CodeDevX/auralis-coder
Text Generation • 2B • Updated
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
format: string
tokenizer: string
original_tokens: int64
new_tokens: int64
total_tokens: int64
requested_new_tokens: int64
documents_used: int64
documents_skipped: int64
duplicates_removed: int64
approx_new_source_characters: int64
new_shard_size_tokens: int64
original_shards_preserved: bool
storage: string
note: string
records_skipped: int64
token_format: string
existing_tokens_before_run: int64
total_tokens_after_run: int64
documents_tokenized: int64
existing_shards_preserved: bool
duplicates_skipped: int64
dataset_name: string
new_shards_start: int64
new_tokens_this_run: int64
to
{'dataset_name': Value('string'), 'token_format': Value('string'), 'tokenizer': Value('string'), 'existing_tokens_before_run': Value('int64'), 'new_tokens_this_run': Value('int64'), 'total_tokens_after_run': Value('int64'), 'documents_tokenized': Value('int64'), 'duplicates_skipped': Value('int64'), 'records_skipped': Value('int64'), 'existing_shards_preserved': Value('bool'), 'new_shards_start': Value('int64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
format: string
tokenizer: string
original_tokens: int64
new_tokens: int64
total_tokens: int64
requested_new_tokens: int64
documents_used: int64
documents_skipped: int64
duplicates_removed: int64
approx_new_source_characters: int64
new_shard_size_tokens: int64
original_shards_preserved: bool
storage: string
note: string
records_skipped: int64
token_format: string
existing_tokens_before_run: int64
total_tokens_after_run: int64
documents_tokenized: int64
existing_shards_preserved: bool
duplicates_skipped: int64
dataset_name: string
new_shards_start: int64
new_tokens_this_run: int64
to
{'dataset_name': Value('string'), 'token_format': Value('string'), 'tokenizer': Value('string'), 'existing_tokens_before_run': Value('int64'), 'new_tokens_this_run': Value('int64'), 'total_tokens_after_run': Value('int64'), 'documents_tokenized': Value('int64'), 'duplicates_skipped': Value('int64'), 'records_skipped': Value('int64'), 'existing_shards_preserved': Value('bool'), 'new_shards_start': Value('int64')}
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Auralis-Coder Training Dataset
Packed token dataset for training the Auralis-Coder 1.5B parameter language model.
Dataset Overview
| Property | Value |
|---|---|
| Total tokens | 176,544,387 |
| Tokenizer | Qwen2.5 (Qwen/Qwen2.5-1.5B) |
| Format | uint32 token IDs (packed) |
| Shards | 16 |
| Shard size | ~10M tokens each |
| Documents | 235,219 |
| Duplicates removed | 19 |
Tokenization & Packing
- Tokenized with Qwen2.5 tokenizer (vocab: 151,936)
- Packed into fixed-size shards (10M tokens each)
- Sequence length: 4,096 tokens during training
- Maximum context: 131,072 tokens
Usage
import numpy as np
# Load a shard
tokens = np.fromfile("tokens-000000.bin", dtype=np.uint32)
# Reshape into sequences (e.g., 4096 tokens per sequence)
seq_len = 4096
sequences = tokens.reshape(-1, seq_len)
Files
tokens-000000.binthroughtokens-000015.bin— Packed token shards (uint32)packed_manifest.json— Dataset statistics and metadatadataset.json— Additional metadata
License
Refer to the source dataset licenses (FineWeb, StarCoder, FineWeb-Edu) and the Auralis project distribution.
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