add depr warning for preprocess --iterable

* 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

examples/llama-3/diffusion-3.2-1b-pretrain.yaml

@@ -1,57 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-pretraining_dataset:
-  - path: wikitext
-    name: wikitext-103-raw-v1
-    type: completion
-    field: text
-
-plugins:
-  - diffusion.DiffusionPlugin
-noise_schedule: cosine
-min_mask_ratio: 0.15
-max_mask_ratio: 0.85
-eps: 5e-4
-importance_weighting: true
-mask_token_id: 128002
-generate_samples: true
-generation_interval: 10
-
-output_dir: ./outputs/model-out
-
-sequence_len: 512
-sample_packing: true
-
-gradient_accumulation_steps: 8
-micro_batch_size: 4
-max_steps: 10000
-
-optimizer: adamw_8bit
-lr_scheduler: cosine
-learning_rate: 3e-4
-
-bf16: auto
-tf32: true
-
-gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-sdp_attention: true
-
-warmup_steps: 1000
-
-save_strategy: steps
-save_steps: 1000
-
-special_tokens:
-  pad_token: "<|end_of_text|>"
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

examples/llama-3/diffusion-3.2-1b-sft.yaml

@@ -1,58 +0,0 @@
-base_model: meta-llama/Llama-3.2-1B
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-datasets:
-  - path: teknium/GPT4-LLM-Cleaned
-    type: alpaca
-val_set_size: 0.05
-
-plugins:
-  - diffusion.DiffusionPlugin
-noise_schedule: cosine
-min_mask_ratio: 0.1
-max_mask_ratio: 0.9
-num_diffusion_steps: 128
-eps: 1e-3
-importance_weighting: true
-mask_token_id: 128002
-
-output_dir: ./outputs/model-out
-
-sequence_len: 512
-sample_packing: true
-eval_sample_packing: true
-
-gradient_accumulation_steps: 4
-micro_batch_size: 4
-num_epochs: 1
-
-optimizer: adamw_8bit
-lr_scheduler: cosine
-learning_rate: 1e-5
-
-bf16: auto
-tf32: true
-
-gradient_checkpointing: true
-resume_from_checkpoint:
-logging_steps: 1
-sdp_attention: true
-
-warmup_steps: 1000
-
-save_strategy: steps
-eval_strategy: steps
-save_steps: 500
-eval_steps: 500
-
-special_tokens:
-  pad_token: "<|end_of_text|>"
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

requirements.txt

@@ -13,8 +13,8 @@ liger-kernel==0.6.1
 packaging==23.2
 
 huggingface_hub>=0.33.0
-peft==0.17.0
-transformers==4.55.2
+peft>=0.17.0
+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

@@ -14,9 +14,13 @@ class PreprocessCliArgs:
     prompter: Optional[str] = field(default=None)
     download: Optional[bool] = field(default=True)
     iterable: Optional[bool] = field(
-        default=None,
+        default=False,
         metadata={
-            "help": "Use IterableDataset for streaming processing of large datasets"
+            "help": (
+                "[DEPRECATED] No longer supported. For streaming datasets, use "
+                "'axolotl train' and set 'streaming: true' in your YAML config, or "
+                "pass --streaming instead in the CLI."
+            )
         },
     )
 

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

@@ -35,10 +35,20 @@ def do_preprocess(cfg: DictDefault, cli_args: PreprocessCliArgs) -> None:
     check_accelerate_default_config()
     check_user_token()
 
+    if cli_args.iterable:
+        LOG.error(
+            "The --iterable CLI argument for 'axolotl preprocess' is no longer "
+            "supported. For training, set 'streaming: true' in your YAML config or "
+            "pass '--streaming' in your 'axolotl train' command for on-the-fly "
+            "preprocessing."
+        )
+        return
+
     for key in ["skip_prepare_dataset", "pretraining_dataset"]:
         if cfg.get(key):
             LOG.error(
-                f"You have set `{key}:`. `preprocess` is not needed. Run the `axolotl train` CLI directly instead."
+                f"You have set `{key}:`. `preprocess` is not needed. Run the 'axolotl "
+                "train' CLI directly instead."
             )
             return
 
@@ -97,7 +107,8 @@ 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/core/builders/causal.py

@@ -10,7 +10,6 @@ import transformers
 from transformers import (
     DataCollatorWithFlattening,
     EarlyStoppingCallback,
-    Trainer,
 )
 from trl.trainer.utils import RewardDataCollatorWithPadding
 
@@ -386,11 +385,10 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 **data_collator_kwargs,
             )
         sig = inspect.signature(trainer_cls)
-        if "processing_class" in sig.parameters or issubclass(trainer_cls, Trainer):
+        if "processing_class" in sig.parameters:
             trainer_kwargs["processing_class"] = self.tokenizer
         elif "tokenizer" in sig.parameters:
             trainer_kwargs["tokenizer"] = self.tokenizer
-
         if (
             trainer_cls not in [AxolotlRewardTrainer, AxolotlPRMTrainer]
             and self.cfg.datasets is not None

src/axolotl/core/trainers/base.py

@@ -82,9 +82,7 @@ class AxolotlTrainer(
         super().__init__(*_args, **kwargs)
 
         self.train_data_collator = self.data_collator
-        self._stored_metrics = defaultdict(
-            lambda: defaultdict(lambda: {"values": [], "reduction": "mean"})
-        )
+        self._stored_metrics = defaultdict(lambda: defaultdict(list))
         if self.args.orpo_alpha:
             self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
 
@@ -274,18 +272,6 @@ class AxolotlTrainer(
                     num_workers=self.args.dataloader_num_workers,
                     rank=self.args.process_index,
                 )
-        if (self.args.accelerator_config is not None
-            and self.args.accelerator_config.split_batches
-            and self.args.accelerator_config.dispatch_batches
-        ):
-            if self.args.sample_packing and self.args.pretraining:
-                if not self.args.eval_sample_packing and not is_training:
-                    dataloader_params["batch_size"] *= self.accelerator.num_processes
-                else:
-                    dataloader_params["batch_size"] = self.accelerator.num_processes
-            elif not self.args.sample_packing and self.args.pretraining:
-                dataloader_params["batch_size"] *= self.accelerator.num_processes
-
         if self.args.sample_packing and (
             (is_training and not self.args.pretraining)
             or (not is_training and self.args.eval_sample_packing is not False)
@@ -587,26 +573,9 @@ class AxolotlTrainer(
         """
         # logs either has 'loss' or 'eval_loss'
         train_eval = "train" if "loss" in logs else "eval"
-
-        # Add reduced stored metrics to logs
-        for key, metric_data in self._stored_metrics[train_eval].items():
-            values = torch.tensor(metric_data["values"])
-            reduction_type = metric_data["reduction"]
-
-            if reduction_type == "mean":
-                logs[key] = values.mean().item()
-            elif reduction_type == "min":
-                logs[key] = values.min().item()
-            elif reduction_type == "max":
-                logs[key] = values.max().item()
-            elif reduction_type == "sum":
-                logs[key] = values.sum().item()
-            else:
-                raise NotImplementedError(
-                    "Metric reduction must be one of [mean, min, max, sum]"
-                )
-
-            logs[key] = round(logs[key], 4)
+        # Add averaged stored metrics to logs
+        for key, metrics in self._stored_metrics[train_eval].items():
+            logs[key] = torch.tensor(metrics).mean().item()
 
         if is_main_process():
             # Add memory usage
@@ -623,27 +592,10 @@ class AxolotlTrainer(
         return super().log(logs, start_time)
 
     def store_metrics(
-        self,
-        metrics: dict[str, float] | dict[str, tuple[int | float, str]],
-        train_eval: Literal["train", "eval"] = "train",
-        reduction: Literal["mean", "min", "max", "sum"] = "mean",
+        self, metrics: dict[str, float], train_eval: Literal["train", "eval"] = "train"
     ) -> None:
-        """
-        Store metrics with specified reduction type.
-
-        Args:
-            metrics: Dictionary of metric names to values, or metric names to (value,
-                reduction_type) tuples.
-            train_eval: Whether this is for training or evaluation.
-        """
         for key, value in metrics.items():
-            if isinstance(value, tuple):
-                metric_value, metric_reduction = value
-            else:
-                metric_value, metric_reduction = value, reduction
-
-            self._stored_metrics[train_eval][key]["values"].append(metric_value)
-            self._stored_metrics[train_eval][key]["reduction"] = metric_reduction
+            self._stored_metrics[train_eval][key].append(value)
 
     def _save_checkpoint(self, model, trial, **kwargs):
         # make sure the checkpoint dir exists, since trainer is flakey

src/axolotl/datasets.py

@@ -1,18 +1,19 @@
-"""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. We'll use the collators later on to pad the
+datasets.
+"""
+
+from typing import Any
 
-import torch
 from datasets import Dataset, IterableDataset
 
 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 +43,13 @@ class TokenizedPromptDataset(Dataset):
             **kwargs,
         )
 
-    def process(self, dataset):
-        features = dataset.features.keys()
+    def process(self, dataset: Dataset | IterableDataset) -> Dataset | IterableDataset:
+        """Apply filtering and tokenization."""
+        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 +58,28 @@ 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,
-                desc="Strategy Filtering Rows",
+                **filter_kwargs,
             )
 
+        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,
         )
 
