remove iterable CLI arg

src/axolotl/cli/args.py

@@ -13,12 +13,6 @@ class PreprocessCliArgs:
     debug_num_examples: int = field(default=1)
     prompter: Optional[str] = field(default=None)
     download: Optional[bool] = field(default=True)
-    iterable: Optional[bool] = field(
-        default=None,
-        metadata={
-            "help": "Use IterableDataset for streaming processing of large datasets"
-        },
-    )
 
 
 @dataclass

src/axolotl/common/datasets.py

@@ -55,13 +55,11 @@ def load_datasets(
     """
     tokenizer = load_tokenizer(cfg)
     processor = load_processor(cfg, tokenizer=tokenizer) if cfg.processor_type else None
-    preprocess_iterable = getattr(cli_args, "iterable", False)
 
     train_dataset, eval_dataset, total_num_steps, prompters = prepare_datasets(
         cfg,
         tokenizer,
         processor=processor,
-        preprocess_iterable=preprocess_iterable,
     )
 
     if (

src/axolotl/utils/data/sft.py

@@ -44,24 +44,11 @@ from axolotl.utils.trainer import (
 LOG = get_logger(__name__)
 
 
-def _determine_streaming_mode(cfg: DictDefault) -> bool:
-    """Determine if we should use streaming mode based on config."""
-    if cfg.streaming is not None:
-        return cfg.streaming
-
-    # Default to streaming for pretraining datasets
-    if cfg.pretraining_dataset:
-        return True
-
-    return False
-
-
 @retry_on_request_exceptions(max_retries=3, delay=5)
 def prepare_datasets(
     cfg: DictDefault,
     tokenizer: PreTrainedTokenizer,
     processor: ProcessorMixin | None = None,
-    preprocess_iterable: bool = False,
 ) -> tuple[IterableDataset | Dataset, Dataset | None, int, list[Prompter | None]]:
     """Prepare training and evaluation datasets based on configuration.
 
@@ -69,30 +56,19 @@ def prepare_datasets(
         cfg: Dictionary mapping `axolotl` config keys to values.
         tokenizer: Tokenizer to use for processing text.
         processor: Optional processor for multimodal datasets.
-        preprocess_iterable: Whether to use iterable preprocessing.
 
     Returns:
         Tuple of (train_dataset, eval_dataset, total_steps, prompters).
     """
-    # Determine streaming mode from config
-    streaming_mode = _determine_streaming_mode(cfg)
-
-    # Override preprocess_iterable parameter with streaming config
-    if streaming_mode:
-        preprocess_iterable = True
-
     if cfg.pretraining_dataset:
-        return _prepare_pretraining_dataset(
-            cfg, tokenizer, processor, preprocess_iterable
-        )
-    return _prepare_standard_dataset(cfg, tokenizer, processor, preprocess_iterable)
+        return _prepare_pretraining_dataset(cfg, tokenizer, processor)
+    return _prepare_standard_dataset(cfg, tokenizer, processor)
 
 
 def _prepare_standard_dataset(
     cfg: DictDefault,
     tokenizer: PreTrainedTokenizer,
     processor: ProcessorMixin | None,
-    preprocess_iterable: bool,
 ) -> tuple[Dataset, Dataset | None, int, list[Prompter | None]]:
     """Prepare standard (non-pretraining) datasets."""
 
@@ -103,7 +79,6 @@ def _prepare_standard_dataset(
             cfg,
             split="train",
             processor=processor,
-            preprocess_iterable=preprocess_iterable,
         )
 
