diff --git a/pylock.toml b/pylock.toml
index 2fa1b28e..4c6468c2 100644
--- a/pylock.toml
+++ b/pylock.toml
@@ -3,8 +3,8 @@
 lock-version = "1.0"
 requires-python = "<4.0,>=3.9.0"
 environments = [
-    "python_version ~= \"3.10\"",
-    "python_version < \"3.10\" and python_version >= \"3.9\"",
+    "python_version ~= \"3.12\"",
+    "python_version < \"3.12\" and python_version >= \"3.9\"",
 ]
 extras = ["dev", "recommended"]
 dependency-groups = ["default"]
@@ -44,6 +44,24 @@ dependencies = [
     "tomli>=2.0.1; python_version < \"3.11\"",
 ]
 
+[[packages]]
+name = "blobfile"
+version = "3.1.0"
+requires-python = ">=3.8.0"
+sdist = {name = "blobfile-3.1.0.tar.gz", url = "https://files.pythonhosted.org/packages/f0/6d/2e7567da75ddbb24fe979f52284b708da349d67a41042635af36071a5a6b/blobfile-3.1.0.tar.gz", hashes = {sha256 = "d45b6b1fa3b0920732314c23ddbdb4f494ca12f787c2b6eb6bba6faa51382671"}}
+wheels = [
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+]
+marker = "\"recommended\" in extras"
+
+[packages.tool.pdm]
+dependencies = [
+    "pycryptodomex>=3.8",
+    "urllib3<3,>=1.25.3",
+    "lxml>=4.9",
+    "filelock>=3.0",
+]
+
 [[packages]]
 name = "build"
 version = "1.2.2.post1"
@@ -63,6 +81,21 @@ dependencies = [
     "tomli>=1.1.0; python_version < \"3.11\"",
 ]
 
+[[packages]]
+name = "culsans"
+version = "0.9.0"
+requires-python = ">=3.8"
+sdist = {name = "culsans-0.9.0.tar.gz", url = "https://files.pythonhosted.org/packages/90/5d/12e7e16b0caafaa8cca0728dd817204afd1274ddb35531b029b1c5cf7b2a/culsans-0.9.0.tar.gz", hashes = {sha256 = "942dd3c3c77f20e9ac3383d9a5ef8b7b24c0dac1a593bdb20d46c8a38720a5f3"}}
+wheels = [
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+]
+marker = "\"default\" in dependency_groups"
+
+[packages.tool.pdm]
+dependencies = [
+    "aiologic>=0.13.0",
+]
+
 [[packages]]
 name = "ftfy"
 version = "6.3.1"
@@ -78,6 +111,118 @@ dependencies = [
     "wcwidth",
 ]
 
+[[packages]]
+name = "librosa"
+version = "0.11.0"
+requires-python = ">=3.8"
+sdist = {name = "librosa-0.11.0.tar.gz", url = "https://files.pythonhosted.org/packages/64/36/360b5aafa0238e29758729e9486c6ed92a6f37fa403b7875e06c115cdf4a/librosa-0.11.0.tar.gz", hashes = {sha256 = "f5ed951ca189b375bbe2e33b2abd7e040ceeee302b9bbaeeffdfddb8d0ace908"}}
+wheels = [
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+]
+marker = "\"default\" in dependency_groups"
+
+[packages.tool.pdm]
+dependencies = [
+    "audioread>=2.1.9",
+    "numba>=0.51.0",
+    "numpy>=1.22.3",
+    "scipy>=1.6.0",
+    "scikit-learn>=1.1.0",
+    "joblib>=1.0",
+    "decorator>=4.3.0",
+    "soundfile>=0.12.1",
+    "pooch>=1.1",
+    "soxr>=0.3.2",
+    "typing-extensions>=4.1.1",
+    "lazy-loader>=0.1",
+    "msgpack>=1.0",
+    "standard-aifc; python_version >= \"3.13\"",
+    "standard-sunau; python_version >= \"3.13\"",
+]
+
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+marker = "\"default\" in dependency_groups or \"dev\" in extras"
 
 [packages.tool.pdm]
-dependencies = []
+dependencies = [
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+]
 
 [[packages]]
 name = "h2"
@@ -1664,129 +1556,6 @@ dependencies = [
     "markdown-it-py<4.0.0,>=1.0.0",
 ]
 
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+    {name = "scikit_learn-1.6.1-cp311-cp311-win_amd64.whl",url = "https://files.pythonhosted.org/packages/a1/a6/c5b78606743a1f28eae8f11973de6613a5ee87366796583fb74c67d54939/scikit_learn-1.6.1-cp311-cp311-win_amd64.whl",hashes = {sha256 = "fa909b1a36e000a03c382aade0bd2063fd5680ff8b8e501660c0f59f021a6415"}},
+    {name = "scikit_learn-1.6.1-cp310-cp310-macosx_10_9_x86_64.whl",url = "https://files.pythonhosted.org/packages/2e/3a/f4597eb41049110b21ebcbb0bcb43e4035017545daa5eedcfeb45c08b9c5/scikit_learn-1.6.1-cp310-cp310-macosx_10_9_x86_64.whl",hashes = {sha256 = "d056391530ccd1e501056160e3c9673b4da4805eb67eb2bdf4e983e1f9c9204e"}},
+    {name = "scikit_learn-1.6.1-cp310-cp310-macosx_12_0_arm64.whl",url = "https://files.pythonhosted.org/packages/37/19/0423e5e1fd1c6ec5be2352ba05a537a473c1677f8188b9306097d684b327/scikit_learn-1.6.1-cp310-cp310-macosx_12_0_arm64.whl",hashes = {sha256 = "0c8d036eb937dbb568c6242fa598d551d88fb4399c0344d95c001980ec1c7d36"}},
+    {name = "scikit_learn-1.6.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",url = "https://files.pythonhosted.org/packages/70/95/d5cb2297a835b0f5fc9a77042b0a2d029866379091ab8b3f52cc62277808/scikit_learn-1.6.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",hashes = {sha256 = "8634c4bd21a2a813e0a7e3900464e6d593162a29dd35d25bdf0103b3fce60ed5"}},
+    {name = "scikit_learn-1.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",url = "https://files.pythonhosted.org/packages/b7/91/ab3c697188f224d658969f678be86b0968ccc52774c8ab4a86a07be13c25/scikit_learn-1.6.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",hashes = {sha256 = "775da975a471c4f6f467725dff0ced5c7ac7bda5e9316b260225b48475279a1b"}},
+    {name = "scikit_learn-1.6.1-cp310-cp310-win_amd64.whl",url = "https://files.pythonhosted.org/packages/17/04/d5d556b6c88886c092cc989433b2bab62488e0f0dafe616a1d5c9cb0efb1/scikit_learn-1.6.1-cp310-cp310-win_amd64.whl",hashes = {sha256 = "8a600c31592bd7dab31e1c61b9bbd6dea1b3433e67d264d17ce1017dbdce8002"}},
+    {name = "scikit_learn-1.6.1-cp39-cp39-macosx_10_9_x86_64.whl",url = "https://files.pythonhosted.org/packages/d2/37/b305b759cc65829fe1b8853ff3e308b12cdd9d8884aa27840835560f2b42/scikit_learn-1.6.1-cp39-cp39-macosx_10_9_x86_64.whl",hashes = {sha256 = "6849dd3234e87f55dce1db34c89a810b489ead832aaf4d4550b7ea85628be6c1"}},
+    {name = "scikit_learn-1.6.1-cp39-cp39-macosx_12_0_arm64.whl",url = "https://files.pythonhosted.org/packages/83/74/f64379a4ed5879d9db744fe37cfe1978c07c66684d2439c3060d19a536d8/scikit_learn-1.6.1-cp39-cp39-macosx_12_0_arm64.whl",hashes = {sha256 = "e7be3fa5d2eb9be7d77c3734ff1d599151bb523674be9b834e8da6abe132f44e"}},
+    {name = "scikit_learn-1.6.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",url = "https://files.pythonhosted.org/packages/fd/dc/d5457e03dc9c971ce2b0d750e33148dd060fefb8b7dc71acd6054e4bb51b/scikit_learn-1.6.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",hashes = {sha256 = "44a17798172df1d3c1065e8fcf9019183f06c87609b49a124ebdf57ae6cb0107"}},
+    {name = "scikit_learn-1.6.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",url = "https://files.pythonhosted.org/packages/79/35/b1d2188967c3204c78fa79c9263668cf1b98060e8e58d1a730fe5b2317bb/scikit_learn-1.6.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",hashes = {sha256 = "b8b7a3b86e411e4bce21186e1c180d792f3d99223dcfa3b4f597ecc92fa1a422"}},
+    {name = "scikit_learn-1.6.1-cp39-cp39-win_amd64.whl",url = "https://files.pythonhosted.org/packages/fb/d8/8d603bdd26601f4b07e2363032b8565ab82eb857f93d86d0f7956fcf4523/scikit_learn-1.6.1-cp39-cp39-win_amd64.whl",hashes = {sha256 = "7a73d457070e3318e32bdb3aa79a8d990474f19035464dfd8bede2883ab5dc3b"}},
+]
+marker = "python_version < \"3.12\" and python_version >= \"3.9\" and \"default\" in dependency_groups"
+
+[packages.tool.pdm]
+dependencies = [
+    "numpy>=1.19.5",
+    "scipy>=1.6.0",
+    "joblib>=1.2.0",
+    "threadpoolctl>=3.1.0",
+]
+
+[[packages]]
+name = "zipp"
+version = "3.23.0"
+requires-python = ">=3.9"
+sdist = {name = "zipp-3.23.0.tar.gz", url = "https://files.pythonhosted.org/packages/e3/02/0f2892c661036d50ede074e376733dca2ae7c6eb617489437771209d4180/zipp-3.23.0.tar.gz", hashes = {sha256 = "a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166"}}
+wheels = [
+    {name = "zipp-3.23.0-py3-none-any.whl",url = "https://files.pythonhosted.org/packages/2e/54/647ade08bf0db230bfea292f893923872fd20be6ac6f53b2b936ba839d75/zipp-3.23.0-py3-none-any.whl",hashes = {sha256 = "071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e"}},
+]
+marker = "python_full_version < \"3.10.2\" and python_version >= \"3.9\" and \"dev\" in extras"
+
+[packages.tool.pdm]
+dependencies = []
+
+[[packages]]
+name = "networkx"
+version = "3.2.1"
+requires-python = ">=3.9"
+sdist = {name = "networkx-3.2.1.tar.gz", url = "https://files.pythonhosted.org/packages/c4/80/a84676339aaae2f1cfdf9f418701dd634aef9cc76f708ef55c36ff39c3ca/networkx-3.2.1.tar.gz", hashes = {sha256 = "9f1bb5cf3409bf324e0a722c20bdb4c20ee39bf1c30ce8ae499c8502b0b5e0c6"}}
+wheels = [
+    {name = "networkx-3.2.1-py3-none-any.whl",url = "https://files.pythonhosted.org/packages/d5/f0/8fbc882ca80cf077f1b246c0e3c3465f7f415439bdea6b899f6b19f61f70/networkx-3.2.1-py3-none-any.whl",hashes = {sha256 = "f18c69adc97877c42332c170849c96cefa91881c99a7cb3e95b7c659ebdc1ec2"}},
+]
+marker = "python_version < \"3.12\" and python_version >= \"3.9\" and \"default\" in dependency_groups"
 
 [packages.tool.pdm]
 dependencies = []
 
 [tool.pdm]
-hashes = {sha256 = "4909a619da3f004dcd3f4ece16e07e96f81709904464fc614e66b456c5f8c73e"}
+hashes = {sha256 = "555b4bbcb733817760de0902c1c4437a46a7d37708dba2168675e50f454e6361"}
 strategy = ["inherit_metadata", "static_urls"]
 
 [[tool.pdm.targets]]
-requires_python = "~=3.10"
+requires_python = "~=3.12"
 
 [[tool.pdm.targets]]
-requires_python = ">=3.9,<3.10"
+requires_python = ">=3.9,<3.12"
diff --git a/pyproject.toml b/pyproject.toml
index fbe054ad..3461530d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,6 +13,13 @@ include = ["*"]
 [tool.pdm]
 distribution = true
 
+[[tool.pdm.source]]
+name = "torch"
+type = "find_links"
+#url = "https://download.pytorch.org/whl/cpu/torch_stable.html"
+url = "https://download.pytorch.org/whl/cpu/torch/"
+include_packages = ["torch"]
+
 
 # ************************************************
 # ********** Project Metadata **********
@@ -59,23 +66,21 @@ dependencies = [
     "protobuf",
     "pydantic>=2.11.7",
     "pydantic-settings>=2.0.0",
+    "pydub",
     "pyyaml>=6.0.0",
     "rich",
     "sanic",
     "transformers",
     "uvloop>=0.18",
+    "librosa>=0.11.0",
+    "torch",
 ]
 
 [project.optional-dependencies]
-perf = [
-    "orjson",
-    "msgpack",
-    "msgspec",
-    "uvloop",
-]
+perf = ["orjson", "msgpack", "msgspec", "uvloop"]
 recommended = [
-    "tiktoken>=0.11.0",  # For OpenAI tokenizer
-    "blobfile>=3.1.0",   # For OpenAI tokenizer
+    "tiktoken>=0.11.0", # For OpenAI tokenizer
+    "blobfile>=3.1.0",  # For OpenAI tokenizer
 ]
 dev = [
     # build
@@ -118,7 +123,7 @@ dev = [
 ]
 
 [dependency-groups]
-dev = [ "guidellm[dev]" ]
+dev = ["guidellm[dev]"]
 
 [project.urls]
 homepage = "https://github.com/vllm-project/guidellm"
diff --git a/src/guidellm/__init__.py b/src/guidellm/__init__.py
index f2206e94..dde6e937 100644
--- a/src/guidellm/__init__.py
+++ b/src/guidellm/__init__.py
@@ -7,6 +7,8 @@
 import logging
 import os
 
+from datasets.utils.logging import disable_progress_bar
+
 with (
     open(os.devnull, "w") as devnull,  # noqa: PTH123
     contextlib.redirect_stderr(devnull),
@@ -19,6 +21,7 @@
     os.environ["TOKENIZERS_PARALLELISM"] = "false"  # Silence warnings for tokenizers
     hf_logging.set_verbosity_error()
     logging.getLogger("transformers").setLevel(logging.ERROR)
+    disable_progress_bar()
 
 from .logger import configure_logger, logger
 from .settings import (
diff --git a/src/guidellm/__main__.py b/src/guidellm/__main__.py
index 13a748d5..43939fa7 100644
--- a/src/guidellm/__main__.py
+++ b/src/guidellm/__main__.py
@@ -56,6 +56,7 @@
 from guidellm.benchmark.scenario import (
     GenerativeTextScenario,
 )
+from guidellm.data import GenerativeRequestType
 from guidellm.mock_server import MockServer, MockServerConfig
 from guidellm.preprocess.dataset import ShortPromptStrategy, process_dataset
 from guidellm.scheduler import StrategyType
@@ -143,6 +144,7 @@ def benchmark():
 @click.option(
     "--data",
     type=str,
+    multiple=True,
     help=(
         "The HuggingFace dataset ID, a path to a HuggingFace dataset, "
         "a path to a data file csv, json, jsonl, or txt, "
@@ -171,12 +173,6 @@ def benchmark():
         "For rate-type=synchronous,throughput, this must not be set."
     ),
 )
-@click.option(
-    "--random-seed",
-    default=GenerativeTextScenario.get_default("random_seed"),
-    type=int,
-    help="The random seed to use for benchmarking to ensure reproducibility.",
-)
 # Backend configuration
 @click.option(
     "--backend",
@@ -197,9 +193,7 @@ def benchmark():
     default=None,
     help=(
         "A JSON string containing any arguments to pass to the backend as a "
-        "dict with **kwargs. Headers can be removed by setting their value to "
-        "null. For example: "
-        """'{"headers": {"Authorization": null, "Custom-Header": "Custom-Value"}}'"""
+        "dict with **kwargs."
     ),
 )
 @click.option(
@@ -212,6 +206,24 @@ def benchmark():
     ),
 )
 # Data configuration
+@click.option(
+    "--request-type",
+    default="chat_completions",
+    type=click.Choice(list(get_literal_vals(GenerativeRequestType))),
+    help=(
+        "The type of request to create for each data sample and send to the backend. "
+        f"Supported types: {list(get_literal_vals(GenerativeRequestType))}."
+    ),
+)
+@click.option(
+    "--request-formatter-kwargs",
+    default=None,
+    callback=cli_tools.parse_json,
+    help=(
+        "A JSON string containing any arguments to pass to the request formatter "
+        "as a dict with **kwargs."
+    ),
+)
 @click.option(
     "--processor",
     default=None,
@@ -240,15 +252,52 @@ def benchmark():
         "as a dict with **kwargs."
     ),
 )
+@click.option(
+    "--data-samples",
+    default=-1,
+    type=int,
+    help=(
+        "The number of samples to use from the dataset. If -1 (default), will use all "
+        "samples in the dataset and dynamically generate samples. "
+        "If >1, will precompile that number of items from the dataset configs."
+    ),
+)
+@click.option(
+    "--data-column-mappings",
+    default=None,
+    callback=cli_tools.parse_json,
+    help=(
+        "A JSON string of column mappings to apply to the dataset to map into request "
+        "column types."
+    ),
+)
 @click.option(
     "--data-sampler",
     default=None,
-    type=click.Choice(["random"]),
+    type=click.Choice(["shuffle"]),
+    help="The data sampler type to use.",
+)
+@click.option(
+    "--data-num-workers",
+    default=1,
+    type=int,
+    help="The number of worker processes to use for data loading.",
+)
+@click.option(
+    "--dataloader_kwargs",
+    default=None,
+    callback=cli_tools.parse_json,
     help=(
-        "The data sampler type to use. 'random' will add a random shuffle on the data. "
-        "Defaults to None"
+        "A JSON string containing any arguments to pass to the dataloader constructor "
+        "as a dict with **kwargs."
     ),
 )
+@click.option(
+    "--random-seed",
+    default=GenerativeTextScenario.get_default("random_seed"),
+    type=int,
+    help="The random seed to use for benchmarking to ensure reproducibility.",
+)
 # Output configuration
 @click.option(
     "--output-path",
@@ -378,16 +427,22 @@ def run(
     data,
     profile,
     rate,
-    random_seed,
     # Backend Configuration
     backend,
     backend_kwargs,
     model,
     # Data configuration
+    request_type,
+    request_formatter_kwargs,
     processor,
     processor_args,
     data_args,
+    data_samples,
+    data_column_mappings,
     data_sampler,
+    data_num_workers,
+    dataloader_kwargs,
+    random_seed,
     # Output configuration
     output_path,
     output_formats,
@@ -415,15 +470,21 @@ def run(
     Supports multiple backends, data sources, output formats, and constraint types
     for flexible benchmark configuration.
     """
+    data_request_formatter = (
+        request_type
+        if not request_formatter_kwargs
+        else {"request_type": request_type, **request_formatter_kwargs}
+    )
+
     if HAS_UVLOOP:
         asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
     asyncio.run(
         benchmark_generative_text(
             target=target,
-            data=data,
+            data=list(data),
+            # Benchmark configuration
             profile=profile,
             rate=rate,
-            random_seed=random_seed,
             # Backend configuration
             backend=backend,
             backend_kwargs=backend_kwargs,
@@ -432,7 +493,13 @@ def run(
             processor=processor,
             processor_args=processor_args,
             data_args=data_args,
+            data_samples=data_samples,
+            data_column_mapper=data_column_mappings,
+            data_request_formatter=data_request_formatter,
             data_sampler=data_sampler,
+            data_num_workers=data_num_workers,
+            dataloader_kwargs=dataloader_kwargs,
+            random_seed=random_seed,
             # Output configuration
             output_path=output_path,
             output_formats=[
@@ -455,7 +522,7 @@ def run(
             add_aggregators={"extras": InjectExtrasAggregator(extras=output_extras)},
             warmup=warmup,
             cooldown=cooldown,
-            request_samples=request_samples,
+            sample_requests=request_samples,
             # Constraints configuration
             max_seconds=max_seconds,
             max_requests=max_requests,
diff --git a/src/guidellm/backends/__init__.py b/src/guidellm/backends/__init__.py
index 064722ac..4bcf5683 100644
--- a/src/guidellm/backends/__init__.py
+++ b/src/guidellm/backends/__init__.py
@@ -13,6 +13,7 @@
     GenerationRequest,
     GenerationRequestTimings,
     GenerationResponse,
+    GenerationTokenStats,
 )
 from .openai import OpenAIHTTPBackend
 
@@ -22,5 +23,6 @@
     "GenerationRequest",
     "GenerationRequestTimings",
     "GenerationResponse",
+    "GenerationTokenStats",
     "OpenAIHTTPBackend",
 ]
diff --git a/src/guidellm/backends/backend.py b/src/guidellm/backends/backend.py
index 8f91d5e7..a7d82979 100644
--- a/src/guidellm/backends/backend.py
+++ b/src/guidellm/backends/backend.py
@@ -115,5 +115,6 @@ def requests_limit(self) -> int | None:
     async def default_model(self) -> str | None:
         """
         :return: The default model name or identifier for generation requests.
+            None if no default model is available.
         """
         ...
diff --git a/src/guidellm/backends/objects.py b/src/guidellm/backends/objects.py
index 05280940..88d25949 100644
--- a/src/guidellm/backends/objects.py
+++ b/src/guidellm/backends/objects.py
@@ -6,62 +6,51 @@
 implementations.
 """
 
-import uuid
-from typing import Any, Literal, Optional
+from __future__ import annotations
+
+from typing import Literal
 
 from pydantic import Field
 
+from guidellm.data import (
+    GenerationRequest,
+    GenerationRequestArguments,
+    GenerationRequestTimings,
+)
 from guidellm.scheduler import (
-    MeasuredRequestTimings,
     SchedulerMessagingPydanticRegistry,
 )
 from guidellm.utils import StandardBaseModel
 
 __all__ = [
     "GenerationRequest",
+    "GenerationRequestArguments",
     "GenerationRequestTimings",
     "GenerationResponse",
+    "GenerationTokenStats",
 ]
 
 
 @SchedulerMessagingPydanticRegistry.register()
-class GenerationRequest(StandardBaseModel):
-    """Request model for backend generation operations."""
+class GenerationTokenStats(StandardBaseModel):
+    """Token statistics for generation requests and responses."""
 
-    request_id: str = Field(
-        default_factory=lambda: str(uuid.uuid4()),
-        description="Unique identifier for the request.",
-    )
-    request_type: Literal["text_completions", "chat_completions"] = Field(
-        default="text_completions",
-        description=(
-            "Type of request. 'text_completions' uses backend.text_completions(), "
-            "'chat_completions' uses backend.chat_completions()."
-        ),
-    )
-    content: Any = Field(
-        description=(
-            "Request content. For text_completions: string or list of strings. "
-            "For chat_completions: string, list of messages, or raw content "
-            "(set raw_content=True in params)."
-        )
-    )
-    params: dict[str, Any] = Field(
-        default_factory=dict,
-        description=(
-            "Additional parameters passed to backend methods. "
-            "Common: max_tokens, temperature, stream."
-        ),
+    request: int | None = Field(
+        default=None, description="Number of tokens in the original request."
     )
-    stats: dict[Literal["prompt_tokens"], int] = Field(
-        default_factory=dict,
-        description="Request statistics including prompt token count.",
-    )
-    constraints: dict[Literal["output_tokens"], int] = Field(
-        default_factory=dict,
-        description="Request constraints such as maximum output tokens.",
+    response: int | None = Field(
+        default=None, description="Number of tokens in the generated response."
     )
 
+    def value(
+        self, preference: Literal["request", "response"] | None = None
+    ) -> int | None:
+        if preference == "request":
+            return self.request
+        if preference == "response":
+            return self.response
+        return self.response if self.response is not None else self.request
+
 
 @SchedulerMessagingPydanticRegistry.register()
 class GenerationResponse(StandardBaseModel):
@@ -70,87 +59,32 @@ class GenerationResponse(StandardBaseModel):
     request_id: str = Field(
         description="Unique identifier matching the original GenerationRequest."
     )
-    request_args: dict[str, Any] = Field(
+    request_args: GenerationRequestArguments = Field(
         description="Arguments passed to the backend for this request."
     )
-    value: Optional[str] = Field(
+    text: str | None = Field(
         default=None,
-        description="Complete generated text content. None for streaming responses.",
-    )
-    delta: Optional[str] = Field(
-        default=None, description="Incremental text content for streaming responses."
+        description="The generated response text.",
     )
     iterations: int = Field(
         default=0, description="Number of generation iterations completed."
     )
-    request_prompt_tokens: Optional[int] = Field(
-        default=None, description="Token count from the original request prompt."
-    )
-    request_output_tokens: Optional[int] = Field(
-        default=None,
-        description="Expected output token count from the original request.",
-    )
-    response_prompt_tokens: Optional[int] = Field(
-        default=None, description="Actual prompt token count reported by the backend."
+
+    prompt_stats: GenerationTokenStats = Field(
+        default_factory=GenerationTokenStats,
+        description="Token statistics from the prompt.",
     )
-    response_output_tokens: Optional[int] = Field(
-        default=None, description="Actual output token count reported by the backend."
+    output_stats: GenerationTokenStats = Field(
+        default_factory=GenerationTokenStats,
+        description="Token statistics from the generated output.",
     )
 
-    @property
-    def prompt_tokens(self) -> Optional[int]:
-        """
-        :return: The number of prompt tokens used in the request
-            (response_prompt_tokens if available, otherwise request_prompt_tokens).
-        """
-        return self.response_prompt_tokens or self.request_prompt_tokens
-
-    @property
-    def output_tokens(self) -> Optional[int]:
-        """
-        :return: The number of output tokens generated in the response
-            (response_output_tokens if available, otherwise request_output_tokens).
-        """
-        return self.response_output_tokens or self.request_output_tokens
-
-    @property
-    def total_tokens(self) -> Optional[int]:
-        """
-        :return: The total number of tokens used in the request and response.
-            Sum of prompt_tokens and output_tokens.
-        """
-        if self.prompt_tokens is None or self.output_tokens is None:
-            return None
-        return self.prompt_tokens + self.output_tokens
-
-    def preferred_prompt_tokens(
-        self, preferred_source: Literal["request", "response"]
-    ) -> Optional[int]:
-        if preferred_source == "request":
-            return self.request_prompt_tokens or self.response_prompt_tokens
-        else:
-            return self.response_prompt_tokens or self.request_prompt_tokens
-
-    def preferred_output_tokens(
-        self, preferred_source: Literal["request", "response"]
-    ) -> Optional[int]:
-        if preferred_source == "request":
-            return self.request_output_tokens or self.response_output_tokens
-        else:
-            return self.response_output_tokens or self.request_output_tokens
-
-
-@SchedulerMessagingPydanticRegistry.register()
-@MeasuredRequestTimings.register("generation_request_timings")
-class GenerationRequestTimings(MeasuredRequestTimings):
-    """Timing model for tracking generation request lifecycle events."""
+    def total_tokens(
+        self, preference: Literal["request", "response"] | None = None
+    ) -> int | None:
+        prompt_tokens = self.prompt_stats.value(preference=preference)
+        output_tokens = self.output_stats.value(preference=preference)
 
-    timings_type: Literal["generation_request_timings"] = "generation_request_timings"
-    first_iteration: Optional[float] = Field(
-        default=None,
-        description="Unix timestamp when the first generation iteration began.",
-    )
-    last_iteration: Optional[float] = Field(
-        default=None,
-        description="Unix timestamp when the last generation iteration completed.",
-    )
+        if prompt_tokens is None and output_tokens is None:
+            return None
+        return (prompt_tokens or 0) + (output_tokens or 0)
diff --git a/src/guidellm/backends/openai.py b/src/guidellm/backends/openai.py
index ce83076f..22394afe 100644
--- a/src/guidellm/backends/openai.py
+++ b/src/guidellm/backends/openai.py
@@ -10,17 +10,15 @@
     OpenAIHTTPBackend: HTTP backend for OpenAI-compatible API servers.
 """
 
-import base64
-import contextlib
-import copy
+from __future__ import annotations
+
+import asyncio
 import json
 import time
 from collections.abc import AsyncIterator
-from pathlib import Path
-from typing import Any, ClassVar, Optional, Union
+from typing import Any, cast
 
 import httpx
-from PIL import Image
 from pydantic import dataclasses
 
 from guidellm.backends.backend import Backend
@@ -28,9 +26,18 @@
     GenerationRequest,
     GenerationRequestTimings,
     GenerationResponse,
+    GenerationTokenStats,
 )
 from guidellm.scheduler import ScheduledRequestInfo
 
+try:
+    import orjson
+
+    HAS_ORJSON = True
+except ImportError:
+    orjson = None
+    HAS_ORJSON = False
+
 __all__ = ["OpenAIHTTPBackend", "UsageStats"]
 
 
@@ -38,8 +45,18 @@
 class UsageStats:
     """Token usage statistics for generation requests."""
 
