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Error code: StreamingRowsError Exception: OSError Message: cannot find loader for this HDF5 file Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 323, in compute compute_first_rows_from_parquet_response( File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 88, in compute_first_rows_from_parquet_response rows_index = indexer.get_rows_index( File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 631, in get_rows_index return RowsIndex( File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 512, in __init__ self.parquet_index = self._init_parquet_index( File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 529, in _init_parquet_index response = get_previous_step_or_raise( File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 566, in get_previous_step_or_raise raise CachedArtifactError( libcommon.simple_cache.CachedArtifactError: The previous step failed. During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/src/worker/utils.py", line 92, in get_rows_or_raise return get_rows( File "/src/libs/libcommon/src/libcommon/utils.py", line 183, in decorator return func(*args, **kwargs) File "/src/services/worker/src/worker/utils.py", line 69, in get_rows rows_plus_one = list(itertools.islice(ds, rows_max_number + 1)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1393, in __iter__ example = _apply_feature_types_on_example( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1082, in _apply_feature_types_on_example decoded_example = features.decode_example(encoded_example, token_per_repo_id=token_per_repo_id) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1975, in decode_example return { File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1976, in <dictcomp> column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1341, in decode_nested_example return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 188, in decode_example image.load() # to avoid "Too many open files" errors File "/src/services/worker/.venv/lib/python3.9/site-packages/PIL/ImageFile.py", line 366, in load raise OSError(msg) OSError: cannot find loader for this HDF5 file
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Description of the Dataset
This release integrates the entire data sequence utilized in the CrystalCoder training. It encompasses data sequences from the three pre-training stages, combining information from two prior works: the SlimPajama dataset and StarCoder, totaling approximately 1300 billion tokens. These tokens are distributed across three stages, each with distinct weights.
Stage 1
During this initial stage, half of the SlimPajama data is utilized, equivalent to approximately 345 billion tokens.
Stage 2
In the second stage, the remaining half of the SlimPajama data is employed, along with two epochs of StarCoder data. For the StarCoder data, we apply FIM augmentation with an FIM rate of 0.9 and an SPM rate of 0.5. The total token count for this stage is calculated as 0.5 * 690 + 2 * 291, resulting in 927 billion tokens.
Stage 3
The third stage involves reusing Python and web-related data from the StarCoder data, including HTML, CSS, and JavaScript. This data is utilized for training over three epochs, with the application of FIM at a rate of 0.3 alongside an SPM rate of 0.5. The total token count for this stage is 100 billion. Additionally, a small portion of the SlimPajama dataset, excluding the Github part, is also reused, contributing around 10 billion tokens.
Instruction tuning (Stage 3a)
To enhance the model's proficiency in real chat scenarios, we utilize a diverse set of instruction tuning datasets, totaling approximately 1 billion tokens. Specifically, our data include OASST1-guanaco, SlimOrca, ShareGPT_V4.3, Evol-ShareGPT, CodeAlpaca, Rosetta Code, Evol-CodeAlpaca 1, Evol-CodeAlpaca 2, and a self-generated dataset centered on website creation through the Alpaca pipeline. We will release the full dataset soon.
The detailed breakdown of the tokens is as followed:
Primary Usage
This dataset serves as the foundation for training CrystalCoder and supports further reproduction. For training from scratch, please refer to our training codes. For training from middle checkpoints, please load the dataloader states in checkpoints and follow this tutorial.
License
Pretraining data in langauge model mostly comes from a collection of data sources with various licenses. Any use of all or part of the data here must abide by the terms of the original licenses, including attribution clauses when relevant. We refer users to SlimPajama dataset and StarCoder for detailed license attribution.
We release our work under ODC-BY, hence granting the rights over the dataset, but not the contents of the dataset individually.
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