@@ -79,140 +93,16 @@ def wrap_dataset_for_tokenized_prompt(
         map_kwargs = {}
         if prompt_tokenizer.supports_batched:
             map_kwargs["batched"] = True
-        features = list(dataset.features.keys())
+
+        # Map the dataset and remove original columns
+        # For IterableDataset, features might be None until first iteration
+        remove_columns = None
+        if dataset.features is not None:
+            remove_columns = list(dataset.features.keys())
+
         return dataset.map(
             prompt_tokenizer.tokenize_prompt,
-            remove_columns=features,
+            remove_columns=remove_columns,
             **map_kwargs,
         )
     return TokenizedPromptDataset(prompt_tokenizer, dataset, **kwargs)
-
-
-# TODO this isn't the best since it can't interleave datasets
-class ConstantLengthDataset(IterableDataset):
-    """Iterable dataset that returns constant length chunks of tokens from stream of
-    text files.
-
-    Args:
-        tokenizer: The processor used for processing the data.
-        dataset: Dataset with text files.
-        seq_length: Length of token sequences to return.
-    """
-
-    def __init__(  # pylint: disable=super-init-not-called
-        self,
-        tokenizer,
-        datasets,
-        seq_length=2048,
-    ):
-        self.tokenizer = tokenizer
-        self.concat_token_id = tokenizer.eos_token_id
-        self.datasets: list[IterableDataset] = datasets
-        self.seq_length = seq_length
-
-        vocab_size = len(tokenizer.get_vocab())
-
-        if vocab_size <= torch.iinfo(torch.int16).max:
-            self.tokens_dtype = torch.int16
-        elif vocab_size <= torch.iinfo(torch.int32).max:
-            self.tokens_dtype = torch.int32
-        else:
-            self.tokens_dtype = torch.int64
-
-    def __iter__(self):
-        buffer = {
-            "input_ids": [],
-            "attention_mask": [],
-            "labels": [],
-            "position_ids": [],
-        }
-        buffer_len = 0
-        for dataset in self.datasets:
-            idx = 0
-            iterator = iter(dataset)
-            more_examples = True
-            while more_examples:
-                try:
-                    example = next(iterator)
-                    idx += 1
-                except StopIteration:
-                    more_examples = False
-                    example = None
-
-                add_concat_token = False
-                if example:
-                    example_len = len(example["input_ids"])
-                    add_concat_token = example["input_ids"][-1] != self.concat_token_id
-                else:
-                    example_len = 0
-
-                if not example_len or (
-                    buffer_len + int(add_concat_token) + example_len > self.seq_length
-                ):
-                    if buffer["input_ids"]:
-                        input_ids = torch.cat(buffer["input_ids"], dim=-1)[
-                            : self.seq_length
-                        ]
-                        attention_mask = torch.cat(buffer["attention_mask"], dim=-1)[
-                            : self.seq_length
-                        ]
-                        position_ids = torch.cat(buffer["position_ids"], dim=-1)[
-                            : self.seq_length
-                        ]
-                        labels = torch.cat(buffer["labels"], dim=-1)[: self.seq_length]
-                        if labels.size() == input_ids.size() and (
-                            attention_mask.size() == input_ids.size()
-                        ):
-                            yield {
-                                "input_ids": input_ids,
-                                "labels": labels,
-                                "attention_mask": attention_mask,
-                                "position_ids": position_ids,
-                            }
-                        else:
-                            LOG.warning(
-                                "Dropping batch due to tensor size mismatch "
-                                f"input_ids: {input_ids.size()}, "
-                                f"labels: {labels.size()}, "
-                                f"attention_mask: {attention_mask.size()}"
-                            )
-                    buffer = {
-                        "input_ids": [],
-                        "attention_mask": [],
-                        "labels": [],
-                        "position_ids": [],
-                    }
-                    buffer_len = 0
-                    idx = 1
-
-                if example:
-                    # FIXME
-                    # just going to drop data points that are too long
-                    if len(example["input_ids"]) <= self.seq_length:
-                        input_ids = example["input_ids"]
-                        attention_mask = example["attention_mask"]
-                        labels = example["labels"]
-
-                        if add_concat_token:
-                            input_ids.append(self.concat_token_id)
-                            attention_mask.append(1)
-                            labels.append(self.concat_token_id)
-
-                        input_ids_with_concat = torch.tensor(
-                            input_ids, dtype=self.tokens_dtype
-                        )
-                        attention_mask_with_concat = torch.tensor(
-                            [idx * m for m in attention_mask], dtype=torch.int16
-                        )
-                        labels_with_concat = torch.tensor(
-                            labels, dtype=self.tokens_dtype
-                        )
-                        position_ids = torch.arange(
-                            len(input_ids), dtype=self.tokens_dtype
-                        )
-
-                        buffer["input_ids"].append(input_ids_with_concat)
-                        buffer["attention_mask"].append(attention_mask_with_concat)
-                        buffer["labels"].append(labels_with_concat)
-                        buffer["position_ids"].append(position_ids)
-                        buffer_len += len(input_ids)

src/axolotl/integrations/base.py

@@ -147,7 +147,7 @@ class BasePlugin:
         """
 
     # pylint: disable=unused-argument
-    def get_trainer_cls(self, cfg: DictDefault) -> type[Trainer] | None:
+    def get_trainer_cls(self, cfg: DictDefault) -> Trainer | None:
         """Returns a custom class for the trainer.
 
         Args:

src/axolotl/integrations/diffusion/README.md

@@ -1,125 +0,0 @@
-# Diffusion LM Training Plugin for Axolotl
-
-This plugin enables diffusion language model training using the LLaDA (Large Language
-And Diffusion Assistant) approach within the Axolotl framework.
-
-## Overview
-
-LLaDA is a diffusion-based approach to language model training that uses:
-- **Random token masking** during training instead of next-token prediction
-- **Bidirectional attention** to allow the model to see the full context
-- **Importance weighting** based on masking probabilities for stable training
-
-This approach can lead to more robust language models with better understanding of
-bidirectional context.
-
-## Installation
-
-The plugin is included with Axolotl. To use it, simply add the plugin configuration to
-your training config.
-
-## Quickstart
-
-### Basic Configuration
-
-Add the following to your Axolotl configuration YAML:
-
-```yaml
-# Enable diffusion LM training plugin
-plugins:
-  - axolotl.integrations.diffusion.DiffusionPlugin
-
-# Diffusion-specific configuration
-noise_schedule: linear  # or "cosine"
-min_mask_ratio: 0.1
-max_mask_ratio: 0.9
-num_diffusion_steps: 128
-eps: 1e-3
-importance_weighting: true
-mask_token_id: 128002
-
-# Sample generation (optional)
-generate_samples: true
-generation_interval: 100
-num_generation_samples: 3
-generation_steps: 128
-generation_temperature: 0.0
-generation_max_length: 100
-
-# Model configuration
-base_model: meta-llama/Llama-3.2-1B
-model_type: llama
-
-# Standard Axolotl configuration
-datasets:
-  - path: your_dataset
-    ...
-
-# Other config
-sequence_len: 1024
-micro_batch_size: 8
-gradient_accumulation_steps: 4
-learning_rate: 3e-4
-```
-
-## Supported Models
-
-Any models that support 4D attention masks should work out of the box. If not, please
-create an [issue](https://github.com/axolotl-ai-cloud/axolotl/issues)!
-
-## How It Works
-
-### Random Masking
-During training, tokens are randomly masked based on a sampled timestep:
-- Sample timestep `t` uniformly from [0, 1]
-- Calculate masking probability: `p = (1 - eps) * t + eps`
-- Randomly mask tokens with probability `p`
-
-### Bidirectional Attention
-The plugin uses native 4D attention masks to:
-- Enable bidirectional attention without patches
-- Allow all tokens to attend to all other tokens
-- Maintain proper padding masks
-- Work with modern `transformers` models out of the box
-
-### Diffusion Loss
-
-Loss is computed only on masked tokens with (optional) importance weighting:
-
-```python
-loss = sum(cross_entropy(pred, target) / p_mask) / total_tokens
-```
-
-## Sample Generation
-
-When `generate_samples: true`, the plugin generates samples during training:
-
-```
-Sample 1:
-   Original (45 tokens): The quick brown fox jumps over the lazy dog...
-   Masked (18/45 tokens, 40.0%): The [MASK] [MASK] fox [MASK] over [MASK] lazy [MASK]...
-   Generated: The quick brown fox jumps over the lazy dog...
-```
-
-Samples are logged to console and wandb (if enabled).
-
-## Metrics and Monitoring
-
-The plugin adds several metrics to track diffusion training:
-
-- `train/loss`: Weighted diffusion loss
-- `train/accuracy`: Accuracy on masked tokens
-- `train/mask_ratio`: Average fraction of tokens masked
-- `train/num_masked_tokens`: Number of tokens masked
-- `train/avg_p_mask`: Average masking probability
-- `train/ce_loss`: Unweighted cross-entropy loss
-- `train/importance_weight_avg`: Average importance weight
-
-## Limitations
-
-- No flash attention support
-
-## References
-
-- [LLaDA Paper](https://arxiv.org/abs/2404.10406)
-- [Axolotl Documentation](https://docs.axolotl.ai/)

src/axolotl/integrations/diffusion/__init__.py

@@ -1,6 +0,0 @@
-"""Diffusion LM training plugin init."""
-
-from .args import DiffusionArgs
-from .plugin import DiffusionPlugin
-
-__all__ = ["DiffusionArgs", "DiffusionPlugin"]

src/axolotl/integrations/diffusion/args.py

@@ -1,70 +0,0 @@
-"""Config args for diffusion LM training."""
-
-from typing import Literal
-
-from pydantic import BaseModel, Field
-
-
-class DiffusionArgs(BaseModel):
-    """Arguments for diffusion LM training plugin."""
-
-    # Noise schedule config
-    noise_schedule: Literal["linear", "cosine"] = Field(
-        default="linear", description="Type of noise schedule for diffusion training"
-    )
-    min_mask_ratio: float = Field(
-        default=0.1,
-        ge=0.0,
-        le=1.0,
-        description="Minimum masking ratio for diffusion noise schedule",
-    )
-    max_mask_ratio: float = Field(
-        default=0.9,
-        ge=0.0,
-        le=1.0,
-        description="Maximum masking ratio for diffusion noise schedule",
-    )
-    num_diffusion_steps: int = Field(
-        default=128, ge=1, description="Number of diffusion timesteps"
-    )
-    eps: float = Field(
-        default=1e-3,
-        ge=0.0,
-        le=1.0,
-        description="Epsilon value for minimum masking probability in forward process",
-    )
-
-    # Training config
-    importance_weighting: bool = Field(
-        default=True,
-        description="Apply importance weighting to loss based on masking probability",
-    )
-    mask_token_id: int = Field(
-        default=128002,
-        description=(
-            "Token ID to use for masking. Default is 128002 "
-            "(<|reserved_special_token_0|> for Llama 3.2)"
-        ),
-    )
-
-    # Sample generation config
-    generate_samples: bool = Field(
-        default=True, description="Enable sample generation during training"
-    )
-    generation_interval: int = Field(
-        default=100, ge=1, description="Generate samples every N steps"
-    )
-    num_generation_samples: int = Field(
-        default=3, ge=1, description="Number of samples to generate each time"
-    )
-    generation_steps: int = Field(
-        default=128, ge=1, description="Number of diffusion steps for generation"
-    )
-    generation_temperature: float = Field(
-        default=0.0,
-        ge=0.0,
-        description="Temperature for generation sampling (0.0 = deterministic)",
-    )
-    generation_max_length: int = Field(
-        default=100, ge=1, description="Maximum sequence length for generation"
-    )