-    prompt_tokens: Optional[int] = None
-    output_tokens: Optional[int] = None
+    prompt_tokens: int | None = None
+    output_tokens: int | None = None
+
+
+open_ai_paths: dict[str, str] = {
+    "health": "health",
+    "models": "v1/models",
+    "text_completions": "v1/completions",
+    "chat_completions": "v1/chat/completions",
+    "audio_transcriptions": "v1/audio/transcriptions",
+    "audio_translations": "v1/audio/translations",
+}
 
 
 @Backend.register("openai_http")
@@ -66,78 +83,34 @@ class OpenAIHTTPBackend(Backend):
         await backend.process_shutdown()
     """
 
-    HEALTH_PATH: ClassVar[str] = "/health"
-    MODELS_PATH: ClassVar[str] = "/v1/models"
-    TEXT_COMPLETIONS_PATH: ClassVar[str] = "/v1/completions"
-    CHAT_COMPLETIONS_PATH: ClassVar[str] = "/v1/chat/completions"
-
-    MODELS_KEY: ClassVar[str] = "models"
-    TEXT_COMPLETIONS_KEY: ClassVar[str] = "text_completions"
-    CHAT_COMPLETIONS_KEY: ClassVar[str] = "chat_completions"
-
     def __init__(
         self,
         target: str,
-        model: Optional[str] = None,
-        api_key: Optional[str] = None,
-        organization: Optional[str] = None,
-        project: Optional[str] = None,
+        model: str | None = None,
         timeout: float = 60.0,
         http2: bool = True,
         follow_redirects: bool = True,
-        max_output_tokens: Optional[int] = None,
-        stream_response: bool = True,
-        extra_query: Optional[dict] = None,
-        extra_body: Optional[dict] = None,
-        remove_from_body: Optional[list[str]] = None,
-        headers: Optional[dict] = None,
         verify: bool = False,
+        validate_backend: bool | str | dict[str, Any] = True,
     ):
-        """
-        Initialize OpenAI HTTP backend.
-
-        :param target: Target URL for the OpenAI server (e.g., "http://localhost:8000").
-        :param model: Model to use for requests. If None, uses first available model.
-        :param api_key: API key for authentication. Adds Authorization header
-            if provided.
-        :param organization: Organization ID. Adds OpenAI-Organization header
-            if provided.
-        :param project: Project ID. Adds OpenAI-Project header if provided.
-        :param timeout: Request timeout in seconds. Defaults to 60 seconds.
-        :param http2: Whether to use HTTP/2. Defaults to True.
-        :param follow_redirects: Whether to follow redirects. Default True.
-        :param max_output_tokens: Maximum tokens for completions. If None, none is set.
-        :param stream_response: Whether to stream responses by default. Can be
-            overridden per request. Defaults to True.
-        :param extra_query: Additional query parameters. Both general and
-            endpoint-specific with type keys supported.
-        :param extra_body: Additional body parameters. Both general and
-            endpoint-specific with type keys supported.
-        :param remove_from_body: Parameter names to remove from request bodies.
-        :param headers: Additional HTTP headers.
-        :param verify: Whether to verify SSL certificates. Default False.
-        """
         super().__init__(type_="openai_http")
 
         # Request Values
         self.target = target.rstrip("/").removesuffix("/v1")
         self.model = model
-        self.headers = self._build_headers(api_key, organization, project, headers)
 
         # Store configuration
         self.timeout = timeout
         self.http2 = http2
         self.follow_redirects = follow_redirects
         self.verify = verify
-        self.max_output_tokens = max_output_tokens
-        self.stream_response = stream_response
-        self.extra_query = extra_query or {}
-        self.extra_body = extra_body or {}
-        self.remove_from_body = remove_from_body or []
+        self.validate_backend: dict[str, Any] | None = self._resolve_validate_kwargs(
+            validate_backend
+        )
 
         # Runtime state
         self._in_process = False
-        self._async_client: Optional[httpx.AsyncClient] = None
+        self._async_client: httpx.AsyncClient | None = None
 
     @property
     def info(self) -> dict[str, Any]:
@@ -147,20 +120,12 @@ def info(self) -> dict[str, Any]:
         return {
             "target": self.target,
             "model": self.model,
-            "headers": self.headers,
             "timeout": self.timeout,
             "http2": self.http2,
             "follow_redirects": self.follow_redirects,
             "verify": self.verify,
-            "max_output_tokens": self.max_output_tokens,
-            "stream_response": self.stream_response,
-            "extra_query": self.extra_query,
-            "extra_body": self.extra_body,
-            "remove_from_body": self.remove_from_body,
-            "health_path": self.HEALTH_PATH,
-            "models_path": self.MODELS_PATH,
-            "text_completions_path": self.TEXT_COMPLETIONS_PATH,
-            "chat_completions_path": self.CHAT_COMPLETIONS_PATH,
+            "openai_paths": open_ai_paths,
+            "validate_backend": self.validate_backend,
         }
 
     async def process_startup(self):
@@ -206,45 +171,17 @@ async def validate(self):
         """
         self._check_in_process()
 
-        if self.model:
-            with contextlib.suppress(httpx.TimeoutException, httpx.HTTPStatusError):
-                # Model is set, use /health endpoint as first check
-                target = f"{self.target}{self.HEALTH_PATH}"
-                headers = self._get_headers()
-                response = await self._async_client.get(target, headers=headers)  # type: ignore [union-attr]
-                response.raise_for_status()
-
-                return
-
-        with contextlib.suppress(httpx.TimeoutException, httpx.HTTPStatusError):
-            # Check if models endpoint is available next
-            models = await self.available_models()
-            if models and not self.model:
-                self.model = models[0]
-            elif not self.model:
-                raise RuntimeError(
-                    "No model available and could not set a default model "
-                    "from the server's available models."
-                )
-
+        if not self.validate_backend:
             return
 
-        with contextlib.suppress(httpx.TimeoutException, httpx.HTTPStatusError):
-            # Last check, fall back on dummy request to text completions
-            async for _, __ in self.text_completions(
-                prompt="Validate backend",
-                request_id="validate",
-                output_token_count=1,
-                stream_response=False,
-            ):
-                pass
-
-            return
-
-        raise RuntimeError(
-            "Backend validation failed. Could not connect to the server or "
-            "validate the backend configuration."
-        )
+        try:
+            response = await self._async_client.request(**self.validate_backend)
+            response.raise_for_status()
+        except Exception as exc:
+            raise RuntimeError(
+                "Backend validation request failed. Could not connect to the server "
+                "or validate the backend configuration."
+            ) from exc
 
     async def available_models(self) -> list[str]:
         """
@@ -256,15 +193,13 @@ async def available_models(self) -> list[str]:
         """
         self._check_in_process()
 
-        target = f"{self.target}{self.MODELS_PATH}"
-        headers = self._get_headers()
-        params = self._get_params(self.MODELS_KEY)
-        response = await self._async_client.get(target, headers=headers, params=params)  # type: ignore [union-attr]
+        target = f"{self.target}/{open_ai_paths['models']}"
+        response = await self._async_client.get(target)
         response.raise_for_status()
 
         return [item["id"] for item in response.json()["data"]]
 
-    async def default_model(self) -> Optional[str]:
+    async def default_model(self) -> str | None:
         """
         Get the default model for this backend.
 
@@ -276,11 +211,11 @@ async def default_model(self) -> Optional[str]:
         models = await self.available_models()
         return models[0] if models else None
 
-    async def resolve(
+    async def resolve(  # noqa: C901
         self,
         request: GenerationRequest,
         request_info: ScheduledRequestInfo,
-        history: Optional[list[tuple[GenerationRequest, GenerationResponse]]] = None,
+        history: list[tuple[GenerationRequest, GenerationResponse]] | None = None,
     ) -> AsyncIterator[tuple[GenerationResponse, ScheduledRequestInfo]]:
         """
         Process a generation request and yield progressive responses.
@@ -300,350 +235,207 @@ async def resolve(
                 "Multi-turn requests with conversation history are not yet supported"
             )
 
-        response = GenerationResponse(
-            request_id=request.request_id,
-            request_args={
-                "request_type": request.request_type,
-                "output_token_count": request.constraints.get("output_tokens"),
-                **request.params,
-            },
-            value="",
-            request_prompt_tokens=request.stats.get("prompt_tokens"),
-            request_output_tokens=request.constraints.get("output_tokens"),
-        )
         request_info.request_timings = GenerationRequestTimings()
-        request_info.request_timings.request_start = time.time()
-
-        completion_method = (
-            self.text_completions
-            if request.request_type == "text_completions"
-            else self.chat_completions
-        )
-        completion_kwargs = (
-            {
-                "prompt": request.content,
-                "request_id": request.request_id,
-                "output_token_count": request.constraints.get("output_tokens"),
-                "stream_response": request.params.get("stream", self.stream_response),
-                **request.params,
-            }
-            if request.request_type == "text_completions"
-            else {
-                "content": request.content,
-                "request_id": request.request_id,
-                "output_token_count": request.constraints.get("output_tokens"),
-                "stream_response": request.params.get("stream", self.stream_response),
-                **request.params,
-            }
+        request.arguments.url = (
+            request.arguments.url or f"{self.target}/{request.arguments.path}"
+            if request.arguments.path is not None
+            else f"{self.target}/{open_ai_paths[request.request_type]}"
         )
+        request_info.request_timings.request_start = time.time()
 
-        async for delta, usage_stats in completion_method(**completion_kwargs):
-            if request_info.request_timings.request_start is None:
-                request_info.request_timings.request_start = time.time()
-
-            if delta is not None:
-                if request_info.request_timings.first_iteration is None:
-                    request_info.request_timings.first_iteration = time.time()
-                response.value += delta  # type: ignore [operator]
-                response.delta = delta
-                request_info.request_timings.last_iteration = time.time()
-                response.iterations += 1
-
-            if usage_stats is not None:
-                request_info.request_timings.request_end = time.time()
-                response.response_output_tokens = usage_stats.output_tokens
-                response.response_prompt_tokens = usage_stats.prompt_tokens
-
-            yield response, request_info
-
-        if request_info.request_timings.request_end is None:
-            request_info.request_timings.request_end = time.time()
-        response.delta = None
-        yield response, request_info
-
-    async def text_completions(
-        self,
-        prompt: Union[str, list[str]],
-        request_id: Optional[str],  # noqa: ARG002
-        output_token_count: Optional[int] = None,
-        stream_response: bool = True,
-        **kwargs,
-    ) -> AsyncIterator[tuple[Optional[str], Optional[UsageStats]]]:
-        """
-        Generate text completions using the /v1/completions endpoint.
-
-        :param prompt: Text prompt(s) for completion. Single string or list.
-        :param request_id: Request identifier for tracking.
-        :param output_token_count: Maximum tokens to generate. Overrides default
-            if specified.
-        :param stream_response: Whether to stream response progressively.
-        :param kwargs: Additional request parameters (temperature, top_p, etc.).
-        :yields: Tuples of (generated_text, usage_stats). First yield is (None, None).
-        :raises RuntimeError: If backend is not initialized.
-        :raises HTTPError: If API request fails.
-        """
-        self._check_in_process()
-        target = f"{self.target}{self.TEXT_COMPLETIONS_PATH}"
-        headers = self._get_headers()
-        params = self._get_params(self.TEXT_COMPLETIONS_KEY)
-        body = self._get_body(
-            endpoint_type=self.TEXT_COMPLETIONS_KEY,
-            request_kwargs=kwargs,
-            max_output_tokens=output_token_count,
-            prompt=prompt,
-        )
-        yield None, None  # Initial yield for async iterator to signal start
-
-        if not stream_response:
-            response = await self._async_client.post(  # type: ignore [union-attr]
-                target,
-                headers=headers,
-                params=params,
-                json=body,
+        if not request.arguments.stream:
+            response = await self._async_client.request(
+                request.arguments.method or "POST",
+                request.arguments.url,
+                content=request.arguments.content_body,
+                files=request.arguments.files,
+                json=request.arguments.json_body,
+                params=request.arguments.params,
+                headers=request.arguments.headers,
             )
             response.raise_for_status()
             data = response.json()
+            prompt_stats, output_stats = self._extract_response_stats(data, request)
+            request_info.request_timings.request_end = time.time()
+
             yield (
-                self._get_completions_text_content(data),
-                self._get_completions_usage_stats(data),
+                GenerationResponse(
+                    request_id=request.request_id,
+                    request_args=request.arguments,
+                    text=self._extract_response_text(data),
+                    iterations=0,
+                    prompt_stats=prompt_stats,
+                    output_stats=output_stats,
+                ),
+                request_info,
             )
             return
 
-        body.update({"stream": True, "stream_options": {"include_usage": True}})
-        async with self._async_client.stream(  # type: ignore [union-attr]
-            "POST",
-            target,
-            headers=headers,
-            params=params,
-            json=body,
-        ) as stream:
-            stream.raise_for_status()
-            async for line in stream.aiter_lines():
-                if not line or not line.strip().startswith("data:"):
-                    continue
-                if line.strip() == "data: [DONE]":
-                    break
-                data = json.loads(line.strip()[len("data: ") :])
-                yield (
-                    self._get_completions_text_content(data),
-                    self._get_completions_usage_stats(data),
-                )
-
-    async def chat_completions(
-        self,
-        content: Union[
-            str,
-            list[Union[str, dict[str, Union[str, dict[str, str]]], Path, Image.Image]],
-            Any,
-        ],
-        request_id: Optional[str] = None,  # noqa: ARG002
-        output_token_count: Optional[int] = None,
-        raw_content: bool = False,
-        stream_response: bool = True,
-        **kwargs,
-    ) -> AsyncIterator[tuple[Optional[str], Optional[UsageStats]]]:
-        """
-        Generate chat completions using the /v1/chat/completions endpoint.
-
-        Supports multimodal inputs including text and images with message formatting.
-
-        :param content: Chat content - string, list of mixed content, or raw content
-            when raw_content=True.
-        :param request_id: Request identifier (currently unused).
-        :param output_token_count: Maximum tokens to generate. Overrides default
-            if specified.
-        :param raw_content: If True, passes content directly without formatting.
-        :param stream_response: Whether to stream response progressively.
-        :param kwargs: Additional request parameters (temperature, top_p, tools, etc.).
-        :yields: Tuples of (generated_text, usage_stats). First yield is (None, None).
-        :raises RuntimeError: If backend is not initialized.
-        :raises HTTPError: If API request fails.
-        """
-        self._check_in_process()
-        target = f"{self.target}{self.CHAT_COMPLETIONS_PATH}"
-        headers = self._get_headers()
-        params = self._get_params(self.CHAT_COMPLETIONS_KEY)
-        body = self._get_body(
-            endpoint_type=self.CHAT_COMPLETIONS_KEY,
-            request_kwargs=kwargs,
-            max_output_tokens=output_token_count,
-            messages=self._get_chat_messages(content) if not raw_content else content,
-            **kwargs,
-        )
-        yield None, None  # Initial yield for async iterator to signal start
+        deltas = []
+        prompt_stats = None
+        output_stats = None
+        end_reached = False
+
+        try:
+            async with self._async_client.stream(
+                request.arguments.method or "POST",
+                request.arguments.url,
+                content=request.arguments.content_body,
+                files=request.arguments.files,
+                json=request.arguments.json_body,
+                params=request.arguments.params,
+                headers=request.arguments.headers,
+            ) as stream:
+                stream.raise_for_status()
+                buffer = bytearray()
+
+                async for chunk in stream.aiter_bytes():
+                    if not chunk or end_reached:
+                        continue
+                    buffer.extend(chunk)
+
+                    while (start := buffer.find(b"data:")) != -1 and (
+                        end := buffer.find(b"\n", start)
+                    ) != -1:
+                        line = buffer[start + len(b"data:") : end].strip()
+                        buffer = buffer[end + 1 :]
+
+                        if not line:
+                            continue
+
+                        if line == b"[DONE]":
+                            if request_info.request_timings.request_end is None:
+                                request_info.request_timings.request_end = time.time()
+                            end_reached = True
+                            break
+
+                        data = (
+                            json.loads(line) if not HAS_ORJSON else orjson.loads(line)
+                        )
+
+                        if "usage" in data and data["usage"] is not None:
+                            request_info.request_timings.request_end = time.time()
+                            prompt_stats, output_stats = self._extract_response_stats(
+                                data, request
+                            )
+                        else:
+                            if request_info.request_timings.first_iteration is None:
+                                request_info.request_timings.first_iteration = (
+                                    time.time()
+                                )
+                            request_info.request_timings.last_iteration = time.time()
+                            deltas.append(self._extract_response_text(data))
 
-        if not stream_response:
-            response = await self._async_client.post(  # type: ignore [union-attr]
-                target, headers=headers, params=params, json=body
-            )
-            response.raise_for_status()
-            data = response.json()
             yield (
-                self._get_completions_text_content(data),
-                self._get_completions_usage_stats(data),
+                GenerationResponse(
+                    request_id=request.request_id,
+                    request_args=request.arguments,
+                    text="".join(deltas) if deltas else None,
+                    iterations=len(deltas),
+                    prompt_stats=prompt_stats or GenerationTokenStats(),
+                    output_stats=output_stats or GenerationTokenStats(),
+                ),
+                request_info,
             )
-            return
-
-        body.update({"stream": True, "stream_options": {"include_usage": True}})
-        async with self._async_client.stream(  # type: ignore [union-attr]
-            "POST", target, headers=headers, params=params, json=body
-        ) as stream:
-            stream.raise_for_status()
-            async for line in stream.aiter_lines():
-                if not line or not line.strip().startswith("data:"):
-                    continue
-                if line.strip() == "data: [DONE]":
-                    break
-                data = json.loads(line.strip()[len("data: ") :])
-                yield (
-                    self._get_completions_text_content(data),
-                    self._get_completions_usage_stats(data),
-                )
-
-    def _build_headers(
-        self,
-        api_key: Optional[str],
-        organization: Optional[str],
-        project: Optional[str],
-        user_headers: Optional[dict],
-    ) -> dict[str, str]:
-        headers = {}
-
-        if api_key:
-            headers["Authorization"] = (
-                f"Bearer {api_key}" if not api_key.startswith("Bearer") else api_key
+        except asyncio.CancelledError as err:
+            yield (  # Ensure we yield what we have so far before stopping
+                GenerationResponse(
+                    request_id=request.request_id,
+                    request_args=request.arguments,
+                    text="".join(deltas) if deltas else None,
+                    iterations=len(deltas),
+                    prompt_stats=prompt_stats or GenerationTokenStats(),
+                    output_stats=output_stats or GenerationTokenStats(),
+                ),
+                request_info,
             )
-        if organization:
-            headers["OpenAI-Organization"] = organization
-        if project:
-            headers["OpenAI-Project"] = project
-        if user_headers:
-            headers.update(user_headers)
+            raise err
 
-        return {key: val for key, val in headers.items() if val is not None}
+    def _extract_response_text(self, data: dict) -> str:
+        if not data:
+            return None
 
-    def _check_in_process(self):
-        if not self._in_process or self._async_client is None:
-            raise RuntimeError(
-                "Backend not started up for process, cannot process requests."
-            )
+        object_type = data.get("object") or data.get("type")
 
-    def _get_headers(self) -> dict[str, str]:
-        return {
-            "Content-Type": "application/json",
-            **self.headers,
-        }
+        if object_type == "text_completion":
+            return data.get("choices", [{}])[0].get("text", "")
 
-    def _get_params(self, endpoint_type: str) -> dict[str, str]:
-        if endpoint_type in self.extra_query:
-            return copy.deepcopy(self.extra_query[endpoint_type])
-        return copy.deepcopy(self.extra_query)
+        if object_type == "chat.completion":
+            return data.get("choices", [{}])[0].get("message", {}).get("content", "")
 
-    def _get_chat_messages(
-        self,
-        content: Union[
-            str,
-            list[Union[str, dict[str, Union[str, dict[str, str]]], Path, Image.Image]],
-            Any,
-        ],
-    ) -> list[dict[str, Any]]:
-        if isinstance(content, str):
-            return [{"role": "user", "content": content}]
-
-        if not isinstance(content, list):
-            raise ValueError(f"Unsupported content type: {type(content)}")
-
-        resolved_content = []
-        for item in content:
-            if isinstance(item, dict):
-                resolved_content.append(item)
-            elif isinstance(item, str):
-                resolved_content.append({"type": "text", "text": item})
-            elif isinstance(item, (Image.Image, Path)):
-                resolved_content.append(self._get_chat_message_media_item(item))
-            else:
-                raise ValueError(f"Unsupported content item type: {type(item)}")
-
-        return [{"role": "user", "content": resolved_content}]
-
-    def _get_chat_message_media_item(
-        self, item: Union[Path, Image.Image]
-    ) -> dict[str, Any]:
-        if isinstance(item, Image.Image):
-            encoded = base64.b64encode(item.tobytes()).decode("utf-8")
-            return {
-                "type": "image",
-                "image": {"url": f"data:image/jpeg;base64,{encoded}"},
-            }
+        if object_type == "chat.completion.chunk":
+            return data.get("choices", [{}])[0].get("delta", {}).get("content", "")
 
-        # Handle file paths
-        suffix = item.suffix.lower()
-        if suffix in [".jpg", ".jpeg"]:
-            image = Image.open(item)
-            encoded = base64.b64encode(image.tobytes()).decode("utf-8")
-            return {
-                "type": "image",
-                "image": {"url": f"data:image/jpeg;base64,{encoded}"},
-            }
-        elif suffix == ".wav":
-            encoded = base64.b64encode(item.read_bytes()).decode("utf-8")
-            return {
-                "type": "input_audio",
-                "input_audio": {"data": encoded, "format": "wav"},
-            }
-        else:
-            raise ValueError(f"Unsupported file type: {suffix}")
+        if "text" in data:
+            return data.get("text", "")
 
-    def _get_body(
-        self,
-        endpoint_type: str,
-        request_kwargs: Optional[dict[str, Any]],
-        max_output_tokens: Optional[int] = None,
-        **kwargs,
-    ) -> dict[str, Any]:
-        # Start with endpoint-specific extra body parameters
-        extra_body: dict = self.extra_body.get(endpoint_type, self.extra_body)
-
-        body = copy.deepcopy(extra_body)
-        body.update(request_kwargs or {})
-        body.update(kwargs)
-        body["model"] = self.model
-
-        # Handle token limits
-        max_tokens = max_output_tokens or self.max_output_tokens
-        if max_tokens is not None:
-            body.update(
-                {
-                    "max_tokens": max_tokens,
-                    "max_completion_tokens": max_tokens,
-                }
-            )
-            # Set stop conditions only for request-level limits
-            if max_output_tokens:
-                body.update({"stop": None, "ignore_eos": True})
+        if "delta" in data:
+            return data.get("delta", "")
 
-            if self.remove_from_body:
-                for key in self.remove_from_body:
-                    body.pop(key, None)
+        raise ValueError(f"Unsupported response format: {data}")
 
-        return {key: val for key, val in body.items() if val is not None}
+    def _extract_response_stats(
+        self, data: dict, request: GenerationRequest
+    ) -> tuple[GenerationTokenStats, GenerationTokenStats]:
+        prompt_stats = GenerationTokenStats()
+        output_stats = GenerationTokenStats()
 
-    def _get_completions_text_content(self, data: dict) -> Optional[str]:
-        if not data.get("choices"):
-            return None
+        if not data or not (usage := cast("dict", data.get("usage"))):
+            return prompt_stats, output_stats
 
-        choice: dict = data["choices"][0]
-        return (
-            choice.get("text")
-            or choice.get("delta", {}).get("content")
-            or choice.get("message", {}).get("content")
+        prompt_stats.request = request.stats.get("prompt_tokens")
+        prompt_stats.response = usage.get("prompt_tokens", usage.get("input_tokens"))
+        prompt_token_details = usage.get(
+            "prompt_tokens_details", usage.get("input_tokens_details")
+        )
+        if prompt_token_details:
+            for key, val in prompt_token_details.items():
+                setattr(prompt_stats, key, val)
+
+        output_stats.request = request.stats.get("output_tokens")
+        output_stats.response = usage.get(
+            "completion_tokens", usage.get("output_tokens")
+        )
+        output_token_details = usage.get(
+            "completion_tokens_details", usage.get("output_tokens_details")
         )
+        if output_token_details:
+            for key, val in output_token_details.items():
+                setattr(output_stats, key, val)
 
-    def _get_completions_usage_stats(self, data: dict) -> Optional[UsageStats]:
-        if not data.get("usage"):
+        return prompt_stats, output_stats
+
+    def _resolve_validate_kwargs(
+        self, validate_backend: bool | str | dict[str, Any]
+    ) -> dict[str, Any] | None:
+        if not (validate_kwargs := validate_backend):
             return None
 
-        return UsageStats(
-            prompt_tokens=data["usage"].get("prompt_tokens"),
-            output_tokens=data["usage"].get("completion_tokens"),
-        )
+        if validate_kwargs is True:
+            validate_kwargs = "health"
+
+        if isinstance(validate_kwargs, str) and validate_kwargs in open_ai_paths:
+            validate_kwargs = f"{self.target}/{open_ai_paths[validate_kwargs]}"
+
+        if isinstance(validate_kwargs, str):
+            validate_kwargs = {
+                "method": "GET",
+                "url": validate_kwargs,
+            }
+
+        if not isinstance(validate_kwargs, dict) or "url" not in validate_kwargs:
+            raise ValueError(
+                "validate_backend must be a boolean, string, or dictionary and contain "
+                f"a target URL. Got: {validate_kwargs}"
+            )
+
+        if "method" not in validate_kwargs:
+            validate_kwargs["method"] = "GET"
+
+        return validate_kwargs
+
+    def _check_in_process(self):
+        if not self._in_process or self._async_client is None:
+            raise RuntimeError(
+                "Backend not started up for process, cannot process requests."
+            )
diff --git a/src/guidellm/benchmark/aggregator.py b/src/guidellm/benchmark/aggregator.py
index e965c482..562fc36c 100644
--- a/src/guidellm/benchmark/aggregator.py
+++ b/src/guidellm/benchmark/aggregator.py
@@ -532,116 +532,117 @@ def __call__(
         :return: Updated aggregation state for progress reporting.
         """
         _ = (request,)  # unused
-        if request_info.status not in {"completed", "errored", "cancelled"}:
-            # Only compile progress stats for processed requests
-            return None
 
-        state["updated_generative_stats"] = True
-        start_time = scheduler_state.start_time
-        end_time = (
-            safe_getattr(request_info.request_timings, "request_end")
-            or request_info.scheduler_timings.resolve_end
+        # Request Concurrency
+        state.set_metric(
+            key="requests",
+            value=scheduler_state.processing_requests,
+            type_="avg",
         )
-        duration = end_time - start_time if end_time else None
-
-        for prefix in (request_info.status, None):
-            requests_count = (
-                scheduler_state.processed_requests
-                if prefix is None
-                else scheduler_state.successful_requests
-                if request_info.status == "completed"
-                else scheduler_state.cancelled_requests
-                if request_info.status == "cancelled"
-                else scheduler_state.errored_requests
+
+        if request_info.status in {"completed", "errored", "cancelled"}:
+            # Only compile progress stats for processed requests
+            state["updated_generative_stats"] = True
+            start_time = scheduler_state.start_time
+            end_time = (
+                safe_getattr(request_info.request_timings, "request_end")
+                or request_info.scheduler_timings.resolve_end
             )
+            duration = end_time - start_time if end_time else None
+
+            for prefix in (request_info.status, None):
+                requests_count = (
+                    scheduler_state.processed_requests
+                    if prefix is None
+                    else scheduler_state.successful_requests
+                    if request_info.status == "completed"
+                    else scheduler_state.cancelled_requests
+                    if request_info.status == "cancelled"
+                    else scheduler_state.errored_requests
+                )
 
-            # Requests per Second
-            if duration is not None:
-                state.set_metric(
-                    key="requests",
-                    value=safe_divide(requests_count, duration),
-                    type_="rate",
+                # Requests per Second
+                if duration is not None:
+                    state.set_metric(
+                        key="requests",
+                        value=safe_divide(requests_count, duration),
+                        type_="rate",
+                        prefix=prefix,
+                    )
+
+                # Request Latency
+                state.add_metric(
+                    key="request_latency",
+                    value=safe_getattr(request_info.request_timings, "request_end"),
+                    start_val=safe_getattr(
+                        request_info.request_timings, "request_start"
+                    ),
                     prefix=prefix,
                 )
 
-            # Request Concurrency
-            state.set_metric(
-                key="requests",
-                value=scheduler_state.processing_requests,
-                type_="avg",
-                prefix=prefix,
-            )
-
-            # Request Latency
-            state.add_metric(
-                key="request_latency",
-                value=safe_getattr(request_info.request_timings, "request_end"),
-                start_val=safe_getattr(request_info.request_timings, "request_start"),
-                prefix=prefix,
-            )
-
-            # Time to First Token
-            state.add_metric(
-                key="time_to_first_token",
-                value=safe_getattr(request_info.request_timings, "first_iteration"),
-                start_val=safe_getattr(request_info.request_timings, "request_start"),
-                prefix=prefix,
-            )
+                # Time to First Token
+                state.add_metric(
+                    key="time_to_first_token",
+                    value=safe_getattr(request_info.request_timings, "first_iteration"),
+                    start_val=safe_getattr(
+                        request_info.request_timings, "request_start"
+                    ),
+                    prefix=prefix,
+                )
 
-            output_tokens = safe_getattr(response, "output_tokens")
-            prompt_tokens = safe_getattr(response, "prompt_tokens")
+                output_tokens = response.output_stats.value() if response else None
+                prompt_tokens = response.prompt_stats.value() if response else None
+                total_tokens = response.total_tokens() if response else None
 
-            # Inter Token Latency
-            state.add_metric(
-                key="inter_token_latency",
-                value=safe_getattr(request_info.request_timings, "last_iteration"),
-                start_val=safe_getattr(request_info.request_timings, "first_iteration"),
-                count=(
-                    output_tokens - 1 if output_tokens and output_tokens > 1 else None
-                ),
-                prefix=prefix,
-            )
+                # Inter Token Latency
+                state.add_metric(
+                    key="inter_token_latency",
+                    value=safe_getattr(request_info.request_timings, "last_iteration"),
+                    start_val=safe_getattr(
+                        request_info.request_timings, "first_iteration"
+                    ),
+                    count=(
+                        output_tokens - 1
+                        if output_tokens and output_tokens > 1
+                        else None
+                    ),
+                    prefix=prefix,
+                )
 
-            # Time per Output Token
-            state.add_metric(
-                key="time_per_output_token",
-                value=safe_getattr(request_info.request_timings, "request_start"),
-                start_val=safe_getattr(request_info.request_timings, "last_iteration"),
-                count=output_tokens,
-                prefix=prefix,
-            )
+                # Time per Output Token
+                state.add_metric(
+                    key="time_per_output_token",
+                    value=safe_getattr(request_info.request_timings, "request_start"),
+                    start_val=safe_getattr(
+                        request_info.request_timings, "last_iteration"
+                    ),
+                    count=output_tokens,
+                    prefix=prefix,
+                )
 
-            # Prompt Tokens
-            state.add_metric(
-                key="prompt_tokens",
-                value=prompt_tokens,
-                duration=duration,
-                prefix=prefix,
-            )
+                # Prompt Tokens
+                state.add_metric(
+                    key="prompt_tokens",
+                    value=prompt_tokens,
+                    duration=duration,
+                    prefix=prefix,
+                )
 
-            # Output Tokens
-            state.add_metric(
-                key="output_tokens",
-                value=output_tokens,
-                duration=duration,
-                prefix=prefix,
-            )
+                # Output Tokens
+                state.add_metric(
+                    key="output_tokens",
+                    value=output_tokens,
+                    duration=duration,
+                    prefix=prefix,
+                )
 
-            # Total Tokens
-            state.add_metric(
-                key="total_tokens",
-                value=(
-                    prompt_tokens + output_tokens
-                    if all_defined(prompt_tokens, output_tokens)
-                    else prompt_tokens
-                    if all_defined(prompt_tokens)
-                    else output_tokens
-                    if all_defined(output_tokens)
-                    else None
-                ),
-                duration=duration,
-                prefix=prefix,
-            )
+                # Total Tokens
+                state.add_metric(
+                    key="total_tokens",
+                    value=total_tokens,
+                    duration=duration,
+                    prefix=prefix,
+                )
 
         return state
 
@@ -677,20 +678,20 @@ class GenerativeRequestsAggregator(
     @classmethod
     def validated_kwargs(
         cls,
-        request_samples: int | None = 20,
+        sample_requests: int | None = 20,
         warmup: int | float | None = None,
         cooldown: int | float | None = None,
         **_kwargs,
     ) -> dict[str, Any]:
         return {
-            "request_samples": request_samples,
+            "sample_requests": sample_requests,
             "warmup": warmup,
             "cooldown": cooldown,
         }
 
     type_: Literal["generative_requests"] = Field(default="generative_requests")
 
-    request_samples: int | None = Field(default=20, description="")
+    sample_requests: int | None = Field(default=20, description="")
     warmup: int | float | None = Field(
         default=None,
         description="Number of warmup requests to ignore at benchmark start",
@@ -827,9 +828,9 @@ def compile(
                 list[GenerativeRequestStats],
                 list[GenerativeRequestStats],
             ](
-                successful=self._sample_request_stats(successful, self.request_samples),
-                incomplete=self._sample_request_stats(incomplete, self.request_samples),
-                errored=self._sample_request_stats(errored, self.request_samples),
+                successful=self._sample_request_stats(successful, self.sample_requests),
+                incomplete=self._sample_request_stats(incomplete, self.sample_requests),
+                errored=self._sample_request_stats(errored, self.sample_requests),
             ),
             "metrics": GenerativeMetrics(
                 requests_per_second=self._calculate_requests_per_second(
@@ -929,29 +930,29 @@ def _is_in_cooldown(
     @classmethod
     def _create_generative_request_stats(
         cls,
-        response: GenerationResponse,
+        response: GenerationResponse | None,
         request: GenerationRequest,
         request_info: ScheduledRequestInfo,
     ) -> GenerativeRequestStats:
-        prompt_tokens = response.preferred_prompt_tokens(
-            settings.preferred_prompt_tokens_source
-        )
-        output_tokens = response.preferred_output_tokens(
-            settings.preferred_output_tokens_source
-        )
-
         return GenerativeRequestStats(
             request_id=request.request_id,
             request_type=request.request_type,
-            prompt=str(request.content),
-            request_args=response.request_args,
-            output=response.value,
-            iterations=response.iterations,
-            prompt_tokens=prompt_tokens,
-            output_tokens=output_tokens,
+            request_args=request.arguments,
+            output=response.text if response else None,
+            iterations=response.iterations if response else 0,
+            prompt_tokens=(
+                response.prompt_stats.value(settings.preferred_prompt_tokens_source)
+                if response
+                else None
+            ),
+            output_tokens=(
+                response.output_stats.value(settings.preferred_output_tokens_source)
+                if response
+                else None
+            ),
             total_tokens=(
-                prompt_tokens + output_tokens
-                if prompt_tokens is not None and output_tokens is not None
+                response.total_tokens(settings.preferred_output_tokens_source)
+                if response
                 else None
             ),
             scheduler_info=request_info,
diff --git a/src/guidellm/benchmark/entrypoints.py b/src/guidellm/benchmark/entrypoints.py
index 828402d8..e400907a 100644
--- a/src/guidellm/benchmark/entrypoints.py
+++ b/src/guidellm/benchmark/entrypoints.py
@@ -1,10 +1,9 @@
 from __future__ import annotations
 