         # Overwrite eval_dataset if test data exists
@@ -113,7 +88,6 @@ def _prepare_standard_dataset(
                 cfg,
                 split="test",
                 processor=processor,
-                preprocess_iterable=preprocess_iterable,
             )
 
         return train_dataset, eval_dataset, prompters
@@ -159,7 +133,6 @@ def _prepare_pretraining_dataset(
     cfg: DictDefault,
     tokenizer: PreTrainedTokenizer,
     processor: ProcessorMixin | None,
-    preprocess_iterable: bool,
 ) -> tuple[IterableDataset, Dataset | None, int, list[Prompter | None]]:
     """
     Prepare dataset for pretraining mode.
@@ -180,7 +153,6 @@ def _prepare_pretraining_dataset(
             cfg,
             split="test",
             processor=processor,
-            preprocess_iterable=preprocess_iterable,
         )
 
     if cfg.dataset_exact_deduplication:
@@ -283,7 +255,6 @@ def _load_tokenized_prepared_datasets(
     cfg: DictDefault,
     split: Literal["train", "test"] = "train",
     processor: ProcessorMixin | None = None,
-    preprocess_iterable: bool = False,
 ) -> tuple[Dataset | DatasetDict, list[Prompter | None]]:
     """Load or create tokenized and prepared datasets for training or testing.
 
@@ -292,7 +263,6 @@ def _load_tokenized_prepared_datasets(
         cfg: Configuration object.
         split: Dataset split to load ('train' or 'test').
         processor: Optional processor for multimodal datasets.
-        preprocess_iterable: Whether to use iterable preprocessing.
 
     Returns:
         Tuple of (dataset, prompters list).
@@ -323,7 +293,6 @@ def _load_tokenized_prepared_datasets(
             tokenizer,
             split,
             processor,
-            preprocess_iterable,
         )
 
     return dataset, prompters
@@ -335,7 +304,6 @@ def _load_raw_datasets(
     tokenizer: PreTrainedTokenizer,
     split: str,
     processor: ProcessorMixin | None = None,
-    preprocess_iterable: bool = False,
 ) -> tuple[Dataset, list[Prompter | None]]:
     """Load, process, merge, and save raw datasets."""
     LOG.info("Loading raw datasets...", main_process_only=False)
@@ -356,7 +324,6 @@ def _load_raw_datasets(
             split=split,
             seed=cfg.seed,
             processor=processor,
-            preprocess_iterable=preprocess_iterable,
         )
         datasets.append(dataset_wrapper)
         prompters.append(dataset_prompter)
@@ -388,15 +355,11 @@ def _load_and_process_single_dataset(
     split: str,
     seed: int,
     processor: ProcessorMixin | None = None,
-    preprocess_iterable: bool = False,
 ) -> tuple[Dataset | IterableDataset, Prompter | None]:
     """Load and process a single dataset based on the passed config."""
-    # Load the dataset
     dataset = load_dataset_with_config(
-        dataset_config, cfg.hf_use_auth_token, streaming=preprocess_iterable
+        dataset_config, cfg.hf_use_auth_token, cfg.streaming
     )
-
-    # Parse dataset type
     d_base_type, d_prompt_style = _parse_dataset_type(dataset_config.type)
 
     # Select the appropriate split
@@ -515,7 +478,6 @@ def _load_and_prepare_datasets(
     cfg: DictDefault,
     split: Literal["train", "test"] = "train",
     processor: ProcessorMixin | None = None,
-    preprocess_iterable: bool = False,
 ) -> tuple[Dataset | None, Dataset | None, list[Prompter | None]]:
     """Load and prepare datasets with optional validation split and sharding.
 
@@ -524,7 +486,6 @@ def _load_and_prepare_datasets(
         cfg: Configuration object.
         split: Dataset split to load ('train' or 'test').
         processor: Optional processor for multimodal datasets.
-        preprocess_iterable: Whether to use iterable preprocessing.
 
     Returns:
         Tuple of (train_dataset, eval_dataset, prompters).
@@ -535,7 +496,6 @@ def _load_and_prepare_datasets(
         cfg,
         split=split,
         processor=processor,
-        preprocess_iterable=preprocess_iterable,
     )
 
     # Apply dataset sharding if configured using shared function

src/axolotl/utils/schemas/config.py

@@ -962,7 +962,6 @@ class AxolotlInputConfig(
 
     # INTERNALS - document for now, generally not set externally
     is_preprocess: bool | None = None
-    preprocess_iterable: bool | None = None
 
     total_num_tokens: int | None = Field(
         default=None,