diff --git a/.dev.commit b/.dev.commit
index 204e2f2f04..259caa26e1 100644
--- a/.dev.commit
+++ b/.dev.commit
@@ -1 +1 @@
-af91b6b5cc5aec01458b8e6d24ccb0f4717e49e3
+de1ebd63c7935886150364f9fc1a4150c7bdb37c
diff --git a/docker/Dockerfile.ci b/docker/Dockerfile.ci
index 1ebec925e3..80538a25fa 100644
--- a/docker/Dockerfile.ci
+++ b/docker/Dockerfile.ci
@@ -117,13 +117,17 @@ ARG UV_CACHE_PRUNE_ARGS
 RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
     --mount=type=cache,target=/var/lib/apt,sharing=locked \
     --mount=type=cache,target=/root/.cache/uv \
-    export MAMBA_FORCE_BUILD=TRUE CAUSAL_CONV1D_FORCE_BUILD=TRUE && \
+    export MAMBA_FORCE_BUILD=TRUE CAUSAL_CONV1D_FORCE_BUILD=TRUE \
+        FAST_HADAMARD_TRANSFORM_FORCE_BUILD=TRUE && \
     # flash-mla (Megatron-LM's no_pypi_wheels group) has no PyPI wheel and is built from source by
     # the uv sync below. Point the compilers at the CCCL/libcu++ headers (under cccl/ in this base
     # image, not the default CUDA include path) and scope the build to the target archs.
     export FLASH_MLA_DISABLE_SM90=1 NVCC_THREADS=16 \
         CFLAGS="-I/usr/local/cuda/include/cccl${CFLAGS:+ $CFLAGS} -DNDEBUG" \
         CXXFLAGS="-I/usr/local/cuda/include/cccl${CXXFLAGS:+ $CXXFLAGS} -DNDEBUG" && \
+    # Do not reuse a wheel previously downloaded by the dependency's setup.py. The force-build
+    # variable is not part of uv's cache key, so evict the package before rebuilding locked source.
+    uv cache clean fast-hadamard-transform && \
     # Reinstall nvidia-cutlass-dsl in system site packages
     # uv install can run into non-deterministic install issues
     # Keep this pin compatible with cuDNN Frontend; mismatched versions can break
diff --git a/docker/common/install.sh b/docker/common/install.sh
index 48a4c93ed3..7ab9e6b668 100644
--- a/docker/common/install.sh
+++ b/docker/common/install.sh
@@ -82,6 +82,17 @@ main() {
     unset PIP_CONSTRAINT
 
     if [[ "$USE_UV" == "true" ]]; then
+        # MCore dev requires TransformerEngine as part of its dev extra. TE's bundled NCCL-EP
+        # extension needs newer NCCL device headers than the NGC 25.05 installation-test image
+        # provides. Disable only that optional extension when the gitlink is the recorded dev ref.
+        submodule_sha="$(git -C 3rdparty/Megatron-LM rev-parse HEAD 2>/dev/null || true)"
+        dev_sha="$(tr -d '[:space:]' <.dev.commit)"
+        main_sha="$(tr -d '[:space:]' <.main.commit)"
+        if [[ -n "$submodule_sha" && "$submodule_sha" == "$dev_sha" && "$dev_sha" != "$main_sha" ]]; then
+            export NVTE_WITH_NCCL_EP="${NVTE_WITH_NCCL_EP:-0}"
+            echo "🔧 MCore dev compatibility: NVTE_WITH_NCCL_EP=$NVTE_WITH_NCCL_EP"
+        fi
+
         if [[ "$BASE_IMAGE" == "pytorch" ]]; then
             UV_ARGS=(
                 "--no-install-package" "torch"
diff --git a/pyproject.toml b/pyproject.toml
index af10bc752e..25c48e21f7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -252,10 +252,10 @@ build = [
 # them at test time with scripts/install_diffusion_deps.sh. This group is kept for
 # documentation only; `uv sync --group diffusion` will NOT install them on Linux.
 diffusion = ["imageio", "imageio-ffmpeg", "av"]
-# flash-mla has no PyPI wheel and is built from source by uv (no-build-isolation) during the
-# image's `uv sync --all-groups`. Megatron-LM declares it in the same group, but uv does not
-# install a path dependency's groups, so it is declared here too.
-no_pypi_wheels = ["flash_mla"]
+# flash-mla and fast-hadamard-transform have no usable PyPI wheels and are built from source by uv
+# (no-build-isolation) during the image's `uv sync --all-groups`. Megatron-LM declares them in the
+# same group, but uv does not install a path dependency's groups, so they are declared here too.
+no_pypi_wheels = ["flash_mla", "fast-hadamard-transform"]
 