-from collections.abc import Iterable
 from pathlib import Path
-from typing import Any, Literal
+from typing import Any, Callable, Literal
 
-from datasets import Dataset, DatasetDict, IterableDataset, IterableDatasetDict
+from torch.utils.data import Sampler
 from transformers import (  # type: ignore[import]
     PreTrainedTokenizerBase,
 )
@@ -26,6 +25,7 @@
 from guidellm.benchmark.benchmarker import Benchmarker
 from guidellm.benchmark.objects import GenerativeBenchmark, GenerativeBenchmarksReport
 from guidellm.benchmark.output import (
+    GenerativeBenchmarkerConsole,
     GenerativeBenchmarkerOutput,
 )
 from guidellm.benchmark.profile import Profile, ProfileType
@@ -33,8 +33,14 @@
     BenchmarkerProgress,
     BenchmarkerProgressGroup,
 )
-from guidellm.benchmark.scenario import GenerativeTextScenario, Scenario
-from guidellm.request import GenerativeRequestLoader
+from guidellm.data import (
+    DataLoader,
+    DatasetPreprocessor,
+    GenerativeRequestCollator,
+    PreprocessorRegistry,
+    ProcessorFactory,
+)
+from guidellm.data.preprocessors import GenerativeColumnMapper
 from guidellm.scheduler import (
     ConstraintInitializer,
     NonDistributedEnvironment,
@@ -44,7 +50,6 @@
 
 __all__ = [
     "benchmark_generative_text",
-    "benchmark_with_scenario",
     "reimport_benchmarks_report",
 ]
 
@@ -52,115 +57,14 @@
 _CURRENT_WORKING_DIR = Path.cwd()
 
 
-# Data types
-
-DataType = (
-    Iterable[str]
-    | Iterable[dict[str, Any]]
-    | Dataset
-    | DatasetDict
-    | IterableDataset
-    | IterableDatasetDict
-    | str
-    | Path
-)
-
-OutputFormatType = (
-    tuple[str, ...]
-    | list[str]
-    | dict[str, str | dict[str, Any] | GenerativeBenchmarkerOutput]
-    | None
-)
-
-
-# Helper functions
-
-async def initialize_backend(
-    backend: BackendType | Backend,
-    target: str,
-    model: str | None,
-    backend_kwargs: dict[str, Any] | None,
-) -> Backend:
-    backend = (
-        Backend.create(
-            backend, target=target, model=model, **(backend_kwargs or {})
-        )
-        if not isinstance(backend, Backend)
-        else backend
-    )
-    await backend.process_startup()
-    await backend.validate()
-    return backend
-
-
-async def resolve_profile(
-    constraint_inputs: dict[str, int | float],
-    profile: Profile | str | None,
-    rate: list[float] | None,
-    random_seed: int,
-    constraints: dict[str, ConstraintInitializer | Any],
-):
-    for key, val in constraint_inputs.items():
-        if val is not None:
-            constraints[key] = val
-    if not isinstance(profile, Profile):
-        if isinstance(profile, str):
-            profile = Profile.create(
-                rate_type=profile,
-                rate=rate,
-                random_seed=random_seed,
-                constraints={**constraints},
-            )
-        else:
-            raise ValueError(f"Expected string for profile; got {type(profile)}")
-
-    elif constraints:
-        raise ValueError(
-            "Constraints must be empty when providing a Profile instance. "
-            f"Provided constraints: {constraints} ; provided profile: {profile}"
-        )
-    return profile
-
-async def resolve_output_formats(
-    output_formats: OutputFormatType,
-    output_path: str | Path | None,
-) -> dict[str, GenerativeBenchmarkerOutput]:
-    output_formats = GenerativeBenchmarkerOutput.resolve(
-        output_formats=(output_formats or {}), output_path=output_path
-    )
-    return output_formats
-
-async def finalize_outputs(
-    report: GenerativeBenchmarksReport,
-    resolved_output_formats: dict[str, GenerativeBenchmarkerOutput]
-):
-    output_format_results = {}
-    for key, output in resolved_output_formats.items():
-        output_result = await output.finalize(report)
-        output_format_results[key] = output_result
-    return output_format_results
-
-
-# Complete entrypoints
-
-async def benchmark_with_scenario(scenario: Scenario, **kwargs):
-    """
-    Run a benchmark using a scenario and specify any extra arguments
-    """
-
-    if isinstance(scenario, GenerativeTextScenario):
-        return await benchmark_generative_text(**vars(scenario), **kwargs)
-    else:
-        raise ValueError(f"Unsupported Scenario type {type(scenario)}")
-
-
 # @validate_call(config={"arbitrary_types_allowed": True})
-async def benchmark_generative_text(  # noqa: C901
+async def benchmark_generative_text(  # noqa: C901, PLR0915, PLR0912
+    # Required
     target: str,
-    data: DataType,
-    profile: StrategyType | ProfileType | Profile,
+    data: list[Any],
+    # Benchmark configuration
+    profile: StrategyType | ProfileType | Profile = "sweep",
     rate: float | list[float] | None = None,
-    random_seed: int = 42,
     # Backend configuration
     backend: BackendType | Backend = "openai_http",
     backend_kwargs: dict[str, Any] | None = None,
@@ -168,11 +72,27 @@ async def benchmark_generative_text(  # noqa: C901
     # Data configuration
     processor: str | Path | PreTrainedTokenizerBase | None = None,
     processor_args: dict[str, Any] | None = None,
-    data_args: dict[str, Any] | None = None,
-    data_sampler: Literal["random"] | None = None,
+    data_args: list[dict[str, Any]] | None = None,
+    data_samples: int = -1,
+    data_column_mapper: (
+        DatasetPreprocessor | dict[str, str] | Literal["generative_column_mapper"]
+    ) = "generative_column_mapper",
+    data_request_formatter: (
+        DatasetPreprocessor | dict[str, str] | str
+    ) = "chat_completions",
+    data_collator: Callable | Literal["generative"] | None = "generative",
+    data_sampler: Sampler[int] | Literal["shuffle"] | None = None,
+    data_num_workers: int | None = 1,
+    dataloader_kwargs: dict[str, Any] | None = None,
+    random_seed: int = 42,
     # Output configuration
     output_path: str | Path | None = _CURRENT_WORKING_DIR,
-    output_formats: OutputFormatType = ("console", "json", "html", "csv"),
+    output_formats: (
+        tuple[str, ...]
+        | list[str]
+        | dict[str, str | dict[str, Any] | GenerativeBenchmarkerOutput]
+        | None
+    ) = ("console", "json", "html", "csv"),
     # Updates configuration
     progress: tuple[str, ...] | list[str] | list[BenchmarkerProgress] | None = None,
     print_updates: bool = False,
@@ -182,7 +102,7 @@ async def benchmark_generative_text(  # noqa: C901
     ) = None,
     warmup: float | None = None,
     cooldown: float | None = None,
-    request_samples: int | None = 20,
+    sample_requests: int | None = 10,
     # Constraints configuration
     max_seconds: int | float | None = None,
     max_requests: int | None = None,
@@ -196,9 +116,27 @@ async def benchmark_generative_text(  # noqa: C901
     with console.print_update_step(
         title=f"Initializing backend {backend}"
     ) as console_step:
-        backend = await initialize_backend(backend, target, model, backend_kwargs)
+        backend = (
+            Backend.create(
+                backend, target=target, model=model, **(backend_kwargs or {})
+            )
+            if not isinstance(backend, Backend)
+            else backend
+        )
+        console_step.update(f"{backend.__class__.__name__} backend initialized")
+        await backend.process_startup()
+        await backend.validate()
+        if model is None:
+            console_step.update(
+                title="Resolving default model from backend.default_model",
+                status_level="info",
+            )
+            model = await backend.default_model()
+        await backend.process_shutdown()
         console_step.finish(
-            title=f"{backend.__class__.__name__} backend initialized",
+            title=(
+                f"{backend.__class__.__name__} backend validated with model {model}"
+            ),
             details=backend.info,
             status_level="success",
         )
@@ -210,45 +148,64 @@ async def benchmark_generative_text(  # noqa: C901
                 details=f"Using processor '{processor}'",
                 status_level="success",
             )
-        elif model is not None:
-            console_step.finish(
-                title="Processor resolved",
-                details=f"Using model '{model}' as processor",
-                status_level="success",
-            )
-            processor = model
         else:
-            console_step.update(
-                title="Resolving processor from backend.default_model",
-                status_level="info",
-            )
-            processor = await backend.default_model()
+            processor = model
             console_step.finish(
                 title="Processor resolved",
-                details=(
-                    f"Using model '{processor}' from backend "
-                    f"{backend.__class__.__name__} as processor"
-                ),
+                details=f"Using model '{processor}' as processor",
                 status_level="success",
             )
-        await backend.process_shutdown()
 
     with console.print_update_step(
         title=f"Initializing request loader from {data}"
     ) as console_step:
-        request_loader = GenerativeRequestLoader(
+        if not isinstance(data_column_mapper, DatasetPreprocessor):
+            column_mappings = (
+                data_column_mapper if isinstance(data_column_mapper, dict) else None
+            )
+            data_column_mapper = GenerativeColumnMapper(
+                column_mappings=column_mappings,
+            )
+        if not isinstance(data_request_formatter, DatasetPreprocessor):
+            request_type = (
+                data_request_formatter
+                if isinstance(data_request_formatter, str)
+                else data_request_formatter.pop("request_type", "chat_completions")
+            )
+            data_request_formatter = PreprocessorRegistry.get_registered_object(
+                request_type
+            )(
+                model=model,
+                **(
+                    data_request_formatter
+                    if isinstance(data_request_formatter, dict)
+                    else {}
+                ),
+            )
+
+        request_loader = DataLoader(
             data=data,
             data_args=data_args,
-            processor=processor,
-            processor_args=processor_args,
-            shuffle=data_sampler == "random",
+            data_samples=data_samples,
+            processor_factory=ProcessorFactory(
+                processor=processor, processor_args=processor_args
+            ),
+            preprocessors=[data_column_mapper, data_request_formatter],
+            collator=(
+                data_collator
+                if callable(data_collator)
+                else GenerativeRequestCollator()
+            ),
+            sampler=data_sampler,
+            num_workers=data_num_workers,
             random_seed=random_seed,
+            **(dataloader_kwargs or {}),
         )
-        unique_requests = request_loader.num_unique_items(raise_err=False)
         console_step.finish(
             title=(
-                f"Request loader initialized with {unique_requests} unique requests "
-                f"from {data}"
+                f"Request loader initialized with "
+                f"{data_samples if data_samples > 0 else 'inf'} "
+                f"unique requests from {data}"
             ),
             details=InfoMixin.extract_from_obj(request_loader),
             status_level="success",
@@ -257,19 +214,27 @@ async def benchmark_generative_text(  # noqa: C901
     with console.print_update_step(
         title=f"Resolving profile {profile}"
     ) as console_step:
-        profile = await resolve_profile(
-            {
-                "max_seconds": max_seconds,
-                "max_requests": max_requests,
-                "max_errors": max_errors,
-                "max_error_rate": max_error_rate,
-                "max_global_error_rate": max_global_error_rate,
-            },
-            profile,
-            rate,
-            random_seed,
-            constraints,
-        )
+        for key, val in {
+            "max_seconds": max_seconds,
+            "max_requests": max_requests,
+            "max_errors": max_errors,
+            "max_error_rate": max_error_rate,
+            "max_global_error_rate": max_global_error_rate,
+        }.items():
+            if val is not None:
+                constraints[key] = val
+        if not isinstance(profile, Profile):
+            profile = Profile.create(
+                rate_type=profile,
+                rate=rate,
+                random_seed=random_seed,
+                constraints={**constraints},
+            )
+        elif constraints:
+            raise ValueError(
+                "Constraints must be empty when providing a Profile instance. "
+                f"Provided constraints: {constraints} ; provided profile: {profile}"
+            )
         console_step.finish(
             title=f"{profile.__class__.__name__} profile resolved",
             details=InfoMixin.extract_from_obj(profile),
@@ -283,7 +248,7 @@ async def benchmark_generative_text(  # noqa: C901
             "scheduler_stats": SchedulerStatsAggregator(),
             "requests_progress": GenerativeStatsProgressAggregator(),
             "requests": GenerativeRequestsAggregator(
-                request_samples=request_samples,
+                request_samples=sample_requests,
                 warmup=warmup,
                 cooldown=cooldown,
             ),
@@ -296,10 +261,12 @@ async def benchmark_generative_text(  # noqa: C901
         )
 
     with console.print_update_step(title="Resolving output formats") as console_step:
-        resolved_output_formats = await resolve_output_formats(output_formats, output_path)
+        output_formats = GenerativeBenchmarkerOutput.resolve(
+            output_formats=(output_formats or {}), output_path=output_path
+        )
         console_step.finish(
             title="Output formats resolved",
-            details={key: str(val) for key, val in resolved_output_formats.items()},
+            details={key: str(val) for key, val in output_formats.items()},
             status_level="success",
         )
 
@@ -335,11 +302,14 @@ async def benchmark_generative_text(  # noqa: C901
         if benchmark:
             report.benchmarks.append(benchmark)
 
-    output_format_results = await finalize_outputs(report, resolved_output_formats)
+    output_format_results = {}
+    for key, output in output_formats.items():
+        output_result = await output.finalize(report)
+        output_format_results[key] = output_result
 
     console.print("\n\n")
     console.print_update(
-        title=f"Benchmarking complete; generated {len(report.benchmarks)} benchmark(s)",
+        title=f"Benchmarking complete, generated {len(report.benchmarks)} benchmark(s)",
         status="success",
     )
     for key, value in output_format_results.items():
@@ -348,34 +318,20 @@ async def benchmark_generative_text(  # noqa: C901
     return report, output_format_results
 
 
-async def reimport_benchmarks_report(
-    file: Path,
-    output_path: Path | None,
-    output_formats: OutputFormatType = ("console", "json", "html", "csv"),
-) -> tuple[GenerativeBenchmarksReport, dict[str, Any]]:
+def reimport_benchmarks_report(file: Path, output_path: Path | None) -> None:
     """
     The command-line entry point for re-importing and displaying an
-    existing benchmarks report. Can also specify an output format.
+    existing benchmarks report. Can also specify
     Assumes the file provided exists.
     """
+    report = GenerativeBenchmarksReport.load_file(file)
+    console_output = GenerativeBenchmarkerConsole()
+    console_output.finalize(report)
     console = Console()
-    with console.print_update_step(
-        title=f"Loading benchmarks from {file}"
-    ) as console_step:
-        report = GenerativeBenchmarksReport.load_file(file)
-        console_step.finish(f"Import of old benchmarks complete; loaded {len(report.benchmarks)} benchmark(s)")
 
