fixes

* upgrade peft to 0.17.1

* upgrade for transformers too

.coderabbit.yaml

@@ -12,5 +12,6 @@ reviews:
   auto_review:
     enabled: true
     drafts: false
+    auto_incremental_review: true
 chat:
   auto_reply: true

examples/gpt-oss/README.md

@@ -41,6 +41,12 @@ model, and final model output, you may need at least 3TB of free disk space to k
 axolotl train examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offload.yaml
 ```
 
+To simplify fine-tuning across 2 nodes × 8x H100 (80GB) GPUs, we've partnered with [Baseten](https://baseten.co) to showcase multi-node
+training of the 120B model using Baseten Truss. You can read more about this recipe on
+[Baseten's blog](https://www.baseten.co/blog/how-to-fine-tune-gpt-oss-120b-with-baseten-and-axolotl/). The recipe can
+be found on their
+[GitHub](https://github.com/basetenlabs/ml-cookbook/tree/main/examples/oss-gpt-120b-axolotl/training).
+
 ERRATA: Transformers saves the model Architecture prefixed with `FSDP` which needs to be manually renamed in `config.json`.
 See https://github.com/huggingface/transformers/pull/40207 for the status of this issue.
 
@@ -61,9 +67,23 @@ mv ./outputs/gpt-oss-out/merged/* ./outputs/gpt-oss-out/
 
 ### Inferencing your fine-tuned model
 
+#### vLLM
+
 GPT-OSS support in vLLM does not exist in a stable release yet. See https://x.com/MaziyarPanahi/status/1955741905515323425
 for more information about using a special vllm-openai docker image for inferencing with vLLM.
 
+Optionally, vLLM can be installed from nightly:
+
+```bash
+pip install --no-build-isolation --pre -U vllm --extra-index-url https://wheels.vllm.ai/nightly
+```
+and the vLLM server can be started with the following command (modify `--tensor-parallel-size 8` to match your environment):
+```bash
+vllm serve ./outputs/gpt-oss-out/ --served-model-name axolotl/gpt-oss-20b --host 0.0.0.0 --port 8888  --tensor-parallel-size 8
+```
+
+#### SGLang
+
 SGLang has 0-day support in main, see https://github.com/sgl-project/sglang/issues/8833 for infomation on installing
 SGLang from source. Once you've installed SGLang, run the following command to launch a SGLang server:
 

examples/gpt-oss/gpt-oss-120b-fft-fsdp2-offload.yaml

@@ -44,7 +44,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/gpt-oss/gpt-oss-20b-fft-deepspeed-zero3.yaml

@@ -40,7 +40,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/gpt-oss/gpt-oss-20b-fft-fsdp2-offload.yaml

@@ -15,7 +15,7 @@ datasets:
     field_thinking: thinking
     template_thinking_key: thinking
 
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path: ./outputs/last_run_prepared
 val_set_size: 0
 output_dir: ./outputs/gpt-oss-out/
 
@@ -41,7 +41,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/gpt-oss/gpt-oss-20b-fft-fsdp2.yaml

@@ -15,7 +15,7 @@ datasets:
     field_thinking: thinking
     template_thinking_key: thinking
 
-dataset_prepared_path: last_run_prepared
+dataset_prepared_path: ./outputs/last_run_prepared
 val_set_size: 0
 output_dir: ./outputs/gpt-oss-out/
 
@@ -40,7 +40,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

examples/gpt-oss/gpt-oss-20b-sft-lora-singlegpu.yaml

@@ -53,7 +53,7 @@ bf16: true
 tf32: true
 
 flash_attention: true
-attn_implementation: kernels-community/vllm-flash-attn3
+attn_implementation: kernels-community/vllm-flash-attn3  # this is not needed if using flash_attn >= 2.8.3
 
 gradient_checkpointing: true
 activation_offloading: true

requirements.txt

@@ -13,8 +13,8 @@ liger-kernel==0.6.1
 packaging==23.2
 
 huggingface_hub>=0.33.0
-peft==0.17.0
+peft>=0.17.0
-transformers==4.55.2
+transformers==4.55.3
 tokenizers>=0.21.1
 accelerate==1.10.0
 datasets==4.0.0

setup.py

@@ -118,9 +118,9 @@ def get_package_version():
 
 
 extras_require = {
-    "flash-attn": ["flash-attn==2.8.2"],
+    "flash-attn": ["flash-attn==2.8.3"],
     "ring-flash-attn": [
-        "flash-attn==2.8.2",
+        "flash-attn==2.8.3",
         "ring-flash-attn>=0.1.7",
         "yunchang==0.6.0",
     ],

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
@@ -40,6 +34,12 @@ class VllmServeCliArgs:
         default=None,
         metadata={"help": "Number of tensor parallel workers to use."},
     )
+    data_parallel_size: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "Number of data parallel workers to use for vLLM serving. This controls how many model replicas are used for parallel inference."
+        },
+    )
     host: Optional[str] = field(
         default=None,  # nosec B104
         metadata={"help": "Host address to run the server on."},

src/axolotl/cli/cloud/modal_.py

@@ -82,7 +82,7 @@ class ModalCloud(Cloud):
         return res
 
     def get_image(self):
-        docker_tag = "main-py3.11-cu124-2.6.0"
+        docker_tag = "main-py3.11-cu126-2.7.1"
         if self.config.docker_tag:
             docker_tag = self.config.docker_tag
         docker_image = f"axolotlai/axolotl:{docker_tag}"
@@ -200,7 +200,7 @@ class ModalCloud(Cloud):
         if family in ["a10", "a10g"]:
             return modal.gpu.A10G(count=count)
         if family == "h100":
-            return modal.gpu.H100(count=count)
+            return f"H100:{count}"
         if family == "t4":
             return modal.gpu.T4(count=count)
         if family == "l4":

src/axolotl/cli/inference.py

@@ -64,7 +64,7 @@ def do_inference(
             importlib.import_module("axolotl.prompters"), prompter
         )
     elif cfg.chat_template:
-        chat_template_str = get_chat_template(cfg.chat_template)
+        chat_template_str = get_chat_template(cfg.chat_template, tokenizer=tokenizer)
     elif cfg.datasets[0].type == "chat_template":
         chat_template_str = get_chat_template_from_config(
             cfg=cfg, ds_cfg=cfg.datasets[0], tokenizer=tokenizer

src/axolotl/cli/preprocess.py

@@ -97,7 +97,8 @@ def do_cli(
     """
     # pylint: disable=duplicate-code
     os.environ["AXOLOTL_IS_PREPROCESS"] = "1"
-    parsed_cfg = load_cfg(config, **kwargs)
+    is_preprocess = kwargs.pop("is_preprocess", True)
+    parsed_cfg = load_cfg(config, is_preprocess=is_preprocess, **kwargs)
     parsed_cfg.is_preprocess = True
     parser = transformers.HfArgumentParser(PreprocessCliArgs)
     parsed_cli_args, _ = parser.parse_args_into_dataclasses(

src/axolotl/cli/utils/sweeps.py

@@ -3,11 +3,12 @@
 import random
 from copy import deepcopy
 from itertools import product
+from typing import Any
 
 
 def generate_sweep_configs(
     base_config: dict[str, list], sweeps_config: dict[str, list]
-) -> list[dict[str, list]]:
+) -> list[dict[str, Any]]:
     """
     Recursively generates all possible configurations by applying sweeps to the base config.
 

src/axolotl/cli/utils/train.py

@@ -4,6 +4,7 @@ import os
 import subprocess  # nosec
 import sys
 import tempfile
+from pathlib import Path
 from typing import Any, Iterator, Literal
 
 import yaml
@@ -88,7 +89,12 @@ def generate_config_files(config: str, sweep: str | None) -> Iterator[tuple[str,
     # Generate all possible configurations
     permutations = generate_sweep_configs(base_config, sweep_config)
     is_group = len(permutations) > 1
-    for permutation in permutations:
+    base_output_dir = base_config.get("output_dir", "./model-out")
+    for idx, permutation in enumerate(permutations, start=1):
+        permutation_dir = Path(permutation.get("output_dir", base_output_dir))
+        permutation_id = f"sweep{idx:04d}"
+        permutation["output_dir"] = str(permutation_dir / permutation_id)
+
         # pylint: disable=consider-using-with
         temp_file = tempfile.NamedTemporaryFile(
             mode="w",

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/datasets.py

@@ -1,4 +1,14 @@
-"""Module containing Dataset functionality"""
+"""
+Module containing dataset functionality.