 [project.entry-points."nemo_run.cli"]
 lm = "megatron.bridge"
diff --git a/src/megatron/bridge/models/distillation_provider.py b/src/megatron/bridge/models/distillation_provider.py
index e4c4808aa9..1b60513e22 100644
--- a/src/megatron/bridge/models/distillation_provider.py
+++ b/src/megatron/bridge/models/distillation_provider.py
@@ -13,13 +13,16 @@
 # limitations under the License.
 
 import logging
+from copy import deepcopy
 from dataclasses import dataclass, fields
 from typing import TYPE_CHECKING, Any, Optional
 
 import modelopt.torch.distill as mtd
 import modelopt.torch.distill.plugins.megatron as mtd_mcore
 from megatron.core.models.common.language_module.language_module import LanguageModule
+from megatron.core.process_groups_config import ProcessGroupCollection
 from megatron.core.utils import unwrap_model
+from megatron.training.models.base import ModelConfig
 
 from megatron.bridge.models.gpt_provider import GPTModelProvider
 from megatron.bridge.models.hybrid.hybrid_provider import HybridModelProvider
@@ -33,6 +36,110 @@
 logger = logging.getLogger(__name__)
 
 
+def _clone_model_config(config: ModelConfig) -> ModelConfig:
+    """Clone a model config without resolving proxied ``__deepcopy__`` methods."""
+    config_copy = type(config).__new__(type(config))
+    memo = {id(config): config_copy}
+    for name, value in config.__dict__.items():
+        object.__setattr__(config_copy, name, deepcopy(value, memo))
+    return config_copy
+
+
+class DistillationModelConfig:
+    """Builder-backed model config for knowledge distillation.
+
+    Instances retain the student's concrete config class and builder while
+    registering a pre-wrap hook that builds the teacher from its own config and
+    applies ModelOpt distillation after both models are constructed.
+    """
+
+    _SHARED_ATTRIBUTES = (
+        "tensor_model_parallel_size",
+        "pipeline_model_parallel_size",
+        "context_parallel_size",
+        "seq_length",
+        "pipeline_dtype",
+    )
+
+    def _initialize_distillation(
+        self,
+        teacher: ModelConfig,
+        kd_config: Optional["ModelOptDistillConfig"],
+        distill_submodule: Optional[str],
+    ) -> None:
+        """Attach teacher state and validate the shared distributed settings."""
+        object.__setattr__(self, "teacher", teacher)
+        object.__setattr__(self, "kd_config", kd_config)
+        object.__setattr__(self, "distill_submodule", distill_submodule)
+        object.__setattr__(self, "_mirror_to_teacher", True)
+
+        self.cross_entropy_fusion_impl = "native"
+
+    def as_dict(self) -> dict[str, Any]:
+        """Serialize as the original student config for checkpoint restoration."""
+        result = super().as_dict()
+        student_class = self._student_config_class
+        result["_target_"] = f"{student_class.__module__}.{student_class.__qualname__}"
+        return result
+
+    def finalize(self) -> None:
+        """Finalize student and teacher configs without mirroring derived values."""
+        object.__setattr__(self, "_mirror_to_teacher", False)
+        try:
+            super().finalize()
+            if hasattr(self.teacher, "finalize"):
+                self.teacher.finalize()
+        finally:
+            object.__setattr__(self, "_mirror_to_teacher", True)
+        for attribute_name in self._SHARED_ATTRIBUTES:
+            if getattr(self, attribute_name) != getattr(self.teacher, attribute_name):
+                raise ValueError(f"Student and teacher configs must have the same {attribute_name}.")
+
+    def _convert_hook(self, model_chunks: list) -> list:
+        """Build the teacher and convert the student after model construction."""
+        assert len(model_chunks) == 1, "ModelOpt KD does not support virtual pipeline (>1 model chunk)."
+        student_model = unwrap_model(model_chunks[0])
+
+        teacher_builder = self.teacher.get_builder_cls()(self.teacher)
+        teacher_chunks = teacher_builder.build_distributed_models(
+            pg_collection=ProcessGroupCollection.use_mpu_process_groups(),
+            wrap_with_ddp=False,
+            mixed_precision_wrapper=None,
+        )
+        assert len(teacher_chunks) == 1, "ModelOpt KD does not support virtual pipeline (>1 model chunk)."
+        teacher_model = unwrap_model(teacher_chunks[0])
+
+        if self.distill_submodule is not None:
+            self.full_model = student_model
+            student_model = getattr(student_model, self.distill_submodule)
+            teacher_model = getattr(teacher_model, self.distill_submodule)
+
+        kd_cfg = mtd_mcore.setup_distillation_config(self.kd_config, student_model.config, teacher_model.config)
+        modelopt_cfg = {
+            "teacher_model": teacher_model,
+            "criterion": kd_cfg.criterion,
+            "loss_balancer": kd_cfg.loss_balancer,
+        }
+        kd_model = mtd.convert(student_model, mode=[("kd_loss", modelopt_cfg)])
+        if self.distill_submodule is not None:
+            assert getattr(self.full_model, self.distill_submodule) is kd_model
+            model_chunks[0] = self.full_model
+        else:
+            model_chunks[0] = kd_model
+        mtd_mcore.adjust_distillation_model_for_mcore(kd_model, kd_cfg)
+        return model_chunks
+
+    def __setattr__(self, name: str, value: Any) -> None:
+        if name in {"teacher", "kd_config", "distill_submodule", "full_model", "_mirror_to_teacher"}:
+            object.__setattr__(self, name, value)
+            return
+
+        super().__setattr__(name, value)
+        teacher = getattr(self, "teacher", None)
+        if self._mirror_to_teacher and teacher is not None and hasattr(teacher, name):
+            setattr(teacher, name, value)
+
+
 @dataclass
 class DistillationProvider(TransformerConfig):
     """Provider for Bridge language models in distillation mode.
@@ -160,12 +267,12 @@ def __setattr__(self, name, value):
 