src/axolotl/integrations/diffusion/callbacks.py

@@ -1,113 +0,0 @@
-"""Callbacks for diffusion training."""
-
-import wandb
-from transformers.trainer_callback import TrainerCallback, TrainerControl, TrainerState
-from transformers.training_args import TrainingArguments
-
-from axolotl.utils.logging import get_logger
-
-from .generation import generate_samples
-
-LOG = get_logger(__name__)
-
-
-class DiffusionGenerationCallback(TrainerCallback):
-    """Callback for generating samples during diffusion training."""
-
-    def __init__(self, trainer):
-        self.trainer = trainer
-
-    # pylint: disable=unused-argument
-    def on_step_end(
-        self,
-        args: TrainingArguments,
-        state: TrainerState,
-        control: TrainerControl,
-        **kwargs,
-    ):
-        """Generate samples at specified intervals."""
-        if (
-            state.global_step > 0
-            and state.global_step % self.trainer.config.generation_interval == 0
-        ):
-            # Use eval dataloader if available, otherwise use train dataloader
-            if (
-                hasattr(self.trainer, "eval_dataset")
-                and self.trainer.eval_dataset is not None
-            ):
-                dataloader = self.trainer.callback_handler.eval_dataloader
-            else:
-                dataloader = self.trainer.callback_handler.train_dataloader
-
-            # Generate samples
-            samples = generate_samples(
-                model=self.trainer.model,
-                tokenizer=self.trainer.tokenizer,
-                dataloader=dataloader,
-                num_generation_samples=self.trainer.config.num_generation_samples,
-                max_length=self.trainer.config.generation_max_length,
-                num_diffusion_steps=self.trainer.config.generation_steps,
-                temperature=self.trainer.config.generation_temperature,
-                mask_token_id=self.trainer.config.mask_token_id,
-            )
-
-            # Log samples
-            self._log_samples(samples, state.global_step)
-
-    def _log_samples(self, samples: list, step: int):
-        """Log generated samples."""
-        if not samples:
-            return
-
-        LOG.info("=" * 60)
-        LOG.info("GENERATED SAMPLES")
-        LOG.info("=" * 60)
-
-        for i, sample_data in enumerate(samples, 1):
-            original = sample_data["original"]
-            masked = sample_data["masked"]
-            generated = sample_data["generated"]
-            mask_ratio = sample_data["mask_ratio"]
-            masked_tokens = sample_data["masked_tokens"]
-            total_tokens = sample_data["total_tokens"]
-
-            LOG.info(f"\nSample {i}:")
-            LOG.info(f"\tOriginal ({total_tokens} tokens): {original}")
-            LOG.info(
-                f"\tMasked ({masked_tokens}/{total_tokens} tokens, "
-                f"{mask_ratio:.1%}): {masked}"
-            )
-            LOG.info(f"\tGenerated: {generated}")
-
-        LOG.info("=" * 60)
-
-        if self.trainer.config.use_wandb and self.trainer.state.is_world_process_zero:
-            if wandb.run is not None:
-                wandb.log(
-                    {
-                        "generated_samples": wandb.Table(
-                            columns=[
-                                "step",
-                                "original",
-                                "masked",
-                                "generated",
-                                "mask_ratio",
-                                "masked_tokens",
-                                "total_tokens",
-                            ],
-                            data=[
-                                [
-                                    step,
-                                    sample["original"],
-                                    sample["masked"],
-                                    sample["generated"],
-                                    f"{sample['mask_ratio']:.1%}",
-                                    sample["masked_tokens"],
-                                    sample["total_tokens"],
-                                ]
-                                for sample in samples
-                            ],
-                        )
-                    },
-                    step=step,
-                )

src/axolotl/integrations/diffusion/generation.py

@@ -1,269 +0,0 @@
-"""Sample generation utilities for diffusion training."""
-
-import logging
-from typing import Any, List, Optional
-
-import torch
-
-logger = logging.getLogger(__name__)
-
-
-def generate_samples(
-    model: torch.nn.Module,
-    tokenizer: Any,
-    dataloader: Optional[Any] = None,
-    num_generation_samples: int = 3,
-    max_length: int = 100,
-    num_diffusion_steps: int = 128,
-    temperature: float = 0.0,
-    mask_token_id: int = 32000,
-) -> List[dict]:
-    """
-    Generate text samples using the diffusion model by randomly masking sequences from
-    the given dataset and running the reverse diffusion process.
-
-    Args:
-        model: The wrapped or unwrapped model
-        tokenizer: Tokenizer for encoding/decoding
-        dataloader: Validation dataloader (for sampling sequences)
-        num_generation_samples: Number of samples to generate
-        max_length: Maximum length of sequences to use
-        num_diffusion_steps: Number of diffusion steps for generation
-        temperature: Temperature for sampling (0.0 = deterministic)
-        mask_token_id: Token ID used for masking
-
-    Returns:
-        List of dictionaries with original text, masked text, and generated text
-    """
-    if dataloader is None:
-        logger.warning("No validation dataloader provided, cannot generate samples")
-        return []
-
-    # Get the actual model (unwrap if needed)
-    unwrapped_model = model.module if hasattr(model, "module") else model
-    unwrapped_model.eval()
-    generations = []
-
-    # Sample sequences from validation dataset
-    sampled_sequences = _sample_sequences_from_dataloader(
-        dataloader, num_generation_samples, max_length, unwrapped_model.device
-    )
-    logger.info(f"Sampled {len(sampled_sequences)} sequences from validation dataset")
-
-    # Generate samples using reverse diffusion process
-    with torch.no_grad():
-        for original_sequence in sampled_sequences:
-            generation_result = _generate(
-                unwrapped_model,
-                tokenizer,
-                original_sequence,
-                num_diffusion_steps,
-                temperature,
-                mask_token_id,
-            )
-            generations.append(generation_result)
-
-    unwrapped_model.train()
-    return generations
-
-
-def _sample_sequences_from_dataloader(
-    dataloader: Any, num_samples: int, max_length: int, device: torch.device
-) -> List[torch.Tensor]:
-    """Sample sequences from validation dataloader."""
-    sampled_sequences = []
-    sample_count = 0
-
-    # Add randomness by skipping a random number of batches
-    skip_batches = torch.randint(0, 6, (1,)).item()
-    batch_count = 0
-
-    for batch in dataloader:
-        # Skip some batches for variety
-        if batch_count < skip_batches:
-            batch_count += 1
-            continue
-
-        if sample_count >= num_samples:
-            break
-
-        batch_count += 1
-        input_ids = batch["input_ids"]
-        attention_mask = batch.get("attention_mask")
-
-        # Randomly sample from sequences in this batch
-        batch_indices = torch.randperm(input_ids.size(0)).tolist()
-
-        for i in batch_indices:
-            if sample_count >= num_samples:
-                break
-
-            # Get actual sequence length (non-padded)
-            if attention_mask is not None:
-                seq_len = attention_mask[i].sum().item()
-            else:
-                seq_len = input_ids.size(1)
-
-            # Limit sequence length to max_length
-            actual_length = min(seq_len, max_length)
-            if actual_length < 10:  # Skip very short sequences
-                continue
-
-            # Extract the sequence
-            sequence = input_ids[i][:actual_length].unsqueeze(0).to(device)
-            sampled_sequences.append(sequence)
-            sample_count += 1
-
-    return sampled_sequences
-
-
-def _generate(
-    model: torch.nn.Module,
-    tokenizer: Any,
-    original_sequence: torch.Tensor,
-    num_diffusion_steps: int,
-    temperature: float,
-    mask_token_id: int,
-) -> dict:
-    """Generate a single sample using reverse diffusion."""
-    # Get original text for comparison
-    original_text = tokenizer.decode(
-        original_sequence[0].cpu(), skip_special_tokens=True
-    )
-
-    # Apply custom masking with random ratio (10% to 70%)
-    total_tokens = original_sequence.size(1)
-    min_ratio, max_ratio = 0.1, 0.7
-    target_mask_ratio = torch.rand(1).item() * (max_ratio - min_ratio) + min_ratio
-    target_masked_tokens = int(total_tokens * target_mask_ratio)
-
-    # Create random mask indices
-    mask_positions = torch.randperm(total_tokens)[:target_masked_tokens]
-    masked_indices = torch.zeros(
-        1, total_tokens, dtype=torch.bool, device=original_sequence.device
-    )
-    masked_indices[0, mask_positions] = True
-
-    # Create masked sequence
-    masked_sequence = original_sequence.clone()
-    masked_sequence[masked_indices] = mask_token_id
-
-    # Calculate actual mask ratio
-    masked_tokens = masked_indices.sum().item()
-    mask_ratio = masked_tokens / total_tokens
-
-    # Get masked text for comparison
-    masked_text = tokenizer.decode(masked_sequence[0].cpu(), skip_special_tokens=False)
-    # Clean up mask token representation
-    masked_text = _clean_masked_text(masked_text, tokenizer, mask_token_id)
-
-    # Run reverse diffusion process
-    sequence = masked_sequence.clone()
-    for step in range(num_diffusion_steps):
-        sequence = _diffusion_step(
-            model, sequence, step, num_diffusion_steps, temperature, mask_token_id
-        )
-
-    # Get final generated text
-    generated_text = tokenizer.decode(sequence[0].cpu(), skip_special_tokens=True)
-
-    return {
-        "original": original_text,
-        "masked": masked_text,
-        "generated": generated_text,
-        "mask_ratio": mask_ratio,
-        "masked_tokens": masked_tokens,
-        "total_tokens": total_tokens,
-        "formatted": (
-            f"Original: '{original_text}' → Masked: '{masked_text}' "
-            f"({mask_ratio:.1%}) → Generated: '{generated_text}'"
-        ),
-    }
-
-
-def _clean_masked_text(masked_text: str, tokenizer: Any, mask_token_id: int) -> str:
-    """Clean up masked text for display."""
-    mask_token_repr = tokenizer.decode([mask_token_id], skip_special_tokens=False)
-    cleaned = masked_text.replace(mask_token_repr, "[MASK]")
-
-    if hasattr(tokenizer, "special_tokens_map"):
-        for token_value in tokenizer.special_tokens_map.values():
-            if token_value and isinstance(token_value, str):
-                cleaned = cleaned.replace(token_value, "")
-
-    cleaned = " ".join(cleaned.split()).strip()
-
-    return cleaned
-
-
-def _diffusion_step(
-    model: torch.nn.Module,
-    sequence: torch.Tensor,
-    step: int,
-    num_diffusion_steps: int,
-    temperature: float,
-    mask_token_id: int,
-) -> torch.Tensor:
-    """Perform a single diffusion step with remasking."""
-    # Only process if there are masked tokens remaining
-    current_mask = sequence == mask_token_id
-    if not current_mask.any():
-        return sequence
-
-    # Create bidirectional attention mask for diffusion
-    batch_size, seq_len = sequence.shape
-    attention_mask = torch.ones(
-        batch_size, 1, seq_len, seq_len, dtype=torch.bool, device=sequence.device
-    )
-
-    # Forward pass
-    outputs = model(input_ids=sequence, attention_mask=attention_mask)
-    logits = outputs.logits
-
-    # Only sample at currently masked positions
-    if current_mask.any():
-        masked_logits = logits[current_mask]
-
-        # Apply temperature scaling
-        if temperature > 0:
-            scaled_logits = masked_logits / temperature
-        else:
-            scaled_logits = masked_logits
-
-        # Suppress mask token in outputs
-        scaled_logits[:, mask_token_id] = -float("inf")
-
-        # Sample predictions
-        if temperature > 0:
-            # Add Gumbel noise for sampling
-            gumbel_noise = -torch.log(
-                -torch.log(torch.rand_like(scaled_logits, dtype=torch.float32))
-            )
-            gumbel_logits = scaled_logits + gumbel_noise
-            predicted_tokens = torch.argmax(gumbel_logits, dim=-1)
-        else:
-            # Deterministic sampling when temperature is 0
-            predicted_tokens = torch.argmax(scaled_logits, dim=-1)
-
-        # Calculate probabilities for confidence scoring
-        probs = torch.softmax(scaled_logits, dim=-1)
-        predicted_token_probs = probs[range(len(predicted_tokens)), predicted_tokens]
-
-        # Determine how many tokens to unmask this step
-        remaining_masked = current_mask.sum().item()
-        if step == num_diffusion_steps - 1:
-            num_to_unmask = remaining_masked
-        else:
-            unmask_ratio = 1.0 / (num_diffusion_steps - step)
-            num_to_unmask = max(1, int(remaining_masked * unmask_ratio))
-
-        # Select highest confidence predictions to unmask
-        if num_to_unmask >= remaining_masked:
-            sequence[current_mask] = predicted_tokens
-        else:
-            _, top_indices = predicted_token_probs.topk(num_to_unmask)
-            mask_positions = torch.where(current_mask)[1]
-            positions_to_unmask = mask_positions[top_indices]
-            sequence[0, positions_to_unmask] = predicted_tokens[top_indices]
-
-    return sequence