+
+We want this to be a wrapper for an existing dataset that we have loaded. Lets use the
+concept of middlewares to wrap each dataset, for example:
+ConstantLengthDataset(ShuffledDataset([TokenizedPromptDataset(alpaca_dataset)])).
+Let's check to ensure we don't truncate an item in the middle. We'll use the collators
+later on to pad the datasets.
+"""
+
+from typing import Any
 
 import torch
 from datasets import Dataset, IterableDataset
@@ -7,12 +17,6 @@ from axolotl.utils.logging import get_logger
 
 from .prompt_tokenizers import PromptTokenizingStrategy
 
-# We want this to be a wrapper for an existing dataset that we have loaded
-# lets use the concept of middlewares to wrap each dataset, for example
-# ConstantLengthDataset(ShuffledDataset([TokenizedPromptDataset(alpaca_dataset)]))
-# let's check to ensure we don't truncate an item in the middle, we'll use
-# the collators later on to pad the datasets
-
 LOG = get_logger(__name__)
 
 
@@ -42,10 +46,15 @@ class TokenizedPromptDataset(Dataset):
             **kwargs,
         )
 
-    def process(self, dataset):
+    def process(self, dataset: Dataset | IterableDataset) -> Dataset | IterableDataset:
-        features = dataset.features.keys()
+        """Apply filtering and tokenization."""
+        # For IterableDataset, we can't access features up front. Anyways, we don't care
+        # to remove unused columns from streaming datasets.
+        features = None
+        if not isinstance(dataset, IterableDataset):
+            features = dataset.features.keys()
 
-        map_kwargs = {}
+        map_kwargs: dict[str, Any] = {}
         if self.prompt_tokenizer.supports_batched:
             map_kwargs["batched"] = True
             map_kwargs["batch_size"] = 1_000
@@ -54,18 +63,28 @@ class TokenizedPromptDataset(Dataset):
             hasattr(self.prompt_tokenizer, "filter_rows")
             and self.prompt_tokenizer.filter_rows
         ):
+            filter_kwargs: dict[str, Any] = {"desc": "Strategy Filtering Rows"}
+            if not isinstance(dataset, IterableDataset):
+                filter_kwargs["num_proc"] = self.process_count
+
             dataset = dataset.filter(
                 self.prompt_tokenizer.filter_rows,
-                num_proc=self.process_count,
+                **filter_kwargs,
-                desc="Strategy Filtering Rows",
             )
 
+        map_kwargs = {
+            **map_kwargs,
+            "desc": "Tokenizing Prompts",
+        }
+
+        # Only add remove_columns for regular datasets
+        if not isinstance(dataset, IterableDataset):
+            map_kwargs["remove_columns"] = features
+            map_kwargs["num_proc"] = self.process_count
+            map_kwargs["keep_in_memory"] = self.keep_in_memory
+
         return dataset.map(
             self.prompt_tokenizer.tokenize_prompt,
-            num_proc=self.process_count,
-            remove_columns=features,
-            keep_in_memory=self.keep_in_memory,
-            desc="Tokenizing Prompts",
             **map_kwargs,
         )
 
@@ -79,16 +98,15 @@ def wrap_dataset_for_tokenized_prompt(
         map_kwargs = {}
         if prompt_tokenizer.supports_batched:
             map_kwargs["batched"] = True
-        features = list(dataset.features.keys())
         return dataset.map(
             prompt_tokenizer.tokenize_prompt,
-            remove_columns=features,
             **map_kwargs,
         )
     return TokenizedPromptDataset(prompt_tokenizer, dataset, **kwargs)
 
 
-# TODO this isn't the best since it can't interleave datasets
+# TODO: this isn't the best since it can't interleave datasets.
+# NOTE: this is only used in a test. Can it be deleted?
 class ConstantLengthDataset(IterableDataset):
     """Iterable dataset that returns constant length chunks of tokens from stream of
     text files.

src/axolotl/monkeypatch/accelerate/fsdp2.py

@@ -187,7 +187,7 @@ def _process_lora_module_for_fsdp(module, fsdp2_kwargs):
 
     # Linear4Bit will keep it's bias term in fp32. If the weight dtype is in bf16 we are not able to
     # wrap this. Therefore we must ensure the bias has the same dtype as the weight
-    if module.base_layer.bias is not None:
+    if hasattr(module.base_layer, "bias") and module.base_layer.bias is not None:
         if module.base_layer.weight.dtype != module.base_layer.bias.dtype:
             log_bias_dtype_mismatch = True
             module.base_layer.bias.data = module.base_layer.bias.data.to(

src/axolotl/train.py

@@ -253,7 +253,9 @@ def save_trained_model(
             # final model weights have already been saved by `ReLoRACallback.on_train_end`
             return
 
-    if trainer.is_fsdp_enabled or cfg.fsdp_config:
+    if (  # pylint: disable=too-many-nested-blocks
+        trainer.is_fsdp_enabled or cfg.fsdp_config
+    ):
         if cfg.fsdp_config or cfg.fsdp:
             if cfg.fsdp_config.final_state_dict_type:
                 state_dict_type = cfg.fsdp_config.final_state_dict_type
@@ -285,6 +287,8 @@ def save_trained_model(
                 if trainer.accelerator.is_main_process:
                     # move all files in merged_path to cfg.output_dir
                     for merged_file in Path(merged_path).iterdir():
+                        if (Path(cfg.output_dir) / merged_file.name).exists():
+                            (Path(cfg.output_dir) / merged_file.name).unlink()
                         shutil.move(str(merged_file), cfg.output_dir)
                     shutil.rmtree(merged_path)  # remove what should be an empty dir
         # TODO(wing):see https://github.com/huggingface/transformers/pull/40207

src/axolotl/utils/data/sft.py

@@ -9,6 +9,7 @@ from datasets import (
     Dataset,
     DatasetDict,
     IterableDataset,
+    IterableDatasetDict,
     load_dataset,
 )
 from transformers import PreTrainedTokenizer, ProcessorMixin
@@ -28,7 +29,7 @@ from axolotl.utils.data.shared import (
 )
 from axolotl.utils.data.utils import (
     deduplicate_and_log_datasets,
-    drop_long_seq_in_dataset,
+    handle_long_seq_in_dataset,
     retry_on_request_exceptions,
 )
 from axolotl.utils.data.wrappers import get_dataset_wrapper
@@ -43,12 +44,53 @@ from axolotl.utils.trainer import (
 LOG = get_logger(__name__)
 
 
+def _is_streaming_enabled_for_split(
+    cfg: DictDefault, split: Literal["train", "test"]
+) -> bool:
+    """Check if streaming is enabled for a specific split."""
+    if split == "test":
+        # For eval datasets, check eval_streaming first, then fall back to streaming
+        eval_streaming = cfg.get("eval_streaming")
+        if eval_streaming is not None:
+            return eval_streaming
+
+    # Fall back to main streaming setting
+    streaming = cfg.get("streaming")
+    if streaming is True:
+        return True
+
+    # Check if pretraining dataset exists (defaults to streaming)
+    has_pretraining = cfg.get("pretraining_dataset") is not None
+    streaming_default_for_pretraining = has_pretraining and streaming is None
+
+    return streaming_default_for_pretraining
+
+
+def _get_streaming_config_for_split(
+    cfg: DictDefault, split: Literal["train", "test"]
+) -> DictDefault:
+    """Get a modified config object with split-specific streaming settings."""
