Datasets:

tomg-group-umd
/

gemstones_data_order_sequential

@@ -4,6 +4,7 @@ configs:
   data_files:
   - split: train
     path: "*.parquet"
 ---
 Gemstones Training Dataset - Sequential version
@@ -17,6 +18,12 @@ reproduce the training batches across the gpus is/was the run the training code.
 This repo is the result of an attempt to simulate the way in which the training code loaded the data and
 stream it out to a portable file format for use in downstream analyses of the model suite.
 # Sharding format: sequential
 This version of the dataset approximates the order of the dataset _as if_ a model was being trained
@@ -35,7 +42,7 @@ have seen all of the same rows of the dataset during training. The sync granular
 This linearized recreation assumes a single worker is reading every row of the dataset and so at a microbatch size of 8 over packed sequences of 2048 tokens, 21247488 steps worth of "training" is required to reach ~350B tokens.
-At runtime, the the single worker receives the total dataset represented by the thousands of raw training format files (for reference, this format is defined by the `packed_cycle_dataset.py` file in this repo).
 The raw files were first shuffled globally, and then the single worker
 loaded 4 files at a time, and shuffled the "blocks" of 2048 tokens each in a temporary buffer so
 that the contents of the 4 packed files were not read in the exact order in which the tokens appeared in them.
@@ -44,12 +51,7 @@ that the contents of the 4 packed files were not read in the exact order in whic
 a time whose contents (blocks of tokens) are read in a shuffled order, does not exactly match any one of the
 Gemstones model sets. However, the key is that the synchronization argument above still holds and so analyses at a coarser granularity than ~8.6B tokens should be sound.
-The `train_mock_data_order_file.py` performs these operations and writes the resulting data order out to files named like
 `ordered_dataset_shard_{shard}-of-{total_shards}.parquet` where the total number of shards is arbitrary, but chosen to be 256 for
-portability.
-# Loading
-This data should be loadable using `load_dataset` in the standard manner to auto-download the data.
-Alternately, the dataset can be cloned using git to materialize the files locally, and then loaded
-using the default `parquet` builder as described here: https://huggingface.co/docs/datasets/en/loading#parquet

   data_files:
   - split: train
     path: "*.parquet"
+license: odc-by
 ---
 Gemstones Training Dataset - Sequential version
 This repo is the result of an attempt to simulate the way in which the training code loaded the data and
 stream it out to a portable file format for use in downstream analyses of the model suite.
+# Loading
+This data should be loadable using `load_dataset` in the standard manner to auto-download the data.
+Alternately, the dataset can be cloned using git to materialize the files locally, and then loaded
+using the default `parquet` builder as described here: https://huggingface.co/docs/datasets/en/loading#parquet
 # Sharding format: sequential
 This version of the dataset approximates the order of the dataset _as if_ a model was being trained
 This linearized recreation assumes a single worker is reading every row of the dataset and so at a microbatch size of 8 over packed sequences of 2048 tokens, 21247488 steps worth of "training" is required to reach ~350B tokens.
+In this setup, a single worker received the total dataset represented by the thousands of raw training format files (for reference, this format is defined by the `packed_cycle_dataset.py` file in this repo).
 The raw files were first shuffled globally, and then the single worker
 loaded 4 files at a time, and shuffled the "blocks" of 2048 tokens each in a temporary buffer so
 that the contents of the 4 packed files were not read in the exact order in which the tokens appeared in them.
 a time whose contents (blocks of tokens) are read in a shuffled order, does not exactly match any one of the
 Gemstones model sets. However, the key is that the synchronization argument above still holds and so analyses at a coarser granularity than ~8.6B tokens should be sound.
+The `train_mock_data_order_file.py` performs these operations and writes the resulting data order out to files.
+Each shard named like
 `ordered_dataset_shard_{shard}-of-{total_shards}.parquet` where the total number of shards is arbitrary, but chosen to be 256 for
+portability, represents a contiguous subset of the approximated total ordering of the rows int the training dataset.