 
 def convert_to_distillation_provider(
-    student_provider: GPTModelProvider | HybridModelProvider,
-    teacher_provider: GPTModelProvider | HybridModelProvider,
+    student_provider: GPTModelProvider | HybridModelProvider | ModelConfig,
+    teacher_provider: GPTModelProvider | HybridModelProvider | ModelConfig,
     kd_config: Optional["ModelOptDistillConfig"] = None,
     *,
     distill_submodule: Optional[str] = None,
-) -> "DistillationProvider":
+) -> "DistillationProvider | DistillationModelConfig":
     """Convert a given model provider to a DistillationProvider.
 
     The KD conversion runs in a pre-wrap hook (after the student's weights are loaded), not in
@@ -182,8 +289,24 @@ def convert_to_distillation_provider(
             submodule is trained).
     """
 
+    if isinstance(student_provider, ModelConfig):
+        assert isinstance(teacher_provider, ModelConfig), "Teacher config must be a ModelConfig."
+        student_class = type(student_provider)
+        student_copy = _clone_model_config(student_provider)
+        distillation_class = type(
+            f"Distillation{student_class.__name__}",
+            (DistillationModelConfig, student_class),
+            {"__module__": __name__},
+        )
+        student_copy.__class__ = distillation_class
+        object.__setattr__(student_copy, "_student_config_class", student_class)
+        student_copy._initialize_distillation(teacher_provider, kd_config, distill_submodule)
+        hooks = [*student_copy.pre_wrap_hooks, student_copy._convert_hook]
+        object.__setattr__(student_copy, "pre_wrap_hooks", hooks)
+        return student_copy
+
     assert isinstance(student_provider, (GPTModelProvider, HybridModelProvider)), (
-        "Student provider must be a subclass of GPTModelProvider or HybridModelProvider."
+        "Student provider must be a subclass of GPTModelProvider, HybridModelProvider, or ModelConfig."
     )
     assert isinstance(teacher_provider, (GPTModelProvider, HybridModelProvider)), (
         "Teacher provider must be a subclass of GPTModelProvider or HybridModelProvider."
diff --git a/src/megatron/bridge/training/checkpointing.py b/src/megatron/bridge/training/checkpointing.py
index debf151f39..cea8200dbc 100644
--- a/src/megatron/bridge/training/checkpointing.py
+++ b/src/megatron/bridge/training/checkpointing.py
@@ -346,6 +346,16 @@ def read_metadata(tracker_filename: str) -> tuple[int, bool]:
     return max_iter, release
 