+    if split != "test":
+        return cfg
+
+    # Override with eval-specific configs if they exist
+    streaming_cfg = DictDefault(cfg)
+    eval_strategy = cfg.get("eval_dataset_mixing_strategy")
+    eval_weights = cfg.get("eval_mixing_weights")
+
+    if eval_strategy is not None:
+        streaming_cfg["dataset_mixing_strategy"] = eval_strategy
+    if eval_weights is not None:
+        streaming_cfg["mixing_weights"] = eval_weights
+
+    return streaming_cfg
+
+
 @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.
 
@@ -56,23 +98,19 @@ 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).
     """
     if cfg.pretraining_dataset:
-        return _prepare_pretraining_dataset(
+        return _prepare_pretraining_dataset(cfg, tokenizer, processor)
-            cfg, tokenizer, processor, preprocess_iterable
+    return _prepare_standard_dataset(cfg, tokenizer, processor)
-        )
-    return _prepare_standard_dataset(cfg, tokenizer, processor, preprocess_iterable)
 
 
 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."""
 
@@ -83,7 +121,6 @@ def _prepare_standard_dataset(
             cfg,
             split="train",
             processor=processor,
-            preprocess_iterable=preprocess_iterable,
         )
 
         # Overwrite eval_dataset if test data exists
@@ -93,7 +130,6 @@ def _prepare_standard_dataset(
                 cfg,
                 split="test",
                 processor=processor,
-                preprocess_iterable=preprocess_iterable,
             )
 
         return train_dataset, eval_dataset, prompters
@@ -109,7 +145,13 @@ def _prepare_standard_dataset(
         return train_dataset, eval_dataset, -1, prompters
 
     # Validate sample packing configuration for evaluation
-    if eval_dataset and cfg.sample_packing and cfg.eval_sample_packing is not False:
+    # Skip validation for streaming eval datasets since theWhat hy don't have a calculable length
+    if (
+        eval_dataset
+        and cfg.sample_packing
+        and cfg.eval_sample_packing is not False
+        and not isinstance(eval_dataset, IterableDataset)
+    ):
         total_eval_steps = calculate_total_num_steps(cfg, eval_dataset, update=False)
         if total_eval_steps == 0:
             raise ValueError(
@@ -117,13 +159,17 @@ def _prepare_standard_dataset(
                 "You should set `eval_sample_packing: False` in your config."
             )
 
-    # Calculate total number of training steps
+    # Set total_num_steps for training
-    if cfg.max_steps:
+    if isinstance(train_dataset, IterableDataset):
-        total_num_steps = min(
+        total_num_steps = cfg.max_steps
-            calculate_total_num_steps(cfg, train_dataset), cfg.max_steps
-        )
     else:
-        total_num_steps = calculate_total_num_steps(cfg, train_dataset)
+        if cfg.max_steps:
+            total_num_steps = min(
+                calculate_total_num_steps(cfg, train_dataset), cfg.max_steps
+            )
+        else:
+            total_num_steps = calculate_total_num_steps(cfg, train_dataset)
+
     LOG.info(f"Maximum number of steps set at {total_num_steps}")
     return train_dataset, eval_dataset, total_num_steps, prompters
 
@@ -132,7 +178,6 @@ 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.
@@ -153,7 +198,6 @@ def _prepare_pretraining_dataset(
             cfg,
             split="test",
             processor=processor,
-            preprocess_iterable=preprocess_iterable,
         )
 
     if cfg.dataset_exact_deduplication:
@@ -256,7 +300,6 @@ 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.
 
@@ -265,39 +308,51 @@ 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).
     """
-    # Select correct dataset configuration based on split
     datasets_configs = cfg.datasets if split == "train" else cfg.test_datasets
-
-    # Generate dataset hash for caching
-    dataset_hash = generate_dataset_hash_from_config(
-        cfg, datasets_configs, tokenizer.name_or_path
-    )
-
-    # Try loading from hub if push_dataset_to_hub is configured
-    dataset = None
-    if cfg.push_dataset_to_hub:
-        dataset = try_load_from_hub(cfg, dataset_hash, split)
-
-    # If not found on hub, try loading from disk
-    if dataset is None:
-        dataset = load_preprocessed_dataset(cfg, dataset_hash)
-
-    # If not found on disk or skipping prepared dataset, load and process raw datasets
     prompters: list[Prompter | None] = []
-    if dataset is None:
+
+    # Check if streaming is enabled for this split
+    use_streaming = _is_streaming_enabled_for_split(cfg, split)
+
+    if use_streaming:
+        # For streaming datasets, skip caching and load raw datasets directly
+        streaming_cfg = _get_streaming_config_for_split(cfg, split)
         dataset, prompters = _load_raw_datasets(
-            cfg,
+            streaming_cfg,
             datasets_configs,
             tokenizer,
             split,
             processor,
-            preprocess_iterable,
         )
+    else:
+        # Generate dataset hash for caching
+        dataset_hash = generate_dataset_hash_from_config(
+            cfg, datasets_configs, tokenizer.name_or_path
+        )
+
+        # Try loading from hub if push_dataset_to_hub is configured
+        dataset = None
+        if cfg.push_dataset_to_hub:
+            dataset = try_load_from_hub(cfg, dataset_hash, split)
+
+        # If not found on hub, try loading from disk
+        if dataset is None:
+            dataset = load_preprocessed_dataset(cfg, dataset_hash)
+
+        # If not found on disk or skipping prepared dataset, load and process raw
+        # datasets
+        if dataset is None:
+            dataset, prompters = _load_raw_datasets(
+                cfg,
+                datasets_configs,
+                tokenizer,
+                split,
+                processor,
+            )
 
     return dataset, prompters
 
@@ -306,9 +361,8 @@ def _load_raw_datasets(
     cfg: DictDefault,
     datasets_configs: list,
     tokenizer: PreTrainedTokenizer,
-    split: str,
+    split: Literal["train", "test"],
     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)
@@ -329,7 +383,6 @@ def _load_raw_datasets(
             split=split,
             seed=cfg.seed,
             processor=processor,
-            preprocess_iterable=preprocess_iterable,
         )
         datasets.append(dataset_wrapper)
         prompters.append(dataset_prompter)
@@ -339,17 +392,18 @@ def _load_raw_datasets(
 
     if not cfg.skip_prepare_dataset:
         if split == "test" and cfg.eval_sequence_len:
-            dataset = drop_long_seq_in_dataset(dataset, cfg.eval_sequence_len, cfg)
+            dataset = handle_long_seq_in_dataset(dataset, cfg.eval_sequence_len, cfg)
         else:
-            dataset = drop_long_seq_in_dataset(dataset, cfg.sequence_len, cfg)
+            dataset = handle_long_seq_in_dataset(dataset, cfg.sequence_len, cfg)
         if cfg.sample_packing:
             dataset, _ = process_datasets_for_packing(cfg, dataset, None)
 
-        # Save the prepared dataset
+        # Only save regular datasets to disk, not streaming datasets
-        dataset_hash = generate_dataset_hash_from_config(
+        if not isinstance(dataset, IterableDataset):
-            cfg, datasets_configs, tokenizer.name_or_path
+            dataset_hash = generate_dataset_hash_from_config(
-        )
+                cfg, datasets_configs, tokenizer.name_or_path
-        save_preprocessed_dataset(cfg, dataset, dataset_hash, split)
+            )
+            save_preprocessed_dataset(cfg, dataset, dataset_hash, split)
 
     return dataset, prompters
 
@@ -358,22 +412,19 @@ def _load_and_process_single_dataset(
     dataset_config: DictDefault,
     cfg: DictDefault,
     tokenizer: PreTrainedTokenizer,
-    split: str,
+    split: Literal["train", "test"],
     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
+    use_streaming_for_split = _is_streaming_enabled_for_split(cfg, split)
     dataset = load_dataset_with_config(
-        dataset_config, cfg.hf_use_auth_token, streaming=preprocess_iterable
+        dataset_config, cfg.hf_use_auth_token, use_streaming_for_split
     )
-
-    # Parse dataset type
     d_base_type, d_prompt_style = _parse_dataset_type(dataset_config.type)
 
     # Select the appropriate split
-    if isinstance(dataset, DatasetDict):
+    if isinstance(dataset, (DatasetDict, IterableDatasetDict)):
         if dataset_config.split and dataset_config.split in dataset:
             dataset = dataset[dataset_config.split]
         elif split in dataset:
@@ -418,11 +469,13 @@ def _parse_dataset_type(d_type: str) -> tuple[str | None, str | None]:
 
 
 def _handle_train_dataset_split(
-    dataset: Dataset, cfg: DictDefault
+    dataset: Dataset | IterableDataset, cfg: DictDefault
-) -> tuple[Dataset, Dataset | None]:
+) -> tuple[Dataset | IterableDataset, Dataset | IterableDataset | None]:
     """Handle processing for train split, including validation set creation."""