-    with console.print_update_step(title="Resolving output formats") as console_step:
-        resolved_output_formats = await resolve_output_formats(output_formats, output_path)
-        console_step.finish(
-            title="Output formats resolved",
-            details={key: str(val) for key, val in resolved_output_formats.items()},
-            status_level="success",
-        )
-
-    output_format_results = await finalize_outputs(report, resolved_output_formats)
-
-    for key, value in output_format_results.items():
-        console.print_update(title=f"  {key:<8}: {value}", status="debug")
-
-    return report, output_format_results
+    if output_path:
+        with console.print_update_step(
+            title=f"Saving benchmarks report to {output_path}..."
+        ) as console_step:
+            saved_path = report.save_file(output_path)
+            console_step.finish(title=f"Benchmarks report saved to {saved_path}")
diff --git a/src/guidellm/benchmark/objects.py b/src/guidellm/benchmark/objects.py
index 8afabba9..a9b5ff79 100644
--- a/src/guidellm/benchmark/objects.py
+++ b/src/guidellm/benchmark/objects.py
@@ -34,6 +34,9 @@
 from guidellm.benchmark.profile import (
     Profile,
 )
+from guidellm.data import (
+    GenerationRequestArguments,
+)
 from guidellm.scheduler import (
     ScheduledRequestInfo,
     SchedulerState,
@@ -214,9 +217,8 @@ class GenerativeRequestStats(BenchmarkRequestStats):
     request_type: Literal["text_completions", "chat_completions"] = Field(
         description="Type of generative request: text or chat completion"
     )
-    prompt: str = Field(description="Input text prompt for generation")
-    request_args: dict[str, Any] = Field(
-        description="Generation parameters and configuration options"
+    request_args: GenerationRequestArguments | None = Field(
+        default=None, description="Arguments passed to the backend for this request"
     )
     output: str | None = Field(
         description="Generated text output, if request completed successfully"
diff --git a/src/guidellm/benchmark/profile.py b/src/guidellm/benchmark/profile.py
index d2f9d70c..fd2a3850 100644
--- a/src/guidellm/benchmark/profile.py
+++ b/src/guidellm/benchmark/profile.py
@@ -681,7 +681,10 @@ def next_strategy(
                 prev_benchmark.metrics.requests_per_second.successful.mean
             )
             if self.synchronous_rate <= 0 and self.throughput_rate <= 0:
-                raise RuntimeError("Invalid rates in sweep; aborting. Were there any successful requests?")
+                raise RuntimeError(
+                    "Invalid rates in sweep; aborting. "
+                    "Were there any successful requests?"
+                )
             self.measured_rates = list(
                 np.linspace(
                     self.synchronous_rate,
diff --git a/src/guidellm/data/__init__.py b/src/guidellm/data/__init__.py
index 8a48204e..d25c719a 100644
--- a/src/guidellm/data/__init__.py
+++ b/src/guidellm/data/__init__.py
@@ -1,4 +1,42 @@
-"""
-Required for python < 3.12
-https://docs.python.org/3/library/importlib.resources.html#importlib.resources.files
-"""
+from .collators import GenerativeRequestCollator
+from .deserializers import (
+    DataNotSupportedError,
+    DatasetDeserializer,
+    DatasetDeserializerFactory,
+)
+from .loaders import DataLoader
+from .objects import (
+    GenerationRequest,
+    GenerationRequestArguments,
+    GenerationRequestTimings,
+    GenerativeDatasetColumnType,
+    GenerativeRequestType,
+)
+from .preprocessors import (
+    DataDependentPreprocessor,
+    DatasetPreprocessor,
+    PreprocessorRegistry,
+)
+from .processor import ProcessorFactory
+
+__all__ = [
+    "ColumnMapper",
+    "ColumnMapperRegistry",
+    "DataDependentPreprocessor",
+    "DataLoader",
+    "DataNotSupportedError",
+    "DatasetDeserializer",
+    "DatasetDeserializerFactory",
+    "DatasetPreprocessor",
+    "GenerationRequest",
+    "GenerationRequestArguments",
+    "GenerationRequestTimings",
+    "GenerativeDatasetArgs",
+    "GenerativeDatasetColumnType",
+    "GenerativeRequestCollator",
+    "GenerativeRequestType",
+    "PreprocessorRegistry",
+    "ProcessorFactory",
+    "RequestFormatter",
+    "RequestFormatterRegistry",
+]
diff --git a/src/guidellm/data/collators.py b/src/guidellm/data/collators.py
new file mode 100644
index 00000000..4d12f0c0
--- /dev/null
+++ b/src/guidellm/data/collators.py
@@ -0,0 +1,16 @@
+from __future__ import annotations
+
+from guidellm.data.objects import GenerationRequest
+
+__all__ = ["GenerativeRequestCollator"]
+
+
+class GenerativeRequestCollator:
+    def __call__(self, batch: list) -> GenerationRequest:
+        if len(batch) != 1:
+            raise NotImplementedError(
+                f"Batch size greater than 1 is not currently supported. "
+                f"Got batch size: {len(batch)}"
+            )
+
+        return batch[0]
diff --git a/src/guidellm/data/deserializers/__init__.py b/src/guidellm/data/deserializers/__init__.py
new file mode 100644
index 00000000..1062f2b7
--- /dev/null
+++ b/src/guidellm/data/deserializers/__init__.py
@@ -0,0 +1,53 @@
+from .deserializer import (
+    DataNotSupportedError,
+    DatasetDeserializer,
+    DatasetDeserializerFactory,
+)
+from .file import (
+    ArrowFileDatasetDeserializer,
+    CSVFileDatasetDeserializer,
+    DBFileDatasetDeserializer,
+    HDF5FileDatasetDeserializer,
+    JSONFileDatasetDeserializer,
+    ParquetFileDatasetDeserializer,
+    TarFileDatasetDeserializer,
+    TextFileDatasetDeserializer,
+)
+from .huggingface import HuggingFaceDatasetDeserializer
+from .memory import (
+    InMemoryCsvDatasetDeserializer,
+    InMemoryDictDatasetDeserializer,
+    InMemoryDictListDatasetDeserializer,
+    InMemoryItemListDatasetDeserializer,
+    InMemoryJsonStrDatasetDeserializer,
+)
+from .synthetic import (
+    SyntheticTextDatasetConfig,
+    SyntheticTextDatasetDeserializer,
+    SyntheticTextGenerator,
+    SyntheticTextPrefixBucketConfig,
+)
+
+__all__ = [
+    "ArrowFileDatasetDeserializer",
+    "CSVFileDatasetDeserializer",
+    "DBFileDatasetDeserializer",
+    "DataNotSupportedError",
+    "DatasetDeserializer",
+    "DatasetDeserializerFactory",
+    "HDF5FileDatasetDeserializer",
+    "HuggingFaceDatasetDeserializer",
+    "InMemoryCsvDatasetDeserializer",
+    "InMemoryDictDatasetDeserializer",
+    "InMemoryDictListDatasetDeserializer",
+    "InMemoryItemListDatasetDeserializer",
+    "InMemoryJsonStrDatasetDeserializer",
+    "JSONFileDatasetDeserializer",
+    "ParquetFileDatasetDeserializer",
+    "SyntheticTextDatasetConfig",
+    "SyntheticTextDatasetDeserializer",
+    "SyntheticTextGenerator",
+    "SyntheticTextPrefixBucketConfig",
+    "TarFileDatasetDeserializer",
+    "TextFileDatasetDeserializer",
+]
diff --git a/src/guidellm/data/deserializers/deserializer.py b/src/guidellm/data/deserializers/deserializer.py
new file mode 100644
index 00000000..c7e2f1da
--- /dev/null
+++ b/src/guidellm/data/deserializers/deserializer.py
@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+import contextlib
+from collections.abc import Callable
+from typing import Any, Protocol, Union, runtime_checkable
+
+from datasets import Dataset, IterableDataset
+from transformers import PreTrainedTokenizerBase
+
+from guidellm.data.utils import resolve_dataset_split
+from guidellm.utils import RegistryMixin
+
+__all__ = [
+    "DataNotSupportedError",
+    "DatasetDeserializer",
+    "DatasetDeserializerFactory",
+]
+
+
+class DataNotSupportedError(Exception):
+    """Exception raised when data format is not supported by deserializer."""
+
+
+@runtime_checkable
+class DatasetDeserializer(Protocol):
+    def __call__(
+        self,
+        data: Any,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        random_seed: int,
+        **data_kwargs: dict[str, Any],
+    ) -> dict[str, list]: ...
+
+
+class DatasetDeserializerFactory(
+    RegistryMixin[Union["type[DatasetDeserializer]", DatasetDeserializer]],
+):
+    @classmethod
+    def deserialize(
+        cls,
+        data: Any,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        random_seed: int = 42,
+        type_: str | None = None,
+        resolve_split: bool = True,
+        **data_kwargs: dict[str, Any],
+    ) -> Dataset | IterableDataset:
+        dataset = None
+
+        if type_ is None:
+            for deserializer in cls.registered_objects():
+                deserializer_fn: DatasetDeserializer = (
+                    deserializer() if isinstance(deserializer, type) else deserializer
+                )
+
+                with contextlib.suppress(DataNotSupportedError):
+                    dataset = deserializer_fn(
+                        data=data,
+                        processor_factory=processor_factory,
+                        random_seed=random_seed,
+                        **data_kwargs,
+                    )
+        elif deserializer := cls.get_registered_object(type_) is not None:
+            deserializer_fn: DatasetDeserializer = (
+                deserializer() if isinstance(deserializer, type) else deserializer
+            )
+
+            dataset = deserializer_fn(
+                data=data,
+                processor_factory=processor_factory,
+                random_seed=random_seed,
+                **data_kwargs,
+            )
+
+        if dataset is None:
+            raise DataNotSupportedError(
+                f"No suitable deserializer found for data {data} "
+                f"with kwargs {data_kwargs} and type_ {type_}."
+            )
+
+        return resolve_dataset_split(dataset) if resolve_split else dataset
diff --git a/src/guidellm/data/deserializers/file.py b/src/guidellm/data/deserializers/file.py
new file mode 100644
index 00000000..54b18edb
--- /dev/null
+++ b/src/guidellm/data/deserializers/file.py
@@ -0,0 +1,221 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any, Callable
+
+import pandas as pd
+from datasets import Dataset, load_dataset
+from transformers import PreTrainedTokenizerBase
+
+from guidellm.data.deserializers.deserializer import (
+    DataNotSupportedError,
+    DatasetDeserializer,
+    DatasetDeserializerFactory,
+)
+
+__all__ = [
+    "ArrowFileDatasetDeserializer",
+    "CSVFileDatasetDeserializer",
+    "DBFileDatasetDeserializer",
+    "HDF5FileDatasetDeserializer",
+    "JSONFileDatasetDeserializer",
+    "ParquetFileDatasetDeserializer",
+    "TarFileDatasetDeserializer",
+    "TextFileDatasetDeserializer",
+]
+
+
+@DatasetDeserializerFactory.register("text_file")
+class TextFileDatasetDeserializer(DatasetDeserializer):
+    def __call__(
+        self,
+        data: Any,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        random_seed: int,
+        **data_kwargs: dict[str, Any],
+    ) -> dict[str, list]:
+        _ = (processor_factory, random_seed)  # Ignore unused args format errors
+
+        if (
+            not isinstance(data, (str, Path))
+            or not (path := Path(data)).exists()
+            or not path.is_file()
+            or path.suffix.lower() not in {".txt", ".text"}
+        ):
+            raise DataNotSupportedError(
+                "Unsupported data for TextFileDatasetDeserializer, "
+                f"expected str or Path to a local .txt or .text file, got {data}"
+            )
+
+        with path.open() as file:
+            lines = file.readlines()
+
+        return Dataset.from_dict({"text": lines}, **data_kwargs)
+
+
+@DatasetDeserializerFactory.register("csv_file")
+class CSVFileDatasetDeserializer(DatasetDeserializer):
+    def __call__(
+        self,
+        data: Any,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        random_seed: int,
+        **data_kwargs: dict[str, Any],
+    ) -> dict[str, list]:
+        _ = (processor_factory, random_seed)
+        if (
+            not isinstance(data, (str, Path))
+            or not (path := Path(data)).exists()
+            or not path.is_file()
+            or path.suffix.lower() != ".csv"
+        ):
+            raise DataNotSupportedError(
+                "Unsupported data for CSVFileDatasetDeserializer, "
+                f"expected str or Path to a local .csv file, got {data}"
+            )
+
+        return load_dataset("csv", data_files=str(path), **data_kwargs)
+
+
+@DatasetDeserializerFactory.register("json_file")
+class JSONFileDatasetDeserializer(DatasetDeserializer):
+    def __call__(
+        self,
+        data: Any,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        random_seed: int,
+        **data_kwargs: dict[str, Any],
+    ) -> dict[str, list]:
+        _ = (processor_factory, random_seed)
+        if (
+            not isinstance(data, (str, Path))
+            or not (path := Path(data)).exists()
+            or not path.is_file()
+            or path.suffix.lower() not in {".json", ".jsonl"}
+        ):
+            raise DataNotSupportedError(
+                f"Unsupported data for JSONFileDatasetDeserializer, "
+                f"expected str or Path to a local .json or .jsonl file, got {data}"
+            )
+
+        return load_dataset("json", data_files=str(path), **data_kwargs)
+
+
+@DatasetDeserializerFactory.register("parquet_file")
+class ParquetFileDatasetDeserializer(DatasetDeserializer):
+    def __call__(
+        self,
+        data: Any,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        random_seed: int,
+        **data_kwargs: dict[str, Any],
+    ) -> dict[str, list]:
+        _ = (processor_factory, random_seed)
+        if (
+            not isinstance(data, (str, Path))
+            or not (path := Path(data)).exists()
+            or not path.is_file()
+            or path.suffix.lower() != ".parquet"
+        ):
+            raise DataNotSupportedError(
+                f"Unsupported data for ParquetFileDatasetDeserializer, "
+                f"expected str or Path to a local .parquet file, got {data}"
+            )
+
+        return load_dataset("parquet", data_files=str(path), **data_kwargs)
+
+
+@DatasetDeserializerFactory.register("arrow_file")
+class ArrowFileDatasetDeserializer(DatasetDeserializer):
+    def __call__(
+        self,
+        data: Any,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        random_seed: int,
+        **data_kwargs: dict[str, Any],
+    ) -> dict[str, list]:
+        _ = (processor_factory, random_seed)
+        if (
+            not isinstance(data, (str, Path))
+            or not (path := Path(data)).exists()
+            or not path.is_file()
+            or path.suffix.lower() != ".arrow"
+        ):
+            raise DataNotSupportedError(
+                f"Unsupported data for ArrowFileDatasetDeserializer, "
+                f"expected str or Path to a local .arrow file, got {data}"
+            )
+
+        return load_dataset("arrow", data_files=str(path), **data_kwargs)
+
+
+@DatasetDeserializerFactory.register("hdf5_file")
+class HDF5FileDatasetDeserializer(DatasetDeserializer):
+    def __call__(
+        self,
+        data: Any,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        random_seed: int,
+        **data_kwargs: dict[str, Any],
+    ) -> dict[str, list]:
+        _ = (processor_factory, random_seed)
+        if (
+            not isinstance(data, (str, Path))
+            or not (path := Path(data)).exists()
+            or not path.is_file()
+            or path.suffix.lower() not in {".hdf5", ".h5"}
+        ):
+            raise DataNotSupportedError(
+                f"Unsupported data for HDF5FileDatasetDeserializer, "
+                f"expected str or Path to a local .hdf5 or .h5 file, got {data}"
+            )
+
+        return Dataset.from_pandas(pd.read_hdf(str(path)), **data_kwargs)
+
+
+@DatasetDeserializerFactory.register("db_file")
+class DBFileDatasetDeserializer(DatasetDeserializer):
+    def __call__(
+        self,
+        data: Any,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        random_seed: int,
+        **data_kwargs: dict[str, Any],
+    ) -> dict[str, list]:
+        _ = (processor_factory, random_seed)
+        if (
+            not isinstance(data, (str, Path))
+            or not (path := Path(data)).exists()
+            or not path.is_file()
+            or path.suffix.lower() != ".db"
+        ):
+            raise DataNotSupportedError(
+                f"Unsupported data for DBFileDatasetDeserializer, "
+                f"expected str or Path to a local .db file, got {data}"
+            )
+
+        return Dataset.from_sql(con=str(path), **data_kwargs)
+
+
+@DatasetDeserializerFactory.register("tar_file")
+class TarFileDatasetDeserializer(DatasetDeserializer):
+    def __call__(
+        self,
+        data: Any,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        random_seed: int,
+        **data_kwargs: dict[str, Any],
+    ) -> dict[str, list]:
+        _ = (processor_factory, random_seed)
+        if (
+            not isinstance(data, (str, Path))
+            or not (path := Path(data)).exists()
+            or not path.is_file()
+            or path.suffix.lower() != ".tar"
+        ):
+            raise DataNotSupportedError(
+                f"Unsupported data for TarFileDatasetDeserializer, "
+                f"expected str or Path to a local .tar file, got {data}"
+            )
+
+        return load_dataset("webdataset", data_files=str(path), **data_kwargs)
diff --git a/src/guidellm/data/deserializers/huggingface.py b/src/guidellm/data/deserializers/huggingface.py
new file mode 100644
index 00000000..3e0cf090
--- /dev/null
+++ b/src/guidellm/data/deserializers/huggingface.py
@@ -0,0 +1,75 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any, Callable
+
+from datasets import (
+    Dataset,
+    DatasetDict,
+    IterableDataset,
+    IterableDatasetDict,
+    load_dataset,
+    load_from_disk,
+)
+from transformers import PreTrainedTokenizerBase
+
+from guidellm.data.deserializers.deserializer import (
+    DataNotSupportedError,
+    DatasetDeserializer,
+    DatasetDeserializerFactory,
+)
+
+__all__ = ["HuggingFaceDatasetDeserializer"]
+
+
+@DatasetDeserializerFactory.register("huggingface")
+class HuggingFaceDatasetDeserializer(DatasetDeserializer):
+    def __call__(
+        self,
+        data: Any,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        random_seed: int,
+        **data_kwargs: dict[str, Any],
+    ) -> dict[str, list]:
+        _ = (processor_factory, random_seed)
+
+        if isinstance(
+            data, (Dataset, IterableDataset, DatasetDict, IterableDatasetDict)
+        ):
+            return data
+
+        load_error = None
+
+        if (
+            isinstance(data, (str, Path))
+            and (path := Path(data)).exists()
+            and ((path.is_file() and path.suffix == ".py") or path.is_dir())
+        ):
+            # Handle python script or nested python script in a directory
+            try:
+                return load_dataset(str(data), **data_kwargs)
+            except Exception as err:  # noqa: BLE001
+                load_error = err
+
+        if (
+            isinstance(data, (str, Path))
+            and (path := Path(data)).exists()
+            and path.is_dir()
+        ):
+            # Handle local dataset directory
+            try:
+                return load_from_disk(str(data), **data_kwargs)
+            except Exception as err:  # noqa: BLE001
+                load_error = err
+
+        not_supported = DataNotSupportedError(
+            "Unsupported data for HuggingFaceDatasetDeserializer, "
+            "expected Dataset, IterableDataset, DatasetDict, IterableDatasetDict, "
+            "str or Path to a local dataset directory or a local .py dataset script, "
+            f"got {data} and HF load error: {load_error}"
+        )
+
+        if load_error is not None:
+            raise not_supported from load_error
+        else:
+            raise not_supported
diff --git a/src/guidellm/data/deserializers/memory.py b/src/guidellm/data/deserializers/memory.py
new file mode 100644
index 00000000..ddca64a9
--- /dev/null
+++ b/src/guidellm/data/deserializers/memory.py
@@ -0,0 +1,191 @@
+from __future__ import annotations
+
+import contextlib
+import csv
+import json
+from io import StringIO
+from typing import Any, Callable, cast
+
+from datasets import Dataset
+from transformers import PreTrainedTokenizerBase
+
+from guidellm.data.deserializers.deserializer import (
+    DataNotSupportedError,
+    DatasetDeserializer,
+    DatasetDeserializerFactory,
+)
+
+__all__ = [
+    "InMemoryCsvDatasetDeserializer",
+    "InMemoryDictDatasetDeserializer",
+    "InMemoryDictListDatasetDeserializer",
+    "InMemoryItemListDatasetDeserializer",
+    "InMemoryJsonStrDatasetDeserializer",
+]
+
+
+@DatasetDeserializerFactory.register("in_memory_dict")
+class InMemoryDictDatasetDeserializer(DatasetDeserializer):
+    def __call__(
+        self,
+        data: Any,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        random_seed: int,
+        **data_kwargs: dict[str, Any],
+    ) -> dict[str, list]:
+        _ = (processor_factory, random_seed)  # Ignore unused args format errors
+
+        if (
+            not data
+            or not isinstance(data, dict)
+            or not all(
+                isinstance(key, str) and isinstance(val, list)
+                for key, val in data.items()
+            )
+        ):
+            raise DataNotSupportedError(
+                f"Unsupported data for InMemoryDictDatasetDeserializer, "
+                f"expected dict[str, list], got {data}"
+            )
+
+        rows = len(list(data.values())[0])
+        if not all(len(val) == rows for val in data.values()):
+            raise DataNotSupportedError(
+                "All lists in the data dictionary must have the same length, "
+                f"expected {rows} for all keys {list(data.keys())}"
+            )
+
+        return Dataset.from_dict(data, **data_kwargs)
+
+
+@DatasetDeserializerFactory.register("in_memory_dict_list")
+class InMemoryDictListDatasetDeserializer(DatasetDeserializer):
+    def __call__(
+        self,
+        data: Any,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        random_seed: int,
+        **data_kwargs: dict[str, Any],
+    ) -> dict[str, list]:
+        _ = (processor_factory, random_seed)  # Ignore unused args format errors
+
+        if (
+            not data
+            or not isinstance(data, list)
+            or not all(isinstance(item, dict) for item in data)
+            or not all(isinstance(key, str) for item in data for key in item)
+        ):
+            raise DataNotSupportedError(
+                f"Unsupported data for InMemoryDictListDatasetDeserializer, "
+                f"expected list of dicts, got {data}"
+            )
+
+        data: list[dict[str, Any]] = cast("list[dict[str, Any]]", data)
+        first_keys = set(data[0].keys())
+        for index, item in enumerate(data):
+            if set(item.keys()) != first_keys:
+                raise DataNotSupportedError(
+                    f"All dictionaries must have the same keys. "
+                    f"Expected keys: {first_keys}, "
+                    f"got keys at index {index}: {set(item.keys())}"
+                )
+
+        # Convert list of dicts to dict of lists
+        result_dict = {key: [] for key in first_keys}
+        for item in data:
+            for key, value in item.items():
+                result_dict[key].append(value)
+
+        return Dataset.from_dict(result_dict, **data_kwargs)
+
+
+@DatasetDeserializerFactory.register("in_memory_item_list")
+class InMemoryItemListDatasetDeserializer(DatasetDeserializer):
+    def __call__(
+        self,
+        data: Any,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        random_seed: int,
+        **data_kwargs: dict[str, Any],
+    ) -> dict[str, list]:
+        _ = (processor_factory, random_seed)  # Ignore unused args format errors
+
+        primitive_types = (str, int, float, bool, type(None))
+        if (
+            not data
+            or not isinstance(data, list)
+            or not all(isinstance(item, primitive_types) for item in data)
+        ):
+            raise DataNotSupportedError(
+                f"Unsupported data for InMemoryItemListDatasetDeserializer, "
+                f"expected list of primitive items, got {data}"
+            )
+
+        column_name = data_kwargs.pop("column_name", "data")
+
+        return Dataset.from_dict({column_name: data}, **data_kwargs)
+
+
+@DatasetDeserializerFactory.register("in_memory_json_str")
+class InMemoryJsonStrDatasetDeserializer(DatasetDeserializer):
+    def __call__(
+        self,
+        data: Any,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        random_seed: int,
+        **data_kwargs: dict[str, Any],
+    ) -> dict[str, list]:
+        if (
+            isinstance(data, str)
+            and (json_str := data.strip())
+            and (
+                (json_str.startswith("{") and json_str.endswith("}"))
+                or (json_str.startswith("[") and json_str.endswith("]"))
+            )
+        ):
+            with contextlib.suppress(Exception):
+                parsed = json.loads(data)
+
+            for deserializer in [
+                InMemoryDictDatasetDeserializer,
+                InMemoryDictListDatasetDeserializer,
+                InMemoryItemListDatasetDeserializer,
+            ]:
+                with contextlib.suppress(DataNotSupportedError):
+                    return deserializer()(
+                        parsed, data_kwargs, processor_factory, random_seed
+                    )
+
+        raise DataNotSupportedError(
+            f"Unsupported data for InMemoryJsonStrDatasetDeserializer, "
+            f"expected JSON string with a list or dict of items, got {data}"
+        )
+
+
+@DatasetDeserializerFactory.register("in_memory_csv_str")
+class InMemoryCsvDatasetDeserializer(DatasetDeserializer):
+    def __call__(
+        self,
+        data: Any,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        random_seed: int,
+        **data_kwargs: dict[str, Any],
+    ) -> dict[str, list]:
+        if (
+            isinstance(data, str)
+            and (csv_str := data.strip())
+            and len(csv_str.split("\n")) > 0
+        ):
+            with contextlib.suppress(Exception):
+                csv_buffer = StringIO(data)
+                reader = csv.DictReader(csv_buffer)
+                rows = list(reader)
+
+                return InMemoryDictListDatasetDeserializer()(
+                    rows, processor_factory, random_seed, **data_kwargs
+                )
+
+        raise DataNotSupportedError(
+            f"Unsupported data for InMemoryCsvDatasetDeserializer, "
+            f"expected CSV string, got {type(data)}"
+        )
diff --git a/src/guidellm/data/deserializers/synthetic.py b/src/guidellm/data/deserializers/synthetic.py
new file mode 100644
index 00000000..c2078f1a
--- /dev/null
+++ b/src/guidellm/data/deserializers/synthetic.py
@@ -0,0 +1,344 @@
+from __future__ import annotations
+
+import math
+from collections.abc import Iterator
+from pathlib import Path
+from random import Random
+from typing import Any, Callable
+
+import yaml
+from datasets import Features, IterableDataset, Value
+from faker import Faker
+from pydantic import ConfigDict, Field, model_validator
+from transformers import PreTrainedTokenizerBase
+
+from guidellm.data.deserializers.deserializer import (
+    DataNotSupportedError,
+    DatasetDeserializer,
+    DatasetDeserializerFactory,
+)
+from guidellm.utils import IntegerRangeSampler, StandardBaseModel
+
+__all__ = [
+    "SyntheticTextDatasetConfig",
+    "SyntheticTextDatasetDeserializer",
+    "SyntheticTextGenerator",
+    "SyntheticTextPrefixBucketConfig",
+]
+
+
+class SyntheticTextPrefixBucketConfig(StandardBaseModel):
+    bucket_weight: int = Field(
+        description="Weight of this bucket in the overall distribution.",
+        gt=0,
+        default=100,
+    )
+    prefix_count: int = Field(
+        description="The number of unique prefixes to generate for this bucket.",
+        ge=1,
+        default=1,
+    )
+    prefix_tokens: int = Field(
+        description="The number of prefix tokens per-prompt for this bucket.",
+        ge=0,
+        default=0,
+    )
+
+
+class SyntheticTextDatasetConfig(StandardBaseModel):
+    model_config = ConfigDict(
+        extra="allow",
+    )
+
+    prefix_buckets: list[SyntheticTextPrefixBucketConfig] | None = Field(
+        description="Buckets for the prefix tokens distribution.",
+        default=None,
+    )
+    prompt_tokens: int = Field(
+        description="The average number of text tokens generated for prompts.",
+        gt=0,
+    )
+    prompt_tokens_stdev: int | None = Field(
+        description="The standard deviation of the tokens generated for prompts.",
+        gt=0,
+        default=None,
+    )
+    prompt_tokens_min: int | None = Field(
+        description="The minimum number of text tokens generated for prompts.",
+        gt=0,
+        default=None,
+    )
+    prompt_tokens_max: int | None = Field(
+        description="The maximum number of text tokens generated for prompts.",
+        gt=0,
+        default=None,
+    )
+    output_tokens: int = Field(
+        description="The average number of text tokens generated for outputs.",
+        gt=0,
+    )
+    output_tokens_stdev: int | None = Field(
+        description="The standard deviation of the tokens generated for outputs.",
+        gt=0,
+        default=None,
+    )
+    output_tokens_min: int | None = Field(
+        description="The minimum number of text tokens generated for outputs.",
+        gt=0,
+        default=None,
+    )
+    output_tokens_max: int | None = Field(
+        description="The maximum number of text tokens generated for outputs.",
+        gt=0,
+        default=None,
+    )
+    source: str = Field(
+        description="The source of the text data to be used for generation.",
+        default="data:prideandprejudice.txt.gz",
+    )
+
+    @model_validator(mode="after")
+    def check_prefix_options(self) -> SyntheticTextDatasetConfig:
+        prefix_count = self.__pydantic_extra__.get("prefix_count", None)  # type: ignore[attr-defined]
+        prefix_tokens = self.__pydantic_extra__.get("prefix_count", None)  # type: ignore[attr-defined]
+        if prefix_count is not None or prefix_tokens is not None:
+            if self.prefix_buckets:
+                raise ValueError(
+                    "prefix_buckets is mutually exclusive"
+                    " with prefix_count and prefix_tokens"
+                )
+
+            self.prefix_buckets = [
+                SyntheticTextPrefixBucketConfig(
+                    prefix_count=prefix_count or 1,
+                    prefix_tokens=prefix_tokens or 0,
+                )
+            ]
+
+        return self
+
+
+class SyntheticTextGenerator:
+    def __init__(
+        self,
+        config: SyntheticTextDatasetConfig,
+        processor: PreTrainedTokenizerBase,
+        random_seed: int = 42,
+    ):
+        self.config = config
+        self.processor = processor
+        self.random_seed = random_seed
+
+    def __iter__(self) -> Iterator[dict[str, Any]]:
+        samples_generated = 0
+
+        faker = Faker()
+        faker.seed_instance(self.random_seed)
+        prompt_tokens_sampler = iter(
+            IntegerRangeSampler(
+                average=self.config.prompt_tokens,
+                variance=self.config.prompt_tokens_stdev,
+                min_value=self.config.prompt_tokens_min,
+                max_value=self.config.prompt_tokens_max,
+                random_seed=self.random_seed,
+            )
+        )
+        output_tokens_sampler = iter(
+            IntegerRangeSampler(
+                average=self.config.output_tokens,
+                variance=self.config.output_tokens_stdev,
+                min_value=self.config.output_tokens_min,
+                max_value=self.config.output_tokens_max,
+                random_seed=self.random_seed + 1,  # ensure diff dist from prompts
+            )
+        )
+
+        # Create a shared prefix if specified
+        rand = Random(self.random_seed + 3)
+        prefix_iter = self._create_prefix_iter(faker, rand)
+
+        while True:
+            prompt_tokens_count = next(prompt_tokens_sampler)
+            output_tokens_count = next(output_tokens_sampler)
+
+            yield {
+                "prefix": next(prefix_iter),
+                "prompt": self._create_prompt(
+                    prompt_tokens_count, faker, f"{samples_generated} "
+                ),
+                "prompt_tokens_count": prompt_tokens_count,
+                "output_tokens_count": output_tokens_count,
+            }
+            samples_generated += 1
+
+    def _create_prompt(
+        self, prompt_tokens_count: int, faker: Faker, unique: str = ""
+    ) -> str:
+        prompt_token_ids = []
+        avg_chars_per_token = 5
+        margin_of_safety = 1.5
+        attempts = 0
+
+        while len(prompt_token_ids) < prompt_tokens_count:
+            attempts += 1
+            num_chars = (
+                prompt_tokens_count * avg_chars_per_token * margin_of_safety * attempts
+            )
+            text = unique + faker.text(max_nb_chars=num_chars)
+            prompt_token_ids = self.processor.encode(text)
+
+        return self.processor.decode(
+            prompt_token_ids[:prompt_tokens_count], skip_special_tokens=True
+        )
+
+    def _create_prefix_iter(self, faker: Faker, rand: Random) -> Iterator[str]:
+        if not self.config.prefix_buckets:
+            while True:
+                yield ""
+
+        # Increase weights to ensure an integer number of samples per per-prefix
+        least_common_prefix_count = math.lcm(
+            *(bucket.prefix_count for bucket in self.config.prefix_buckets)
+        )
+        unnorm_weights = [
+            least_common_prefix_count * bucket.bucket_weight // bucket.prefix_count
+            for bucket in self.config.prefix_buckets
+        ]
+        # Use GCD to reduce the weights to smallest integer ratio
+        common_divisor = math.gcd(*unnorm_weights)
+
+        # Create prefix list maintaining the correct distribution
+        prefixes = []
+        for bucket, weight in zip(self.config.prefix_buckets, unnorm_weights):
+            bucket_prefixes = [
+                self._create_prompt(bucket.prefix_tokens, faker)
+                for _ in range(bucket.prefix_count)
+            ]
+            sample_count = weight // common_divisor
+            prefixes.extend(bucket_prefixes * sample_count)
+
+        while True:
+            yield rand.choice(prefixes)
+
+
+@DatasetDeserializerFactory.register("synthetic_text")
+class SyntheticTextDatasetDeserializer(DatasetDeserializer):
+    def __call__(
+        self,
+        data: Any,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        random_seed: int,
+        **data_kwargs: dict[str, Any],
+    ) -> IterableDataset:
+        # Config file pathways, deserialize and call self again
+        if (config := self._load_config_file(data)) is not None:
+            return self(config, processor_factory, random_seed, **data_kwargs)
+
+        # Config str pathways, deserialize and call self again
+        if (config := self._load_config_str(data)) is not None:
+            return self(config, processor_factory, random_seed, **data_kwargs)
+
+        if not isinstance(data, SyntheticTextDatasetConfig):
+            raise DataNotSupportedError(
+                "Unsupported data for SyntheticTextDatasetDeserializer, "
+                "expected SyntheticTextDatasetConfig, str or Path to a config file, "
+                f"got {data}"
+            )
+
+        return IterableDataset.from_generator(
+            SyntheticTextGenerator,
+            gen_kwargs={
+                "config": data,
+                "processor": processor_factory(),
+                "random_seed": random_seed,
+            },
+            features=Features(
+                {
+                    "prefix": Value("string"),
+                    "prompt": Value("string"),
+                    "prompt_tokens_count": Value("int32"),
+                    "output_tokens_count": Value("int32"),
+                }
+            ),
+        )
+
+    def _load_config_file(self, data: Any) -> SyntheticTextDatasetConfig | None:
+        if (not isinstance(data, str) and not isinstance(data, Path)) or (
+            not Path(data).is_file()
+        ):
+            return None
+
+        data_path = Path(data) if isinstance(data, str) else data
+        error = None
+
+        if Path(data).is_file() and data_path.suffix.lower() == ".json":
+            try:
+                return SyntheticTextDatasetConfig.model_validate_json(
+                    data_path.read_text()
+                )
+            except Exception as err:  # noqa: BLE001
+                error = err
+
+        if Path(data).is_file() and data_path.suffix.lower() in {
+            ".yaml",
+            ".yml",
+            ".config",
+        }:
+            try:
+                return SyntheticTextDatasetConfig.model_validate(
+                    yaml.safe_load(data_path.read_text())
+                )
+            except Exception as err:  # noqa: BLE001
+                error = err
+
+        err_message = (
+            f"Unsupported file {data_path} for "
+            f"SyntheticTextDatasetDeserializer, expected .json, "
+            f".yaml, .yml, or .config"
+        )
+
+        if error is not None:
+            err_message += f" with error: {error}"
+            raise DataNotSupportedError(err_message) from error
+        raise DataNotSupportedError(err_message)
+
+    def _load_config_str(self, data: str) -> SyntheticTextDatasetConfig | None:
+        if not isinstance(data, str):
+            return None
+
+        data_str = data.strip()
+        error = None
+
+        if (data_str.startswith("{") and data_str.endswith("}")) or (
+            data_str.startswith("[") and data_str.endswith("]")
+        ):
+            try:
+                return SyntheticTextDatasetConfig.model_validate_json(data_str)
+            except Exception as err:  # noqa: BLE001
+                error = err
+
+        if data_str.count("=") > 1:
+            # key=value pairs separated by commas
+            try:
+                config_dict = {}
+                items = data_str.split(",")
+                for item in items:
+                    key, value = item.split("=")
+                    config_dict[key.strip()] = (
+                        int(value.strip())
+                        if value.strip().isnumeric()
+                        else value.strip()
+                    )
+
+                return SyntheticTextDatasetConfig.model_validate(config_dict)
+            except Exception as err:  # noqa: BLE001
+                error = err
+
+        err_message = (
+            "Unsupported string data for SyntheticTextDatasetDeserializer, "
+            f"expected JSON or key-value pairs, got {data}"
+        )
+        if error is not None:
+            err_message += f" with error: {error}"
+            raise DataNotSupportedError(err_message) from error
+        raise DataNotSupportedError(err_message)
diff --git a/src/guidellm/data/loaders.py b/src/guidellm/data/loaders.py
new file mode 100644
index 00000000..303e5a8d
--- /dev/null
+++ b/src/guidellm/data/loaders.py
@@ -0,0 +1,111 @@
+from __future__ import annotations
+
+import contextlib
+import math
+from collections.abc import Callable, Iterator
+from typing import Any, Literal
+
+from datasets import Dataset, IterableDataset
+from torch.utils.data import Sampler
+from torch.utils.data.dataloader import DataLoader as PyTorchDataLoader
+from transformers import PreTrainedTokenizerBase
+
+from guidellm.data.deserializers import DatasetDeserializerFactory
+from guidellm.data.objects import GenerationRequest
+from guidellm.data.preprocessors import DataDependentPreprocessor, DatasetPreprocessor
+
+__all__ = ["DataLoader", "datasets_item_iterator"]
+
+
+def datasets_item_iterator(
+    datasets: list[Dataset | IterableDataset],
+    data_samples: int,
+    preprocessors: tuple[DatasetPreprocessor | DataDependentPreprocessor],
+) -> Iterator[Any]:
+    gen_count = 0
+    dataset_iters = [iter(dataset) for dataset in datasets]
+
+    with contextlib.suppress(StopIteration):
+        while gen_count < data_samples or data_samples == math.inf:
+            row = {"items": [next(dataset_iter) for dataset_iter in dataset_iters]}
+            for preprocessor in preprocessors:
+                row = preprocessor(row)
+            yield row
+            gen_count += 1
+
+    if data_samples != math.inf and gen_count < data_samples:
+        raise ValueError(
+            f"Requested {data_samples} samples, but only {gen_count} "
+            "available from the provided datasets."
+        )
+
+
+class DataLoader(PyTorchDataLoader[GenerationRequest]):
+    def __init__(
+        self,
+        data: list[Any],
+        data_args: list[dict[str, Any]] | None,
+        data_samples: int,
+        processor_factory: Callable[[], PreTrainedTokenizerBase],
+        preprocessors: list[DatasetPreprocessor | DataDependentPreprocessor],
+        collator: Callable,
+        sampler: Sampler[int] | Literal["shuffle"] | None = None,
+        num_workers: int | None = 1,
+        random_seed: int = 42,
+        **kwargs: Any,
+    ):
+        if not data or not isinstance(data, list):
+            raise ValueError(f"Data must be a non-empty list, got {data}.")
+
+        if data_args is None:
+            data_args = [{} for _ in data]
+
+        if len(data) != len(data_args):
+            raise ValueError(
+                f"Length of data ({len(data)}) must match length of data_args "
+                f"({len(data_args)})."
+            )
+
+        datasets = []
+        for datum, data_kwargs in zip(data, data_args):
+            type_ = data_kwargs.pop("type_") if "type_" in data_kwargs else None
+            datasets.append(
+                DatasetDeserializerFactory.deserialize(
+                    data=datum,
+                    data_kwargs=data_args,
+                    processor_factory=processor_factory,
+                    random_seed=random_seed,
+                    type_=type_,
+                    **data_kwargs,
+                )
+            )
+        for preprocessor in preprocessors:
+            if isinstance(preprocessor, DataDependentPreprocessor):
+                preprocessor.setup_data(
+                    datasets=datasets,
+                    data_args=data_args,
+                )
+        if data_samples != math.inf and data_samples > 0:
+            cached_samples = list(
+                datasets_item_iterator(datasets, data_samples, tuple(preprocessors))
+            )
+            dataset = IterableDataset.from_generator(lambda: cached_samples)
+        else:
+            dataset = IterableDataset.from_generator(
+                datasets_item_iterator,
+                gen_kwargs={
+                    "datasets": datasets,
+                    "data_samples": math.inf,
+                    "preprocessors": tuple(preprocessors),
+                },
+            )
+
+        super().__init__(
+            dataset=dataset,
+            batch_size=1,
+            shuffle=sampler == "shuffle",
+            sampler=sampler if sampler != "shuffle" else None,
+            collate_fn=collator,
+            num_workers=num_workers,
+            **kwargs,
+        )
diff --git a/src/guidellm/data/objects.py b/src/guidellm/data/objects.py
new file mode 100644
index 00000000..2a4b3857
--- /dev/null
+++ b/src/guidellm/data/objects.py
@@ -0,0 +1,147 @@
+from __future__ import annotations
+
+import uuid
+from typing import Any, Literal
+
+from pydantic import Field
+
+from guidellm.scheduler import (
+    MeasuredRequestTimings,
+    SchedulerMessagingPydanticRegistry,
+)
+from guidellm.utils import StandardBaseDict, StandardBaseModel
+
+__all__ = [
+    "GenerationRequest",
+    "GenerationRequestArguments",
+    "GenerationRequestTimings",
+    "GenerativeDatasetColumnType",
+    "GenerativeRequestType",
+]
+
+
+GenerativeRequestType = Literal[
+    "text_completions",
+    "chat_completions",
+    "audio_transcriptions",
+    "audio_translations",
+]
+
+GenerativeDatasetColumnType = Literal[
+    "prompt_tokens_count_column",
+    "output_tokens_count_column",
+    "prefix_column",
+    "text_column",
+    "image_column",
+    "video_column",
+    "audio_column",
+]
+
+
+class GenerationRequestArguments(StandardBaseDict):
+    @classmethod
+    def model_combine_dict(  # noqa: C901, PLR0912
+        cls, *arguments: GenerationRequestArguments | dict[str, Any]
+    ) -> dict[str, Any]:
+        combined = {}
+
+        for args in arguments:
+            args_dict = args if isinstance(args, dict) else args.model_dump()
+            combined["url"] = args_dict.get("url", combined.get("url"))
+            combined["path"] = args_dict.get("path", combined.get("path"))
+            combined["method"] = args_dict.get("method", combined.get("method"))
+            combined["stream"] = args_dict.get("stream", combined.get("stream"))
+            combined["content_body"] = args_dict.get(
+                "content_body", combined.get("content_body")
+            )
+
+            if (json_body := args_dict.get("json_body")) is not None:
+                combined["json_body"] = combined.get("json_body", {}) + json_body
+            if (files := args_dict.get("files")) is not None:
+                combined["files"] = combined.get("files", {}) + files
+            if (params := args_dict.get("params")) is not None:
+                combined["params"] = combined.get("params", {}) + params
+            if (headers := args_dict.get("headers")) is not None:
+                combined["headers"] = combined.get("headers", {}) + headers
+
+        return combined
+
+    url: str | None = Field(
+        default=None,
+        description="The URL endpoint to which the request will be sent.",
+    )
+    path: str | None = Field(
+        default=None,
+        description="The path to append to the base URL for the request.",
+    )
+    method: str | None = Field(
+        default=None,
+        description="The HTTP method to use for the request (e.g., 'POST', 'GET').",
+    )
+    stream: bool | None = Field(
+        default=None,
+        description="Whether to stream the response, if applicable.",
+    )
+    content_body: Any | None = Field(
+        default=None,
+        description="Raw content to send in the request body, if applicable.",
+    )
+    json_body: dict[str, Any] | None = Field(
+        default=None,
+        description="JSON content to include in the request body, if applicable.",
+    )
+    files: dict[str, Any] | None = Field(
+        default=None,
+        description="Files to include in the request, if applicable.",
+    )
+    params: dict[str, Any] | None = Field(
+        default=None,
+        description="Query parameters to include in the request URL, if applicable.",
+    )
+    headers: dict[str, str] | None = Field(
+        default=None,
+        description="HTTP headers to include in the request, if applicable.",
+    )
+
+
+@SchedulerMessagingPydanticRegistry.register()
+class GenerationRequest(StandardBaseModel):
+    """Request model for backend generation operations."""
+
+    request_id: str = Field(
+        default_factory=lambda: str(uuid.uuid4()),
+        description="Unique identifier for the request.",
+    )
+    request_type: GenerativeRequestType | str = Field(
+        description=(
+            "Type of request. If url is not provided in arguments, "
+            "this will be used to determine the request url."
+        ),
+    )
+    arguments: GenerationRequestArguments = Field(
+        description=(
+            "Payload for the request, structured as a dictionary of arguments to pass "
+            "to the respective backend method. For example, can contain "
+            "'json', 'headers', 'files', etc."
+        )
+    )
+    stats: dict[Literal["prompt_tokens", "output_tokens"], int] = Field(
+        default_factory=dict,
+        description="Request statistics including prompt and output token counts.",
+    )
+
+
+@SchedulerMessagingPydanticRegistry.register()
+@MeasuredRequestTimings.register("generation_request_timings")
+class GenerationRequestTimings(MeasuredRequestTimings):
+    """Timing model for tracking generation request lifecycle events."""
+
+    timings_type: Literal["generation_request_timings"] = "generation_request_timings"
+    first_iteration: float | None = Field(
+        default=None,
+        description="Unix timestamp when the first generation iteration began.",
+    )
+    last_iteration: float | None = Field(
+        default=None,
+        description="Unix timestamp when the last generation iteration completed.",
+    )
diff --git a/src/guidellm/data/preprocessors/__init__.py b/src/guidellm/data/preprocessors/__init__.py
new file mode 100644
index 00000000..664e196b
--- /dev/null
+++ b/src/guidellm/data/preprocessors/__init__.py
@@ -0,0 +1,25 @@
+from .formatters import (
+    GenerativeAudioTranscriptionRequestFormatter,
+    GenerativeAudioTranslationRequestFormatter,
+    GenerativeChatCompletionsRequestFormatter,
+    GenerativeTextCompletionsRequestFormatter,
+)
+from .mappers import GenerativeColumnMapper
+from .preprocessor import (
+    DataDependentPreprocessor,
+    DatasetPreprocessor,
+    PreprocessorRegistry,
+)
+
+__all__ = [
+    "ColumnMapper",
+    "ColumnMapperRegistry",
+    "DataDependentPreprocessor",
+    "DatasetPreprocessor",
+    "GenerativeAudioTranscriptionRequestFormatter",
+    "GenerativeAudioTranslationRequestFormatter",
+    "GenerativeChatCompletionsRequestFormatter",
+    "GenerativeColumnMapper",
+    "GenerativeTextCompletionsRequestFormatter",
+    "PreprocessorRegistry",
+]
diff --git a/src/guidellm/data/preprocessors/formatters.py b/src/guidellm/data/preprocessors/formatters.py
new file mode 100644
index 00000000..c41ce936
--- /dev/null
+++ b/src/guidellm/data/preprocessors/formatters.py
@@ -0,0 +1,303 @@
+from __future__ import annotations
+
+from typing import Any, Literal
+
+from guidellm.data.objects import (
+    GenerationRequest,
+    GenerationRequestArguments,
+    GenerativeDatasetColumnType,
+)
+from guidellm.data.preprocessors.preprocessor import (
+    DatasetPreprocessor,
+    PreprocessorRegistry,
+)
+from guidellm.data.utils import (
+    encode_audio_as_dict,
+    encode_audio_as_file,
+    encode_image,
+    encode_video,
+)
+
+__all__ = [
+    "GenerativeAudioTranscriptionRequestFormatter",
+    "GenerativeAudioTranslationRequestFormatter",
+    "GenerativeChatCompletionsRequestFormatter",
+    "GenerativeTextCompletionsRequestFormatter",
+]
+
+
+@PreprocessorRegistry.register("text_completions")
+class GenerativeTextCompletionsRequestFormatter(DatasetPreprocessor):
+    def __init__(
+        self,
+        model: str,
+        extras: dict[str, Any] | GenerationRequestArguments | None = None,
+        stream: bool = True,
+        max_tokens: int | None = None,
+        max_completion_tokens: int | None = None,
+    ):
+        self.model: str | None = model
+        self.extras = (
+            GenerationRequestArguments(**extras)
+            if extras and isinstance(extras, dict)
+            else extras
+        )
+        self.stream: bool = stream
+        self.max_tokens: int | None = max_tokens or max_completion_tokens
+
+    def __call__(
+        self, columns: dict[GenerativeDatasetColumnType, list[Any]]
+    ) -> GenerationRequest:
+        arguments = {"json_body": {}}
+        stats = {}
+
+        # Add model
+        if self.model is not None:
+            arguments["json_body"]["model"] = self.model
+
+        # Configure streaming
+        if self.stream:
+            arguments["json_body"].update(
+                {"stream": True, "stream_options": {"include_usage": True}}
+            )
+            arguments["stream"] = True
+
+        # Handle output tokens
+        if output_tokens := columns.get("output_tokens_count_column", []):
+            output_count = output_tokens[0]
+            stats["output_tokens"] = output_count
+            arguments["json_body"].update(
+                {"max_tokens": output_count, "stop": None, "ignore_eos": True}
+            )
+        elif self.max_tokens is not None:
+            arguments["json_body"]["max_tokens"] = self.max_tokens
+
+        # Handle prompt tokens
+        if prompt_tokens := columns.get("prompt_tokens_count_column", []):
+            stats["prompt_tokens"] = prompt_tokens[0]
+
+        # Apply extra arguments
+        if self.extras:
+            arguments = GenerationRequestArguments.model_combine_dict(
+                arguments, self.extras
+            )
+
+        # Build prompt
+        arguments["json_body"]["prompt"] = "".join(
+            columns.get("prefix_column", []) + columns.get("text_column", [])
+        )
+
+        return GenerationRequest(
+            request_type="text_completions",
+            arguments=GenerationRequestArguments(**arguments),
+            stats=stats,
+        )
+
+
+@PreprocessorRegistry.register("chat_completions")
+class GenerativeChatCompletionsRequestFormatter(DatasetPreprocessor):
+    def __init__(
+        self,
+        model: str,
+        extras: dict[str, Any] | GenerationRequestArguments | None = None,
+        stream: bool = True,
+        max_tokens: int | None = None,
+        max_completion_tokens: int | None = None,
+        encode_kwargs: dict[str, Any] | None = None,
+    ):
+        self.model = model
+        self.extras = (
+            GenerationRequestArguments(**extras)
+            if extras and isinstance(extras, dict)
+            else extras
+        )
+        self.stream = stream
+        self.max_completion_tokens = max_tokens or max_completion_tokens
+        self.encode_image_kwargs = (
+            encode_kwargs.get("image", {}) if encode_kwargs else {}
+        )
+        self.encode_video_kwargs = (
+            encode_kwargs.get("video", {}) if encode_kwargs else {}
+        )
+        self.encode_audio_kwargs = (
+            encode_kwargs.get("audio", {}) if encode_kwargs else {}
+        )
+
+    def __call__(
+        self, columns: dict[GenerativeDatasetColumnType, list[Any]]
+    ) -> GenerationRequest:
+        arguments = {"json_body": {}}
+        stats = {}
+
+        # Add model
+        if self.model is not None:
+            arguments["json_body"]["model"] = self.model
+
+        # Configure streaming
+        if self.stream:
+            arguments["json_body"].update(
+                {"stream": True, "stream_options": {"include_usage": True}}
+            )
+            arguments["stream"] = True
+
+        # Handle output tokens
+        if output_tokens := columns.pop("output_tokens_count_column", []):
+            output_count = output_tokens[0]
+            stats["output_tokens"] = output_count
+            arguments["json_body"].update(
+                {
+                    "max_completion_tokens": output_count,
+                    "stop": None,
+                    "ignore_eos": True,
+                }
+            )
+        elif self.max_completion_tokens is not None:
+            arguments["json_body"]["max_completion_tokens"] = self.max_completion_tokens
+
+        # Handle prompt tokens
+        if prompt_tokens := columns.pop("prompt_tokens_count_column", []):
+            stats["prompt_tokens"] = prompt_tokens[0]
+
+        # Apply extra arguments
+        if self.extras:
+            arguments = GenerationRequestArguments.model_combine_dict(
+                arguments, self.extras
+            )
+
+        # Build messages
+        arguments["json_body"]["messages"] = (
+            [
+                {"role": "system", "content": prefix}
+                for prefix in columns.pop("prefix_column", [])
+            ]
+            + [
+                {"role": "user", "content": [{"type": "text", "text": text}]}
+                for text in columns.pop("text_column", [])
+            ]
+            + [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image_url",
+                            "image_url": encode_image(
+                                image, **self.encode_image_kwargs
+                            ),
+                        }
+                    ],
+                }
+                for image in columns.pop("image_column", [])
+            ]
+            + [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "video_url",
+                            "video_url": encode_video(
+                                video, **self.encode_video_kwargs
+                            ),
+                        }
+                    ],
+                }