src/axolotl/integrations/diffusion/plugin.py

@@ -1,41 +0,0 @@
-"""Diffusion LM training plugin for Axolotl."""
-
-from peft import PeftModel
-from transformers import PreTrainedModel
-
-from axolotl.integrations.base import BasePlugin
-from axolotl.utils.dict import DictDefault
-from axolotl.utils.logging import get_logger
-
-from .trainer import DiffusionTrainer
-
-LOG = get_logger(__name__)
-
-
-class DiffusionPlugin(BasePlugin):
-    """
-    Plugin for diffusion language model training.
-
-    This plugin enables diffusion-based training using the LLaDA approach, which uses
-    random masking and bidirectional attention to train language models.
-    """
-
-    def __init__(self):
-        super().__init__()
-        self.cfg = None
-
-    def get_input_args(self) -> str:
-        """Returns the pydantic model for LLaDA plugin arguments."""
-        return "axolotl.integrations.diffusion.DiffusionArgs"
-
-    def post_model_load(self, cfg: DictDefault, model: PreTrainedModel | PeftModel):
-        """Perform actions after model is loaded."""
-        self.cfg = cfg
-
-    def get_trainer_cls(self, cfg: DictDefault) -> type[DiffusionTrainer] | None:
-        """Return custom trainer class for diffusion training."""
-        return DiffusionTrainer
-
-    def post_trainer_create(self, cfg: DictDefault, trainer: DiffusionTrainer):
-        """Configure trainer after creation."""
-        trainer.set_config(cfg)

src/axolotl/integrations/diffusion/trainer.py

@@ -1,336 +0,0 @@
-"""Custom trainer for diffusion LM training."""
-
-from typing import Any, Literal
-
-import torch
-import torch.nn.functional as F
-from torch import nn
-from transformers.masking_utils import find_packed_sequence_indices
-
-from axolotl.core.trainers.base import AxolotlTrainer
-from axolotl.integrations.diffusion.utils import create_bidirectional_block_mask
-from axolotl.utils.dict import DictDefault
-from axolotl.utils.logging import get_logger
-
-from .callbacks import DiffusionGenerationCallback
-
-LOG = get_logger(__name__)
-
-
-class DiffusionTrainer(AxolotlTrainer):  # pylint: disable=too-many-ancestors
-    """Custom trainer for diffusion LM training that overrides loss computation."""
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.config = None
-        self._special_token_ids = None
-
-    def set_config(self, config: DictDefault):
-        """Set config for diffusion training."""
-        self.config = config
-        self._cache_special_token_ids()
-
-        if config.generate_samples:
-            generation_callback = DiffusionGenerationCallback(self)
-            self.add_callback(generation_callback)
-
-    def compute_loss(
-        self,
-        model: nn.Module,
-        inputs: dict[str, torch.Tensor],
-        return_outputs: bool = False,
-        num_items_in_batch: torch.Tensor | None = None,
-    ) -> torch.Tensor | tuple[torch.Tensor, dict[str, torch.Tensor]]:
-        """Override compute_loss to use diffusion loss."""
-        input_ids = inputs.get("input_ids")
-        attention_mask = inputs.get("attention_mask")
-        labels = inputs.get("labels")
-        position_ids = inputs.get("position_ids")
-
-        if input_ids is None:
-            raise ValueError("input_ids is required for diffusion training")
-
-        loss, outputs = self._compute_diffusion_loss(
-            model, input_ids, attention_mask, labels, position_ids
-        )
-
-        if return_outputs:
-            return loss, outputs
-        return loss
-
-    def _cache_special_token_ids(self):
-        """Cache special token IDs to avoid repeated tokenizer access."""
-        if self.processing_class is None:
-            self._special_token_ids = set()
-            return
-
-        tokenizer = self.processing_class
-        special_tokens = set()
-
-        if hasattr(tokenizer, "bos_token_id") and tokenizer.bos_token_id is not None:
-            special_tokens.add(tokenizer.bos_token_id)
-        if hasattr(tokenizer, "eos_token_id") and tokenizer.eos_token_id is not None:
-            special_tokens.add(tokenizer.eos_token_id)
-        if hasattr(tokenizer, "pad_token_id") and tokenizer.pad_token_id is not None:
-            special_tokens.add(tokenizer.pad_token_id)
-
-        self._special_token_ids = special_tokens
-
-    @torch.compile
-    def _forward_process(
-        self,
-        input_ids: torch.Tensor,
-        attention_mask: torch.Tensor | None = None,
-        labels: torch.Tensor | None = None,
-        eps: float = 1e-3,
-        min_p: float = 0.0,
-        max_p: float = 1.0,
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        """
-        Forward noising process. A timestep is sampled along the process, and tokens are
-        masked with probability determined by the configured noise schedule.
-
-        Args:
-            input_ids: Input token ids [batch_size, seq_len].
-            attention_mask: Attention mask [batch_size, seq_len].
-            labels: Labels for SFT training [batch_size, seq_len].
-            eps: Small epsilon value for minimum masking probability.
-
-        Returns:
-            noisy_batch: Input with some tokens masked.
-            masked_indices: Boolean mask indicating which tokens were masked.
-            p_mask: Masking probabilities for each token [batch_size, seq_len].
-        """
-        batch_size, seq_len = input_ids.shape
-        device = input_ids.device
-
-        # Sample random timesteps for each sample in batch
-        t = torch.rand(batch_size, device=device)
-
-        # Calculate masking probability with epsilon
-        p_mask = min_p + (max_p - min_p) * (1 - eps) * t + eps  # [batch_size]
-        p_mask = p_mask[:, None].repeat(1, seq_len)  # [batch_size, seq_len]
-
-        # Don't mask padding tokens if attention_mask is provided
-        if attention_mask is not None:
-            valid_mask = attention_mask.bool()
-            p_mask = p_mask * valid_mask.float()
-
-        # Create mask to exclude special tokens
-        special_token_mask = torch.zeros_like(input_ids, dtype=torch.bool)
-        if self._special_token_ids:
-            for token_id in self._special_token_ids:
-                special_token_mask |= input_ids == token_id
-
-        # Create random mask based on p_mask
-        masked_indices = torch.rand((batch_size, seq_len), device=device) < p_mask
-        masked_indices = masked_indices & ~special_token_mask
-        if attention_mask is not None:
-            masked_indices = masked_indices & attention_mask.bool()
-
-        # For SFT data, only mask answer tokens
-        if labels is not None:
-            answer_mask = labels != -100
-            masked_indices = masked_indices & answer_mask
-
-        # Create masked input
-        mask_token_id = self.config.mask_token_id
-        noisy_batch = torch.where(masked_indices, mask_token_id, input_ids)
-
-        return noisy_batch, masked_indices, p_mask
-
-    @torch.compile
-    def _create_bidirectional_attention_mask(
-        self, input_ids: torch.Tensor, attention_mask: torch.Tensor | None = None, position_ids: torch.Tensor | None = None
-    ) -> torch.Tensor:
-        """
-        Create bidirectional attention mask to override default causal masking. Handles
-        sample-packed sequences where different samples are identified by different
-        attention mask values.
-
-        Args:
-            input_ids: Input token ids [batch_size, seq_len].
-            attention_mask: Attention mask [batch_size, seq_len]
-            position_ids: Position ids [batch_size, seq_len]
-
-        Returns:
-            bidirectional_mask: 4D attention mask [batch_size, 1, seq_len, seq_len].
-        """
-        batch_size, seq_len = input_ids.shape
-        device = input_ids.device
-
-        if attention_mask is None or not self.config.sample_packing:
-            return torch.ones(
-                batch_size, 1, seq_len, seq_len, dtype=torch.bool, device=device
-            )
-
-        if position_ids is None:
-            # Create attention mask by comparing sample IDs element-wise
-            mask_i = attention_mask.unsqueeze(2)  # [batch_size, seq_len, 1]
-            mask_j = attention_mask.unsqueeze(1)  # [batch_size, 1, seq_len]
-
-            # Tokens can attend to each other if they have the same non-zero sample ID
-            bidirectional_mask = (mask_i == mask_j) & (mask_i > 0)
-
-            # Add head dimension: [batch_size, 1, seq_len, seq_len]
-            bidirectional_mask = bidirectional_mask.unsqueeze(1)
-
-            return bidirectional_mask
-
-        if self._config.flex_attention:
-            block_mask = create_bidirectional_block_mask(
-                input_ids, attention_mask, position_ids
-            )
-        else:
-            packed_seq_mask = find_packed_sequence_indices(position_ids)
-            block_mask = packed_seq_mask.unsqueeze(2) == packed_seq_mask.unsqueeze(1)
-
-        return block_mask
-
-    def _compute_diffusion_loss(
-        self,
-        model: nn.Module,
-        input_ids: torch.Tensor,
-        attention_mask: torch.Tensor | None = None,
-        labels: torch.Tensor | None = None,
-        position_ids: torch.Tensor | None = None,
-    ) -> tuple[torch.Tensor, torch.Tensor | Any]:
-        """
-        Compute diffusion loss.
-
-        Args:
-            model: The model to compute loss for.
-            input_ids: Ground truth token ids [batch_size, seq_len].
-            attention_mask: Attention mask [batch_size, seq_len].
-            labels: Labels for SFT training [batch_size, seq_len].
-            position_ids: Position ids [batch_size, seq_len].
-
-        Returns:
-            loss: Cross-entropy loss.
-            metrics: Dictionary of metrics.
-        """
-        # Apply forward process
-        noisy_batch, masked_indices, p_mask = self._forward_process(
-            input_ids, attention_mask, labels, self._config.eps, self._config.min_mask_ratio, self._config.max_mask_ratio
-        )
-
-        # Create bidirectional attention mask (optional: use causal if you want strict AR behavior)
-        bidirectional_mask = self._create_bidirectional_attention_mask(
-            input_ids, attention_mask, position_ids
-        )
-
-        # Forward pass
-        outputs = model(
-            input_ids=noisy_batch,
-            attention_mask=bidirectional_mask,
-        )
-        logits = outputs.logits  # [B, L, V]
-
-        # ----- AR label shift toggle -----
-        use_ar_shift = False
-        if use_ar_shift:
-            # Predict token at t from logits at t-1: drop last logit step, drop first target step
-            logits_eff = logits[:, :-1, :]
-            input_ids_eff = input_ids[:, 1:]
-            masked_indices_eff = masked_indices[:, 1:]
-            p_mask_eff = p_mask[:, 1:]
-            labels_eff = labels[:, 1:] if labels is not None else None
-        else:
-            logits_eff = logits
-            input_ids_eff = input_ids
-            masked_indices_eff = masked_indices
-            p_mask_eff = p_mask
-            labels_eff = labels
-
-        if masked_indices_eff.sum() > 0:
-            valid_indices = torch.where(masked_indices_eff)
-            batch_indices, seq_indices = valid_indices
-
-            masked_logits = logits_eff[batch_indices, seq_indices]
-            masked_targets = input_ids_eff[batch_indices, seq_indices]
-            masked_p_mask = p_mask_eff[batch_indices, seq_indices]
-
-            # Compute cross-entropy loss without reduction
-            token_loss = F.cross_entropy(
-                masked_logits.float(), masked_targets, reduction="none"
-            )
-
-            if self.config.importance_weighting:
-                masked_p_mask = masked_p_mask.float().clamp_min(1e-6)
-                weighted_loss = token_loss / masked_p_mask
-            else:
-                weighted_loss = token_loss
-
-            # Final loss: sum weighted losses, normalize
-            if labels_eff is not None:
-                # For SFT data: normalize by answer length per sample
-                answer_mask = labels_eff != -100
-                answer_lengths = answer_mask.sum(dim=1).float()  # [batch_size]
-
-                # Get batch indices for masked tokens
-                masked_batch_indices = batch_indices
-
-                # Sum losses per sample and divide by answer length
-                loss_per_sample = torch.zeros(
-                    input_ids.shape[0], device=input_ids.device
-                )
-                for i in range(input_ids.shape[0]):
-                    sample_mask = masked_batch_indices == i
-                    if sample_mask.any():
-                        sample_loss = weighted_loss[sample_mask].sum()
-                        loss_per_sample[i] = sample_loss / answer_lengths[i]
-
-                loss = loss_per_sample.mean()
-            else:
-                # Original normalization for non-SFT data
-                loss = weighted_loss.sum() / (input_ids.shape[0] * input_ids.shape[1])
-
-            ce_loss = token_loss.mean()
-
-            # Compute accuracy on masked tokens
-            with torch.no_grad():
-                pred_tokens = masked_logits.argmax(dim=-1)
-                accuracy = (pred_tokens == masked_targets).float().mean()
-        else:
-            loss = torch.tensor(0.0, device=input_ids.device, requires_grad=True)
-            accuracy = torch.tensor(0.0, device=input_ids.device)
-            ce_loss = torch.tensor(0.0, device=input_ids.device)
-            masked_p_mask = torch.tensor(1.0, device=input_ids.device)
-
-            # Keep eff tensors around for metrics
-            masked_indices_eff = masked_indices
-            p_mask_eff = p_mask
-            labels_eff = labels
-
-        # Metrics (aligned to the effective tensors)
-        if masked_indices_eff.any():
-            avg_p = p_mask_eff[masked_indices_eff].float().mean().item()
-            num_masked = int(masked_indices_eff.sum().item())
-            mask_ratio = masked_indices_eff.float().mean().item()
-        else:
-            avg_p = 0.0
-            num_masked = 0
-            mask_ratio = 0.0
-
-        metrics = {
-            "loss": float(loss.detach()),
-            "accuracy": float(accuracy.detach()),
-            "mask_ratio": mask_ratio,
-            "num_masked_tokens": (num_masked, "sum"),
-            "avg_p_mask": avg_p,
-            "ce_loss": float(ce_loss.detach()),
-        }
-
-        # SFT-specific metrics (aligned)
-        if labels_eff is not None:
-            answer_mask = labels_eff != -100
-            metrics["answer_ratio"] = answer_mask.float().mean().item()
-            metrics["avg_answer_length"] = answer_mask.sum(dim=1).float().mean().item()
-
-        if self.config.importance_weighting:
-            metrics["importance_weight_avg"] = (1.0 / masked_p_mask).mean().item()
-
-        train_eval: Literal["train", "eval"] = "train" if model.training else "eval"
-        self.store_metrics(metrics, train_eval=train_eval)
-
-        return loss, outputs