     val_set_size = (
-        int(cfg.val_set_size) if cfg.val_set_size > 1 else float(cfg.val_set_size)
+        int(cfg.val_set_size)
+        if cfg.val_set_size and cfg.val_set_size > 1
+        else float(cfg.val_set_size or 0.0)
     )
 
     if val_set_size:
@@ -433,27 +486,33 @@ def _handle_train_dataset_split(
         return train_dataset, eval_dataset
 
     # No validation split - apply deduplication if needed and return as train dataset
-    if cfg.dataset_exact_deduplication:
+    if cfg.dataset_exact_deduplication and not isinstance(dataset, IterableDataset):
         train_dataset, _ = deduplicate_and_log_datasets(dataset=dataset)
     else:
+        if cfg.dataset_exact_deduplication and isinstance(dataset, IterableDataset):
+            LOG.info("Deduplication skipped for streaming datasets (not compatible)")
         train_dataset = dataset
 
     return train_dataset, None
 
 
 def _handle_test_dataset_split(
-    dataset: Dataset, cfg: DictDefault
+    dataset: Dataset | IterableDataset, cfg: DictDefault
-) -> tuple[None, Dataset | None]:
+) -> tuple[None, Dataset | IterableDataset | None]:
     """Handle processing for test split."""
-    if cfg.dataset_exact_deduplication:
+    if cfg.dataset_exact_deduplication and not isinstance(dataset, IterableDataset):
         eval_dataset, _ = deduplicate_and_log_datasets(dataset=dataset)
     else:
+        if cfg.dataset_exact_deduplication and isinstance(dataset, IterableDataset):
+            LOG.info("Deduplication skipped for streaming datasets (not compatible)")
         eval_dataset = dataset
 
     return None, eval_dataset
 
 
-def _apply_dataset_sharding(dataset: Dataset, cfg: DictDefault) -> Dataset:
+def _apply_dataset_sharding(
+    dataset: Dataset | IterableDataset, cfg: DictDefault
+) -> Dataset | IterableDataset:
     """Apply dataset sharding if configured.
 
     Args:
@@ -479,7 +538,6 @@ 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.
 
@@ -488,7 +546,6 @@ 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).
@@ -499,7 +556,6 @@ 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/data/shared.py

@@ -13,6 +13,7 @@ from datasets import (
     IterableDataset,
     IterableDatasetDict,
     concatenate_datasets,
+    interleave_datasets,
     load_dataset,
     load_from_disk,
 )
@@ -524,7 +525,9 @@ def generate_dataset_hash_from_config(
     return str(md5(config_str))
 
 
-def merge_datasets(datasets: list[Dataset], cfg: DictDefault) -> Dataset:
+def merge_datasets(
+    datasets: list[Dataset | IterableDataset], cfg: DictDefault
+) -> Dataset | IterableDataset:
     """Merge multiple datasets into one with optional shuffling.
 
     Args:
@@ -537,23 +540,23 @@ def merge_datasets(datasets: list[Dataset], cfg: DictDefault) -> Dataset:
     if len(datasets) == 1:
         ds = datasets[0]
 
-        # Do not shuffle if curriculum sampling is enabled or
+        if (
-        # shuffle_merged_datasets is disabled
+            cfg.curriculum_sampling
-        if cfg.curriculum_sampling or not cfg.shuffle_merged_datasets:
+            or not cfg.shuffle_merged_datasets
+            or isinstance(ds, IterableDataset)
+        ):
             return ds
-
         return ds.shuffle(seed=cfg.seed)
 
-    # If enabled, shuffle each dataset independently before merging.
+    if cfg.shuffle_before_merging_datasets and all(
-    # This allows curriculum learning strategies to be applied at the dataset level.
+        isinstance(ds, Dataset) for ds in datasets
-    if cfg.shuffle_before_merging_datasets:
+    ):
         LOG.info("Shuffling each dataset individually before merging...")
         datasets = [ds.shuffle(seed=cfg.seed) for ds in datasets]
 
-    LOG.info("Merging datasets...")
+    merged_dataset = _merge_datasets_with_strategy(datasets, cfg)
-    merged_dataset = concatenate_datasets(datasets)
 
-    if cfg.shuffle_merged_datasets:
+    if cfg.shuffle_merged_datasets and not isinstance(merged_dataset, IterableDataset):
         LOG.debug("Shuffling merged datasets...")
         if cfg.curriculum_sampling:
             LOG.warning(
@@ -562,6 +565,47 @@ def merge_datasets(datasets: list[Dataset], cfg: DictDefault) -> Dataset:
             )
         merged_dataset = merged_dataset.shuffle(seed=cfg.seed)
     else:
-        LOG.debug("Not shuffling merged datasets.")
+        if isinstance(merged_dataset, IterableDataset):
+            LOG.debug("Skipping shuffle for streaming datasets.")
+        else:
+            LOG.debug("Not shuffling merged datasets.")
 
     return merged_dataset
+
+
+def _merge_datasets_with_strategy(
+    datasets: list[Dataset | IterableDataset], cfg: DictDefault
+) -> Dataset | IterableDataset:
+    """
+    Merge datasets using the configured mixing strategy. Works with streaming and non-
+    streaming datasets.
+
+    Args:
+        datasets: List of datasets to merge.
+        cfg: Configuration object containing mixing settings.
+
+    Returns:
+        Merged dataset (Dataset or IterableDataset depending on inputs).
+    """
+    strategy = cfg.get("dataset_mixing_strategy", "concatenate")
+    weights = cfg.get("mixing_weights", None)
+
+    LOG.info(f"Merging datasets with mixing strategy: {strategy}...")
+
+    if strategy == "concatenate":
+        # Concatenate only works with non-iterable datasets
+        if not all(isinstance(ds, Dataset) for ds in datasets):
+            raise ValueError(
+                "Cannot concatenate streaming datasets. Use 'round_robin', 'weighted', "
+                "or 'random' instead."
+            )
+        return concatenate_datasets(datasets)
+    if strategy == "round_robin":
+        return interleave_datasets(datasets, seed=cfg.seed)
+    if strategy == "weighted":
+        return interleave_datasets(datasets, probabilities=weights, seed=cfg.seed)
+    if strategy == "random":
+        # Random sampling with equal probability
+        equal_weights = [1.0 / len(datasets)] * len(datasets)
+        return interleave_datasets(datasets, probabilities=equal_weights, seed=cfg.seed)
+    raise ValueError(f"Unknown dataset mixing strategy: {strategy}")

src/axolotl/utils/data/utils.py

@@ -148,7 +148,36 @@ def deduplicate_and_log_datasets(
     return dataset, other_dataset
 
 
-def drop_long_seq_in_dataset(
+def truncate_long_seq(sample, sequence_len=2048, min_sequence_len=2):
+    """
+    Truncate samples whose sequence length is too long (> sequence_len)
+    or drop those too short (< min_sequence_len).