+                for video in columns.pop("video_column", [])
+            ]
+            + [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "input_audio",
+                            "input_audio": encode_audio_as_dict(
+                                audio, **self.encode_audio_kwargs
+                            ),
+                        }
+                    ],
+                }
+                for audio in columns.pop("audio_column", [])
+            ]
+        )
+
+        return GenerationRequest(
+            request_type="chat_completions",
+            arguments=GenerationRequestArguments(**arguments),
+            stats=stats,
+        )
+
+
+@PreprocessorRegistry.register("audio_transcriptions")
+class GenerativeAudioTranscriptionRequestFormatter(DatasetPreprocessor):
+    def __init__(
+        self,
+        model: str,
+        extra_args: dict[str, Any] | GenerationRequestArguments | None = None,
+        stream: bool = True,
+        encode_kwargs: dict[str, Any] | None = None,
+    ):
+        self.model = model
+        self.extra_args = extra_args
+        self.stream = stream
+        self.encode_audio_kwargs = encode_kwargs or {}
+
+    def __call__(
+        self, columns: dict[GenerativeDatasetColumnType, list[Any]]
+    ) -> GenerationRequest:
+        arguments = {"json_body": {}}
+        stats = {}
+
+        # Add model
+        if self.model is not None:
+            arguments["json_body"]["model"] = self.model
+
+        # Configure streaming
+        if self.stream:
+            arguments["json_body"].update(
+                {"stream": True, "stream_options": {"include_usage": True}}
+            )
+
+        # Apply extra arguments
+        if self.extra_args:
+            arguments = GenerationRequestArguments.model_combine_dict(
+                arguments, self.extra_args
+            )
+
+        # Handle stats tokens
+        if output_tokens := columns.get("output_tokens_count_column", []):
+            output_count = output_tokens[0]
+            stats["output_tokens"] = output_count
+        if prompt_tokens := columns.get("prompt_tokens_count_column", []):
+            stats["prompt_tokens"] = prompt_tokens[0]
+
+        # Build audio input
+        if audio := columns.get("audio_column", []):
+            arguments["files"]["file"] = encode_audio_as_file(
+                audio[0], **self.encode_audio_kwargs
+            )
+        else:
+            raise ValueError("No audio column found for audio transcription request.")
+
+        # Build prompt
+        if (prefix := columns.get("prefix_column", [])) or (
+            text := columns.get("text_column", [])
+        ):
+            arguments["json_body"]["prompt"] = "".join(prefix) + "".join(text)
+
+        return {
+            "request": {
+                "request_type": "audio_transcriptions",
+                "arguments": arguments,
+                "stats": stats,
+            }
+        }
+
+
+@PreprocessorRegistry.register("audio_translations")
+class GenerativeAudioTranslationRequestFormatter(
+    GenerativeAudioTranscriptionRequestFormatter
+):
+    def __call__(
+        self, columns: dict[GenerativeDatasetColumnType, list[Any]]
+    ) -> dict[Literal["request"], dict[Literal["request_type"], Any]]:
+        result = super().__call__(columns)
+        result["request"]["request_type"] = "audio_translations"
+        return result
diff --git a/src/guidellm/data/preprocessors/mappers.py b/src/guidellm/data/preprocessors/mappers.py
new file mode 100644
index 00000000..56ca0342
--- /dev/null
+++ b/src/guidellm/data/preprocessors/mappers.py
@@ -0,0 +1,182 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from typing import Any, ClassVar
+
+from datasets import Dataset, IterableDataset
+
+from guidellm.data.objects import GenerativeDatasetColumnType
+from guidellm.data.preprocessors.preprocessor import (
+    DataDependentPreprocessor,
+    PreprocessorRegistry,
+)
+
+__all__ = ["GenerativeColumnMapper"]
+
+
+@PreprocessorRegistry.register("generative_column_mapper")
+class GenerativeColumnMapper(DataDependentPreprocessor):
+    defaults: ClassVar[dict[str, list[str]]] = {
+        "prompt_tokens_count_column": ["prompt_tokens_count", "input_tokens_count"],
+        "output_tokens_count_column": [
+            "output_tokens_count",
+            "completion_tokens_count",
+        ],
+        "prefix_column": [
+            "system_prompt",
+            "system",
+            "prefix",
+        ],
+        "text_column": [
+            "prompt",
+            "instruction",
+            "question",
+            "input",
+            "context",
+            "content",
+            "conversation",
+            "turn",
+            "text",
+        ],
+        "image_column": [
+            "image",
+            "picture",
+            "photo",
+            "img",
+        ],
+        "video_column": [
+            "video",
+            "clip",
+            "movie",
+            "footage",
+            "mp4",
+            "mov",
+            "avi",
+        ],
+        "audio_column": [
+            "audio",
+            "sound",
+            "voice",
+            "speech",
+            "wav",
+            "mp3",
+        ],
+    }
+
+    @classmethod
+    def datasets_default_mappings(
+        cls, datasets: list[Dataset | IterableDataset]
+    ) -> dict[str, list[tuple[int, str]]]:
+        mappings: dict[GenerativeDatasetColumnType, list[tuple[int, str]]] = (
+            defaultdict(list)
+        )
+
+        for index, dataset in enumerate(datasets):
+            dataset_columns = dataset.column_names or list(next(iter(dataset)).keys())
+
+            for column_type in cls.defaults:
+                if column_type in mappings:
+                    continue
+
+                type_names = [
+                    variant
+                    for name in cls.defaults.get(column_type, [])
+                    for plural in [name, f"{name}s", f"{name}es"]
+                    for variant in [
+                        plural,
+                        plural.lower(),
+                        plural.upper(),
+                        plural.capitalize(),
+                    ]
+                ]
+
+                for name in type_names:
+                    if name in dataset_columns:
+                        mappings[column_type].append((index, name))
+                        break
+
+        return mappings
+
+    @classmethod
+    def datasets_mappings(
+        cls,
+        datasets: list[Dataset | IterableDataset],
+        input_mappings: dict[GenerativeDatasetColumnType, str | list[str]],
+    ) -> dict[GenerativeDatasetColumnType, list[tuple[int, str]]]:
+        mappings: dict[GenerativeDatasetColumnType, list[tuple[int, str]]] = (
+            defaultdict(list)
+        )
+        datasets_named_indices = {
+            (
+                dataset.info.dataset_name
+                if dataset.info and dataset.info.dataset_name
+                else index
+            ): index
+            for index, dataset in enumerate(datasets)
+        }
+        datasets_columns = {
+            index: dataset.column_names or list(next(iter(dataset)).keys())
+            for index, dataset in enumerate(datasets)
+        }
+
+        for column_type, names in input_mappings.items():
+            mappings[column_type] = []
+
+            for name in names if isinstance(names, list) else [names]:
+                dataset, column_name = name.split(".", 1)
+                dataset_index = (
+                    int(dataset)
+                    if dataset.isdigit()
+                    else datasets_named_indices.get(dataset)
+                )
+                if dataset_index is None or dataset_index >= len(datasets):
+                    raise ValueError(
+                        f"Dataset '{dataset}' not found in datasets: "
+                        f"{datasets_named_indices}."
+                    )
+                if column_name not in datasets_columns[dataset_index]:
+                    raise ValueError(
+                        f"Column '{column_name}' not found in dataset '{dataset}' "
+                        f"columns: {datasets_columns[dataset_index]}."
+                    )
+                mappings[column_type].append((dataset_index, column_name))
+
+        return mappings
+
+    def __init__(
+        self,
+        column_mappings: dict[GenerativeDatasetColumnType, str | list[str]]
+        | None = None,
+    ):
+        self.input_mappings = column_mappings
+        self.datasets_column_mappings: (
+            dict[GenerativeDatasetColumnType, list[tuple[int, str]]] | None
+        )
+
+    def __call__(self, row: dict[int, list[dict[str, Any]]]) -> dict[str, list[Any]]:
+        if self.datasets_column_mappings is None:
+            raise ValueError("DefaultGenerativeColumnMapper not setup with data.")
+
+        items = row.pop("items")
+        mapped: dict[GenerativeDatasetColumnType, list[Any]] = defaultdict(list)
+
+        for column_type, column_mappings in self.datasets_column_mappings.items():
+            for (
+                dataset_index,
+                dataset_column,
+            ) in column_mappings:
+                mapped[column_type].append(items[dataset_index][dataset_column])
+
+        return dict(mapped)
+
+    def setup_data(
+        self,
+        datasets: list[Dataset | IterableDataset],
+        data_args: list[dict[str, Any]],
+    ):
+        _ = data_args  # Unused for this mapper
+        self.datasets_column_mappings = (
+            self.datasets_default_mappings(datasets)
+            if self.input_mappings is None
+            else self.datasets_mappings(datasets, self.input_mappings)
+        )
diff --git a/src/guidellm/data/preprocessors/preprocessor.py b/src/guidellm/data/preprocessors/preprocessor.py
new file mode 100644
index 00000000..eefb53d3
--- /dev/null
+++ b/src/guidellm/data/preprocessors/preprocessor.py
@@ -0,0 +1,29 @@
+from __future__ import annotations
+
+from typing import Any, Protocol, Union, runtime_checkable
+
+from datasets import Dataset, IterableDataset
+
+from guidellm.utils import RegistryMixin
+
+__all__ = ["DataDependentPreprocessor", "DatasetPreprocessor", "PreprocessorRegistry"]
+
+
+@runtime_checkable
+class DatasetPreprocessor(Protocol):
+    def __call__(self, item: dict[str, Any]) -> dict[str, Any]: ...
+
+
+@runtime_checkable
+class DataDependentPreprocessor(DatasetPreprocessor, Protocol):
+    def setup_data(
+        self,
+        datasets: list[Dataset | IterableDataset],
+        data_args: list[dict[str, Any]],
+    ): ...
+
+
+class PreprocessorRegistry(
+    RegistryMixin[Union[DataDependentPreprocessor, type[DataDependentPreprocessor]]]
+):
+    pass
diff --git a/src/guidellm/data/prideandprejudice.txt.gz b/src/guidellm/data/prideandprejudice.txt.gz
deleted file mode 100644
index 8c7a1072..00000000
Binary files a/src/guidellm/data/prideandprejudice.txt.gz and /dev/null differ
diff --git a/src/guidellm/data/processor.py b/src/guidellm/data/processor.py
new file mode 100644
index 00000000..645683c4
--- /dev/null
+++ b/src/guidellm/data/processor.py
@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+from typing import Any
+
+from transformers import (  # type: ignore[import]
+    AutoTokenizer,
+    PreTrainedTokenizerBase,
+)
+
+__all__ = ["ProcessorFactory"]
+
+
+class ProcessorFactory:
+    def __init__(
+        self,
+        processor: str | PreTrainedTokenizerBase,
+        processor_args: dict[str, Any] | None = None,
+    ) -> None:
+        self.processor = processor
+        self.processor_args = processor_args or {}
+
+    def __call__(self) -> PreTrainedTokenizerBase:
+        if isinstance(self.processor, PreTrainedTokenizerBase):
+            return self.processor
+        else:
+            self.processor = AutoTokenizer.from_pretrained(
+                self.processor,
+                **(self.processor_args or {}),
+            )
+            return self.processor
diff --git a/src/guidellm/data/utils/__init__.py b/src/guidellm/data/utils/__init__.py
new file mode 100644
index 00000000..aac657f8
--- /dev/null
+++ b/src/guidellm/data/utils/__init__.py
@@ -0,0 +1,34 @@
+from .dataset import DEFAULT_SPLITS, resolve_dataset_split
+from .functions import (
+    download_audio,
+    download_image,
+    download_video,
+    encode_audio,
+    encode_audio_as_dict,
+    encode_audio_as_file,
+    encode_image,
+    encode_image_base64,
+    encode_video,
+    encode_video_base64,
+    get_file_format,
+    is_url,
+    resize_image,
+)
+
+__all__ = [
+    "DEFAULT_SPLITS",
+    "download_audio",
+    "download_image",
+    "download_video",
+    "encode_audio",
+    "encode_audio_as_dict",
+    "encode_audio_as_file",
+    "encode_image",
+    "encode_image_base64",
+    "encode_video",
+    "encode_video_base64",
+    "get_file_format",
+    "is_url",
+    "resize_image",
+    "resolve_dataset_split",
+]
diff --git a/src/guidellm/data/utils/dataset.py b/src/guidellm/data/utils/dataset.py
new file mode 100644
index 00000000..9656c1a7
--- /dev/null
+++ b/src/guidellm/data/utils/dataset.py
@@ -0,0 +1,94 @@
+from __future__ import annotations
+
+from typing import Literal
+
+from datasets import Dataset, DatasetDict, IterableDataset, IterableDatasetDict
+
+__all__ = ["DEFAULT_SPLITS", "resolve_dataset_split"]
+
+
+DEFAULT_SPLITS: dict[Literal["train", "calib", "val", "test"], list[str]] = {
+    "train": [
+        "train",
+        "training",
+        "train_set",
+        "training_set",
+        "train_dataset",
+        "training_dataset",
+        "train_data",
+        "training_data",
+        "pretrain",
+        "pretrain_set",
+        "pretrain_dataset",
+        "pretrain_data",
+        "pretraining",
+    ],
+    "calib": [
+        "calibration",
+        "calib",
+        "cal",
+        "calibration_set",
+        "calib_set",
+        "cal_set",
+        "calibration_dataset",
+        "calib_dataset",
+        "cal_set",
+        "calibration_data",
+        "calib_data",
+        "cal_data",
+    ],
+    "val": [
+        "validation",
+        "val",
+        "valid",
+        "validation_set",
+        "val_set",
+        "validation_dataset",
+        "val_dataset",
+        "validation_data",
+        "val_data",
+        "dev",
+        "dev_set",
+        "dev_dataset",
+        "dev_data",
+    ],
+    "test": [
+        "test",
+        "testing",
+        "test_set",
+        "testing_set",
+        "test_dataset",
+        "testing_dataset",
+        "test_data",
+        "testing_data",
+        "eval",
+        "eval_set",
+        "eval_dataset",
+        "eval_data",
+    ],
+}
+
+
+def resolve_dataset_split(
+    dataset: Dataset | IterableDataset | DatasetDict | IterableDatasetDict,
+    split: str | None = None,
+) -> Dataset | IterableDataset:
+    if split is not None and isinstance(dataset, (DatasetDict, IterableDatasetDict)):
+        if split in dataset:
+            return dataset[split]
+
+        raise ValueError(f"Requested split '{split}' not found in dataset: {dataset}.")
+    elif split is not None:
+        raise ValueError(
+            f"Requested split '{split}' but dataset has no splits: {dataset}."
+        )
+
+    if isinstance(dataset, (Dataset, IterableDataset)):
+        return dataset
+
+    for _, default_splits in DEFAULT_SPLITS.items():
+        for default_split in default_splits:
+            if default_split in dataset:
+                return dataset[default_split]
+
+    return dataset[list(dataset.keys())[0]]
diff --git a/src/guidellm/data/utils/functions.py b/src/guidellm/data/utils/functions.py
new file mode 100644
index 00000000..c9ca20ed
--- /dev/null
+++ b/src/guidellm/data/utils/functions.py
@@ -0,0 +1,356 @@
+from __future__ import annotations
+
+import base64
+import io
+from pathlib import Path
+from typing import Any, Literal
+
+import datasets
+import httpx
+import librosa
+import numpy as np
+import soundfile
+from PIL import Image as PILImage
+from pydub import AudioSegment
+
+__all__ = [
+    "download_audio",
+    "download_image",
+    "download_video",
+    "encode_audio",
+    "encode_audio_as_dict",
+    "encode_audio_as_file",
+    "encode_image",
+    "encode_image_base64",
+    "encode_video",
+    "encode_video_base64",
+    "get_file_format",
+    "is_url",
+    "resize_image",
+]
+
+
+def is_url(text: Any) -> bool:
+    return isinstance(text, str) and text.startswith(("http://", "https://"))
+
+
+def encode_image(
+    image: bytes | str | Path | np.ndarray | PILImage.Image | datasets.Image,
+    max_size: int | None = None,
+    max_width: int | None = None,
+    max_height: int | None = None,
+    encode_type: Literal["base64", "url"] | None = None,
+) -> str:
+    """
+    Input image types:
+    - bytes: raw image bytes, decoded with Pillow
+    - str: file path on disk, url, or already base64 encoded image string
+    - pathlib.Path: file path on disk
+    - np.ndarray: image array, decoded with Pillow
+    - PIL.Image.Image: Pillow image
+    - datasets.Image: HuggingFace datasets Image object
+
+    max_size: maximum size of the longest edge of the image
+    max_width: maximum width of the image
+    max_height: maximum height of the image
+
+    encode_type: None to return the supported format
+        (url for url, base64 string for others)
+        "base64" to return base64 encoded string (or download URL and encode)
+        "url" to return url (only if input is url, otherwise fails)
+
+    Returns a str of either:
+    - image url
+    - "data:image/{type};base64, {data}" string
+    """
+    url = is_url(image)
+
+    if (
+        url
+        and (encode_type is None or encode_type == "url")
+        and (max_size is not None or max_width is not None or max_height is not None)
+    ):
+        raise ValueError("Cannot resize image when encode_type is 'url'")
+    elif url and (encode_type is None or encode_type == "url"):
+        return image
+    elif url and encode_type == "base64":
+        raise ValueError(f"Cannot convert non-url image to URL {image}")
+
+    return encode_image_base64(
+        image=image,
+        max_size=max_size,
+        max_width=max_width,
+        max_height=max_height,
+    )
+
+
+def encode_image_base64(
+    image: bytes | str | Path | np.ndarray | PILImage.Image,
+    width: int | None = None,
+    height: int | None = None,
+    max_width: int | None = None,
+    max_height: int | None = None,
+    max_size: int | None = None,
+) -> str:
+    if (
+        isinstance(image, str)
+        and image.startswith("data:image/")
+        and ";base64," in image
+    ):
+        return image
+
+    if is_url(image):
+        image = download_image(image)
+
+    if isinstance(image, bytes):
+        image = PILImage.open(io.BytesIO(image))
+    elif isinstance(image, (str, Path)):
+        image = PILImage.open(image)
+    elif isinstance(image, np.ndarray):
+        image = PILImage.fromarray(image)
+    elif not isinstance(image, PILImage.Image):
+        raise ValueError(f"Unsupported image type: {type(image)}")
+
+    image = resize_image(
+        image,
+        width=width,
+        height=height,
+        max_width=max_width,
+        max_height=max_height,
+        max_size=max_size,
+    )
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+
+    buffer = io.BytesIO()
+    image.save(buffer, format="JPEG")
+    image_bytes = buffer.getvalue()
+    image_base64 = base64.b64encode(image_bytes).decode("utf-8")
+
+    return f"data:image/jpeg;base64,{image_base64}"
+
+
+def resize_image(
+    image: PILImage.Image,
+    width: int | None = None,
+    height: int | None = None,
+    max_width: int | None = None,
+    max_height: int | None = None,
+    max_size: int | None = None,
+) -> PILImage.Image:
+    if not isinstance(image, PILImage.Image):
+        raise ValueError(f"Unsupported image type: {type(image)}")
+
+    if width is not None and height is not None:
+        return image.resize((width, height), PILImage.Resampling.BILINEAR)
+
+    orig_w, orig_h = image.size
+    aspect = orig_w / orig_h
+
+    if width is not None:
+        target_w = width
+        target_h = round(width / aspect)
+    elif height is not None:
+        target_h = height
+        target_w = round(height * aspect)
+    else:
+        target_w, target_h = orig_w, orig_h
+
+    # Normalize max_size → max_width/max_height
+    if max_size is not None:
+        max_width = max_width or max_size
+        max_height = max_height or max_size
+
+    # Apply max constraints (preserve aspect ratio)
+    if max_width or max_height:
+        scale_w = max_width / target_w if max_width else 1.0
+        scale_h = max_height / target_h if max_height else 1.0
+        scale = min(scale_w, scale_h, 1.0)  # never upscale
+        target_w = round(target_w * scale)
+        target_h = round(target_h * scale)
+
+    if (target_w, target_h) != (orig_w, orig_h):
+        image = image.resize((target_w, target_h), PILImage.Resampling.BILINEAR)
+
+    return image
+
+
+def download_image(url: str) -> bytes:
+    response = httpx.get(url)
+    response.raise_for_status()
+    return response.content
+
+
+def encode_video(
+    video: bytes | str | Path | datasets.Video,
+    encode_type: Literal["base64", "url"] | None = None,
+) -> str:
+    """
+    Input video types:
+    - bytes: raw video bytes
+    - str: file path on disk, url, or already base64 encoded video string
+    - pathlib.Path: file path on disk
+    - datasets.Video: HuggingFace datasets Video object
+
+    encode_type: None to return the supported format
+        (url for url, base64 string for others)
+        "base64" to return base64 encoded string (or download URL and encode)
+        "url" to return url (only if input is url, otherwise fails)
+
+    Returns a str of either:
+    - video url
+    - "data:video/{type};base64, {data}" string
+    """
+    url = is_url(video)
+
+    if url and (encode_type is None or encode_type == "url"):
+        return video
+    elif url and encode_type == "base64":
+        raise ValueError(f"Cannot encode URL video {video}")
+
+    return encode_video_base64(video=video)
+
+
+def encode_video_base64(video: bytes | str | Path) -> str:
+    if (
+        isinstance(video, str)
+        and video.startswith("data:video/")
+        and ";base64," in video
+    ):
+        return video
+
+    video_format = "unknown"
+
+    if is_url(video):
+        video, video_format = download_video(video)
+
+    if isinstance(video, (str, Path)):
+        path = Path(video)
+        video = path.read_bytes()
+        video_format = get_file_format(path)
+    elif not isinstance(video, bytes):
+        raise ValueError(f"Unsupported video type: {type(video)}")
+
+    video_base64 = base64.b64encode(video).decode("utf-8")
+    return f"data:video/{video_format};base64,{video_base64}"
+
+
+def download_video(url: str) -> tuple[bytes, str]:
+    response = httpx.get(url)
+    response.raise_for_status()
+    return response.content, get_file_format(url)
+
+
+def encode_audio_as_dict(
+    audio: bytes | str | Path | dict | np.ndarray,
+    sample_rate: int | None = 16000,
+    max_duration: float | None = None,
+    mono: bool = True,
+    audio_format: str = "mp3",
+    bitrate: str = "64k",
+) -> dict[Literal["data", "format"], Any]:
+    content, file_name, file_format = encode_audio(
+        audio=audio,
+        sample_rate=sample_rate or 16000,
+        max_duration=max_duration,
+        mono=mono,
+        audio_format=audio_format,
+        bitrate=bitrate,
+    )
+
+    return {
+        "data": base64.b64encode(content).decode("utf-8"),
+        "format": file_format,
+    }
+
+
+def encode_audio_as_file(
+    audio: bytes | str | Path | dict | np.ndarray,
+    sample_rate: int | None = 16000,
+    max_duration: float | None = None,
+    mono: bool = True,
+    audio_format: str = "mp3",
+    bitrate: str = "64k",
+) -> tuple[str, bytes, str]:
+    content, file_name, file_format = encode_audio(
+        audio=audio,
+        sample_rate=sample_rate or 16000,
+        max_duration=max_duration,
+        mono=mono,
+        audio_format=audio_format,
+        bitrate=bitrate,
+    )
+
+    return file_name, content, f"audio/{file_format}"
+
+
+def encode_audio(
+    audio: bytes | str | Path | dict,
+    sample_rate: int = 16000,
+    max_duration: float | None = None,
+    mono: bool = True,
+    audio_format: str = "mp3",
+    bitrate: str = "64k",
+) -> tuple[bytes, str, str]:
+    file_name = "audio.wav"
+
+    if is_url(audio):
+        audio, file_name, _ = download_audio(audio)
+    elif isinstance(audio, dict):
+        file_name = audio.get("name", "audio")
+        audio = base64.b64decode(audio["data"])
+    elif isinstance(audio, (str, Path)):
+        path = Path(audio)
+        file_name = get_file_name(path)
+        audio = path.read_bytes()
+    elif not isinstance(audio, bytes):
+        raise ValueError(f"Unsupported audio type: {type(audio)}")
+
+    processed_audio, sample_rate = librosa.load(
+        io.BytesIO(audio),
+        sr=sample_rate,
+        mono=mono,
+        duration=max_duration,
+    )
+
+    # Encode to target format
+    buffer = io.BytesIO()
+    if audio_format.lower() == "mp3":
+        temp_wav = io.BytesIO()
+        soundfile.write(
+            temp_wav,
+            processed_audio,
+            sample_rate,
+            format="WAV",
+            subtype="PCM_16",
+        )
+        temp_wav.seek(0)
+        AudioSegment.from_wav(temp_wav).export(buffer, format="mp3", bitrate=bitrate)
+    else:
+        soundfile.write(
+            buffer,
+            processed_audio,
+            sample_rate,
+            format=audio_format.upper(),
+        )
+
+    return buffer.getvalue(), file_name, audio_format.lower()
+
+
+def download_audio(url: str) -> tuple[bytes, str, str]:
+    response = httpx.get(url)
+    response.raise_for_status()
+    content = response.content
+
+    return content, get_file_name(url), get_file_format(url)
+
+
+def get_file_name(path: Path | str) -> str:
+    """Get file name from path."""
+    return Path(path).name
+
+
+def get_file_format(path: Path | str) -> str:
+    """Get file format from path extension."""
+    suffix = Path(path).suffix.lower()
+    return suffix[1:] if suffix.startswith(".") else "unknown"
diff --git a/src/guidellm/dataset/__init__.py b/src/guidellm/dataset/__init__.py
deleted file mode 100644
index b90b72ff..00000000
--- a/src/guidellm/dataset/__init__.py
+++ /dev/null
@@ -1,22 +0,0 @@
-from .creator import ColumnInputTypes, DatasetCreator
-from .entrypoints import load_dataset
-from .file import FileDatasetCreator
-from .hf_datasets import HFDatasetsCreator
-from .in_memory import InMemoryDatasetCreator
-from .synthetic import (
-    SyntheticDatasetConfig,
-    SyntheticDatasetCreator,
-    SyntheticTextItemsGenerator,
-)
-
-__all__ = [
-    "ColumnInputTypes",
-    "DatasetCreator",
-    "FileDatasetCreator",
-    "HFDatasetsCreator",
-    "InMemoryDatasetCreator",
-    "SyntheticDatasetConfig",
-    "SyntheticDatasetCreator",
-    "SyntheticTextItemsGenerator",
-    "load_dataset",
-]
diff --git a/src/guidellm/dataset/creator.py b/src/guidellm/dataset/creator.py
deleted file mode 100644
index a74ec8c0..00000000
--- a/src/guidellm/dataset/creator.py
+++ /dev/null
@@ -1,213 +0,0 @@
-from abc import ABC, abstractmethod
-from pathlib import Path
-from typing import Any, Literal, Optional, Union
-
-from datasets import Dataset, DatasetDict, IterableDataset, IterableDatasetDict
-from transformers import PreTrainedTokenizerBase  # type: ignore[import]
-
-__all__ = ["ColumnInputTypes", "DatasetCreator"]
-
-ColumnInputTypes = Literal[
-    "prompt_column",
-    "text_column",
-    "prompt_tokens_count_column",
-    "output_tokens_count_column",
-]
-
-
-class DatasetCreator(ABC):
-    DEFAULT_SPLITS_TRAIN = [
-        "train",
-        "training",
-        "train_set",
-        "training_set",
-        "train_dataset",
-        "training_dataset",
-        "train_data",
-        "training_data",
-        "pretrain",
-        "pretrain_set",
-        "pretrain_dataset",
-        "pretrain_data",
-        "pretraining",
-    ]
-    DEFAULT_SPLITS_CALIB = [
-        "calibration",
-        "calib",
-        "cal",
-        "calibration_set",
-        "calib_set",
-        "cal_set",
-        "calibration_dataset",
-        "calib_dataset",
-        "cal_set",
-        "calibration_data",
-        "calib_data",
-        "cal_data",
-    ]
-    DEFAULT_SPLITS_VAL = [
-        "validation",
-        "val",
-        "valid",
-        "validation_set",
-        "val_set",
-        "validation_dataset",
-        "val_dataset",
-        "validation_data",
-        "val_data",
-        "dev",
-        "dev_set",
-        "dev_dataset",
-        "dev_data",
-    ]
-    DEFAULT_SPLITS_TEST = [
-        "test",
-        "testing",
-        "test_set",
-        "testing_set",
-        "test_dataset",
-        "testing_dataset",
-        "test_data",
-        "testing_data",
-        "eval",
-        "eval_set",
-        "eval_dataset",
-        "eval_data",
-    ]
-    DEFAULT_SPLITS_DATASET: dict[str, str] = {}
-
-    @classmethod
-    def create(
-        cls,
-        data: Any,
-        data_args: Optional[dict[str, Any]],
-        processor: Optional[Union[str, Path, PreTrainedTokenizerBase]],
-        processor_args: Optional[dict[str, Any]],
-        random_seed: int = 42,
-        split_pref_order: Optional[list[str]] = None,
-    ) -> tuple[Union[Dataset, IterableDataset], dict[ColumnInputTypes, str]]:
-        if not cls.is_supported(data, data_args):
-            raise ValueError(f"Unsupported data type: {type(data)} given for {data}. ")
-
-        split = cls.extract_args_split(data_args)
-        column_mappings = cls.extract_args_column_mappings(data_args)
-        dataset = cls.handle_create(
-            data, data_args, processor, processor_args, random_seed
-        )
-
-        if isinstance(dataset, (DatasetDict, IterableDatasetDict)):
-            dataset = cls.extract_dataset_split(dataset, split, split_pref_order)
-
-        if not isinstance(dataset, (Dataset, IterableDataset)):
-            raise ValueError(
-                f"Unsupported data type: {type(dataset)} given for {dataset}."
-            )
-
-        return dataset, column_mappings
-
-    @classmethod
-    def extract_args_split(cls, data_args: Optional[dict[str, Any]]) -> str:
-        split = "auto"
-
-        if data_args and "split" in data_args:
-            split = data_args["split"]
-            del data_args["split"]
-
-        return split
-
-    @classmethod
-    def extract_args_column_mappings(
-        cls,
-        data_args: Optional[dict[str, Any]],
-    ) -> dict[ColumnInputTypes, str]:
-        columns: dict[ColumnInputTypes, str] = {}
-
-        if data_args:
-            if "prompt_column" in data_args:
-                columns["prompt_column"] = data_args["prompt_column"]
-                del data_args["prompt_column"]
-
-            if "prompt_tokens_count_column" in data_args:
-                columns["prompt_tokens_count_column"] = data_args[
-                    "prompt_tokens_count_column"
-                ]
-                del data_args["prompt_tokens_count_column"]
-
-            if "output_tokens_count_column" in data_args:
-                columns["output_tokens_count_column"] = data_args[
-                    "output_tokens_count_column"
-                ]
-                del data_args["output_tokens_count_column"]
-
-        return columns
-
-    @classmethod
-    def extract_dataset_name(
-        cls, dataset: Union[Dataset, IterableDataset, DatasetDict, IterableDatasetDict]
-    ) -> Optional[str]:
-        if isinstance(dataset, (DatasetDict, IterableDatasetDict)):
-            dataset = dataset[list(dataset.keys())[0]]
-
-        if isinstance(dataset, (Dataset, IterableDataset)):
-            if not hasattr(dataset, "info") or not hasattr(
-                dataset.info, "dataset_name"
-            ):
-                return None
-
-            return dataset.info.dataset_name
-
-        raise ValueError(f"Unsupported data type: {type(dataset)} given for {dataset}.")
-
-    @classmethod
-    def extract_dataset_split(
-        cls,
-        dataset: Union[DatasetDict, IterableDatasetDict],
-        specified_split: Union[Literal["auto"], str] = "auto",
-        split_pref_order: Optional[Union[Literal["auto"], list[str]]] = "auto",
-    ) -> Union[Dataset, IterableDataset]:
-        if not isinstance(dataset, (DatasetDict, IterableDatasetDict)):
-            raise ValueError(
-                f"Unsupported data type: {type(dataset)} given for {dataset}."
-            )
-
-        if specified_split != "auto":
-            if specified_split not in dataset:
-                raise ValueError(
-                    f"Split {specified_split} not found in dataset {dataset}."
-                )
-
-            return dataset[specified_split]
-
-        dataset_name = cls.extract_dataset_name(dataset)
-
-        if dataset_name and dataset_name in cls.DEFAULT_SPLITS_DATASET:
-            return dataset[cls.DEFAULT_SPLITS_DATASET[dataset_name]]
-
-        if split_pref_order == "auto":
-            split_pref_order = [
-                *cls.DEFAULT_SPLITS_TEST,
-                *cls.DEFAULT_SPLITS_VAL,
-                *cls.DEFAULT_SPLITS_CALIB,
-                *cls.DEFAULT_SPLITS_TRAIN,
-            ]
-
-        for test_split in split_pref_order or []:
-            if test_split in dataset:
-                return dataset[test_split]
-
-        return dataset[list(dataset.keys())[0]]
-
-    @classmethod
-    @abstractmethod
-    def is_supported(cls, data: Any, data_args: Optional[dict[str, Any]]) -> bool: ...
-
-    @classmethod
-    @abstractmethod
-    def handle_create(
-        cls,
-        data: Any,
-        data_args: Optional[dict[str, Any]],
-        processor: Optional[Union[str, Path, PreTrainedTokenizerBase]],
-        processor_args: Optional[dict[str, Any]],
-        random_seed: int,
-    ) -> Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]: ...
diff --git a/src/guidellm/dataset/entrypoints.py b/src/guidellm/dataset/entrypoints.py
deleted file mode 100644
index cf689956..00000000
--- a/src/guidellm/dataset/entrypoints.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from pathlib import Path
-from typing import Any, Optional, Union
-
-from datasets import Dataset, IterableDataset
-from transformers import PreTrainedTokenizerBase  # type: ignore[import]
-
-from guidellm.dataset.creator import ColumnInputTypes
-from guidellm.dataset.file import FileDatasetCreator
-from guidellm.dataset.hf_datasets import HFDatasetsCreator
-from guidellm.dataset.in_memory import InMemoryDatasetCreator
-from guidellm.dataset.synthetic import SyntheticDatasetCreator
-
-__all__ = ["load_dataset"]
-
-
-def load_dataset(
-    data: Any,
-    data_args: Optional[dict[str, Any]],
-    processor: Optional[Union[str, Path, PreTrainedTokenizerBase]],
-    processor_args: Optional[dict[str, Any]],
-    random_seed: int = 42,
-    split_pref_order: Optional[list[str]] = None,
-) -> tuple[Union[Dataset, IterableDataset], dict[ColumnInputTypes, str]]:
-    creators = [
-        InMemoryDatasetCreator,
-        SyntheticDatasetCreator,
-        FileDatasetCreator,
-        HFDatasetsCreator,
-    ]
-
-    for creator in creators:
-        if creator.is_supported(data, data_args):
-            return creator.create(
-                data,
-                data_args,
-                processor,
-                processor_args,
-                random_seed,
-                split_pref_order,
-            )
-
-    raise ValueError(f"Unsupported data type: {type(data)} given for {data}. ")
diff --git a/src/guidellm/dataset/file.py b/src/guidellm/dataset/file.py
deleted file mode 100644
index 5d6df1d9..00000000
--- a/src/guidellm/dataset/file.py
+++ /dev/null
@@ -1,92 +0,0 @@
-from pathlib import Path
-from typing import Any, Optional, Union
-
-import pandas as pd  # type: ignore[import]
-from datasets import (
-    Dataset,
-    DatasetDict,
-    IterableDataset,
-    IterableDatasetDict,
-    load_dataset,
-)
-from transformers import PreTrainedTokenizerBase  # type: ignore[import]
-
-from guidellm.dataset.creator import DatasetCreator
-
-__all__ = ["FileDatasetCreator"]
-
-
-class FileDatasetCreator(DatasetCreator):
-    SUPPORTED_TYPES = {
-        ".txt",
-        ".text",
-        ".csv",
-        ".json",
-        ".jsonl",
-        ".parquet",
-        ".arrow",
-        ".hdf5",
-        ".tar",
-    }
-
-    @classmethod
-    def is_supported(cls, data: Any, data_args: Optional[dict[str, Any]]) -> bool:  # noqa: ARG003
-        if isinstance(data, (str, Path)) and (path := Path(data)).exists():
-            # local folder or py file, assume supported
-            return path.suffix.lower() in cls.SUPPORTED_TYPES
-
-        return False
-
-    @classmethod
-    def handle_create(
-        cls,
-        data: Any,
-        data_args: Optional[dict[str, Any]],
-        processor: Optional[Union[str, Path, PreTrainedTokenizerBase]],  # noqa: ARG003
-        processor_args: Optional[dict[str, Any]],  # noqa: ARG003
-        random_seed: int,  # noqa: ARG003
-    ) -> Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
-        if not isinstance(data, (str, Path)):
-            raise ValueError(f"Unsupported data type: {type(data)} given for {data}. ")
-
-        path = Path(data)
-        if not path.exists():
-            raise FileNotFoundError(f"File not found: {path}")
-
-        if not path.is_file():
-            raise ValueError(f"Unsupported data type: {path} given for {path}. ")
-
-        if path.suffix.lower() not in cls.SUPPORTED_TYPES:
-            raise ValueError(f"Unsupported file type: {path.suffix} given for {path}. ")
-
-        return cls.load_dataset(path, data_args)
-
-    @classmethod
-    def load_dataset(
-        cls, path: Path, data_args: Optional[dict[str, Any]]
-    ) -> Union[Dataset, IterableDataset]:
-        if path.suffix.lower() in {".txt", ".text"}:
-            with path.open("r") as file:
-                items = file.readlines()
-
-            dataset = Dataset.from_dict({"text": items}, **(data_args or {}))
-        elif path.suffix.lower() == ".csv":
-            dataset = load_dataset("csv", data_files=str(path), **(data_args or {}))
-        elif path.suffix.lower() in {".json", ".jsonl"}:
-            dataset = load_dataset("json", data_files=str(path), **(data_args or {}))
-        elif path.suffix.lower() == ".parquet":
-            dataset = load_dataset("parquet", data_files=str(path), **(data_args or {}))
-        elif path.suffix.lower() == ".arrow":
-            dataset = load_dataset("arrow", data_files=str(path), **(data_args or {}))
-        elif path.suffix.lower() == ".hdf5":
-            dataset = Dataset.from_pandas(pd.read_hdf(str(path)), **(data_args or {}))
-        elif path.suffix.lower() == ".db":
-            dataset = Dataset.from_sql(con=str(path), **(data_args or {}))
-        elif path.suffix.lower() == ".tar":
-            dataset = load_dataset(
-                "webdataset", data_files=str(path), **(data_args or {})
-            )
-        else:
-            raise ValueError(f"Unsupported file type: {path.suffix} given for {path}. ")
-
-        return dataset
diff --git a/src/guidellm/dataset/hf_datasets.py b/src/guidellm/dataset/hf_datasets.py
deleted file mode 100644
index 7f91facd..00000000
--- a/src/guidellm/dataset/hf_datasets.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from pathlib import Path
-from typing import Any, Optional, Union
-
-from datasets import (
-    Dataset,
-    DatasetDict,
-    IterableDataset,
-    IterableDatasetDict,
-    get_dataset_config_info,
-    load_dataset,
-)
-from transformers import PreTrainedTokenizerBase  # type: ignore[import]
-
-from guidellm.dataset.creator import DatasetCreator
-
-__all__ = ["HFDatasetsCreator"]
-
-
-class HFDatasetsCreator(DatasetCreator):
-    @classmethod
-    def is_supported(cls, data: Any, data_args: Optional[dict[str, Any]]) -> bool:  # noqa: ARG003
-        if isinstance(
-            data, (Dataset, DatasetDict, IterableDataset, IterableDatasetDict)
-        ):
-            # base type is supported
-            return True
-
-        if isinstance(data, (str, Path)) and (path := Path(data)).exists():
-            # local folder or py file, assume supported
-            return path.is_dir() or path.suffix == ".py"
-
-        if isinstance(data, (str, Path)):
-            try:
-                # try to load dataset
-                return get_dataset_config_info(data) is not None
-            except Exception:  # noqa: BLE001, S110
-                pass
-
-        return False
-
-    @classmethod
-    def handle_create(
-        cls,
-        data: Any,
-        data_args: Optional[dict[str, Any]],
-        processor: Optional[Union[str, Path, PreTrainedTokenizerBase]],  # noqa: ARG003
-        processor_args: Optional[dict[str, Any]],  # noqa: ARG003
-        random_seed: int,  # noqa: ARG003
-    ) -> Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
-        if isinstance(data, (str, Path)):
-            data = load_dataset(data, **(data_args or {}))
-        elif data_args:
-            raise ValueError(
-                f"data_args should not be provided when data is a {type(data)}"
-            )
-
-        if isinstance(
-            data, (Dataset, DatasetDict, IterableDataset, IterableDatasetDict)
-        ):
-            return data
-
-        raise ValueError(f"Unsupported data type: {type(data)} given for {data}. ")
diff --git a/src/guidellm/dataset/in_memory.py b/src/guidellm/dataset/in_memory.py
deleted file mode 100644
index af84f658..00000000
--- a/src/guidellm/dataset/in_memory.py
+++ /dev/null
@@ -1,132 +0,0 @@
-from collections.abc import Iterable
-from pathlib import Path
-from typing import Any, Optional, Union
-
-from datasets import (
-    Dataset,
-    DatasetDict,
-    IterableDataset,
-    IterableDatasetDict,
-)
-from transformers import PreTrainedTokenizerBase  # type: ignore[import]
-
-from guidellm.dataset.creator import DatasetCreator
-
-__all__ = ["InMemoryDatasetCreator"]
-
-
-class InMemoryDatasetCreator(DatasetCreator):
-    @classmethod
-    def is_supported(cls, data: Any, data_args: Optional[dict[str, Any]]) -> bool:  # noqa: ARG003
-        return isinstance(data, Iterable) and not isinstance(data, str)
-
-    @classmethod
-    def handle_create(
-        cls,
-        data: Any,
-        data_args: Optional[dict[str, Any]],
-        processor: Optional[Union[str, Path, PreTrainedTokenizerBase]],  # noqa: ARG003
-        processor_args: Optional[dict[str, Any]],  # noqa: ARG003
-        random_seed: int,  # noqa: ARG003
-    ) -> Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
-        if not isinstance(data, Iterable):
-            raise TypeError(
-                f"Unsupported data format. Expected Iterable[Any], got {type(data)}"
-            )
-
-        if not data:
-            raise ValueError("Data is empty")
-
-        if isinstance(data, dict):
-            # assume data is a dictionary of columns and values: {"c1": ["i1", "i2"]}
-            data_dict = cls.format_data_dict(data)
-        elif isinstance(data[0], dict):  # type: ignore[index]
-            # assume data is a list of dictionaries: [{"c1": "i1"}, {"c1": "i2"}]
-            data_dict = cls.format_data_iterable_dicts(data)
-        else:
-            # assume data is a list of items with no columns: ["i1", "i2"]
-            data_dict = cls.format_data_iterable_values(data)
-
-        return Dataset.from_dict(data_dict, **(data_args or {}))
-
-    @classmethod
-    def format_data_dict(cls, data: dict[Any, Any]) -> dict[str, Any]:
-        if not isinstance(data, dict):
-            raise TypeError(
-                f"Unsupported data format. Expected Dict[str, Iterable[Any]], "
-                f"got {type(data)}"
-            )
-
-        if not all(
-            isinstance(key, str) and isinstance(val, Iterable)
-            for key, val in data.items()
-        ):
-            raise TypeError(
-                "Unsupported data format. Expected Dict[str, Iterable[Any]], "
-                f"got {type(data)}"
-            )
-
-        samples = len(list(data.values())[0])
-        if not all(len(val) == samples for val in data.values()):
-            raise ValueError(
-                "Unsupported data format. Not all columns have the same number samples "
-                f"for {data}"
-            )
-
-        return data
-
-    @classmethod
-    def format_data_iterable_dicts(
-        cls, data: Iterable[dict[Any, Any]]
-    ) -> dict[str, Any]:
-        if not isinstance(data, Iterable):
-            raise TypeError(
-                f"Unsupported data format. Expected Iterable[Dict[str, Any]], "
-                f"got {type(data)}"
-            )
-
-        if not all(isinstance(item, dict) for item in data):
-            raise TypeError(
-                f"Unsupported data format. Expected Iterable[Dict[str, Any]], "
-                f"got {type(data)}"
-            )
-
-        if not all(isinstance(key, str) for key in data[0]):  # type: ignore[index]
-            raise TypeError(
-                "Unsupported data format. Expected Dict[str, Any], "
-                f"but one of the items had a non string column for {data}"
-            )
-
-        columns = list(data[0].keys())  # type: ignore[index]
-        if not all(
-            len(item) == len(columns) and all(key in item for key in columns)
-            for item in data
-        ):
-            raise ValueError(
-                "Unsupported data format. Not all items have the same columns "
-                f"for {data}"
-            )
-
-        data_dict: dict[str, Any] = {key: [] for key in columns}
-        for item in data:
-            for key, value in item.items():
-                data_dict[key].append(value)
-
-        return data_dict
-
-    @classmethod
-    def format_data_iterable_values(cls, data: Iterable[Any]) -> dict[str, Any]:
-        if not isinstance(data, Iterable):
-            raise TypeError(
-                f"Unsupported data format. Expected Iterable[Iterable[Any]], "
-                f"got {type(data)}"
-            )
-
-        first_item = next(iter(data), None)
-        first_type = type(first_item)
-        if not all(isinstance(item, first_type) for item in data):
-            raise TypeError(
-                f"Unsupported data format. Not all types are the same for {data}"
-            )
-
-        return {"data": list(data)}
diff --git a/src/guidellm/dataset/synthetic.py b/src/guidellm/dataset/synthetic.py
deleted file mode 100644
index 8c30f0f7..00000000
--- a/src/guidellm/dataset/synthetic.py
+++ /dev/null
@@ -1,287 +0,0 @@
-import json
-import random
-from collections.abc import Iterable, Iterator
-from itertools import cycle
-from pathlib import Path
-from typing import Any, Literal, Optional, Union
-
-import yaml
-from datasets import (
-    Dataset,
-    DatasetDict,
-    IterableDataset,
-    IterableDatasetDict,
-)
-from pydantic import BaseModel, Field
-from transformers import PreTrainedTokenizerBase  # type: ignore[import]
-
-from guidellm.dataset.creator import ColumnInputTypes, DatasetCreator
-from guidellm.utils import EndlessTextCreator, IntegerRangeSampler, check_load_processor
-
-__all__ = [
-    "SyntheticDatasetConfig",
-    "SyntheticDatasetCreator",
-    "SyntheticTextItemsGenerator",
-]
-
-
-class SyntheticDatasetConfig(BaseModel):
-    prefix_tokens: int = Field(
-        description="The number of shared prefix tokens to prepend to each prompt.",
-        ge=0,
-        default=0,
-    )
-    prompt_tokens: int = Field(
-        description="The average number of text tokens generated for prompts.",
-        gt=0,
-    )
-    prompt_tokens_stdev: Optional[int] = Field(
-        description="The standard deviation of the tokens generated for prompts.",
-        gt=0,
-        default=None,
-    )
-    prompt_tokens_min: Optional[int] = Field(
-        description="The minimum number of text tokens generated for prompts.",
-        gt=0,
-        default=None,
-    )
-    prompt_tokens_max: Optional[int] = Field(
-        description="The maximum number of text tokens generated for prompts.",
-        gt=0,
-        default=None,
-    )
-    output_tokens: int = Field(
-        description="The average number of text tokens generated for outputs.",
-        gt=0,
-    )
-    output_tokens_stdev: Optional[int] = Field(
-        description="The standard deviation of the tokens generated for outputs.",
-        gt=0,
-        default=None,
-    )
-    output_tokens_min: Optional[int] = Field(
-        description="The minimum number of text tokens generated for outputs.",
-        gt=0,
-        default=None,
-    )
-    output_tokens_max: Optional[int] = Field(
-        description="The maximum number of text tokens generated for outputs.",
-        gt=0,
-        default=None,
-    )
-    samples: int = Field(
-        description="The number of samples to generate for the dataset.",
-        gt=0,
-        default=1000,
-    )
-    source: str = Field(
-        description="The source of the text data to be used for generation.",
-        default="data:prideandprejudice.txt.gz",
-    )
-
-    @staticmethod
-    def parse_str(data: Union[str, Path]) -> "SyntheticDatasetConfig":
-        if (
-            isinstance(data, Path)
-            or data.strip().endswith(".config")
-            or data.strip().endswith(".yaml")
-        ):
-            return SyntheticDatasetConfig.parse_config_file(data)
-
-        if data.strip().startswith("{"):
-            return SyntheticDatasetConfig.parse_json(data)
-
-        if data.count("=") > 1:
-            return SyntheticDatasetConfig.parse_key_value_pairs(data)
-
-        raise ValueError(
-            f"Unsupported data format. Expected JSON or key-value pairs, got {data}"
-        )
-
-    @staticmethod
-    def parse_json(data: str) -> "SyntheticDatasetConfig":
-        config_dict = json.loads(data.strip())
-
-        return SyntheticDatasetConfig(**config_dict)
-
-    @staticmethod
-    def parse_key_value_pairs(data: str) -> "SyntheticDatasetConfig":
-        config_dict = {}
-        items = data.strip().split(",")
-        for item in items:
-            key, value = item.split("=")
-            config_dict[key.strip()] = (
-                int(value.strip()) if value.strip().isnumeric() else value.strip()
-            )
-
-        return SyntheticDatasetConfig(**config_dict)  # type: ignore[arg-type]
-
-    @staticmethod
-    def parse_config_file(data: Union[str, Path]) -> "SyntheticDatasetConfig":
-        with Path(data).open("r") as file:
-            config_dict = yaml.safe_load(file)
-
-        return SyntheticDatasetConfig(**config_dict)
-
-
-class SyntheticTextItemsGenerator(
-    Iterable[
-        dict[
-            Literal["prompt", "prompt_tokens_count", "output_tokens_count"],
-            Union[str, int],
-        ]
-    ]
-):
-    def __init__(
-        self,
-        config: SyntheticDatasetConfig,
-        processor: PreTrainedTokenizerBase,
-        random_seed: int,
-    ):
-        self.config = config
-        self.processor = processor
-        self.random_seed = random_seed
-        self.text_creator = EndlessTextCreator(
-            data=config.source,
-        )
-
-    def __iter__(
-        self,
-    ) -> Iterator[
-        dict[
-            Literal["prompt", "prompt_tokens_count", "output_tokens_count"],
-            Union[str, int],
-        ]
-    ]:
-        prompt_tokens_sampler = IntegerRangeSampler(
-            average=self.config.prompt_tokens,
-            variance=self.config.prompt_tokens_stdev,
-            min_value=self.config.prompt_tokens_min,
-            max_value=self.config.prompt_tokens_max,
-            random_seed=self.random_seed,
-        )
-        output_tokens_sampler = IntegerRangeSampler(
-            average=self.config.output_tokens,
-            variance=self.config.output_tokens_stdev,
-            min_value=self.config.output_tokens_min,
-            max_value=self.config.output_tokens_max,
-            random_seed=self.random_seed + 1,  # ensure diff dist from prompts
-        )
-        # ensure diff distribution from output tokens
-        rand = random.Random(self.random_seed + 2)  # noqa: S311
-        unique_prefix_iter = cycle(self.processor.get_vocab().values())
-
-        prefix_index = rand.randint(0, len(self.text_creator.words))
-        prefix_tokens = self._create_prompt(self.config.prefix_tokens, prefix_index)
-
-        for _, prompt_tokens, output_tokens in zip(
-            range(self.config.samples),
-            prompt_tokens_sampler,
-            output_tokens_sampler,
-        ):
-            start_index = rand.randint(0, len(self.text_creator.words))
-            prompt_text = self.processor.decode(
-                prefix_tokens
-                + self._create_prompt(
-                    prompt_tokens, start_index, next(unique_prefix_iter)
-                ),
-                skip_special_tokens=True,
-            )
-            yield {
-                "prompt": prompt_text,
-                "prompt_tokens_count": self.config.prefix_tokens + prompt_tokens,
-                "output_tokens_count": output_tokens,
-            }
-
-    def _create_prompt(
-        self, prompt_tokens: int, start_index: int, unique_prefix: Optional[int] = None
-    ) -> list[int]:
-        if prompt_tokens <= 0:
-            return []
-
-        left = start_index
-        right = start_index + 4 * prompt_tokens
-        start_tokens = [unique_prefix] if unique_prefix else []
-
-        while left < right:
-            mid = (left + right) // 2
-            test_prompt = self.text_creator.create_text(start_index, mid - start_index)
-            test_tokens = start_tokens + self.processor.encode(test_prompt)
-
-            if len(test_tokens) == prompt_tokens:
-                return test_tokens
-            elif len(test_tokens) < prompt_tokens:
-                left = mid + 1
-            else:
-                right = mid
-
-        final_text = self.text_creator.create_text(start_index, left - start_index)
-        return start_tokens + self.processor.encode(final_text)
-
-
-class SyntheticDatasetCreator(DatasetCreator):
-    @classmethod
-    def is_supported(
-        cls,
-        data: Any,
-        data_args: Optional[dict[str, Any]],  # noqa: ARG003
-    ) -> bool:
-        if (
-            isinstance(data, Path)
-            and data.exists()
-            and data.suffix in {".config", ".yaml"}
-        ):
-            return True
-
-        if isinstance(data, str):
-            data_str: str = data.strip()
-            if (
-                data_str.startswith("{")
-                or data_str.count("=") > 1
-                or data_str.endswith((".config", ".yaml"))
-            ):
-                return True
-
-        return False
-
-    @classmethod
-    def handle_create(
-        cls,
-        data: Any,
-        data_args: Optional[dict[str, Any]],
-        processor: Optional[Union[str, Path, PreTrainedTokenizerBase]],
-        processor_args: Optional[dict[str, Any]],
-        random_seed: int,
-    ) -> Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
-        processor = check_load_processor(
-            processor,
-            processor_args,
-            error_msg=(
-                "Processor/tokenizer required for synthetic dataset generation."
-            ),
-        )
-
-        config = SyntheticDatasetConfig.parse_str(data)
-        generator = SyntheticTextItemsGenerator(config, processor, random_seed)
-        items = list(generator)
-
-        return Dataset.from_list(items, **(data_args or {}))
-
-    @classmethod
-    def extract_args_column_mappings(
-        cls,
-        data_args: Optional[dict[str, Any]],
-    ) -> dict[ColumnInputTypes, str]:
-        data_args_columns = super().extract_args_column_mappings(data_args)
-
-        if data_args_columns:
-            raise ValueError(
-                f"Column mappings are not supported for synthetic datasets. "
-                f"Got {data_args_columns}"
-            )
-
-        return {
-            "prompt_column": "prompt",
-            "prompt_tokens_count_column": "prompt_tokens_count",
-            "output_tokens_count_column": "output_tokens_count",
-        }
diff --git a/src/guidellm/logger.py b/src/guidellm/logger.py
index 70259bad..da3464f9 100644
--- a/src/guidellm/logger.py
+++ b/src/guidellm/logger.py
@@ -72,7 +72,7 @@ def configure_logger(config: LoggingSettings = settings.logging):
         sys.stdout,
         level=config.console_log_level.upper(),
         format="<green>{time:YY-MM-DD HH:mm:ss}</green>|<level>{level: <8}</level> \
-        |<cyan>{name}:{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>"
+        |<cyan>{name}:{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>",
     )
 