src/axolotl/integrations/diffusion/utils.py

@@ -1,50 +0,0 @@
-import torch
-from torch.nn.attention.flex_attention import BlockMask, create_block_mask
-from transformers.masking_utils import find_packed_sequence_indices, packed_sequence_mask_function
-
-
-def create_bidirectional_block_mask(
-    input_ids: torch.Tensor,
-    attention_mask: torch.Tensor | None = None,
-    position_ids: torch.Tensor | None = None,
-) -> "BlockMask":
-    """
-    Creates a bidirectional block mask for FlexAttention.
-
-    Args:
-        input_ids: Input token ids [batch_size, seq_len]
-        attention_mask: Padding mask [batch_size, seq_len]
-
-    Returns:
-        BlockMask for bidirectional attention with padding
-    """
-    batch_size, seq_len = input_ids.shape
-
-    if position_ids is not None:
-        packed_seq_mask = find_packed_sequence_indices(position_ids)
-        mask_fn =packed_sequence_mask_function(packed_seq_mask, batch_size, seq_len)
-    elif attention_mask is None:
-        # If no padding mask, all positions can attend to all positions
-        def mask_fn(b, h, q_idx, kv_idx):
-            # Always return True for bidirectional attention
-            return True
-    else:
-        # Convert attention_mask to boolean if needed
-        attention_mask = attention_mask.bool()
-
-        def mask_fn(b, h, q_idx, kv_idx):
-            # Both query and key positions must be valid (not padding)
-            return attention_mask[b, q_idx] & attention_mask[b, kv_idx]
-
-    # Create the block mask
-    block_mask = create_block_mask(
-        mask_fn,
-        B=batch_size,
-        H=None,  # Will be set by the attention layer
-        Q_LEN=seq_len,
-        KV_LEN=seq_len,
-        device=input_ids.device,
-        _compile=True,
-    )
-
-    return block_mask

src/axolotl/integrations/spectrum/__init__.py

@@ -57,7 +57,7 @@ class SpectrumPlugin(BasePlugin):
     Spectrum Plugin to automatically generate unfrozen parameters based on SNR data.
     """
 
-    base_url = "https://raw.githubusercontent.com/QuixiAI/spectrum/main/model_snr_results/"
+    base_url = "https://raw.githubusercontent.com/cognitivecomputations/spectrum/main/model_snr_results/"
     base_path = "./model_snr_results/"
     snr_file_template = "snr_results_{model_name_slug}.json"
 

src/axolotl/loaders/model.py

@@ -681,23 +681,6 @@ class ModelLoader:
 
         return hf_ds_cfg
 
-    def _load_model_from_config(self) -> PreTrainedModel:
-        """Load model with random initialization using from_config."""
-        if self.auto_model_loader in [AutoModelForCausalLM, AutoModelForVision2Seq]:
-            return self.auto_model_loader.from_config(config=self.model_config)
-        return self.auto_model_loader(config=self.model_config)
-
-    def _load_model_from_pretrained(self, model_loader_class=None) -> PreTrainedModel:
-        """Load model from pretrained weights."""
-        loader = model_loader_class or self.auto_model_loader
-        kwargs = {
-            **self.model_kwargs,
-            "config": self.model_config,
-            "trust_remote_code": self.cfg.trust_remote_code or False,
-            **self.model_kwargs,
-        }
-        return loader.from_pretrained(self.base_model, **kwargs)
-
     def _build_model(self) -> bool:
         """Load model, with load strategy depending on config."""
         skip_move_to_device = False
@@ -712,8 +695,7 @@ class ModelLoader:
         if self.is_fsdp_enabled:
             if self.cfg.fsdp_config.cpu_ram_efficient_loading:
                 skip_move_to_device = True
-                # Don't delete device_map for QLoRA + FSDP - it was set correctly in
-                # _set_device_map
+                # Don't delete device_map for QLoRA + FSDP - it was set correctly in _set_device_map
                 if (
                     "device_map" in self.model_kwargs
                     and not self.is_qlora_and_fsdp_enabled
@@ -742,11 +724,6 @@ class ModelLoader:
                 or self.cfg.qlora_sharded_model_loading
             )
         ):
-            if self.cfg.reinit_weights:
-                LOG.warning(
-                    "reinit_weights is not supported with sharded quantized loading. "
-                    "Loading from pretrained weights instead."
-                )
             quant_storage = self.cfg.torch_dtype
             quantization_config = getattr(
                 self.model_config, "quantization_config", None
@@ -762,12 +739,33 @@ class ModelLoader:
                 quantization_config=quantization_config,
             )
             skip_move_to_device = True
-        elif self.model_type == "MambaLMHeadModel":
-            if self.cfg.reinit_weights:
-                LOG.warning(
-                    "reinit_weights is not supported with MambaLMHeadModel. "
-                    "Loading from pretrained weights instead."
+        elif (
+            self.model_config.model_type in ["llama", "llama4"]
+            and not self.cfg.trust_remote_code
+            and not self.cfg.gptq
+        ):
+            # Please don't remove underscore binding without reading the fn docstring.
+            _ = self._configure_zero3_memory_efficient_loading()
+
+            # Load model with random initialization if specified
+            if self.cfg.random_init_weights:
+                # AutoModel classes support the from_config method
+                if self.auto_model_loader in [
+                    AutoModelForCausalLM,
+                    AutoModelForVision2Seq,
+                ]:
+                    self.model = self.auto_model_loader.from_config(
+                        config=self.model_config,
+                    )
+                else:
+                    self.model = self.auto_model_loader(config=self.model_config)
+            else:
+                self.model = self.auto_model_loader.from_pretrained(
+                    self.base_model,
+                    config=self.model_config,
+                    **self.model_kwargs,
                 )
+        elif self.model_type == "MambaLMHeadModel":
             # FIXME this is janky at best and hacked together to make it work
             MambaLMHeadModel = fix_mamba_attn_for_loss()  # pylint: disable=invalid-name
 