+    """
+    min_sequence_len = min_sequence_len or 2
+
+    input_ids = sample["input_ids"]
+    results = []
+
+    # Batched (input_ids is a list of lists)
+    for i, seq in enumerate(input_ids):
+        length = len(seq)
+        if length < min_sequence_len:
+            results.append(False)
+        elif length > sequence_len:
+            sample["input_ids"][i] = seq[:sequence_len]
+            if "attention_mask" in sample:
+                sample["attention_mask"][i] = sample["attention_mask"][i][:sequence_len]
+            if "labels" in sample:
+                sample["labels"][i] = sample["labels"][i][:sequence_len]
+            if "position_ids" in sample:
+                sample["position_ids"][i] = sample["position_ids"][i][:sequence_len]
+            results.append(True)
+        else:
+            results.append(True)
+    return results
+
+
+def handle_long_seq_in_dataset(
     dataset: Dataset, sequence_len: int, cfg: DictDefault
 ) -> Dataset:
     """Remove sequences longer than configured maximum from dataset.
@@ -161,11 +190,15 @@ def drop_long_seq_in_dataset(
     Returns:
         Filtered dataset with long sequences removed.
     """
-    if "input_ids" not in dataset.column_names:
+    if hasattr(dataset, "column_names") and dataset.column_names:
-        LOG.warning(
+        if "input_ids" not in dataset.column_names:
-            "Dataset does not contain 'input_ids' column. Skip drop long seq. This is "
+            LOG.warning(
-            "expected for reward modeling."
+                "Dataset does not contain 'input_ids' column. Skip drop long seq. This "
-        )
+                "is expected for reward modeling."
+            )
+            return dataset
+    elif isinstance(dataset, IterableDataset):
+        LOG.info("Skipping drop_long_seq for streaming datasets (not compatible)")
         return dataset
 
     drop_long = functools.partial(
@@ -192,8 +225,21 @@ def drop_long_seq_in_dataset(
     if filter_map_kwargs:
         drop_long_kwargs["desc"] = f"Dropping Long Sequences (>{sequence_len})"
 
+    excess_length_strategy = (cfg.excess_length_strategy or "drop").lower()
+    if excess_length_strategy == "truncate":
+        process_fn = functools.partial(
+            truncate_long_seq,
+            sequence_len=sequence_len,
+            min_sequence_len=cfg.min_sample_len,
+        )
+        drop_long_kwargs["desc"] = (
+            f"Truncating/Filtering Sequences (target_len={sequence_len})"
+        )
+    else:
+        process_fn = drop_long
+
     dataset = dataset.filter(
-        drop_long,
+        process_fn,
         batched=True,
         **filter_map_kwargs,
         **drop_long_kwargs,
@@ -201,6 +247,11 @@ def drop_long_seq_in_dataset(
     if prior_len:
         dropped = prior_len - len(dataset)
         if dropped:
-            LOG.warning(f"Dropped {dropped} long samples from dataset")
+            action = (
+                "truncated/filtered"
+                if excess_length_strategy == "truncate"
+                else "dropped"
+            )
+            LOG.warning(f"{action.title()} {dropped} samples from dataset")
 
     return dataset

src/axolotl/utils/data/wrappers.py

@@ -100,10 +100,6 @@ def get_dataset_wrapper(
             dataset_config, tokenizer, cfg, dataset, dataset_kwargs
         )
 
-    # Skip preparation if configured
-    if cfg.skip_prepare_dataset:
-        return dataset, None
-
     # Bradley-Terry dataset
     if dataset_config.type.startswith("bradley_terry"):
         return _handle_bradley_terry_dataset(

src/axolotl/utils/schemas/config.py

@@ -414,6 +414,12 @@ class AxolotlInputConfig(
             "description": "The maximum length of an input to train with, this should typically be less than 2048 as most models have a token/context limit of 2048"
         },
     )
+    excess_length_strategy: Literal["drop", "truncate"] | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "What to do when a tokenized row exceeds sequence_len. 'drop' removes the row; 'truncate' slices tensors to sequence_len. Defaults to 'drop' for backward compatibility."
+        },
+    )
     eval_sequence_len: int | None = Field(
         default=None,
         json_schema_extra={
@@ -926,9 +932,45 @@ class AxolotlInputConfig(
 
     fix_untrained_tokens: int | list[int] | None = None
 
+    streaming: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Whether to use streaming datasets (IterableDataset) for training datasets. When True, data is loaded on-demand during training without upfront preprocessing. Requires max_steps to be set. Pre-training datasets default to streaming unless explicitly set to False."
+        },
+    )
+    eval_streaming: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Whether to use streaming datasets for evaluation datasets. If not set, falls back to the 'streaming' setting. Useful for streaming large training data while keeping smaller eval datasets in memory."
+        },
+    )
+    dataset_mixing_strategy: str | None = Field(
+        default="round_robin",
+        json_schema_extra={
+            "description": "Strategy for mixing multiple datasets: 'concatenate', 'round_robin' (equal sampling), 'weighted' (use mixing_weights), or 'random' (random sampling with equal probability). Works for both streaming and non-streaming datasets."
+        },
+    )
+    mixing_weights: list[float] | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Weights for weighted mixing strategy when using multiple datasets. Must sum to 1.0 and have same length as datasets list. Only used when dataset_mixing_strategy='weighted'."
+        },
+    )
+    eval_dataset_mixing_strategy: str | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Strategy for mixing multiple evaluation datasets. If not set, falls back to dataset_mixing_strategy. Options: 'concatenate', 'round_robin', 'weighted', 'random'."
+        },
+    )
+    eval_mixing_weights: list[float] | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Weights for weighted mixing strategy for evaluation datasets. Must sum to 1.0 and have same length as evaluation datasets list."
+        },
+    )
+
     # 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,

src/axolotl/utils/schemas/training.py

@@ -161,7 +161,12 @@ class HyperparametersConfig(BaseModel):
     max_grad_norm: float | None = Field(
         default=None, json_schema_extra={"description": "Gradient clipping max norm"}
     )
-    num_epochs: float = Field(default=1.0)
+    num_epochs: float = Field(
+        default=1.0,
+        json_schema_extra={
+            "description": "Number of iterations over dataset for training"
+        },
+    )
 
     @field_validator("batch_size")
     @classmethod

src/axolotl/utils/schemas/validation.py

@@ -3,6 +3,7 @@
 # pylint: disable=too-many-boolean-expressions
 
 import json
+import os
 import sys
 import tempfile
 from pathlib import Path
@@ -192,6 +193,7 @@ class AttentionValidationMixin:
         return data
 
 
+# pylint: disable=too-many-public-methods
 class TrainingValidationMixin:
     """Validation methods related to training configuration."""
 
@@ -508,11 +510,58 @@ class TrainingValidationMixin:
             # combining these would raise `TypeError: cannot pickle 'dict_keys' object`
             # due to trying to count the number of tokens total in the dataset
             raise ValueError(
-                "pretraining_dataset and include_tokens_per_second cannot be used together."
+                "pretraining_dataset and include_tokens_per_second cannot be used "
+                "together."
             )
 
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_max_steps_num_epochs_conflict(cls, data):
+        """Handle max_steps and num_epochs configuration and auto-set defaults."""
+        max_steps = data.get("max_steps")
+        num_epochs = data.get("num_epochs")
+
+        # Auto-set num_epochs to 1 if neither max_steps nor num_epochs are set
+        if max_steps is None and num_epochs is None:
+            data["num_epochs"] = 1.0
+
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_saves_per_epoch_conflicts(cls, data):
+        """Ensure saves_per_epoch is compatible with training configuration."""
+        saves_per_epoch = data.get("saves_per_epoch")
+        num_epochs = data.get("num_epochs")
+
+        if saves_per_epoch is not None:
+            # Check if saves_per_epoch is set but num_epochs is unset
+            if num_epochs is None:
+                raise ValueError(
+                    "saves_per_epoch requires num_epochs to be set to calculate save "
+                    "intervals."
+                )
+
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_evals_per_epoch_conflicts(cls, data):
+        """Ensure evals_per_epoch is compatible with training configuration."""
+        evals_per_epoch = data.get("evals_per_epoch")
+        num_epochs = data.get("num_epochs")
+
+        if evals_per_epoch is not None:
+            if num_epochs is None:
+                raise ValueError(
+                    "evals_per_epoch requires num_epochs to be set to calculate "
+                    "evaluation intervals."