     if config.log_file or config.log_file_level:
diff --git a/src/guidellm/preprocess/dataset.py b/src/guidellm/preprocess/dataset.py
index a94b8a14..9d65dcd6 100644
--- a/src/guidellm/preprocess/dataset.py
+++ b/src/guidellm/preprocess/dataset.py
@@ -11,7 +11,6 @@
 from pydantic import BaseModel, Field
 from transformers import PreTrainedTokenizerBase
 
-from guidellm.dataset import load_dataset as guidellm_load_dataset
 from guidellm.utils import IntegerRangeSampler, check_load_processor
 from guidellm.utils.hf_datasets import SUPPORTED_TYPES, save_dataset_to_file
 
@@ -239,7 +238,7 @@ def process_dataset(
     prompt_tokens: Union[str, Path],
     output_tokens: Union[str, Path],
     processor_args: Optional[dict[str, Any]] = None,
-    data_args: Optional[dict[str, Any]] = None,
+    data_args: Optional[dict[str, Any]] = None,  # noqa: ARG001
     short_prompt_strategy: ShortPromptStrategy = ShortPromptStrategy.IGNORE,
     pad_char: Optional[str] = None,
     concat_delimiter: Optional[str] = None,
@@ -271,9 +270,7 @@ def process_dataset(
         f"Starting dataset conversion | Input: {data} | Output directory: {output_path}"
     )
 