@@ -780,27 +778,41 @@ class ModelLoader:
                 self.base_model,
                 **self.model_kwargs,
             )
+        elif (
+            self.model_type
+            and self.model_type != "AutoModelForCausalLM"
+            and not self.cfg.trust_remote_code
+        ):
+            if self.cfg.gptq:
+                self.model = self.auto_model_loader.from_pretrained(
+                    self.base_model,
+                    config=self.model_config,
+                    trust_remote_code=self.cfg.trust_remote_code or False,
+                    **self.model_kwargs,
+                )
+            else:
+                self.model = getattr(transformers, self.model_type).from_pretrained(
+                    self.base_model,
+                    config=self.model_config,
+                    trust_remote_code=self.cfg.trust_remote_code or False,
+                    **self.model_kwargs,
+                )
+        elif self.cfg.gptq:
+            self.model = self.auto_model_loader.from_pretrained(
+                self.base_model,
+                config=self.model_config,
+                trust_remote_code=self.cfg.trust_remote_code or False,
+                **self.model_kwargs,
+            )
         else:
-            # Please don't remove underscore binding without reading the fn docstring
+            # Please don't remove underscore binding without reading the fn docstring.
             _ = self._configure_zero3_memory_efficient_loading()
-
-            if (
-                self.model_type
-                and self.model_type != "AutoModelForCausalLM"
-                and not self.cfg.trust_remote_code
-                and not self.cfg.gptq
-            ):
-                # Use model type from transformers
-                model_loader_class = getattr(transformers, self.model_type)
-            else:
-                # Use auto model loader (handles gptq and default cases)
-                model_loader_class = self.auto_model_loader
-
-            if self.cfg.reinit_weights:
-                self.model = self._load_model_from_config()
-            else:
-                self.model = self._load_model_from_pretrained(model_loader_class)
-
+            self.model = self.auto_model_loader.from_pretrained(
+                self.base_model,
+                config=self.model_config,
+                trust_remote_code=self.cfg.trust_remote_code or False,
+                **self.model_kwargs,
+            )
         if is_deepspeed_zero3_enabled():
             skip_move_to_device = True
 

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/prompt_tokenizers.py

@@ -75,7 +75,7 @@ class PromptTokenizingStrategy(abc.ABC):
     ) -> BatchEncoding:
         empty = BatchEncoding(data={"input_ids": [], "attention_mask": []})
         if not prompt:
-            LOG.warning_once("Empty text requested for tokenization.")
+            LOG.warning("Empty text requested for tokenization.")
             return empty
 
         result = self.tokenizer(

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
@@ -43,12 +44,24 @@ from axolotl.utils.trainer import (
 LOG = get_logger(__name__)
 
 
+def _is_streaming_enabled(cfg: DictDefault) -> bool:
+    """Check if streaming is enabled for a specific split."""
+    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 = has_pretraining and streaming is None
+
+    return streaming
+
+
 @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 +69,19 @@ 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(
-            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."""
 
@@ -83,7 +92,6 @@ def _prepare_standard_dataset(
             cfg,
             split="train",
             processor=processor,
-            preprocess_iterable=preprocess_iterable,
         )
 
         # Overwrite eval_dataset if test data exists
@@ -93,7 +101,6 @@ def _prepare_standard_dataset(
                 cfg,
                 split="test",
                 processor=processor,
-                preprocess_iterable=preprocess_iterable,
             )
 
         return train_dataset, eval_dataset, prompters
@@ -109,7 +116,12 @@ 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:
+    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 +129,17 @@ def _prepare_standard_dataset(
                 "You should set `eval_sample_packing: False` in your config."
             )
 
-    # Calculate total number of training steps
-    if cfg.max_steps:
-        total_num_steps = min(
-            calculate_total_num_steps(cfg, train_dataset), cfg.max_steps
-        )
+    # Set total_num_steps for training
+    if isinstance(train_dataset, IterableDataset):
+        total_num_steps = 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 +148,6 @@ 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 +168,6 @@ def _prepare_pretraining_dataset(
             cfg,
             split="test",
             processor=processor,
-            preprocess_iterable=preprocess_iterable,
         )
 
     if cfg.dataset_exact_deduplication:
@@ -256,7 +270,6 @@ 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 +278,51 @@ 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:
+
+    use_streaming = False
+    if split == "train":
+        use_streaming = _is_streaming_enabled(cfg)
+
+    if use_streaming:
+        # For streaming datasets, skip caching and load raw datasets directly
         dataset, prompters = _load_raw_datasets(
             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 +331,8 @@ 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 +353,6 @@ def _load_raw_datasets(
             split=split,
             seed=cfg.seed,
             processor=processor,
-            preprocess_iterable=preprocess_iterable,
         )
         datasets.append(dataset_wrapper)
         prompters.append(dataset_prompter)
@@ -345,11 +368,12 @@ def _load_raw_datasets(
         if cfg.sample_packing:
             dataset, _ = process_datasets_for_packing(cfg, dataset, None)
 
-        # Save the prepared dataset
-        dataset_hash = generate_dataset_hash_from_config(
-            cfg, datasets_configs, tokenizer.name_or_path
-        )
-        save_preprocessed_dataset(cfg, dataset, dataset_hash, split)
+        # Only save regular datasets to disk, not streaming datasets
+        if not isinstance(dataset, IterableDataset):
+            dataset_hash = generate_dataset_hash_from_config(
+                cfg, datasets_configs, tokenizer.name_or_path
+            )
+            save_preprocessed_dataset(cfg, dataset, dataset_hash, split)
 
     return dataset, prompters
 
@@ -358,22 +382,22 @@ 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
-    dataset = load_dataset_with_config(
-        dataset_config, cfg.hf_use_auth_token, streaming=preprocess_iterable
-    )
+    use_streaming = False
+    if split == "train":
+        use_streaming = _is_streaming_enabled(cfg)
 
-    # Parse dataset type
+    dataset = load_dataset_with_config(
+        dataset_config, cfg.hf_use_auth_token, use_streaming
+    )
     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 +442,13 @@ def _parse_dataset_type(d_type: str) -> tuple[str | None, str | None]:
 
 
 def _handle_train_dataset_split(
-    dataset: Dataset, cfg: DictDefault
-) -> tuple[Dataset, Dataset | None]:
+    dataset: Dataset | IterableDataset, cfg: DictDefault
+) -> 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 +459,33 @@ 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
-) -> tuple[None, Dataset | None]:
+    dataset: Dataset | IterableDataset, cfg: DictDefault
+) -> 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 +511,6 @@ 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 +519,6 @@ 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 +529,6 @@ 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
-        # shuffle_merged_datasets is disabled
-        if cfg.curriculum_sampling or not cfg.shuffle_merged_datasets:
+        if (
+            cfg.curriculum_sampling
+            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.
-    # This allows curriculum learning strategies to be applied at the dataset level.
-    if cfg.shuffle_before_merging_datasets:
+    if cfg.shuffle_before_merging_datasets and all(
+        isinstance(ds, Dataset) for ds in 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 = concatenate_datasets(datasets)
+    merged_dataset = _merge_datasets_with_strategy(datasets, cfg)
 
-    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 +565,45 @@ 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":
+        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":
+        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

@@ -190,11 +190,15 @@ def handle_long_seq_in_dataset(
     Returns:
         Filtered dataset with long sequences removed.
     """
-    if "input_ids" not in dataset.column_names:
-        LOG.warning(
-            "Dataset does not contain 'input_ids' column. Skip drop long seq. This is "
-            "expected for reward modeling."
-        )
+    if hasattr(dataset, "column_names") and dataset.column_names:
+        if "input_ids" not in dataset.column_names:
+            LOG.warning(
+                "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(

src/axolotl/utils/environment.py

@@ -16,7 +16,7 @@ from packaging.version import Version, parse
 def check_cuda_p2p_ib_support():
     if not accelerate_check_cuda_p2p_ib_support():
         return False
-    unsupported_devices = {"RTX 6000 Ada", "L40S", "A40"}
+    unsupported_devices = {"RTX 6000 Ada", "L40S"}
     try:
         device_names, device_count = get_gpu_info()
         if 1 < device_count < 8:

src/axolotl/utils/schemas/config.py

@@ -109,12 +109,6 @@ class AxolotlInputConfig(
             "description": "Don't upcast the embeddings to float32 when using PEFT. Useful for low-VRAM GPUs"
         },
     )
-    reinit_weights: bool | None = Field(
-        default=None,
-        json_schema_extra={
-            "description": "Reinitialize model weights randomly instead of loading pretrained weights"
-        },
-    )
 
     trainer_cls: str | None = Field(
         default=None,
@@ -938,9 +932,27 @@ 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."
+        },
+    )
+    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'."
+        },
+    )
+
     # 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 +1127,27 @@ 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 streaming is enabled for training
+        streaming = data.get("streaming", False)
+
+        # If streaming is enabled, configure accelerator
+        if 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 +1406,128 @@ class GRPOVllmValidationMixin:
         return self
 