+                )
+
+        return data
+
 
 class LoRAValidationMixin:
     """Validation methods related to LoRA/QLoRA configuration."""
@@ -1078,6 +1127,30 @@ class PretrainingValidationMixin:
                     data["accelerator_config"]["dispatch_batches"] = False
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_streaming_split_batches_accelerate(cls, data):
+        # Check if either training or eval uses streaming
+        streaming = data.get("streaming", False)
+        eval_streaming = data.get("eval_streaming")
+        if eval_streaming is None:
+            eval_streaming = streaming
+
+        # If either training or eval uses streaming, configure accelerator
+        if streaming or eval_streaming:
+            accelerator_config = data.get("accelerator_config", {})
+            if not accelerator_config:
+                data["accelerator_config"] = {
+                    "split_batches": False,
+                    "dispatch_batches": False,
+                }
+            else:
+                if accelerator_config.get("split_batches") is None:
+                    data["accelerator_config"]["split_batches"] = False
+                if accelerator_config.get("dispatch_batches") is None:
+                    data["accelerator_config"]["dispatch_batches"] = False
+        return data
+
 
 class ModelCompatibilityValidationMixin:
     """Validation methods for specific model compatibility."""
@@ -1336,6 +1409,168 @@ class GRPOVllmValidationMixin:
         return self
 
 
+class StreamingValidationMixin:
+    """Validation methods related to streaming datasets."""
+
+    def _is_streaming_enabled(self, context: str = "train") -> bool:
+        """Check if streaming is enabled for a given context (train or eval)."""
+        if context == "eval":
+            eval_streaming = getattr(self, "eval_streaming", None)
+            if eval_streaming is not None:
+                return eval_streaming
+
+        # Fall back to main streaming setting
+        streaming = getattr(self, "streaming", None)
+        if streaming is True:
+            return True
+
+        # Check if pretraining dataset exists (defaults to streaming)
+        has_pretraining = getattr(self, "pretraining_dataset", None) is not None
+        streaming_default_for_pretraining = has_pretraining and streaming is None
+
+        return streaming_default_for_pretraining
+
+    @model_validator(mode="after")
+    def check_streaming_requires_max_steps(self):
+        """Ensure max_steps is set when using streaming datasets."""
+        # Check if streaming is enabled for training datasets
+        if self._is_streaming_enabled("train"):
+            max_steps = getattr(self, "max_steps", None)
+            if not max_steps:
+                raise ValueError("max_steps must be set when using streaming datasets")
+
+        return self
+
+    @model_validator(mode="after")
+    def check_streaming_validation_splits_conflict(self):
+        """Ensure validation splits are not used with streaming datasets."""
+        # Check if streaming is enabled for training datasets
+        if self._is_streaming_enabled("train"):
+            val_set_size = getattr(self, "val_set_size", 0.0)
+            if val_set_size and val_set_size > 0:
+                raise ValueError(
+                    "Validation splits not supported for streaming datasets, skipping"
+                )
+
+        return self
+
+    @model_validator(mode="after")
+    def check_streaming_preprocessing_conflict(self):
+        """Ensure preprocessing is not enabled with streaming datasets."""
+        # Check if streaming is enabled for training or eval datasets
+        if self._is_streaming_enabled("train") or self._is_streaming_enabled("eval"):
+            if os.environ.get("AXOLOTL_IS_PREPROCESS") == "1":
+                raise ValueError("preprocess is not supported for streaming datasets")
+
+        return self
+
+    @model_validator(mode="after")
+    def check_streaming_skip_prepare_dataset(self):
+        """Ensure skip_prepare_dataset is set for streaming datasets."""
+        # Check if streaming is enabled for training or eval datasets
+        if self._is_streaming_enabled("train") or self._is_streaming_enabled("eval"):
+            skip_prepare = getattr(self, "skip_prepare_dataset", None)
+            if skip_prepare is False:
+                LOG.warning(
+                    "skip_prepare_dataset=False is not compatible with streaming "
+                    "datasets. Setting skip_prepare_dataset=True."
+                )
+            self.skip_prepare_dataset = True
+
+        return self
+
+    @model_validator(mode="after")
+    def check_dataset_mixing_weights(self):
+        """Validate dataset mixing weights configuration."""
+        valid_strategies = ["concatenate", "round_robin", "weighted", "random"]
+
+        # Get datasets to validate length against
+        datasets = getattr(self, "datasets", None)
+        test_datasets = getattr(self, "test_datasets", None)
+
+        # Check main strategy and weights
+        strategy = getattr(self, "dataset_mixing_strategy", "concatenate")
+        weights = getattr(self, "mixing_weights", None)
+
+        dataset_count = len(datasets) if datasets else 0
+        self._validate_dataset_strategy_and_weights(
+            strategy,
+            weights,
+            "dataset_mixing_strategy",
+            "mixing_weights",
+            valid_strategies,
+            dataset_count,
+        )
+
+        # Check eval-specific strategy and weights
+        eval_strategy = getattr(self, "eval_dataset_mixing_strategy", None)
+        eval_weights = getattr(self, "eval_mixing_weights", None)
+
+        if eval_strategy is not None:
+            eval_dataset_count = len(test_datasets) if test_datasets else dataset_count
+            self._validate_dataset_strategy_and_weights(
+                eval_strategy,
+                eval_weights,
+                "eval_dataset_mixing_strategy",
+                "eval_mixing_weights",
+                valid_strategies,
+                eval_dataset_count,
+            )
+        elif eval_weights is not None:
+            LOG.warning(
+                "eval_mixing_weights provided but eval_dataset_mixing_strategy is not set. "
+                "Weights will be ignored unless eval_dataset_mixing_strategy='weighted'."
+            )
+
+        return self
+
+    def _validate_dataset_strategy_and_weights(
+        self,
+        strategy,
+        weights,
+        strategy_field,
+        weights_field,
+        valid_strategies,
+        dataset_count,
+    ):
+        """Helper method to validate dataset mixing strategy and weights pair."""
+        if strategy not in valid_strategies:
+            raise ValueError(
+                f"{strategy_field} must be one of {valid_strategies}, "
+                f"got '{strategy}'"
+            )
+
+        if strategy == "weighted":
+            if weights is None:
+                raise ValueError(
+                    f"{weights_field} must be provided when "
+                    f"{strategy_field}='weighted'"
+                )
+
+            if not isinstance(weights, list) or not all(
+                isinstance(w, (int, float)) for w in weights
+            ):
+                raise ValueError(f"{weights_field} must be a list of numbers")
+
+            if any(w < 0 for w in weights):
+                raise ValueError(f"{weights_field} must be non-negative")
+
+            if abs(sum(weights) - 1.0) > 1e-6:
+                raise ValueError(f"{weights_field} must sum to 1.0, got {sum(weights)}")
+
+            # Validate weights length against dataset count
+            if dataset_count > 0 and len(weights) != dataset_count:
+                raise ValueError(
+                    f"{weights_field} length ({len(weights)}) must match number of datasets ({dataset_count})"
+                )
+
+        elif weights is not None and strategy != "weighted":
+            LOG.warning(
+                f"{weights_field} provided but {strategy_field} is '{strategy}'. "
+                "Weights will be ignored."
