0.698608 | 2026-03-03T19:35:38.161811 | 5,725 | 0.698853 | 2026-03-02T02:12:39.548724 | 6,053 | 0.738892 | 2026-03-04T05:28:48.850238 | 5,733 | 0.699829 | 2026-03-28T08:07:53.354419 |
6bbcd86fae5943448cfbce53e01e5b4f | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.447907 | 4,961 | 0.605591 | 2026-03-03T19:35:38.161811 | 4,828 | 0.589355 | 2026-03-02T02:12:39.548724 | 5,164 | 0.630371 | 2026-03-04T05:28:48.850238 | 4,923 | 0.600952 | 2026-03-28T08:07:53.354419 |
400a1334374483b647ecfac9cabb75fd | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.360615 | 4,439 | 0.54187 | 2026-03-03T19:35:38.161811 | 4,330 | 0.528564 | 2026-03-02T02:12:39.548724 | 4,550 | 0.55542 | 2026-03-04T05:28:48.850238 | 4,331 | 0.528687 | 2026-03-28T08:07:53.354419 |
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84e6b68148c76358afccdb28194b8395 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.325414 | 4,139 | 0.505249 | 2026-03-03T19:35:38.161811 | 3,970 | 0.484619 | 2026-03-02T02:12:39.548724 | 4,180 | 0.510254 | 2026-03-04T05:28:48.850238 | 3,984 | 0.486328 | 2026-03-28T08:07:53.354419 |
6d066fab3ece447df6ee12f3cf924a62 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.308957 | 4,033 | 0.49231 | 2026-03-03T19:35:38.161811 | 3,847 | 0.469604 | 2026-03-02T02:12:39.548724 | 4,072 | 0.49707 | 2026-03-04T05:28:48.850238 | 3,887 | 0.474487 | 2026-03-28T08:07:53.354419 |
44881c185a60f9ac8321f15cdcb79a05 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.302564 | 4,042 | 0.493408 | 2026-03-03T19:35:38.161811 | 3,863 | 0.471558 | 2026-03-02T02:12:39.548724 | 4,072 | 0.49707 | 2026-03-04T05:28:48.850238 | 3,885 | 0.474243 | 2026-03-28T08:07:53.354419 |
e01e2c6913f5736e80d08bad8794fbb7 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.293225 | 3,933 | 0.480103 | 2026-03-03T19:35:38.161811 | 3,705 | 0.452271 | 2026-03-02T02:12:39.548724 | 3,914 | 0.477783 | 2026-03-04T05:28:48.850238 | 3,763 | 0.459351 | 2026-03-28T08:07:53.354419 |
56bef9b8da2fad779721ad0353657afd | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.364701 | 4,503 | 0.549683 | 2026-03-03T19:35:38.161811 | 4,376 | 0.53418 | 2026-03-02T02:12:39.548724 | 4,614 | 0.563232 | 2026-03-04T05:28:48.850238 | 4,384 | 0.535156 | 2026-03-28T08:07:53.354419 |
b0b7c4ee4c9c75476ce24662fa45bb39 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.345197 | 4,320 | 0.527344 | 2026-03-03T19:35:38.161811 | 4,128 | 0.503906 | 2026-03-02T02:12:39.548724 | 4,361 | 0.532349 | 2026-03-04T05:28:48.850238 | 4,208 | 0.513672 | 2026-03-28T08:07:53.354419 |
d7c5a1e19bc13c2e8adf77a1c65288dc | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.340562 | 4,339 | 0.529663 | 2026-03-03T19:35:38.161811 | 4,155 | 0.507202 | 2026-03-02T02:12:39.548724 | 4,400 | 0.537109 | 2026-03-04T05:28:48.850238 | 4,218 | 0.514893 | 2026-03-28T08:07:53.354419 |
31d61ee97990a71013ff660834949364 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.258993 | 3,602 | 0.439697 | 2026-03-03T19:35:38.161811 | 3,372 | 0.411621 | 2026-03-02T02:12:39.548724 | 3,599 | 0.439331 | 2026-03-04T05:28:48.850238 | 3,442 | 0.420166 | 2026-03-28T08:07:53.354419 |
df60fd8ce3fb1e435160d95bb0de27a9 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.376525 | 4,544 | 0.554688 | 2026-03-03T19:35:38.161811 | 4,397 | 0.536743 | 2026-03-02T02:12:39.548724 | 4,631 | 0.565308 | 2026-03-04T05:28:48.850238 | 4,429 | 0.540649 | 2026-03-28T08:07:53.354419 |
269529281d90f790379421cb5c0ce000 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.387357 | 4,630 | 0.565186 | 2026-03-03T19:35:38.161811 | 4,528 | 0.552734 | 2026-03-02T02:12:39.548724 | 4,782 | 0.58374 | 2026-03-04T05:28:48.850238 | 4,529 | 0.552856 | 2026-03-28T08:07:53.354419 |