-    dataset, column_mappings = guidellm_load_dataset(
-        data, data_args, processor, processor_args
-    )
+    dataset, column_mappings = None, None
     tokenizer = check_load_processor(
         processor,
         processor_args,
diff --git a/src/guidellm/request/__init__.py b/src/guidellm/request/__init__.py
deleted file mode 100644
index 85b447d6..00000000
--- a/src/guidellm/request/__init__.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from .loader import (
-    GenerativeRequestLoader,
-    GenerativeRequestLoaderDescription,
-    RequestLoader,
-    RequestLoaderDescription,
-)
-from .request import GenerationRequest
-from .types import RequestT, ResponseT
-
-__all__ = [
-    "GenerationRequest",
-    "GenerativeRequestLoader",
-    "GenerativeRequestLoaderDescription",
-    "RequestLoader",
-    "RequestLoaderDescription",
-    "RequestT",
-    "ResponseT",
-]
diff --git a/src/guidellm/request/loader.py b/src/guidellm/request/loader.py
deleted file mode 100644
index 607a7455..00000000
--- a/src/guidellm/request/loader.py
+++ /dev/null
@@ -1,284 +0,0 @@
-from abc import abstractmethod
-from collections.abc import Iterable, Iterator
-from pathlib import Path
-from typing import (
-    Any,
-    Literal,
-    Optional,
-    Union,
-)
-
-from datasets import Dataset, DatasetDict, IterableDataset, IterableDatasetDict
-from transformers import PreTrainedTokenizerBase  # type: ignore[import]
-
-from guidellm.dataset import ColumnInputTypes, load_dataset
-from guidellm.request.request import GenerationRequest
-from guidellm.settings import settings
-from guidellm.utils import StandardBaseModel
-
-__all__ = [
-    "GenerativeRequestLoader",
-    "GenerativeRequestLoaderDescription",
-    "RequestLoader",
-    "RequestLoaderDescription",
-]
-
-
-class RequestLoaderDescription(StandardBaseModel):
-    type_: Literal["request_loader"] = "request_loader"
-
-
-class RequestLoader(Iterable):
-    @abstractmethod
-    def __iter__(self) -> Iterator: ...
-
-    @abstractmethod
-    def __len__(self) -> int: ...
-
-    @property
-    @abstractmethod
-    def description(self) -> RequestLoaderDescription: ...
-
-
-class GenerativeRequestLoaderDescription(RequestLoaderDescription):
-    type_: Literal["generative_request_loader"] = "generative_request_loader"  # type: ignore[assignment]
-    data: str
-    data_args: Optional[dict[str, Any]]
-    processor: str
-    processor_args: Optional[dict[str, Any]]
-
-
-class GenerativeRequestLoader(RequestLoader):
-    DEFAULT_PROMPT_COLUMNS = [
-        "prompt",
-        "prompts",
-        "instruction",
-        "instructions",
-        "question",
-        "questions",
-        "input",
-        "inputs",
-        "context",
-        "content",
-        "conversation",
-        "conversations",
-        "turn",
-        "turns",
-        "text",
-    ]
-
-    def __init__(
-        self,
-        data: Union[
-            str,
-            Path,
-            Iterable[Union[str, dict[str, Any]]],
-            Dataset,
-            DatasetDict,
-            IterableDataset,
-            IterableDatasetDict,
-        ],
-        data_args: Optional[dict[str, Any]],
-        processor: Optional[Union[str, Path, PreTrainedTokenizerBase]],
-        processor_args: Optional[dict[str, Any]],
-        shuffle: bool = True,
-        iter_type: Literal["finite", "infinite"] = "finite",
-        random_seed: int = 42,
-    ):
-        self.data = data
-        self.data_args = data_args
-        dataset, args_column_mappings = load_dataset(
-            data,
-            data_args,
-            processor,
-            processor_args,
-            random_seed,
-        )
-        self.dataset = dataset
-        self.processor = processor
-        self.processor_args = processor_args
-        self.shuffle = shuffle
-        self.iter_type = iter_type
-        self.random_seed = random_seed
-
-        self.column_mappings = self._create_column_mappings(args_column_mappings)
-        self.preserve_iter_state = iter_type == "infinite"  # ensure no caching requests
-        self._preserved_iter = None
-
-    def __iter__(self) -> Iterator[GenerationRequest]:
-        scope_create_count = 0
-
-        while (dataset_iter := self._get_dataset_iter(scope_create_count)) is not None:
-            scope_create_count += 1
-
-            for item in dataset_iter:
-                yield self._create_request(item)
-
-            self._preserved_iter = None
-
-    def __len__(self) -> int:
-        if self.iter_type == "finite":
-            return self.num_unique_items()
-
-        raise ValueError(f"Unable to determine length of dataset: {self.data}")
-
-    @property
-    def description(self) -> GenerativeRequestLoaderDescription:
-        return GenerativeRequestLoaderDescription(
-            data=str(self.data),
-            data_args=self.data_args,
-            processor=str(self.processor),
-            processor_args=self.processor_args,
-        )
-
-    def num_unique_items(self, raise_err: bool = True) -> int:
-        try:
-            return len(self.dataset)
-        except Exception:  # noqa: BLE001, S110
-            pass
-
-        dataset_size = self.dataset.info.dataset_size
-        if dataset_size is not None:
-            return dataset_size
-
-        if raise_err:
-            raise ValueError("Unable to determine number of items in the dataset")
-
-        return -1
-
-    def _create_column_mappings(
-        self,
-        args_column_mappings: dict[ColumnInputTypes, str],
-    ) -> dict[ColumnInputTypes, str]:
-        column_mappings: dict[ColumnInputTypes, str] = {}
-
-        if "text_column" in args_column_mappings:
-            column_mappings["prompt_column"] = args_column_mappings["text_column"]
-        else:
-            column_mappings["prompt_column"] = self._extract_text_column()
-
-        if "prompt_tokens_count_column" in args_column_mappings:
-            column_mappings["prompt_tokens_count_column"] = args_column_mappings[
-                "prompt_tokens_count_column"
-            ]
-        elif prompt_tokens_count_column := self._extract_prompt_tokens_count_column():
-            column_mappings["prompt_tokens_count_column"] = prompt_tokens_count_column
-
-        if "output_tokens_count_column" in args_column_mappings:
-            column_mappings["output_tokens_count_column"] = args_column_mappings[
-                "output_tokens_count_column"
-            ]
-        elif output_tokens_count_column := self._extract_output_tokens_count_column():
-            column_mappings["output_tokens_count_column"] = output_tokens_count_column
-
-        return column_mappings
-
-    def _extract_text_column(self) -> str:
-        column_names = self._dataset_columns(
-            err_msg=(
-                "Unable to determine text column from dataset and it is required. "
-                "To specify the text column, set the 'text_column' key in the "
-                "'data_args' dictionary."
-            )
-        )
-
-        if not column_names:
-            raise ValueError(
-                "Unable to determine text column from dataset and it is required. "
-                "To specify the text column, set the 'text_column' key in the "
-                "'data_args' dictionary."
-            )
-
-        if len(column_names) == 1:
-            return column_names[0]
-
-        for def_column in self.DEFAULT_PROMPT_COLUMNS:
-            if def_column in column_names:
-                return def_column
-
-        raise ValueError(
-            f"Unable to determine text column from dataset columns: {column_names}. "
-            "To specify the text column, set the 'text_column' key in the "
-            "'data_args' dictionary."
-        )
-
-    def _extract_prompt_tokens_count_column(self) -> Optional[str]:
-        column_names = self._dataset_columns()
-
-        if column_names and "prompt_tokens_count" in column_names:
-            return "prompt_tokens_count"
-
-        if column_names and "prompt_tokens" in column_names:
-            return "prompt_tokens"
-
-        return None
-
-    def _extract_output_tokens_count_column(self) -> Optional[str]:
-        column_names = self._dataset_columns()
-
-        if column_names and "output_tokens_count" in column_names:
-            return "output_tokens_count"
-
-        if column_names and "output_tokens" in column_names:
-            return "output_tokens"
-
-        return None
-
-    def _dataset_columns(self, err_msg: Optional[str] = None) -> Optional[list[str]]:
-        try:
-            column_names = self.dataset.column_names
-
-            if not column_names and err_msg:
-                raise ValueError(f"No column names found in dataset: {self.data}")
-        except Exception as err:
-            if err_msg:
-                raise ValueError(err_msg) from err
-
-            column_names = None
-
-        return column_names
-
-    def _get_dataset_iter(
-        self, scope_create_count: int
-    ) -> Optional[Iterator[dict[str, Any]]]:
-        if scope_create_count > 0 and self.iter_type != "infinite":
-            return None
-
-        if self.preserve_iter_state and self._preserved_iter is not None:
-            return self._preserved_iter
-
-        dataset = (
-            self.dataset
-            if not self.shuffle
-            else self.dataset.shuffle(seed=self.random_seed)
-        )
-
-        dataset_iter = iter(dataset)
-
-        if self.preserve_iter_state:
-            self._preserved_iter = dataset_iter
-
-        return dataset_iter
-
-    def _create_request(self, item: dict[str, Any]) -> GenerationRequest:
-        prompt_tokens = (
-            item[self.column_mappings["prompt_tokens_count_column"]]
-            if "prompt_tokens_count_column" in self.column_mappings
-            else None
-        )
-        output_tokens = (
-            item[self.column_mappings["output_tokens_count_column"]]
-            if "output_tokens_count_column" in self.column_mappings
-            else None
-        )
-
-        return GenerationRequest(
-            request_type=settings.preferred_route,
-            content=item[self.column_mappings["prompt_column"]],
-            stats=(
-                {"prompt_tokens": prompt_tokens} if prompt_tokens is not None else {}
-            ),
-            constraints=(
-                {"output_tokens": output_tokens} if output_tokens is not None else {}
-            ),
-        )
diff --git a/src/guidellm/request/request.py b/src/guidellm/request/request.py
deleted file mode 100644
index bf4e59fb..00000000
--- a/src/guidellm/request/request.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import uuid
-from typing import Any, Literal, Optional
-
-from pydantic import Field
-
-from guidellm.utils import StandardBaseModel
-
-__all__ = ["GenerationRequest"]
-
-
-class GenerationRequest(StandardBaseModel):
-    """
-    A class representing a request for generation.
-    This class is used to encapsulate the details of a generation request,
-    including the request ID, type, content, parameters, statistics, and constraints.
-    It is designed to be used with the BackendRequestsWorker class to handle
-    the generation process.
-
-    :param request_id: The unique identifier for the request.
-    :param request_type: The type of request (e.g., text, chat).
-    :param content: The content for the request to send to the backend.
-        If request_type is 'text', this should be a string or list of strings
-        which will be resolved by backend.text_completions.
-        If request_type is 'chat', this should be a string,
-        a list of (str, Dict[str, Union[str, Dict[str, str]], Path, Image]),
-        or Any raw content which will be resolved by backend.chat_completions.
-        If raw content, raw_content=True must be passed in the params.
-    :param params: Additional parameters for the request passed in as kwargs.
-        For an http backend, these are passed into the body of the request.
-    :param stats: Statistics for the request, such as the number of prompt tokens.
-        Used for tracking and reporting purposes.
-    :param constraints: Constraints for the request, such as the maximum number
-        of output tokens. Used for controlling the behavior of the backend.
-    """
-
-    request_id: Optional[str] = Field(
-        default_factory=lambda: str(uuid.uuid4()),
-        description="The unique identifier for the request.",
-    )
-    request_type: Literal["text_completions", "chat_completions"] = Field(
-        default="text_completions",
-        description=(
-            "The type of request (e.g., text, chat). "
-            "If request_type='text_completions', resolved by backend.text_completions. "
-            "If request_typ='chat_completions', resolved by backend.chat_completions."
-        ),
-    )
-    content: Any = Field(
-        description=(
-            "The content for the request to send to the backend. "
-            "If request_type is 'text', this should be a string or list of strings "
-            "which will be resolved by backend.text_completions. "
-            "If request_type is 'chat', this should be a string, "
-            "a list of (str, Dict[str, Union[str, Dict[str, str]], Path, Image]), "
-            "or Any raw content which will be resolved by backend.chat_completions. "
-            "If raw content, raw_content=True must be passed in the params."
-        )
-    )
-    params: dict[str, Any] = Field(
-        default_factory=dict,
-        description=(
-            "Additional parameters for the request that will be passed in as kwargs. "
-            "For an http backend, these are passed into the body of the request. "
-        ),
-    )
-    stats: dict[Literal["prompt_tokens"], int] = Field(
-        default_factory=dict,
-        description=(
-            "Statistics for the request, such as the number of prompt tokens. "
-            "Used for tracking and reporting purposes."
-        ),
-    )
-    constraints: dict[Literal["output_tokens"], int] = Field(
-        default_factory=dict,
-        description=(
-            "Constraints for the request, such as the maximum number of output tokens. "
-            "Used for controlling the behavior of the backend."
-        ),
-    )
diff --git a/src/guidellm/request/types.py b/src/guidellm/request/types.py
deleted file mode 100644
index f82493be..00000000
--- a/src/guidellm/request/types.py
+++ /dev/null
@@ -1,10 +0,0 @@
-from typing import TypeVar
-
-__all__ = [
-    "RequestT",
-    "ResponseT",
-]
-
-
-RequestT = TypeVar("RequestT")
-ResponseT = TypeVar("ResponseT")
diff --git a/src/guidellm/scheduler/scheduler.py b/src/guidellm/scheduler/scheduler.py
index e7d8b2c6..d9bb7c23 100644
--- a/src/guidellm/scheduler/scheduler.py
+++ b/src/guidellm/scheduler/scheduler.py
@@ -124,7 +124,7 @@ async def run(
 
                 # Setup the worker group, sync start with the environment
                 worker_group = WorkerProcessGroup[RequestT, ResponseT](
-                    requests=None,
+                    requests=local_requests,
                     cycle_requests=local_requests,
                     backend=backend,
                     strategy=local_strategy,
diff --git a/src/guidellm/scheduler/worker.py b/src/guidellm/scheduler/worker.py
index 5f2fb74b..1832d25f 100644
--- a/src/guidellm/scheduler/worker.py
+++ b/src/guidellm/scheduler/worker.py
@@ -233,6 +233,12 @@ async def _processing_startup(self):
         self.backend_started = True
         await self.backend.validate()
 
+        # Wait for all processes to be ready
+        await wait_for_sync_barrier(
+            self.startup_barrier,
+            poll_interval=self.messaging.poll_interval,
+        )
+
         # Get messaging system ready
         await self.messaging.start(
             receive_stop_criteria=[self.requests_generated_event],
@@ -240,12 +246,6 @@ async def _processing_startup(self):
         )
         self.messaging_started = True
 
-        # Wait for all processes to be ready
-        await wait_for_sync_barrier(
-            self.startup_barrier,
-            poll_interval=self.messaging.poll_interval,
-        )
-
         self.startup_completed = True
 
     async def _processing_shutdown(self):
diff --git a/src/guidellm/scheduler/worker_group.py b/src/guidellm/scheduler/worker_group.py
index c1d516f1..9baccd1b 100644
--- a/src/guidellm/scheduler/worker_group.py
+++ b/src/guidellm/scheduler/worker_group.py
@@ -98,10 +98,9 @@ def __init__(
         :raises ValueError: If neither requests nor cycle_requests are provided,
             or if cycle_requests is an Iterator rather than Iterable
         """
-        if not requests and not cycle_requests:
+        if requests is None and cycle_requests is None:
             raise ValueError(
                 "At least one of 'requests' or 'cycle_requests' must be provided. "
-                f"Got requests: {requests}, cycle_requests: {cycle_requests}"
             )
 
         if isinstance(cycle_requests, Iterator):
@@ -487,15 +486,16 @@ def requests_generator(
         """
 
         def _iter():
-            if requests:
+            if requests is not None:
                 yield from requests
 
-            if cycle_requests:
+            if cycle_requests is not None:
                 while True:
                     yield from cycle_requests
 
         count = 0
         request_info: ScheduledRequestInfo = None
+
         for request in _iter():
             count += 1
 
@@ -512,6 +512,8 @@ def _iter():
                 scheduler_start_time=self.start_time,
             )
             state_update = self._locked_update(request_info)
+            request_info.scheduler_timings.queued = time.time()
+
             yield (request, request_info)
 
             if state_update.stop_queueing:
diff --git a/src/guidellm/settings.py b/src/guidellm/settings.py
index 20d9ff96..5c360eff 100644
--- a/src/guidellm/settings.py
+++ b/src/guidellm/settings.py
@@ -145,7 +145,7 @@ class Settings(BaseSettings):
     mp_max_pending_buffer_percent: float = 0.5
     mp_max_worker_buffer_percent: float = 0.2
     max_concurrency: int = 512
-    max_worker_processes: int = 10
+    max_worker_processes: int = 2
     scheduler_start_delay_non_distributed: float = 1.0
     constraint_error_window_size: float = 30
     constraint_error_min_processed: float = 30
diff --git a/src/guidellm/utils/messaging.py b/src/guidellm/utils/messaging.py
index c56ec29a..2f631a87 100644
--- a/src/guidellm/utils/messaging.py
+++ b/src/guidellm/utils/messaging.py
@@ -610,6 +610,8 @@ def _send_messages_task_thread(  # noqa: C901, PLR0912
                 except (culsans.QueueFull, queue.Full):
                     pass
 
+            time.sleep(0)  # Yield to other threads
+
     def _receive_messages_task_thread(  # noqa: C901
         self,
         receive_callback: Callable[[Any], Any] | None,
@@ -649,6 +651,8 @@ def _receive_messages_task_thread(  # noqa: C901
                 except (culsans.QueueFull, queue.Full):
                     pass
 