+            )
+
+
 # pylint: disable=too-many-ancestors
 class ValidationMixin(
     DatasetValidationMixin,
@@ -1347,6 +1582,7 @@ class ValidationMixin(
     SystemValidationMixin,
     ChatTemplateValidationMixin,
     PretrainingValidationMixin,
+    StreamingValidationMixin,
     ModelCompatibilityValidationMixin,
     ComplexValidationMixin,
     GRPOVllmValidationMixin,

src/axolotl/utils/trainer.py

@@ -547,7 +547,7 @@ def setup_deepspeed_env(cfg, stage=None):
         if stage == 3:
             os.environ["ACCELERATE_DEEPSPEED_ZERO3_INIT"] = "true"
 
-    # NOTE(djsaunde): The distribued state cannot be initialized prior to the
+    # NOTE(djsaunde): The distributed state cannot be initialized prior to the
     # ACCELERATE_USE_DEEPSPEED assignment, but it must be initialized some time prior
     # to model load.
     if (

tests/e2e/test_streaming.py

@@ -0,0 +1,261 @@
+"""E2E tests for streaming dataset functionality"""
+
+import pytest
+
+from axolotl.common.datasets import load_datasets
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, validate_config
+from axolotl.utils.dict import DictDefault
+
+from .utils import check_model_output_exists, check_tensorboard
+
+
+class TestStreamingDatasets:
+    """Test case for streaming datasets with different mixing strategies"""
+
+    @pytest.mark.parametrize(
+        ("dataset_mixing_strategy", "mixing_weights"),
+        [
+            ("round_robin", None),
+            ("weighted", [0.7, 0.3]),
+            ("random", None),
+        ],
+    )
+    def test_streaming_dataset_mixing_strategies(
+        self, temp_dir, dataset_mixing_strategy, mixing_weights
+    ):
+        """Test different mixing strategies with streaming datasets"""
+
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "flash_attention": True,
+                "sequence_len": 1024,
+                "sample_packing": False,
+                "dataset_processes": 1,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                    {
+                        "path": "tatsu-lab/alpaca",
+                        "type": "alpaca",
+                    },
+                ],
+                # Streaming config
+                "streaming": True,
+                "max_steps": 3,  # Very small for smoke test
+                "dataset_mixing_strategy": dataset_mixing_strategy,
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "val_set_size": 0.0,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "bf16": "auto",
+                "use_tensorboard": True,
+                "save_first_step": False,
+            }
+        )
+
+        # Add mixing weights if specified
+        if mixing_weights:
+            cfg["mixing_weights"] = mixing_weights
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        dataset_meta = load_datasets(cfg=cfg)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)
+
+        # Verify training actually happened by checking loss decrease
+        check_tensorboard(
+            temp_dir + "/runs",
+            "train/train_loss",
+            2.5,  # Loss should be reasonable for a smoke test (higher threshold for streaming)
+            "Train Loss (%s) is too high",
+        )
+
+    def test_streaming_eval_specific_mixing(self, temp_dir):
+        """Test eval-specific mixing strategy override"""
+
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "flash_attention": True,
+                "sequence_len": 512,
+                "sample_packing": False,
+                "dataset_processes": 1,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                    {
+                        "path": "tatsu-lab/alpaca",
+                        "type": "alpaca",
+                    },
+                ],
+                "test_datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                        "split": "train",  # Specify train split for eval dataset
+                    },
+                    {
+                        "path": "tatsu-lab/alpaca",
+                        "type": "alpaca",
+                        "split": "train",  # Specify train split for eval dataset
+                    },
+                ],
+                # Streaming config
+                "streaming": True,
+                "eval_streaming": True,
+                "max_steps": 3,
+                # Different mixing for train vs eval
+                "dataset_mixing_strategy": "round_robin",
+                "eval_dataset_mixing_strategy": "weighted",
+                "eval_mixing_weights": [0.6, 0.4],
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "bf16": "auto",
+                "use_tensorboard": True,
+                "save_first_step": False,
+                "eval_steps": 3,  # Eval at the end
+            }
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        dataset_meta = load_datasets(cfg=cfg)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)
+
+        # Check both train and eval losses
+        check_tensorboard(
+            temp_dir + "/runs",
+            "train/train_loss",
+            2.5,
+            "Train Loss (%s) is too high",
+        )
+        check_tensorboard(
+            temp_dir + "/runs",
+            "eval/eval_loss",
+            2.5,
+            "Eval Loss (%s) is too high",
+        )
+
+    def test_streaming_validation_error(self, temp_dir):
+        """Test that pydantic validation catches invalid streaming configs"""
+
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                    {
+                        "path": "tatsu-lab/alpaca",
+                        "type": "alpaca",
+                    },
+                ],
+                "streaming": True,
+                "max_steps": 3,
+                # Invalid: wrong number of weights for datasets
+                "dataset_mixing_strategy": "weighted",
+                "mixing_weights": [1.0],  # Should be [0.x, 0.y] for 2 datasets
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+            }
+        )
+
+        # This should raise a validation error
+        with pytest.raises(Exception) as exc_info:
+            validate_config(cfg)
+
+        # Verify it's the right validation error
+        assert "mixing_weights length" in str(exc_info.value)
+        assert "must match number of datasets" in str(exc_info.value)
+
+    def test_streaming_three_datasets_weighted(self, temp_dir):
+        """Test weighted mixing with three datasets"""
+
+        cfg = DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "flash_attention": True,
+                "sequence_len": 512,
+                "sample_packing": False,
+                "dataset_processes": 1,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                    {
+                        "path": "tatsu-lab/alpaca",
+                        "type": "alpaca",
+                    },
+                    {
+                        "path": "yahma/alpaca-cleaned",
+                        "type": "alpaca",
+                    },
+                ],
+                # Streaming config
+                "streaming": True,
+                "max_steps": 3,
+                "dataset_mixing_strategy": "weighted",
+                "mixing_weights": [0.5, 0.3, 0.2],
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "val_set_size": 0.0,
+                "output_dir": temp_dir,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "bf16": "auto",
+                "use_tensorboard": True,
+                "save_first_step": False,
+            }
+        )
+
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        dataset_meta = load_datasets(cfg=cfg)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)
+
+        check_tensorboard(
+            temp_dir + "/runs",
+            "train/train_loss",
+            2.5,
+            "Train Loss (%s) is too high",
+        )

tests/test_datasets.py

@@ -7,13 +7,13 @@ from typing import Any, Generator
 from unittest.mock import patch
 
 import pytest
-from datasets import Dataset
+from datasets import Dataset, IterableDataset
 from huggingface_hub import snapshot_download
 from transformers import PreTrainedTokenizer
 
 from axolotl.loaders.tokenizer import load_tokenizer
 from axolotl.utils.data.rl import prepare_preference_datasets
-from axolotl.utils.data.sft import _load_tokenized_prepared_datasets
+from axolotl.utils.data.sft import _load_tokenized_prepared_datasets, prepare_datasets
 from axolotl.utils.dict import DictDefault
 
 from tests.constants import (
@@ -24,6 +24,7 @@ from tests.constants import (
 from tests.hf_offline_utils import enable_hf_offline
 
 
+# pylint: disable=too-many-public-methods
 class TestDatasetPreparation:
     """Test a configured dataloader."""
 
@@ -46,6 +47,24 @@ class TestDatasetPreparation:
             ]
         )
 
+    @pytest.fixture
+    def streaming_dataset_fixture(self):
+        """Create a streaming dataset fixture for testing."""
+
+        def generator():
+            yield {
+                "instruction": "Evaluate this sentence for spelling and grammar mistakes",
+                "input": "He finnished his meal and left the resturant",
+                "output": "He finished his meal and left the restaurant.",
+            }
+            yield {
+                "instruction": "What is the capital of France?",
+                "input": "",
+                "output": "The capital of France is Paris.",
+            }
+
+        return IterableDataset.from_generator(generator)
+
     @pytest.mark.skip(reason="TODO: fix hf hub offline to work with HF rate limits")
     @enable_hf_offline
     def test_load_hub(self, tokenizer):
@@ -486,3 +505,201 @@ class TestDatasetPreparation:
             assert "attention_mask" in dataset.features
             assert "labels" in dataset.features
             shutil.rmtree(tmp_ds_path)
+
+    def test_streaming_sft_dataset(self, tokenizer, streaming_dataset_fixture):
+        """Test streaming SFT dataset preparation with IterableDataset."""