91a3572dc58822383210ffc729b35f17 | 014f5ccb342104fe99a81a5c6906ff55 | model.layers.0.input_layernorm.weight | 4096 | 8,192 | null | 0.578688 | 5,955 | 0.726929 | 2025-12-05T16:52:04.321338 | 5,539 | 0.676147 | 2026-02-19T16:56:07.689881 | 5,883 | 0.71814 | 2026-03-04T05:28:48.850238 | 5,799 | 0.707886 | 2026-03-28T08:07:53.354419 |
0aa24edc9370a27884bd5d52d1e0f5fc | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.340807 | 4,362 | 0.532471 | 2026-03-03T19:35:38.161811 | 4,206 | 0.513428 | 2026-03-02T02:12:39.548724 | 4,422 | 0.539795 | 2026-03-04T05:28:48.850238 | 4,229 | 0.516235 | 2026-03-28T08:07:53.354419 |
dedbcd260ec171d034d5491d1e724df8 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.354313 | 4,491 | 0.548218 | 2026-03-03T19:35:38.161811 | 4,375 | 0.534058 | 2026-03-02T02:12:39.548724 | 4,595 | 0.560913 | 2026-03-04T05:28:48.850238 | 4,400 | 0.537109 | 2026-03-28T08:07:53.354419 |
ab77915b293abeebce7e48c522822e5e | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.37022 | 4,515 | 0.551147 | 2026-03-03T19:35:38.161811 | 4,401 | 0.537231 | 2026-03-02T02:12:39.548724 | 4,637 | 0.56604 | 2026-03-04T05:28:48.850238 | 4,440 | 0.541992 | 2026-03-28T08:07:53.354419 |
0e5f93618f1ad10a3cf97a0421353489 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.382529 | 4,508 | 0.550293 | 2026-03-03T19:35:38.161811 | 4,378 | 0.534424 | 2026-03-02T02:12:39.548724 | 4,620 | 0.563965 | 2026-03-04T05:28:48.850238 | 4,402 | 0.537354 | 2026-03-28T08:07:53.354419 |
87c6f205b38b231f2da465624de5af14 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.365693 | 4,614 | 0.563232 | 2026-03-03T19:35:38.161811 | 4,624 | 0.564453 | 2026-03-02T02:12:39.548724 | 4,732 | 0.577637 | 2026-03-04T05:28:48.850238 | 4,472 | 0.545898 | 2026-03-28T08:07:53.354419 |
e05e941626a652cd5199c668c3efd629 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.394279 | 4,625 | 0.564575 | 2026-03-03T19:35:38.161811 | 4,458 | 0.544189 | 2026-03-02T02:12:39.548724 | 4,741 | 0.578735 | 2026-03-04T05:28:48.850238 | 4,490 | 0.548096 | 2026-03-28T08:07:53.354419 |
7d87b0153415b60c280efb748441359d | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.373076 | 4,461 | 0.544556 | 2026-03-03T19:35:38.161811 | 4,372 | 0.533691 | 2026-03-02T02:12:39.548724 | 4,579 | 0.55896 | 2026-03-04T05:28:48.850238 | 4,352 | 0.53125 | 2026-03-28T08:07:53.354419 |
a8a52529b8c151bc63d3ea08db883d6b | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.360894 | 4,464 | 0.544922 | 2026-03-03T19:35:38.161811 | 4,401 | 0.537231 | 2026-03-02T02:12:39.548724 | 4,561 | 0.556763 | 2026-03-04T05:28:48.850238 | 4,329 | 0.528442 | 2026-03-28T08:07:53.354419 |
1fb7d82aee1dff9c5b5838d0d33620dd | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.435783 | 4,980 | 0.60791 | 2026-03-03T19:35:38.161811 | 4,835 | 0.59021 | 2026-03-02T02:12:39.548724 | 5,164 | 0.630371 | 2026-03-04T05:28:48.850238 | 4,876 | 0.595215 | 2026-03-28T08:07:53.354419 |
223febc4bf18d5f7bb38d33d225f5c46 | 8bf0998a3c8a072007996de4d1efaf2a | model.layers.0.input_layernorm.weight | (4096,) | 8,192 | null | 0.343569 | 4,283 | 0.522827 | 2026-03-03T19:35:38.161811 | 4,148 | 0.506348 | 2026-03-02T02:12:39.548724 | 4,325 | 0.527954 | 2026-03-04T05:28:48.850238 | 4,105 | 0.501099 | 2026-03-28T08:07:53.354419 |
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5d1af597300558ef6bc2befb18a15a7d | aecd8e5a3fa91f6d4c7cb69cd5a095e7 | model.layers.1.input_layernorm.weight | 4096 | 8,192 | null | 0.038494 | 224 | 0.027344 | 2025-12-05T16:39:37.174549 | 277 | 0.033813 | 2026-02-20T01:02:20.149594 | 1,212 | 0.147949 | 2026-03-06T19:24:02.563615300+00:00 | 268 | 0.032715 | 2026-03-28T19:35:55 |