 
+class StreamingValidationMixin:
+    """Validation methods related to streaming datasets."""
+
+    def _is_streaming_enabled(self) -> bool:
+        """Check if streaming is enabled."""
+        # 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 = has_pretraining and streaming is None
+
+        return streaming
+
+    @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():
+            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():
+            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, please "
+                    "use test_datasets: ... instead"
+                )
+
+        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 datasets
+        if self._is_streaming_enabled():
+            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_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)
+
+        # 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,
+        )
+
+        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 +1539,7 @@ class ValidationMixin(
     SystemValidationMixin,
     ChatTemplateValidationMixin,
     PretrainingValidationMixin,
+    StreamingValidationMixin,
     ModelCompatibilityValidationMixin,
     ComplexValidationMixin,
     GRPOVllmValidationMixin,

src/axolotl/utils/trainer.py

@@ -10,7 +10,6 @@ from typing import List, Optional
 
 import numpy as np
 import torch
-import torch.cuda
 from datasets import IterableDataset, disable_caching, enable_caching
 from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
 from transformers.utils import is_torch_bf16_gpu_available
@@ -23,6 +22,65 @@ from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 LOG = get_logger(__name__)
 
 
+def _create_filtered_iterable_dataset(dataset, filter_fn, batched=False):
+    """
+    Create a filtered IterableDataset that works around a HuggingFace datasets
+    limitation.
+    """
+
+    def filtered_generator():
+        """Generator that yields only samples that pass the filter function."""
+        if batched:
+            batch = []
+            batch_size = 1000  # Process in batches of 1000
+
+            for sample in dataset:
+                batch.append(sample)
+
+                if len(batch) >= batch_size:
+                    # Create a batch dict from list of samples
+                    batch_dict = {}
+                    for key in batch[0].keys():
+                        batch_dict[key] = [sample[key] for sample in batch]
+
+                    # Apply filter function to batch
+                    keep_mask = filter_fn(batch_dict)
+
+                    # Yield samples that should be kept
+                    for i, keep in enumerate(keep_mask):
+                        if keep:
+                            yield batch[i]
+
+                    batch = []
+
+            # Process remaining samples in batch
+            if batch:
+                batch_dict = {}
+                for key in batch[0].keys():
+                    batch_dict[key] = [sample[key] for sample in batch]
+
+                keep_mask = filter_fn(batch_dict)
+
+                for i, keep in enumerate(keep_mask):
+                    if keep:
+                        yield batch[i]
+        else:
+            # For non-batched filtering, apply filter to each sample individually
+            for sample in dataset:
+                if filter_fn(sample):
+                    yield sample
+
+    # Create new IterableDataset from the filtered generator
+    filtered_dataset = IterableDataset.from_generator(filtered_generator)
+
+    # Preserve the original features if they exist
+    # pylint:disable=protected-access
+    if hasattr(dataset, "_info") and dataset._info.features is not None:
+        filtered_dataset._info.features = dataset._info.features
+
+    return filtered_dataset
+
+
 @torch.jit.script
 def weighted_cross_entropy(
     logits: torch.Tensor, labels: torch.Tensor, weights: torch.Tensor
@@ -282,12 +340,21 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
     drop_long_kwargs = {}
     if filter_map_kwargs:
         drop_long_kwargs["desc"] = "Drop Samples with Zero Trainable Tokens"
-    train_dataset = train_dataset.filter(
-        drop_no_trainable_tokens,
-        batched=True,
-        **filter_map_kwargs,
-        **drop_long_kwargs,
-    )
+
+    # For IterableDatasets, always use custom filtering to avoid features issues
+    if isinstance(train_dataset, IterableDataset):
+        # IterableDatasets often have None features after transformations,
+        # so we use our custom filter implementation that doesn't rely on features
+        train_dataset = _create_filtered_iterable_dataset(
+            train_dataset, drop_no_trainable_tokens, batched=True
+        )
+    else:
+        train_dataset = train_dataset.filter(
+            drop_no_trainable_tokens,
+            batched=True,
+            **filter_map_kwargs,
+            **drop_long_kwargs,
+        )
     if prior_len:
         dropped = prior_len - len(train_dataset)
         if dropped:
@@ -472,7 +539,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
             )
 
             data_loader = DataLoader(
-                train_dataset.remove_columns(["length"]),
+                train_dataset,
                 batch_sampler=sampler,
             )
             data_loader_len = len(data_loader) * cfg.micro_batch_size // cfg.batch_size
@@ -547,7 +614,7 @@ 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/integrations/test_kd.py

@@ -25,7 +25,7 @@ def min_cfg(temp_dir):
         "liger_rms_norm": True,
         "liger_glu_activation": True,
         "torch_compile": True,
-        "chat_template": "llama3",
+        "chat_template": "qwen3",
         "kd_trainer": True,
         "kd_ce_alpha": 0.1,
         "kd_alpha": 0.9,

tests/e2e/test_diffusion.py

@@ -1,119 +0,0 @@
-"""E2E smoke test for diffusion training plugin."""
-
-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 tests.e2e.utils import check_model_output_exists
-
-
-class TestDiffusion:
-    """Test case for diffusion training plugin."""
-
-    def test_diffusion_smoke_test(self, temp_dir):
-        """
-        Smoke test for diffusion training to ensure the plugin loads and trains without
-        error.
-        """
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "tokenizer_type": "AutoTokenizer",
-                "trust_remote_code": True,
-                "sequence_len": 256,
-                "val_set_size": 0.1,
-                "special_tokens": {
-                    "pad_token": "<|endoftext|>",
-                },
-                "datasets": [
-                    {
-                        "path": "mhenrichsen/alpaca_2k_test",
-                        "type": "alpaca",
-                    },
-                ],
-                "num_epochs": 1,
-                "max_steps": 3,
-                "micro_batch_size": 1,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.0001,
-                "optimizer": "adamw_torch",
-                "lr_scheduler": "cosine",
-                "bf16": True,
-                "save_safetensors": True,
-                "save_first_step": False,
-                "logging_steps": 1,
-                "eval_steps": 3,
-                # Diffusion-specific config
-                "plugins": ["axolotl.integrations.diffusion.DiffusionPlugin"],
-                "diffusion_mask_token_id": 16,
-                "diffusion_eps": 1e-3,
-                "diffusion_importance_weighting": 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)
-
-    def test_diffusion_sft_labels(self, temp_dir):
-        """Test that diffusion training properly handles SFT data with labels."""
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "tokenizer_type": "AutoTokenizer",
-                "trust_remote_code": True,
-                "sequence_len": 256,
-                "val_set_size": 0.1,
-                "special_tokens": {
-                    "pad_token": "<|endoftext|>",
-                },
-                "datasets": [
-                    {
-                        "path": "mhenrichsen/alpaca_2k_test",
-                        "type": "alpaca",
-                    },
-                ],
-                "num_epochs": 1,
-                "max_steps": 3,
-                "micro_batch_size": 1,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.0001,
-                "optimizer": "adamw_torch",
-                "lr_scheduler": "cosine",
-                "bf16": True,
-                "save_safetensors": True,
-                "save_first_step": False,
-                "logging_steps": 1,
-                "eval_steps": 2,
-                # Diffusion-specific config
-                "plugins": ["axolotl.integrations.diffusion.DiffusionPlugin"],
-                "diffusion_mask_token_id": 16,
-                "diffusion_eps": 1e-3,
-                "diffusion_importance_weighting": True,
-                # Ensure we have proper SFT labels
-                "train_on_inputs": False,
-            }
-        )
-
-        cfg = validate_config(cfg)
-        normalize_config(cfg)
-        dataset_meta = load_datasets(cfg=cfg)
-
-        # Verify that the dataset has labels
-        sample = dataset_meta.train_dataset[0]
-        assert "labels" in sample, "SFT dataset should have labels"
-
-        # Check that some labels are -100 (prompt tokens)
-        labels = sample["labels"]
-        if hasattr(labels, "tolist"):
-            labels = labels.tolist()
-        assert -100 in labels, "SFT dataset should have -100 labels for prompt tokens"
-
-        train(cfg=cfg, dataset_meta=dataset_meta)
-        check_model_output_exists(temp_dir, cfg)

tests/e2e/test_streaming.py

@@ -0,0 +1,185 @@
+"""E2E tests for streaming dataset functionality"""
+
+# pylint: disable=duplicate-code
+
+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_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/integrations/test_diffusion.py