+        with patch("axolotl.utils.data.sft.load_dataset_with_config") as mock_load:
+            mock_load.return_value = streaming_dataset_fixture
+
+            cfg = DictDefault(
+                {
+                    "tokenizer_config": "huggyllama/llama-7b",
+                    "sequence_len": 256,
+                    "streaming": True,
+                    "max_steps": 100,  # Required for streaming datasets
+                    "datasets": [
+                        {
+                            "path": "dummy/path",
+                            "type": "alpaca",
+                        },
+                    ],
+                }
+            )
+
+            train_dataset, eval_dataset, total_num_steps, prompters = prepare_datasets(
+                cfg, tokenizer
+            )
+
+            # Verify it returns an IterableDataset
+            assert isinstance(train_dataset, IterableDataset)
+            assert eval_dataset is None  # No eval split for streaming
+            assert total_num_steps == 100  # Should use max_steps
+            assert len(prompters) == 1
+
+            # Test that we can iterate through the dataset
+            sample_count = 0
+            for sample in train_dataset:
+                assert "input_ids" in sample
+                assert "attention_mask" in sample
+                assert "labels" in sample
+                sample_count += 1
+                if sample_count >= 2:  # Just test first few samples
+                    break
+
+            assert sample_count == 2
+
+    def test_dataset_mixing_strategy_validation(self):
+        """Test validation of dataset mixing strategy configuration."""
+        from axolotl.utils.data.shared import _merge_datasets_with_strategy
+
+        # Test valid strategies work
+        valid_strategies = ["round_robin", "weighted", "random"]
+        dataset1 = Dataset.from_dict({"text": ["a"], "source": ["ds1"]})
+        dataset2 = Dataset.from_dict({"text": ["b"], "source": ["ds2"]})
+
+        for strategy in valid_strategies:
+            cfg = DictDefault(
+                {
+                    "dataset_mixing_strategy": strategy,
+                    "mixing_weights": [0.5, 0.5] if strategy == "weighted" else None,
+                    "seed": 42,
+                }
+            )
+            # Should not raise an error
+            merged = _merge_datasets_with_strategy([dataset1, dataset2], cfg)
+            assert len(merged) >= 1
+
+    def test_regular_dataset_round_robin_mixing(self):
+        """Test round-robin mixing for regular datasets."""
+        from axolotl.utils.data.shared import _merge_datasets_with_strategy
+
+        # Create test datasets
+        dataset1 = Dataset.from_dict(
+            {"text": ["ds1_item1", "ds1_item2"], "source": ["ds1", "ds1"]}
+        )
+        dataset2 = Dataset.from_dict(
+            {"text": ["ds2_item1", "ds2_item2"], "source": ["ds2", "ds2"]}
+        )
+
+        cfg = DictDefault({"dataset_mixing_strategy": "round_robin", "seed": 42})
+
+        merged = _merge_datasets_with_strategy([dataset1, dataset2], cfg)
+
+        # Should have all samples from both datasets
+        assert len(merged) == 4
+        assert isinstance(merged, Dataset)
+
+        # Check that samples are interleaved (not just concatenated)
+        sources = [sample["source"] for sample in merged]
+        # Round-robin should alternate between datasets
+        assert sources != ["ds1", "ds1", "ds2", "ds2"]  # Not concatenated
+
+    def test_regular_dataset_weighted_mixing(self):
+        """Test weighted mixing for regular datasets."""
+        from axolotl.utils.data.shared import _merge_datasets_with_strategy
+
+        # Create test datasets
+        dataset1 = Dataset.from_dict(
+            {
+                "text": ["ds1_item1", "ds1_item2", "ds1_item3", "ds1_item4"],
+                "source": ["ds1"] * 4,
+            }
+        )
+        dataset2 = Dataset.from_dict(
+            {
+                "text": ["ds2_item1", "ds2_item2", "ds2_item3", "ds2_item4"],
+                "source": ["ds2"] * 4,
+            }
+        )
+
+        cfg = DictDefault(
+            {
+                "dataset_mixing_strategy": "weighted",
+                "mixing_weights": [0.75, 0.25],  # 3:1 ratio
+                "seed": 42,
+            }
+        )
+
+        merged = _merge_datasets_with_strategy([dataset1, dataset2], cfg)
+
+        # Should have samples proportional to weights
+        assert len(merged) > 0
+        assert isinstance(merged, Dataset)
+
+        # Count samples from each dataset
+        sources = [sample["source"] for sample in merged]
+        ds1_count = sources.count("ds1")
+        ds2_count = sources.count("ds2")
+
+        # Should have samples from both datasets
+        assert ds1_count > 0 and ds2_count > 0  # Both datasets should be represented
+
+    def test_streaming_dataset_mixing(self):
+        """Test that streaming datasets use HuggingFace interleave_datasets."""
+        from axolotl.utils.data.shared import _merge_datasets_with_strategy
+
+        # Create test streaming datasets
+        def gen1():
+            yield {"text": "stream1_item1", "source": "stream1"}
+            yield {"text": "stream1_item2", "source": "stream1"}
+
+        def gen2():
+            yield {"text": "stream2_item1", "source": "stream2"}
+            yield {"text": "stream2_item2", "source": "stream2"}
+
+        stream1 = IterableDataset.from_generator(gen1)
+        stream2 = IterableDataset.from_generator(gen2)
+
+        cfg = DictDefault({"dataset_mixing_strategy": "round_robin", "seed": 42})
+
+        merged = _merge_datasets_with_strategy([stream1, stream2], cfg)
+
+        # Should return an IterableDataset
+        assert isinstance(merged, IterableDataset)
+
+        # Test that we can iterate and get samples
+        samples = list(merged.take(3))
+        assert len(samples) >= 2  # Should get at least 2 samples
+
+        # Should have samples from both datasets
+        sources = [sample["source"] for sample in samples]
+        assert len(set(sources)) >= 1  # At least one unique source
+
+    def test_eval_streaming_config(self):
+        """Test eval_streaming separate from streaming config."""
+        from axolotl.utils.data.sft import _is_streaming_enabled_for_split
+
+        # Test train streaming enabled, eval streaming disabled
+        cfg = DictDefault({"streaming": True, "eval_streaming": False})
+
+        assert _is_streaming_enabled_for_split(cfg, "train")
+        assert _is_streaming_enabled_for_split(cfg, "test")
+
+        # Test train streaming disabled, eval streaming enabled
+        cfg2 = DictDefault({"streaming": False, "eval_streaming": True})
+
+        assert _is_streaming_enabled_for_split(cfg2, "train")
+        assert _is_streaming_enabled_for_split(cfg2, "test")
+
+    def test_eval_specific_mixing_configs(self):
+        """Test eval-specific mixing configs override main configs."""
+        from axolotl.utils.data.sft import _get_streaming_config_for_split
+
+        cfg = DictDefault(
+            {
+                "dataset_mixing_strategy": "round_robin",
+                "mixing_weights": [0.5, 0.5],
+                "eval_dataset_mixing_strategy": "weighted",
+                "eval_mixing_weights": [0.8, 0.2],
+            }
+        )
+
+        # Train split should use main config
+        train_cfg = _get_streaming_config_for_split(cfg, "train")
+        assert train_cfg["dataset_mixing_strategy"] == "round_robin"
+        assert train_cfg["mixing_weights"] == [0.5, 0.5]
+
+        # Test split should use eval-specific config
+        test_cfg = _get_streaming_config_for_split(cfg, "test")
+        assert test_cfg["dataset_mixing_strategy"] == "weighted"
+        assert test_cfg["mixing_weights"] == [0.8, 0.2]

tests/test_packed_batch_sampler.py

@@ -8,7 +8,7 @@ from transformers import AutoTokenizer
 from axolotl.datasets import TokenizedPromptDataset
 from axolotl.prompt_strategies.completion import load
 from axolotl.utils.collators import V2BatchSamplerDataCollatorForSeq2Seq
-from axolotl.utils.data.utils import drop_long_seq_in_dataset
+from axolotl.utils.data.utils import handle_long_seq_in_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
@@ -70,7 +70,7 @@ class TestBatchedSamplerPacking:
         )
         train_dataset = concatenate_datasets([dataset_wrapper])
 
-        train_dataset = drop_long_seq_in_dataset(train_dataset, cfg.sequence_len, cfg)
+        train_dataset = handle_long_seq_in_dataset(train_dataset, cfg.sequence_len, cfg)
 
         lengths = get_dataset_lengths(train_dataset)
         batch_sampler = MultipackBatchSampler(