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b659ea13609451fe41be6aeb49a0f189 | acb4eef1decdbd970ac8906680154a72 | model.layers.30.post_attention_layernorm.weight | 4096 | 8,192 | 0.006332 | 0.044878 | 502 | 0.061279 | 2025-12-05T16:39:37.174549 | 439 | 0.053589 | 2026-02-13T18:24:37.653069 | 1,280 | 0.15625 | 2026-03-06T02:30:56.842390818+00:00 | null | null | null |
86cb5b8443ae547da1e5841d53fdca5c | aecd8e5a3fa91f6d4c7cb69cd5a095e7 | model.layers.1.input_layernorm.weight | 4096 | 8,192 | null | 0.196689 | 2,697 | 0.329224 | 2025-12-05T16:39:37.174549 | 1,921 | 0.234497 | 2026-02-20T01:02:20.149594 | 2,483 | 0.303101 | 2026-03-06T04:10:16.794041082+00:00 | 2,067 | 0.252319 | 2026-03-28T19:35:55 |
End of preview.
TensorDex — Artifact Evaluation Cache
Data companion for the TensorDex SOSP artifact
(code repo → ae/). It holds the
pre-computed results and the raw tensors needed to reproduce the paper's
figures and to verify them from scratch, so reviewers don't re-run the full
40 TB pipeline.
The code repo's ae/download_cache.py pulls this into ae/cache/.
Contents
| Path | Size | Role |
|---|---|---|
results.db |
5.2 GB | the 11.4 M-pair compression cache (every figure's numbers) |
sample_blobs/<xx>/<yy>/<id>.safetensors |
~2 GB | raw tensor blobs for the Tier-1 sample (content-addressed) |
sample_pairs.tsv |
— | the (target, base) pairs the sample covers |
data/tensordb_s3/metadata.db |
567 MB | slim: tensor sizes + model→tensor map (for reduction charts) |
model_hub_crawl/ |
~127 MB | Hugging Face crawl stats (Fig 2, Fig 4) |
tests/output/, compression_data/, model_level_reduction/ |
~500 MB | baseline plans & traces the chart modules read |
results.db is a self-contained SQLite file (WAL folded in), slimmed to
exactly the columns the AE charts and verification scripts read (original
row ids are preserved, so subsampled charts reproduce the paper's figures
byte-for-byte). Blobs are keyed by XXH3-128 of the raw tensor bytes —
id == hash(bytes); that is what Tier-1 verify_sample.py re-checks.
results.db column guide
One row per compressed (target, base) tensor pair of the trace:
| Column(s) | Meaning |
|---|---|
target_id, base_id |
XXH3-128 content hashes of the two tensors (= blob ids) |
param_name, shape, bytes_in |
tensor identity and raw size |
bcs_dist |
TensorSketch (BCS) distance between the pair — TensorPred's input |
tratio / tbytes_out |
TensorX delta codec (TensorDex-TX, 65.1 %) — re-derived bit-exact by make verify |
fratio / fbytes_out |
FM++ delta codec (the 70.5 % headline) — re-derived bit-exact after make ae-fmpp |
ratio / bytes_out |
BitX (ZipLLM's delta codec) baseline |
aratio / abytes_out, zratio |
dense per-pair delta ratios computed during development — the fitting cache TensorPred is trained and evaluated on (Fig 13; 5.77 M pairs vs ~1 M for FM++); zratio is its zstd fallback for tiny tensors, also used by the entropy/CDF analyses |
pred_ratio |
TensorPred's stored prediction of aratio — make verify-predict re-fits the model from scratch and recovers this column to ~1e-4 |
timestamp, ttimestamp |
row provenance (trace ordering; legacy-row reporting in verify) |
Use
# in the code repo
python ae/download_cache.py --repo <this-dataset-id>
make figures # Tier 0 — re-plot every figure
make verify # Tier 1 — re-derive a random sample from these blobs
Provenance
Generated by the code repo's authoring tools: ae/stage_data.py (chart inputs
- slim metadata.db),
ae/build_sample_bundle.py(the blob sample). Regenerate a larger sample with--budget-gb N.
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