+            time.sleep(0)  # Yield to other threads
+
 
 class InterProcessMessagingManagerQueue(
     InterProcessMessagingQueue[SendMessageT, ReceiveMessageT]
diff --git a/tests/unit/dataset/__init__.py b/tests/unit/data/__init__.py
similarity index 100%
rename from tests/unit/dataset/__init__.py
rename to tests/unit/data/__init__.py
diff --git a/tests/unit/data/deserializers/__init__.py b/tests/unit/data/deserializers/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/unit/data/deserializers/test_synthetic.py b/tests/unit/data/deserializers/test_synthetic.py
new file mode 100644
index 00000000..58b76aee
--- /dev/null
+++ b/tests/unit/data/deserializers/test_synthetic.py
@@ -0,0 +1,587 @@
+"""
+Unit tests for guidellm.data.deserializers.synthetic module.
+"""
+
+import json
+import tempfile
+from pathlib import Path
+from unittest.mock import Mock
+
+import pytest
+import yaml
+from datasets import IterableDataset
+
+from guidellm.data.deserializers.deserializer import DataNotSupportedError
+from guidellm.data.deserializers.synthetic import (
+    SyntheticTextDatasetConfig,
+    SyntheticTextDatasetDeserializer,
+    SyntheticTextGenerator,
+    SyntheticTextPrefixBucketConfig,
+)
+
+
+class TestPrefixBucketConfig:
+    """Test cases for PrefixBucketConfig class.
+
+    ### WRITTEN BY AI ###
+    """
+
+    @pytest.mark.smoke
+    def test_creation_with_valid_params(self):
+        """Test creating PrefixBucketConfig with valid parameters.
+
+        ### WRITTEN BY AI ###
+        """
+        config = SyntheticTextPrefixBucketConfig(
+            bucket_weight=100, prefix_count=1, prefix_tokens=5
+        )
+
+        assert config.bucket_weight == 100
+        assert config.prefix_count == 1
+        assert config.prefix_tokens == 5
+
+    @pytest.mark.sanity
+    def test_creation_with_negative_values(self):
+        """Test creating PrefixBucketConfig with negative values raises ValueError.
+
+        ### WRITTEN BY AI ###
+        """
+        with pytest.raises(ValueError):
+            SyntheticTextPrefixBucketConfig(
+                bucket_weight=-10, prefix_count=1, prefix_tokens=5
+            )
+
+        with pytest.raises(ValueError):
+            SyntheticTextPrefixBucketConfig(
+                bucket_weight=100, prefix_count=-1, prefix_tokens=5
+            )
+
+        with pytest.raises(ValueError):
+            SyntheticTextPrefixBucketConfig(
+                bucket_weight=100, prefix_count=1, prefix_tokens=-5
+            )
+
+    @pytest.mark.regression
+    def test_prefix_bucket_zero_weight_error(self):
+        """Test that zero total weight raises an error.
+
+        ### WRITTEN BY AI ###
+        """
+        # Test validation error for creating PrefixBucketConfig with weight=0
+        with pytest.raises(ValueError):
+            SyntheticTextPrefixBucketConfig(
+                bucket_weight=0, prefix_count=1, prefix_tokens=2
+            )
+
+    @pytest.mark.sanity
+    def test_prefix_bucket_config_validation(self):
+        """Test PrefixBucketConfig validation.
+
+        ### WRITTEN BY AI ###
+        """
+        # Test valid config
+        valid_config = SyntheticTextPrefixBucketConfig(
+            bucket_weight=50, prefix_count=2, prefix_tokens=3
+        )
+        assert valid_config.bucket_weight == 50
+        assert valid_config.prefix_count == 2
+        assert valid_config.prefix_tokens == 3
+
+        # Test invalid bucket_weight
+        with pytest.raises(ValueError):
+            SyntheticTextPrefixBucketConfig(
+                bucket_weight=0, prefix_count=1, prefix_tokens=2
+            )
+
+        # Test invalid prefix_count
+        with pytest.raises(ValueError):
+            SyntheticTextPrefixBucketConfig(
+                bucket_weight=100, prefix_count=0, prefix_tokens=2
+            )
+
+        # Test invalid prefix_tokens
+        with pytest.raises(ValueError):
+            SyntheticTextPrefixBucketConfig(
+                bucket_weight=100, prefix_count=1, prefix_tokens=-1
+            )
+
+
+class TestSyntheticDatasetConfig:
+    """Test cases for SyntheticDatasetConfig class.
+
+    ### WRITTEN BY AI ###
+    """
+
+    @pytest.mark.smoke
+    def test_config_creation_with_all_params(self):
+        """Test creating config with all parameters specified.
+
+        ### WRITTEN BY AI ###
+        """
+        prefix_bucket = SyntheticTextPrefixBucketConfig(
+            bucket_weight=100, prefix_count=1, prefix_tokens=5
+        )
+
+        config = SyntheticTextDatasetConfig(
+            prefix_buckets=[prefix_bucket],
+            prompt_tokens=100,
+            prompt_tokens_stdev=10,
+            prompt_tokens_min=50,
+            prompt_tokens_max=150,
+            output_tokens=30,
+            output_tokens_stdev=5,
+            output_tokens_min=20,
+            output_tokens_max=40,
+            source="custom_text.txt",
+        )
+
+        assert config.prefix_buckets[0].prefix_tokens == 5  # type: ignore [index]
+        assert config.prompt_tokens == 100
+        assert config.prompt_tokens_stdev == 10
+        assert config.prompt_tokens_min == 50
+        assert config.prompt_tokens_max == 150
+        assert config.output_tokens == 30
+        assert config.output_tokens_stdev == 5
+        assert config.output_tokens_min == 20
+        assert config.output_tokens_max == 40
+        assert config.source == "custom_text.txt"
+
+    @pytest.mark.regression
+    def test_parse_json_string(self):
+        """Test parsing JSON string configuration.
+
+        ### WRITTEN BY AI ###
+        """
+        json_str = json.dumps(
+            {
+                "prompt_tokens": 75,
+                "output_tokens": 25,
+                "source": "test.txt",
+                "prefix_buckets": [
+                    {"bucket_weight": 100, "prefix_count": 1, "prefix_tokens": 10}
+                ],
+            }
+        )
+
+        config = SyntheticTextDatasetConfig.model_validate_json(json_str)
+
+        assert config.prompt_tokens == 75
+        assert config.output_tokens == 25
+        assert config.source == "test.txt"
+        assert config.prefix_buckets[0].prefix_tokens == 10  # type: ignore [index]
+
+    @pytest.mark.sanity
+    def test_validation_positive_values(self):
+        """Test that negative or zero values are rejected.
+
+        ### WRITTEN BY AI ###
+        """
+        with pytest.raises(ValueError):
+            SyntheticTextDatasetConfig(prompt_tokens=0, output_tokens=20)
+
+        with pytest.raises(ValueError):
+            SyntheticTextDatasetConfig(prompt_tokens=20, output_tokens=0)
+
+        # Test negative prefix tokens via PrefixBucketConfig validation
+        with pytest.raises(ValueError):
+            SyntheticTextPrefixBucketConfig(prefix_tokens=-1)
+
+    @pytest.mark.regression
+    def test_validation_optional_positive_values(self):
+        """Test that optional parameters reject negative values.
+
+        ### WRITTEN BY AI ###
+        """
+        with pytest.raises(ValueError):
+            SyntheticTextDatasetConfig(
+                prompt_tokens=20, output_tokens=10, prompt_tokens_stdev=-1
+            )
+
+        with pytest.raises(ValueError):
+            SyntheticTextDatasetConfig(
+                prompt_tokens=20, output_tokens=10, prompt_tokens_min=-1
+            )
+
+        with pytest.raises(ValueError):
+            SyntheticTextDatasetConfig(
+                prompt_tokens=20, output_tokens=10, output_tokens_max=0
+            )
+
+
+class TestSyntheticTextGenerator:
+    """Test cases for SyntheticTextGenerator class.
+
+    ### WRITTEN BY AI ###
+    """
+
+    @pytest.fixture
+    def mock_tokenizer(self):
+        """Fixture to provide a mocked tokenizer.
+
+        ### WRITTEN BY AI ###
+        """
+        tokenizer = Mock()
+        tokenizer.encode.side_effect = lambda text: list(range(len(text.split())))
+        tokenizer.decode.side_effect = (
+            lambda tokens, skip_special_tokens=False: " ".join(
+                f"token_{t}" for t in tokens[:5]
+            )
+        )
+        return tokenizer
+
+    @pytest.fixture
+    def simple_config(self):
+        """Fixture for simple configuration.
+
+        ### WRITTEN BY AI ###
+        """
+        return SyntheticTextDatasetConfig(
+            prompt_tokens=15,
+            output_tokens=10,
+            source="The quick brown fox jumps over the lazy dog.",
+        )
+
+    @pytest.fixture
+    def config_with_prefix(self):
+        """Fixture for configuration with prefix tokens.
+
+        ### WRITTEN BY AI ###
+        """
+        prefix_bucket = SyntheticTextPrefixBucketConfig(
+            bucket_weight=100, prefix_count=1, prefix_tokens=3
+        )
+
+        return SyntheticTextDatasetConfig(
+            prefix_buckets=[prefix_bucket],
+            prompt_tokens=15,
+            output_tokens=10,
+            source="The quick brown fox jumps over the lazy dog.",
+        )
+
+    @pytest.mark.smoke
+    def test_generator_initialization(self, simple_config, mock_tokenizer):
+        """Test generator initialization.
+
+        ### WRITTEN BY AI ###
+        """
+        generator = SyntheticTextGenerator(
+            simple_config, mock_tokenizer, random_seed=42
+        )
+
+        assert generator.config == simple_config
+        assert generator.processor == mock_tokenizer
+        assert generator.random_seed == 42
+
+    @pytest.mark.smoke
+    def test_basic_iteration(self, simple_config, mock_tokenizer):
+        """Test basic iteration functionality.
+
+        ### WRITTEN BY AI ###
+        """
+        generator = SyntheticTextGenerator(
+            simple_config, mock_tokenizer, random_seed=42
+        )
+
+        items = []
+        for i, item in enumerate(generator):
+            items.append(item)
+            if i >= 4:  # Only get 5 items
+                break
+
+        # Verify we get the expected number of items
+        assert len(items) == 5
+
+        # Verify each item has the required keys
+        for item in items:
+            assert "prefix" in item
+            assert "prompt" in item
+            assert "prompt_tokens_count" in item
+            assert "output_tokens_count" in item
+            assert isinstance(item["prefix"], str)
+            assert isinstance(item["prompt"], str)
+            assert isinstance(item["prompt_tokens_count"], int)
+            assert isinstance(item["output_tokens_count"], int)
+
+    @pytest.mark.sanity
+    def test_create_prompt_method(self, simple_config, mock_tokenizer):
+        """Test _create_prompt method.
+
+        ### WRITTEN BY AI ###
+        """
+        from faker import Faker
+
+        generator = SyntheticTextGenerator(
+            simple_config, mock_tokenizer, random_seed=42
+        )
+        faker = Faker()
+        faker.seed_instance(42)
+
+        # Test normal case
+        result = generator._create_prompt(5, faker, "unique_prefix ")
+        assert isinstance(result, str)
+        # The result should be the decoded tokens (token_0 token_1 etc.) due to our mock
+        assert "token_" in result
+
+        # Test zero tokens
+        result = generator._create_prompt(0, faker)
+        assert result == ""
+
+    @pytest.mark.regression
+    def test_prefix_tokens_integration(self, config_with_prefix, mock_tokenizer):
+        """Test integration with prefix tokens.
+
+        ### WRITTEN BY AI ###
+        """
+        generator = SyntheticTextGenerator(
+            config_with_prefix, mock_tokenizer, random_seed=42
+        )
+
+        items = []
+        for i, item in enumerate(generator):
+            items.append(item)
+            if i >= 2:  # Only get 3 items
+                break
+
+        # Verify prefix is present in items
+        for item in items:
+            assert isinstance(item["prefix"], str)
+
+    @pytest.mark.regression
+    def test_random_seeding_consistency(self, simple_config, mock_tokenizer):
+        """Test that same seed produces consistent results.
+
+        ### WRITTEN BY AI ###
+        """
+        # Create two generators with same seed
+        generator1 = SyntheticTextGenerator(
+            simple_config, mock_tokenizer, random_seed=42
+        )
+        generator2 = SyntheticTextGenerator(
+            simple_config, mock_tokenizer, random_seed=42
+        )
+
+        items1 = []
+        items2 = []
+        for i, (item1, item2) in enumerate(zip(generator1, generator2)):
+            items1.append(item1)
+            items2.append(item2)
+            if i >= 2:  # Only get 3 items
+                break
+
+        # With same seed and deterministic mocks, results should be identical
+        assert len(items1) == len(items2)
+        for item1, item2 in zip(items1, items2):
+            assert item1["prompt_tokens_count"] == item2["prompt_tokens_count"]
+            assert item1["output_tokens_count"] == item2["output_tokens_count"]
+
+
+class TestSyntheticDatasetDeserializer:
+    """Test cases for SyntheticDatasetDeserializer class.
+
+    ### WRITTEN BY AI ###
+    """
+
+    @pytest.fixture
+    def mock_tokenizer(self):
+        """Fixture to provide a mocked tokenizer.
+
+        ### WRITTEN BY AI ###
+        """
+        tokenizer = Mock()
+        tokenizer.encode.side_effect = lambda text: list(range(len(text.split())))
+        tokenizer.decode.side_effect = (
+            lambda tokens, skip_special_tokens=False: " ".join(
+                f"token_{t}" for t in tokens[:5]
+            )
+        )
+        return tokenizer
+
+    @pytest.mark.sanity
+    def test_load_config_file_yaml(self):
+        """Test loading YAML config file.
+
+        ### WRITTEN BY AI ###
+        """
+        config_data = {
+            "prompt_tokens": 60,
+            "output_tokens": 15,
+            "source": "yaml_test.txt",
+            "prefix_buckets": [
+                {"bucket_weight": 100, "prefix_count": 1, "prefix_tokens": 3}
+            ],
+        }
+
+        with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f:
+            yaml.dump(config_data, f)
+            yaml_path = f.name
+
+        try:
+            deserializer = SyntheticTextDatasetDeserializer()
+            config = deserializer._load_config_file(yaml_path)
+
+            assert config.prompt_tokens == 60
+            assert config.output_tokens == 15
+            assert config.source == "yaml_test.txt"
+            assert config.prefix_buckets[0].prefix_tokens == 3  # type: ignore [index]
+        finally:
+            Path(yaml_path).unlink()
+
+    @pytest.mark.sanity
+    def test_load_config_file_config_extension(self):
+        """Test loading .config file.
+
+        ### WRITTEN BY AI ###
+        """
+        config_data = {
+            "prompt_tokens": 90,
+            "output_tokens": 35,
+            "prefix_buckets": [
+                {"bucket_weight": 100, "prefix_count": 1, "prefix_tokens": 2}
+            ],
+        }
+
+        with tempfile.NamedTemporaryFile(mode="w", suffix=".config", delete=False) as f:
+            yaml.dump(config_data, f)
+            config_path = f.name
+
+        try:
+            deserializer = SyntheticTextDatasetDeserializer()
+            config = deserializer._load_config_file(config_path)
+
+            assert config.prompt_tokens == 90
+            assert config.output_tokens == 35
+            assert config.prefix_buckets[0].prefix_tokens == 2  # type: ignore [index]
+        finally:
+            Path(config_path).unlink()
+
+    @pytest.mark.smoke
+    def test_load_config_str_json(self):
+        """Test loading JSON string config.
+
+        ### WRITTEN BY AI ###
+        """
+        json_str = '{"prompt_tokens": 50, "output_tokens": 25}'
+        deserializer = SyntheticTextDatasetDeserializer()
+        config = deserializer._load_config_str(json_str)
+
+        assert config.prompt_tokens == 50
+        assert config.output_tokens == 25
+
+    @pytest.mark.smoke
+    def test_load_config_str_key_value(self):
+        """Test loading key-value string config.
+
+        ### WRITTEN BY AI ###
+        """
+        kv_str = "prompt_tokens=50,output_tokens=25"
+        deserializer = SyntheticTextDatasetDeserializer()
+        config = deserializer._load_config_str(kv_str)
+
+        assert config.prompt_tokens == 50
+        assert config.output_tokens == 25
+
+    @pytest.mark.sanity
+    def test_load_config_str_invalid_format(self):
+        """Test loading invalid format raises DataNotSupportedError.
+
+        ### WRITTEN BY AI ###
+        """
+        deserializer = SyntheticTextDatasetDeserializer()
+        with pytest.raises(DataNotSupportedError, match="Unsupported string data"):
+            deserializer._load_config_str("invalid_format_string")
+
+    @pytest.mark.regression
+    def test_load_config_file_non_existent(self):
+        """Test loading non-existent file returns None.
+
+        ### WRITTEN BY AI ###
+        """
+        deserializer = SyntheticTextDatasetDeserializer()
+        config = deserializer._load_config_file("/non/existent/path.config")
+        assert config is None
+
+    @pytest.mark.regression
+    def test_load_config_str_non_string(self):
+        """Test loading non-string returns None.
+
+        ### WRITTEN BY AI ###
+        """
+        deserializer = SyntheticTextDatasetDeserializer()
+        config = deserializer._load_config_str(123)
+        assert config is None
+
+    @pytest.mark.smoke
+    def test_call_with_config_object(self, mock_tokenizer):
+        """Test calling deserializer with SyntheticTextDatasetConfig.
+
+        ### WRITTEN BY AI ###
+        """
+        config = SyntheticTextDatasetConfig(prompt_tokens=50, output_tokens=25)
+        deserializer = SyntheticTextDatasetDeserializer()
+
+        result = deserializer(
+            data=config,
+            data_kwargs={},
+            processor_factory=lambda: mock_tokenizer,
+            random_seed=42,
+        )
+
+        assert isinstance(result, IterableDataset)
+
+    @pytest.mark.regression
+    def test_call_with_unsupported_data(self, mock_tokenizer):
+        """Test calling deserializer with unsupported data raises error.
+
+        ### WRITTEN BY AI ###
+        """
+        deserializer = SyntheticTextDatasetDeserializer()
+
+        with pytest.raises(DataNotSupportedError, match="Unsupported data"):
+            deserializer(
+                data=123,
+                data_kwargs={},
+                processor_factory=lambda: mock_tokenizer,
+                random_seed=42,
+            )
+
+    @pytest.mark.regression
+    def test_call_with_json_string(self, mock_tokenizer):
+        """Test calling deserializer with JSON string.
+
+        ### WRITTEN BY AI ###
+        """
+        json_str = '{"prompt_tokens": 50, "output_tokens": 25}'
+        deserializer = SyntheticTextDatasetDeserializer()
+
+        result = deserializer(
+            data=json_str,
+            data_kwargs={},
+            processor_factory=lambda: mock_tokenizer,
+            random_seed=42,
+        )
+
+        assert isinstance(result, IterableDataset)
+
+    @pytest.mark.regression
+    def test_call_with_config_file(self, mock_tokenizer):
+        """Test calling deserializer with config file.
+
+        ### WRITTEN BY AI ###
+        """
+        config_data = {"prompt_tokens": 65, "output_tokens": 45}
+
+        with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f:
+            yaml.dump(config_data, f)
+            config_path = f.name
+
+        try:
+            deserializer = SyntheticTextDatasetDeserializer()
+            result = deserializer(
+                data=config_path,
+                data_kwargs={},
+                processor_factory=lambda: mock_tokenizer,
+                random_seed=42,
+            )
+            assert isinstance(result, IterableDataset)
+        finally:
+            Path(config_path).unlink()
diff --git a/tests/unit/dataset/test_synthetic.py b/tests/unit/dataset/test_synthetic.py
deleted file mode 100644
index e3110fa3..00000000
--- a/tests/unit/dataset/test_synthetic.py
+++ /dev/null
@@ -1,873 +0,0 @@
-"""
-Unit tests for guidellm.dataset.synthetic module.
-"""
-
-import json
-import tempfile
-from pathlib import Path
-from unittest.mock import Mock, patch
-
-import pytest
-import yaml
-
-from guidellm.dataset.synthetic import (
-    SyntheticDatasetConfig,
-    SyntheticDatasetCreator,
-    SyntheticTextItemsGenerator,
-)
-
-
-class TestSyntheticDatasetConfig:
-    """Test cases for SyntheticDatasetConfig class.
-
-    ### WRITTEN BY AI ###
-    """
-
-    @pytest.mark.smoke
-    def test_config_creation_with_all_params(self):
-        """Test creating config with all parameters specified.
-
-        ### WRITTEN BY AI ###
-        """
-        config = SyntheticDatasetConfig(
-            prefix_tokens=5,
-            prompt_tokens=100,
-            prompt_tokens_stdev=10,
-            prompt_tokens_min=50,
-            prompt_tokens_max=150,
-            output_tokens=30,
-            output_tokens_stdev=5,
-            output_tokens_min=20,
-            output_tokens_max=40,
-            samples=500,
-            source="custom_text.txt",
-        )
-
-        assert config.prefix_tokens == 5
-        assert config.prompt_tokens == 100
-        assert config.prompt_tokens_stdev == 10
-        assert config.prompt_tokens_min == 50
-        assert config.prompt_tokens_max == 150
-        assert config.output_tokens == 30
-        assert config.output_tokens_stdev == 5
-        assert config.output_tokens_min == 20
-        assert config.output_tokens_max == 40
-        assert config.samples == 500
-        assert config.source == "custom_text.txt"
-
-    @pytest.mark.regression
-    def test_parse_json_string(self):
-        """Test parsing JSON string configuration.
-
-        ### WRITTEN BY AI ###
-        """
-        json_str = json.dumps(
-            {
-                "prompt_tokens": 75,
-                "output_tokens": 25,
-                "samples": 200,
-                "source": "test.txt",
-                "prefix_tokens": 10,
-            }
-        )
-
-        config = SyntheticDatasetConfig.parse_str(json_str)
-
-        assert config.prompt_tokens == 75
-        assert config.output_tokens == 25
-        assert config.samples == 200
-        assert config.source == "test.txt"
-        assert config.prefix_tokens == 10
-
-    @pytest.mark.regression
-    def test_parse_key_value_pairs(self):
-        """Test parsing key-value pairs configuration.
-
-        ### WRITTEN BY AI ###
-        """
-        kv_str = "prompt_tokens=80,output_tokens=30,samples=300,source=data.txt,prefix_tokens=5"  # noqa: E501
-
-        config = SyntheticDatasetConfig.parse_str(kv_str)
-
-        assert config.prompt_tokens == 80
-        assert config.output_tokens == 30
-        assert config.samples == 300
-        assert config.source == "data.txt"
-        assert config.prefix_tokens == 5
-
-    @pytest.mark.sanity
-    def test_parse_yaml_file(self):
-        """Test parsing YAML file configuration.
-
-        ### WRITTEN BY AI ###
-        """
-        config_data = {
-            "prompt_tokens": 60,
-            "output_tokens": 15,
-            "samples": 100,
-            "source": "yaml_test.txt",
-            "prefix_tokens": 3,
-        }
-
-        with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f:
-            yaml.dump(config_data, f)
-            yaml_path = f.name
-
-        try:
-            config = SyntheticDatasetConfig.parse_str(yaml_path)
-
-            assert config.prompt_tokens == 60
-            assert config.output_tokens == 15
-            assert config.samples == 100
-            assert config.source == "yaml_test.txt"
-            assert config.prefix_tokens == 3
-        finally:
-            Path(yaml_path).unlink()
-
-    @pytest.mark.sanity
-    def test_parse_config_file(self):
-        """Test parsing .config file.
-
-        ### WRITTEN BY AI ###
-        """
-        config_data = {
-            "prompt_tokens": 90,
-            "output_tokens": 35,
-            "samples": 150,
-            "prefix_tokens": 2,
-        }
-
-        with tempfile.NamedTemporaryFile(mode="w", suffix=".config", delete=False) as f:
-            yaml.dump(config_data, f)
-            config_path = f.name
-
-        try:
-            config = SyntheticDatasetConfig.parse_str(config_path)
-
-            assert config.prompt_tokens == 90
-            assert config.output_tokens == 35
-            assert config.samples == 150
-            assert config.prefix_tokens == 2
-        finally:
-            Path(config_path).unlink()
-
-    @pytest.mark.regression
-    def test_parse_path_object(self):
-        """Test parsing with Path object.
-
-        ### WRITTEN BY AI ###
-        """
-        config_data = {"prompt_tokens": 45, "output_tokens": 25}
-
-        with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f:
-            yaml.dump(config_data, f)
-            yaml_path = Path(f.name)
-
-        try:
-            config = SyntheticDatasetConfig.parse_str(yaml_path)
-            assert config.prompt_tokens == 45
-            assert config.output_tokens == 25
-        finally:
-            yaml_path.unlink()
-
-    @pytest.mark.sanity
-    def test_parse_invalid_format(self):
-        """Test parsing invalid format raises ValueError.
-
-        ### WRITTEN BY AI ###
-        """
-        with pytest.raises(ValueError, match="Unsupported data format"):
-            SyntheticDatasetConfig.parse_str("invalid_format_string")
-
-    @pytest.mark.sanity
-    def test_validation_positive_values(self):
-        """Test that negative or zero values are rejected.
-
-        ### WRITTEN BY AI ###
-        """
-        with pytest.raises(ValueError):
-            SyntheticDatasetConfig(prompt_tokens=0, output_tokens=20)
-
-        with pytest.raises(ValueError):
-            SyntheticDatasetConfig(prompt_tokens=20, output_tokens=0)
-
-        with pytest.raises(ValueError):
-            SyntheticDatasetConfig(prompt_tokens=20, output_tokens=10, samples=0)
-
-        with pytest.raises(ValueError):
-            SyntheticDatasetConfig(prompt_tokens=20, output_tokens=10, prefix_tokens=-1)
-
-    @pytest.mark.regression
-    def test_validation_optional_positive_values(self):
-        """Test that optional parameters reject negative values.
-
-        ### WRITTEN BY AI ###
-        """
-        with pytest.raises(ValueError):
-            SyntheticDatasetConfig(
-                prompt_tokens=20, output_tokens=10, prompt_tokens_stdev=-1
-            )
-
-        with pytest.raises(ValueError):
-            SyntheticDatasetConfig(
-                prompt_tokens=20, output_tokens=10, prompt_tokens_min=-1
-            )
-
-        with pytest.raises(ValueError):
-            SyntheticDatasetConfig(
-                prompt_tokens=20, output_tokens=10, output_tokens_max=0
-            )
-
-    @pytest.mark.regression
-    def test_parse_json_method_directly(self):
-        """Test parse_json static method directly.
-
-        ### WRITTEN BY AI ###
-        """
-        json_data = {"prompt_tokens": 100, "output_tokens": 50}
-        json_str = json.dumps(json_data)
-
-        config = SyntheticDatasetConfig.parse_json(json_str)
-
-        assert config.prompt_tokens == 100
-        assert config.output_tokens == 50
-
-    @pytest.mark.regression
-    def test_parse_key_value_pairs_method_directly(self):
-        """Test parse_key_value_pairs static method directly.
-
-        ### WRITTEN BY AI ###
-        """
-        kv_str = "prompt_tokens=75,output_tokens=35"
-
-        config = SyntheticDatasetConfig.parse_key_value_pairs(kv_str)
-
-        assert config.prompt_tokens == 75
-        assert config.output_tokens == 35
-
-    @pytest.mark.regression
-    def test_parse_config_file_method_directly(self):
-        """Test parse_config_file static method directly.
-
-        ### WRITTEN BY AI ###
-        """
-        config_data = {"prompt_tokens": 65, "output_tokens": 45}
-
-        with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as f:
-            yaml.dump(config_data, f)
-            config_path = f.name
-
-        try:
-            config = SyntheticDatasetConfig.parse_config_file(config_path)
-            assert config.prompt_tokens == 65
-            assert config.output_tokens == 45
-        finally:
-            Path(config_path).unlink()
-
-
-class TestSyntheticTextItemsGenerator:
-    """Test cases for SyntheticTextItemsGenerator class.
-
-    ### WRITTEN BY AI ###
-    """
-
-    @pytest.fixture
-    def mock_tokenizer(self):
-        """Fixture to provide a mocked tokenizer.
-
-        ### WRITTEN BY AI ###
-        """
-        tokenizer = Mock()
-        tokenizer.get_vocab.return_value = {f"token_{i}": i for i in range(1000)}
-        tokenizer.encode.side_effect = lambda text: [1, 2, 3] * (len(text) // 10 + 1)
-        tokenizer.decode.side_effect = (
-            lambda tokens, skip_special_tokens=False: " ".join(
-                f"token_{t}" for t in tokens[:5]
-            )
-        )
-        return tokenizer
-
-    @pytest.fixture
-    def simple_config(self):
-        """Fixture for simple configuration.
-
-        ### WRITTEN BY AI ###
-        """
-        return SyntheticDatasetConfig(
-            prompt_tokens=15,
-            output_tokens=10,
-            samples=5,
-            source="The quick brown fox jumps over the lazy dog.",
-        )
-
-    @pytest.fixture
-    def config_with_prefix(self):
-        """Fixture for configuration with prefix tokens.
-
-        ### WRITTEN BY AI ###
-        """
-        return SyntheticDatasetConfig(
-            prefix_tokens=3,
-            prompt_tokens=15,
-            output_tokens=10,
-            samples=5,
-            source="The quick brown fox jumps over the lazy dog.",
-        )
-
-    @pytest.fixture
-    def complex_config(self):
-        """Fixture for complex configuration with variance.
-
-        ### WRITTEN BY AI ###
-        """
-        return SyntheticDatasetConfig(
-            prompt_tokens=20,
-            prompt_tokens_stdev=5,
-            prompt_tokens_min=10,
-            prompt_tokens_max=30,
-            output_tokens=15,
-            output_tokens_stdev=3,
-            output_tokens_min=10,
-            output_tokens_max=20,
-            samples=10,
-            source="The quick brown fox jumps over the lazy dog.",
-        )
-
-    @pytest.mark.smoke
-    @patch("guidellm.dataset.synthetic.EndlessTextCreator")
-    def test_generator_initialization(
-        self, mock_text_creator, simple_config, mock_tokenizer
-    ):
-        """Test generator initialization.
-
-        ### WRITTEN BY AI ###
-        """
-        generator = SyntheticTextItemsGenerator(
-            simple_config, mock_tokenizer, random_seed=42
-        )
-
-        assert generator.config == simple_config
-        assert generator.processor == mock_tokenizer
-        assert generator.random_seed == 42
-        mock_text_creator.assert_called_once_with(data=simple_config.source)
-
-    @pytest.mark.smoke
-    @patch("guidellm.dataset.synthetic.EndlessTextCreator")
-    @patch("guidellm.dataset.synthetic.IntegerRangeSampler")
-    def test_basic_iteration(
-        self, mock_sampler, mock_text_creator, simple_config, mock_tokenizer
-    ):
-        """Test basic iteration functionality.
-
-        ### WRITTEN BY AI ###
-        """
-        # Setup mocks
-        mock_text_creator_instance = Mock()
-        mock_text_creator_instance.words = ["word1", "word2", "word3"] * 100
-        mock_text_creator_instance.create_text.return_value = "sample text"
-        mock_text_creator.return_value = mock_text_creator_instance
-
-        # Mock IntegerRangeSampler to return iterators
-        def mock_sampler_side_effect(*args, **kwargs):
-            mock_instance = Mock()
-            mock_instance.__iter__ = Mock(return_value=iter([15, 15, 15, 15, 15]))
-            return mock_instance
-
-        mock_sampler.side_effect = mock_sampler_side_effect
-
-        generator = SyntheticTextItemsGenerator(
-            simple_config, mock_tokenizer, random_seed=42
-        )
-
-        items = list(generator)
-
-        # Verify we get the expected number of items
-        assert len(items) == simple_config.samples
-
-        # Verify each item has the required keys
-        for item in items:
-            assert "prompt" in item
-            assert "prompt_tokens_count" in item
-            assert "output_tokens_count" in item
-            assert isinstance(item["prompt"], str)
-            assert isinstance(item["prompt_tokens_count"], int)
-            assert isinstance(item["output_tokens_count"], int)
-
-    @pytest.mark.sanity
-    @patch("guidellm.dataset.synthetic.EndlessTextCreator")
-    def test_create_prompt_method(
-        self, mock_text_creator, simple_config, mock_tokenizer
-    ):
-        """Test _create_prompt method.
-
-        ### WRITTEN BY AI ###
-        """
-        mock_text_creator_instance = Mock()
-        mock_text_creator_instance.words = ["word"] * 100
-        mock_text_creator_instance.create_text.return_value = "test text"
-        mock_text_creator.return_value = mock_text_creator_instance
-
-        mock_tokenizer.encode.return_value = [1, 2, 3]
-
-        generator = SyntheticTextItemsGenerator(
-            simple_config, mock_tokenizer, random_seed=42
-        )
-
-        # Test normal case
-        result = generator._create_prompt(5, 0, 42)
-        assert result == [42, 1, 2, 3]
-
-        # Test zero tokens
-        result = generator._create_prompt(0, 0, 42)
-        assert result == []
-
-        # Test without unique prefix
-        result = generator._create_prompt(3, 0)
-        assert result == [1, 2, 3]
-
-    @pytest.mark.regression
-    @patch("guidellm.dataset.synthetic.EndlessTextCreator")
-    def test_create_prompt_binary_search(
-        self, mock_text_creator, simple_config, mock_tokenizer
-    ):
-        """Test binary search logic in _create_prompt.
-
-        ### WRITTEN BY AI ###
-        """
-        mock_text_creator_instance = Mock()
-        mock_text_creator_instance.words = ["word"] * 1000
-        mock_text_creator_instance.create_text.side_effect = lambda start, length: (
-            "text " * max(1, length // 4)
-        ).strip()
-        mock_text_creator.return_value = mock_text_creator_instance
-
-        # Mock tokenizer to return different lengths based on input
-        def mock_encode(text):
-            return [1] * len(text.split())
-
-        mock_tokenizer.encode.side_effect = mock_encode
-
-        generator = SyntheticTextItemsGenerator(
-            simple_config, mock_tokenizer, random_seed=42
-        )
-
-        # Test that binary search finds appropriate length
-        result = generator._create_prompt(5, 0, 42)
-        assert len(result) >= 4  # Should include prefix + some tokens
-
-    @pytest.mark.sanity
-    @patch("guidellm.dataset.synthetic.EndlessTextCreator")
-    @patch("guidellm.dataset.synthetic.IntegerRangeSampler")
-    def test_prefix_tokens_integration(
-        self, mock_sampler, mock_text_creator, config_with_prefix, mock_tokenizer
-    ):
-        """Test integration with prefix tokens.
-
-        ### WRITTEN BY AI ###
-        """
-        # Setup mocks
-        mock_text_creator_instance = Mock()
-        mock_text_creator_instance.words = ["word"] * 100
-        mock_text_creator_instance.create_text.return_value = "sample text"
-        mock_text_creator.return_value = mock_text_creator_instance
-
-        mock_sampler_instance = Mock()
-        mock_sampler_instance.__iter__ = Mock(return_value=iter([15, 15, 15, 15, 15]))
-        mock_sampler.return_value = mock_sampler_instance
-
-        generator = SyntheticTextItemsGenerator(
-            config_with_prefix, mock_tokenizer, random_seed=42
-        )
-
-        items = list(generator)
-
-        # Verify prompt_tokens_count includes prefix
-        for item in items:
-            assert item["prompt_tokens_count"] == config_with_prefix.prefix_tokens + 15
-
-    @pytest.mark.regression
-    @patch("guidellm.dataset.synthetic.EndlessTextCreator")
-    @patch("guidellm.dataset.synthetic.IntegerRangeSampler")
-    def test_random_seeding_consistency(
-        self, mock_sampler, mock_text_creator, simple_config, mock_tokenizer
-    ):
-        """Test that same seed produces consistent results.
-
-        ### WRITTEN BY AI ###
-        """
-        # Setup mocks
-        mock_text_creator_instance = Mock()
-        mock_text_creator_instance.words = ["word"] * 100
-        mock_text_creator_instance.create_text.return_value = "sample text"
-        mock_text_creator.return_value = mock_text_creator_instance
-
-        # Create consistent mock sampler behavior
-        call_count = 0
-
-        def mock_sampler_side_effect(*args, **kwargs):
-            nonlocal call_count
-            mock_instance = Mock()
-            # Return same sequence for both prompt and output tokens
-            if call_count % 2 == 0:  # prompt tokens
-                mock_instance.__iter__ = Mock(return_value=iter([15, 16, 17, 18, 19]))
-            else:  # output tokens
-                mock_instance.__iter__ = Mock(return_value=iter([10, 11, 12, 13, 14]))
-            call_count += 1
-            return mock_instance
-
-        mock_sampler.side_effect = mock_sampler_side_effect
-
-        # Create two generators with same seed
-        generator1 = SyntheticTextItemsGenerator(
-            simple_config, mock_tokenizer, random_seed=42
-        )
-        generator2 = SyntheticTextItemsGenerator(
-            simple_config, mock_tokenizer, random_seed=42
-        )
-
-        items1 = list(generator1)
-        items2 = list(generator2)
-
-        # Results should be identical with same seed
-        assert len(items1) == len(items2)
-        for item1, item2 in zip(items1, items2):
-            assert item1["prompt"] == item2["prompt"]
-            assert item1["prompt_tokens_count"] == item2["prompt_tokens_count"]
-            assert item1["output_tokens_count"] == item2["output_tokens_count"]
-
-    @pytest.mark.regression
-    @patch("guidellm.dataset.synthetic.EndlessTextCreator")
-    @patch("guidellm.dataset.synthetic.IntegerRangeSampler")
-    def test_variance_configuration(
-        self, mock_sampler, mock_text_creator, complex_config, mock_tokenizer
-    ):
-        """Test that variance configuration is properly used.
-
-        ### WRITTEN BY AI ###
-        """
-        # Setup mocks
-        mock_text_creator_instance = Mock()
-        mock_text_creator_instance.words = ["word"] * 100
-        mock_text_creator_instance.create_text.return_value = "sample text"
-        mock_text_creator.return_value = mock_text_creator_instance
-
-        # Fix tokenizer mock to handle the create_text return properly
-        mock_tokenizer.encode.side_effect = (
-            lambda text: [1, 2, 3] if isinstance(text, str) else [1, 2, 3]
-        )
-
-        # Setup mock sampler to track calls
-        def mock_sampler_side_effect(*args, **kwargs):
-            mock_instance = Mock()
-            mock_instance.__iter__ = Mock(return_value=iter([20, 18, 22, 19, 21] * 2))
-            return mock_instance
-
-        mock_sampler.side_effect = mock_sampler_side_effect
-
-        generator = SyntheticTextItemsGenerator(
-            complex_config, mock_tokenizer, random_seed=42
-        )
-
-        # Initialize the generator to trigger sampler creation
-        generator_iter = iter(generator)
-        next(generator_iter)
-
-        # Verify that IntegerRangeSampler is called with correct parameters
-        assert mock_sampler.call_count == 2
-
-        # Check prompt tokens sampler call
-        prompt_call = mock_sampler.call_args_list[0]
-        assert prompt_call[1]["average"] == complex_config.prompt_tokens
-        assert prompt_call[1]["variance"] == complex_config.prompt_tokens_stdev
-        assert prompt_call[1]["min_value"] == complex_config.prompt_tokens_min
-        assert prompt_call[1]["max_value"] == complex_config.prompt_tokens_max
-        assert prompt_call[1]["random_seed"] == 42
-
-        # Check output tokens sampler call
-        output_call = mock_sampler.call_args_list[1]
-        assert output_call[1]["average"] == complex_config.output_tokens
-        assert output_call[1]["variance"] == complex_config.output_tokens_stdev
-        assert output_call[1]["min_value"] == complex_config.output_tokens_min
-        assert output_call[1]["max_value"] == complex_config.output_tokens_max
-        assert output_call[1]["random_seed"] == 43  # 42 + 1
-
-    @pytest.mark.regression
-    @patch("guidellm.dataset.synthetic.EndlessTextCreator")
-    def test_unique_prefix_generation(
-        self, mock_text_creator, simple_config, mock_tokenizer
-    ):
-        """Test that unique prefixes are generated for each request.
-
-        ### WRITTEN BY AI ###
-        """
-        mock_text_creator_instance = Mock()
-        mock_text_creator_instance.words = ["word"] * 100
-        mock_text_creator_instance.create_text.return_value = "sample text"
-        mock_text_creator.return_value = mock_text_creator_instance
-
-        # Mock the cycle to return predictable values
-        with patch("guidellm.dataset.synthetic.cycle") as mock_cycle:
-            mock_cycle.return_value = iter([100, 101, 102, 103, 104])
-
-            generator = SyntheticTextItemsGenerator(
-                simple_config, mock_tokenizer, random_seed=42
-            )
-
-            # Access the iterator to trigger the cycle creation
-            generator_iter = iter(generator)
-            next(generator_iter)
-
-            # Verify cycle was called with vocab values
-            mock_cycle.assert_called_once()
-
-
-class TestSyntheticDatasetCreator:
-    """Test cases for SyntheticDatasetCreator class.
-
-    ### WRITTEN BY AI ###
-    """
-
-    @pytest.mark.sanity
-    def test_is_supported_path_config_file(self):
-        """Test is_supported with config file paths.
-
-        ### WRITTEN BY AI ###
-        """
-        with tempfile.NamedTemporaryFile(suffix=".config", delete=False) as f:
-            config_path = Path(f.name)
-
-        try:
-            assert SyntheticDatasetCreator.is_supported(config_path, None)
-        finally:
-            config_path.unlink()
-
-    @pytest.mark.sanity
-    def test_is_supported_path_yaml_file(self):
-        """Test is_supported with YAML file paths.
-
-        ### WRITTEN BY AI ###
-        """
-        with tempfile.NamedTemporaryFile(suffix=".yaml", delete=False) as f:
-            yaml_path = Path(f.name)
-
-        try:
-            assert SyntheticDatasetCreator.is_supported(yaml_path, None)
-        finally:
-            yaml_path.unlink()
-
-    @pytest.mark.smoke
-    def test_is_supported_json_string(self):
-        """Test is_supported with JSON string.
-
-        ### WRITTEN BY AI ###
-        """
-        json_str = '{"prompt_tokens": 50, "output_tokens": 25}'
-        assert SyntheticDatasetCreator.is_supported(json_str, None)
-
-    @pytest.mark.smoke
-    def test_is_supported_key_value_string(self):
-        """Test is_supported with key-value string.
-
-        ### WRITTEN BY AI ###
-        """
-        kv_str = "prompt_tokens=50,output_tokens=25"
-        assert SyntheticDatasetCreator.is_supported(kv_str, None)
-
-    @pytest.mark.sanity
-    def test_is_supported_config_filename_string(self):
-        """Test is_supported with config filename string.
-
-        ### WRITTEN BY AI ###
-        """
-        assert SyntheticDatasetCreator.is_supported("config.yaml", None)
-        assert SyntheticDatasetCreator.is_supported("settings.config", None)
-
-    @pytest.mark.sanity
-    def test_is_not_supported_regular_string(self):
-        """Test is_supported returns False for regular strings.
-
-        ### WRITTEN BY AI ###
-        """
-        assert not SyntheticDatasetCreator.is_supported("regular string", None)
-        assert not SyntheticDatasetCreator.is_supported("single=pair", None)
-
-    @pytest.mark.regression
-    def test_is_not_supported_non_existent_path(self):
-        """Test is_supported returns False for non-existent paths.
-
-        ### WRITTEN BY AI ###
-        """
-        non_existent_path = Path("/non/existent/path.config")
-        assert not SyntheticDatasetCreator.is_supported(non_existent_path, None)
-
-    @pytest.mark.regression
-    def test_is_not_supported_other_types(self):
-        """Test is_supported returns False for other data types.
-
-        ### WRITTEN BY AI ###
-        """
-        assert not SyntheticDatasetCreator.is_supported(123, None)
-        assert not SyntheticDatasetCreator.is_supported(["list"], None)
-        assert not SyntheticDatasetCreator.is_supported({"dict": "value"}, None)
-
-    @pytest.mark.smoke
-    @patch("guidellm.dataset.synthetic.check_load_processor")
-    @patch("guidellm.dataset.synthetic.SyntheticTextItemsGenerator")
-    @patch("guidellm.dataset.synthetic.Dataset")
-    def test_handle_create_basic(
-        self, mock_dataset, mock_generator, mock_check_processor
-    ):
-        """Test handle_create basic functionality.
-
-        ### WRITTEN BY AI ###
-        """
-        # Setup mocks
-        mock_processor = Mock()
-        mock_check_processor.return_value = mock_processor
-
-        mock_generator_instance = Mock()
-        mock_generator_instance.__iter__ = Mock(
-            return_value=iter(
-                [
-                    {
-                        "prompt": "test",
-                        "prompt_tokens_count": 10,
-                        "output_tokens_count": 5,
-                    }
-                ]
-            )
-        )
-        mock_generator.return_value = mock_generator_instance
-
-        mock_dataset_instance = Mock()
-        mock_dataset.from_list.return_value = mock_dataset_instance
-
-        # Test
-        data = '{"prompt_tokens": 50, "output_tokens": 25}'
-        result = SyntheticDatasetCreator.handle_create(
-            data=data,
-            data_args=None,
-            processor="gpt2",
-            processor_args=None,
-            random_seed=42,
-        )
-
-        # Verify
-        mock_check_processor.assert_called_once_with(
-            "gpt2",
-            None,
-            error_msg="Processor/tokenizer required for synthetic dataset generation.",
-        )
-        mock_generator.assert_called_once()
-        mock_dataset.from_list.assert_called_once()
-        assert result == mock_dataset_instance
-
-    @pytest.mark.sanity
-    @patch("guidellm.dataset.synthetic.check_load_processor")
-    def test_handle_create_processor_required(self, mock_check_processor):
-        """Test handle_create requires processor.
-
-        ### WRITTEN BY AI ###
-        """
-        mock_check_processor.side_effect = ValueError("Processor required")
-
-        data = '{"prompt_tokens": 50, "output_tokens": 25}'
-
-        with pytest.raises(ValueError, match="Processor required"):
-            SyntheticDatasetCreator.handle_create(
-                data=data,
-                data_args=None,
-                processor=None,
-                processor_args=None,
-                random_seed=42,
-            )
-
-    @pytest.mark.regression
-    @patch("guidellm.dataset.synthetic.check_load_processor")
-    @patch("guidellm.dataset.synthetic.SyntheticTextItemsGenerator")
-    @patch("guidellm.dataset.synthetic.Dataset")
-    def test_handle_create_with_data_args(
-        self, mock_dataset, mock_generator, mock_check_processor
-    ):
-        """Test handle_create with data_args.
-
-        ### WRITTEN BY AI ###
-        """
-        # Setup mocks
-        mock_processor = Mock()
-        mock_check_processor.return_value = mock_processor
-
-        mock_generator_instance = Mock()
-        mock_generator_instance.__iter__ = Mock(return_value=iter([]))
-        mock_generator.return_value = mock_generator_instance
-
-        mock_dataset_instance = Mock()
-        mock_dataset.from_list.return_value = mock_dataset_instance
-
-        # Test with data_args
-        data = '{"prompt_tokens": 50, "output_tokens": 25}'
-        data_args = {"features": "custom_features"}
-
-        SyntheticDatasetCreator.handle_create(
-            data=data,
-            data_args=data_args,
-            processor="gpt2",
-            processor_args=None,
-            random_seed=42,
-        )
-
-        # Verify data_args are passed to Dataset.from_list
-        mock_dataset.from_list.assert_called_once_with([], **data_args)
-
-    @pytest.mark.sanity
-    def test_extract_args_column_mappings_empty(self):
-        """Test extract_args_column_mappings with empty data_args.
-
-        ### WRITTEN BY AI ###
-        """
-        result = SyntheticDatasetCreator.extract_args_column_mappings(None)
-
-        expected = {
-            "prompt_column": "prompt",
-            "prompt_tokens_count_column": "prompt_tokens_count",
-            "output_tokens_count_column": "output_tokens_count",
-        }
-        assert result == expected
-
-    @pytest.mark.regression
-    def test_extract_args_column_mappings_with_parent_mappings(self):
-        """Test extract_args_column_mappings rejects column mappings.
-
-        ### WRITTEN BY AI ###
-        """
-        with (
-            patch.object(
-                SyntheticDatasetCreator.__bases__[0],
-                "extract_args_column_mappings",
-                return_value={"prompt_column": "custom_prompt"},
-            ),
-            pytest.raises(ValueError, match="Column mappings are not supported"),
-        ):
-            SyntheticDatasetCreator.extract_args_column_mappings({"some": "args"})
-
-    @pytest.mark.regression
-    def test_extract_args_column_mappings_no_parent_mappings(self):
-        """Test extract_args_column_mappings with no parent mappings.
-
-        ### WRITTEN BY AI ###
-        """
-        with patch.object(
-            SyntheticDatasetCreator.__bases__[0],
-            "extract_args_column_mappings",
-            return_value={},
-        ):
-            result = SyntheticDatasetCreator.extract_args_column_mappings(
-                {"some": "args"}
-            )
-
-            expected = {
-                "prompt_column": "prompt",
-                "prompt_tokens_count_column": "prompt_tokens_count",
-                "output_tokens_count_column": "output_tokens_count",
-            }
-            assert result == expected