@@ -1,271 +0,0 @@
-"""Tests for diffusion trainer integration."""
-
-# pylint: disable=redefined-outer-name,protected-access
-
-from unittest.mock import Mock
-
-import pytest
-import torch
-
-from axolotl.integrations.diffusion.trainer import DiffusionTrainer
-from axolotl.utils.dict import DictDefault
-
-
-@pytest.fixture
-def mock_tokenizer():
-    """Create a mock tokenizer."""
-    tokenizer = Mock()
-    tokenizer.bos_token_id = 1
-    tokenizer.eos_token_id = 2
-    tokenizer.pad_token_id = 0
-    return tokenizer
-
-
-@pytest.fixture
-def diffusion_config():
-    """Create a diffusion config."""
-    return DictDefault(
-        {
-            "mask_token_id": 32000,
-            "eps": 1e-3,
-            "importance_weighting": False,
-            "sample_packing": False,
-        }
-    )
-
-
-@pytest.fixture
-def diffusion_trainer_instance(mock_tokenizer, diffusion_config):
-    """Create a diffusion trainer instance for testing methods directly."""
-    # Create a minimal trainer instance just for testing methods
-    trainer = object.__new__(DiffusionTrainer)  # Bypass __init__
-    trainer.config = diffusion_config
-    trainer._special_token_ids = {0, 1, 2}  # pad, bos, eos
-    trainer.processing_class = mock_tokenizer
-    trainer.store_metrics = Mock()  # Mock metrics storage
-    return trainer
-
-
-class TestDiffusionTrainer:
-    """Test the DiffusionTrainer class."""
-
-    def test_forward_process_basic(self, diffusion_trainer_instance):
-        """Test basic forward process without labels."""
-        input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
-
-        noisy_batch, masked_indices, p_mask = (
-            diffusion_trainer_instance._forward_process(input_ids, eps=0.1)
-        )
-
-        # Check shapes
-        assert noisy_batch.shape == input_ids.shape
-        assert masked_indices.shape == input_ids.shape
-        assert p_mask.shape == input_ids.shape
-
-        # Check that special tokens are not masked
-        special_token_positions = (input_ids == 1) | (input_ids == 2) | (input_ids == 0)
-        assert not masked_indices[special_token_positions].any()
-
-        # Check that mask token is applied
-        mask_token_id = diffusion_trainer_instance._config.mask_token_id
-        masked_positions = masked_indices
-        if masked_positions.any():
-            assert (noisy_batch[masked_positions] == mask_token_id).all()
-
-    def test_forward_process_with_labels(self, diffusion_trainer_instance):
-        """Test forward process with SFT labels."""
-        input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
-        labels = torch.tensor([[-100, -100, 20, 30, 2]], dtype=torch.long)
-
-        noisy_batch, masked_indices, p_mask = (
-            diffusion_trainer_instance._forward_process(
-                input_ids, labels=labels, eps=0.1
-            )
-        )
-
-        # Check shapes
-        assert noisy_batch.shape == input_ids.shape
-        assert masked_indices.shape == input_ids.shape
-        assert p_mask.shape == input_ids.shape
-
-        # Check that only answer tokens can be masked (where labels != -100)
-        non_answer_mask = labels == -100
-
-        # No masking should occur on non-answer tokens
-        assert not masked_indices[non_answer_mask].any()
-
-        # p_mask should be the same for all positions (sampled timestep),
-        # but masking is only applied to answer tokens
-        assert p_mask.shape == input_ids.shape
-        # Verify that masked_indices respects the answer mask
-        assert not masked_indices[non_answer_mask].any()
-
-    def test_forward_process_with_attention_mask(self, diffusion_trainer_instance):
-        """Test forward process with attention mask."""
-        input_ids = torch.tensor([[1, 10, 20, 0]], dtype=torch.long)
-        attention_mask = torch.tensor([[1, 1, 1, 0]], dtype=torch.long)
-
-        _, masked_indices, p_mask = diffusion_trainer_instance._forward_process(
-            input_ids, attention_mask=attention_mask, eps=0.1
-        )
-
-        # Check that padding tokens are not masked
-        padding_positions = attention_mask == 0
-        assert not masked_indices[padding_positions].any()
-        assert (p_mask[padding_positions] == 0).all()
-
-    def test_bidirectional_attention_mask_no_packing(self, diffusion_trainer_instance):
-        """Test bidirectional attention mask without sample packing."""
-        input_ids = torch.tensor([[1, 10, 20, 2]], dtype=torch.long)
-
-        mask = diffusion_trainer_instance._create_bidirectional_attention_mask(
-            input_ids
-        )
-
-        # Should be all-to-all attention
-        expected_shape = (1, 1, 4, 4)
-        assert mask.shape == expected_shape
-        assert mask.all()
-
-    def test_bidirectional_attention_mask_with_packing(
-        self, diffusion_trainer_instance
-    ):
-        """Test bidirectional attention mask with sample packing."""
-        diffusion_trainer_instance._config.sample_packing = True
-        input_ids = torch.tensor([[1, 10, 20, 30, 40, 2]], dtype=torch.long)
-        # Sample IDs: first sample (1), second sample (2)
-        attention_mask = torch.tensor([[1, 1, 1, 2, 2, 2]], dtype=torch.long)
-
-        mask = diffusion_trainer_instance._create_bidirectional_attention_mask(
-            input_ids, attention_mask
-        )
-
-        # Check that tokens within same sample can attend to each other
-        # but not across samples
-        assert mask[0, 0, 0, 1].item()  # First sample tokens can attend to each other
-        assert mask[0, 0, 1, 2].item()
-        assert not mask[0, 0, 0, 3].item()  # Can't attend across samples
-        assert not mask[0, 0, 2, 4].item()
-        assert mask[0, 0, 3, 4].item()  # Second sample tokens can attend to each other
-
-    def test_compute_loss_basic(self, diffusion_trainer_instance):
-        """Test basic loss computation."""
-        # Mock model that returns logits
-        mock_model = Mock()
-        mock_outputs = Mock()
-        vocab_size = 1000
-        seq_len = 5
-        mock_outputs.logits = torch.randn(1, seq_len, vocab_size, requires_grad=True)
-        mock_model.return_value = mock_outputs
-        mock_model.training = True
-
-        input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
-
-        loss, outputs = diffusion_trainer_instance._compute_diffusion_loss(
-            mock_model, input_ids
-        )
-
-        # Check that loss is computed
-        assert isinstance(loss, torch.Tensor)
-        assert loss.requires_grad
-        assert outputs == mock_outputs
-
-        # Check that metrics were stored
-        diffusion_trainer_instance.store_metrics.assert_called_once()
-
-    def test_compute_loss_with_labels(self, diffusion_trainer_instance):
-        """Test loss computation with SFT labels."""
-        # Mock model
-        mock_model = Mock()
-        mock_outputs = Mock()
-        vocab_size = 1000
-        seq_len = 5
-        mock_outputs.logits = torch.randn(1, seq_len, vocab_size, requires_grad=True)
-        mock_model.return_value = mock_outputs
-        mock_model.training = True
-
-        input_ids = torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long)
-        labels = torch.tensor([[-100, -100, 20, 30, 2]], dtype=torch.long)
-
-        loss, _ = diffusion_trainer_instance._compute_diffusion_loss(
-            mock_model, input_ids, labels=labels
-        )
-
-        # Check that loss is computed
-        assert isinstance(loss, torch.Tensor)
-        assert loss.requires_grad
-
-        # Check that SFT metrics were added
-        call_args = diffusion_trainer_instance.store_metrics.call_args[0][0]
-        assert "answer_ratio" in call_args
-        assert "avg_answer_length" in call_args
-
-    def test_compute_loss_no_masked_tokens(self, diffusion_trainer_instance):
-        """Test loss computation when no tokens are masked."""
-        # Mock model
-        mock_model = Mock()
-        mock_outputs = Mock()
-        vocab_size = 1000
-        seq_len = 3
-        mock_outputs.logits = torch.randn(1, seq_len, vocab_size)
-        mock_model.return_value = mock_outputs
-        mock_model.training = True
-
-        # Only special tokens (which won't be masked)
-        input_ids = torch.tensor([[1, 0, 2]], dtype=torch.long)
-
-        loss, _ = diffusion_trainer_instance._compute_diffusion_loss(
-            mock_model, input_ids
-        )
-
-        # Loss should be zero when no tokens are masked
-        assert loss.item() == 0.0
-        assert loss.requires_grad
-
-    def test_cache_special_token_ids(self, diffusion_trainer_instance):
-        """Test caching of special token IDs."""
-        # Should cache BOS, EOS, PAD tokens
-        expected_tokens = {0, 1, 2}  # pad, bos, eos
-        assert diffusion_trainer_instance._special_token_ids == expected_tokens
-
-    def test_cache_special_token_ids_no_tokenizer(self):
-        """Test caching when no tokenizer is available."""
-        trainer = object.__new__(DiffusionTrainer)  # Bypass __init__
-        trainer.processing_class = None
-        trainer._cache_special_token_ids()
-
-        assert trainer._special_token_ids == set()
-
-    def test_main_compute_loss_interface(self, diffusion_trainer_instance):
-        """Test the main compute_loss interface."""
-        # Mock model
-        mock_model = Mock()
-        mock_outputs = Mock()
-        mock_outputs.logits = torch.randn(1, 5, 1000)
-        mock_model.return_value = mock_outputs
-        mock_model.training = True
-
-        inputs = {
-            "input_ids": torch.tensor([[1, 10, 20, 30, 2]], dtype=torch.long),
-            "attention_mask": torch.tensor([[1, 1, 1, 1, 1]], dtype=torch.long),
-            "labels": torch.tensor([[-100, -100, 20, 30, 2]], dtype=torch.long),
-        }
-
-        # Test without return_outputs
-        loss = diffusion_trainer_instance.compute_loss(mock_model, inputs)
-        assert isinstance(loss, torch.Tensor)
-
-        # Test with return_outputs
-        loss, outputs = diffusion_trainer_instance.compute_loss(
-            mock_model, inputs, return_outputs=True
-        )
-        assert isinstance(loss, torch.Tensor)
-        assert outputs == mock_outputs
-
-    def test_missing_input_ids_raises_error(self, diffusion_trainer_instance):
-        """Test that missing input_ids raises ValueError."""
-        mock_model = Mock()
-        inputs = {"attention_mask": torch.tensor([[1, 1, 1]])}
-
-        with pytest.raises(ValueError, match="input_ids is required"):
-            diffusion_trainer_instance.compute_loss(mock_model, inputs)

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 +505,162 @@ 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

tests/test_packed_dataset.py

@@ -1,16 +1,11 @@
 """Module for testing dataset sequence packing"""
 
 import unittest
-from pathlib import Path
 
-from datasets import Dataset, load_dataset
 from transformers import AutoTokenizer
 
 from axolotl.cli.args import TrainerCliArgs
 from axolotl.common.datasets import load_datasets
-from axolotl.datasets import ConstantLengthDataset, TokenizedPromptDataset
-from axolotl.prompt_tokenizers import AlpacaPromptTokenizingStrategy
-from axolotl.prompters import AlpacaPrompter
 from axolotl.train import setup_model_and_trainer
 from axolotl.utils.config import normalize_config, validate_config
 from axolotl.utils.dict import DictDefault
@@ -36,43 +31,6 @@ class TestPacking(unittest.TestCase):
             }
         )
 
-    def test_increments_attention(self):
-        prompter = AlpacaPrompter("chat")
-        strat = AlpacaPromptTokenizingStrategy(
-            prompter,
-            self.tokenizer,
-            False,
-            2048,
-        )
-        dateset = load_dataset(
-            "json",
-            data_files=str(Path(__file__).parent / "fixtures/alpaca/alpaca.json"),
-        )["train"]
-        dataset = Dataset.from_list(list(TokenizedPromptDataset(strat, dateset)))
-
-        constant_len_dataset = ConstantLengthDataset(
-            self.tokenizer,
-            [dataset],
-            seq_length=2048,
-        )
-        packed_dataset = Dataset.from_list(list(constant_len_dataset))
-        example = packed_dataset[0]
-        next_bos_index = (
-            example["input_ids"][1:].index(self.tokenizer.bos_token_id) + 1
-        )  # add one since we sliced
-
-        # first example doesn't have mask reset
-        assert example["input_ids"][0] == self.tokenizer.bos_token_id
-        assert example["attention_mask"][0] == 1
-        assert example["position_ids"][0] == 0
-        assert example["position_ids"][1] == 1
-
-        # but subsequent one does
-        assert example["input_ids"][next_bos_index] == self.tokenizer.bos_token_id
-        assert example["attention_mask"][next_bos_index] == 2
-        assert example["position_ids"][next_bos_index] == 0
-        assert example["position_ids"][next_bos_index + 1] == 1
-
     @with_temp_dir
     def test_lora_packing(self, temp_dir):
         # pylint: disable=duplicate-code