 
+def _get_model_parallel_sizes(model_config: dict[str, Any]) -> tuple[int, int]:
+    """Read tensor and pipeline parallel sizes from provider or builder configs."""
+    transformer_config = model_config.get("transformer")
+    parallel_config = transformer_config if isinstance(transformer_config, dict) else model_config
+    return (
+        parallel_config["tensor_model_parallel_size"],
+        parallel_config["pipeline_model_parallel_size"],
+    )
+
+
 def _extract_megatron_lm_args_from_state_dict(state_dict: dict[str, Any]) -> dict[str, Any]:
     """Extract and convert legacy Megatron-LM args from checkpoint state_dict to Megatron-Bridge config format.
 
@@ -2749,10 +2759,7 @@ def _load_checkpoint_from_path(
             tp_pp_match = True
             mismatch_msg = ""
         else:
-            ckpt_tp_pp = (
-                run_config["model"]["tensor_model_parallel_size"],
-                run_config["model"]["pipeline_model_parallel_size"],
-            )
+            ckpt_tp_pp = _get_model_parallel_sizes(run_config["model"])
             run_tp_pp = (
                 cfg.model.tensor_model_parallel_size,
                 cfg.model.pipeline_model_parallel_size,
@@ -2869,10 +2876,7 @@ def _load_checkpoint_from_path(
             run_config_filename = get_checkpoint_run_config_filename(checkpoint_name)
             if file_exists(run_config_filename):
                 run_config = read_run_config(run_config_filename)
-                ckpt_tp_pp = (
-                    run_config["model"]["tensor_model_parallel_size"],
-                    run_config["model"]["pipeline_model_parallel_size"],
-                )
+                ckpt_tp_pp = _get_model_parallel_sizes(run_config["model"])
                 run_tp_pp = (
                     cfg.model.tensor_model_parallel_size,
                     cfg.model.pipeline_model_parallel_size,
diff --git a/src/megatron/bridge/training/distill.py b/src/megatron/bridge/training/distill.py
index ca5ac0222a..e059af2f1c 100644
--- a/src/megatron/bridge/training/distill.py
+++ b/src/megatron/bridge/training/distill.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from megatron.bridge.models.distillation_provider import DistillationProvider
+from megatron.bridge.models.distillation_provider import DistillationModelConfig, DistillationProvider
 from megatron.bridge.training.config import ConfigContainer
 from megatron.bridge.training.gpt_step import forward_step_modelopt
 from megatron.bridge.training.pretrain import pretrain
@@ -32,6 +32,8 @@ def distill(
         This is an experimental API and is subject to change in backwards
         incompatible ways without notice.
     """
-    assert isinstance(config.model, DistillationProvider), "Distillation requires a DistillationProvider"
+    assert isinstance(config.model, (DistillationProvider, DistillationModelConfig)), (
+        "Distillation requires a DistillationProvider or DistillationModelConfig"
+    )
 
     return pretrain(config, forward_step_modelopt)
diff --git a/tests/functional_tests/test_groups/recipes/test_llama_recipes_distill_3b-1b.py b/tests/functional_tests/test_groups/recipes/test_llama_recipes_distill_3b-1b.py
index dc180cebb9..840f808b64 100644
--- a/tests/functional_tests/test_groups/recipes/test_llama_recipes_distill_3b-1b.py
+++ b/tests/functional_tests/test_groups/recipes/test_llama_recipes_distill_3b-1b.py
@@ -101,7 +101,6 @@ def run_distill_recipe_test(
         config.checkpoint.load = str(checkpoint_dir)
         config.logger.tensorboard_dir = str(tensorboard_dir)
 
-        # Combine into a distillation provider
         config.model = convert_to_distillation_provider(config.model, teacher_config.model)
 
         # Set default distillation configuration
diff --git a/tests/unit_tests/models/test_distillation_provider.py b/tests/unit_tests/models/test_distillation_provider.py
index aec4ebbce4..3abf9fb004 100644
--- a/tests/unit_tests/models/test_distillation_provider.py
+++ b/tests/unit_tests/models/test_distillation_provider.py
@@ -16,16 +16,113 @@
 
 import pytest
 import torch
-
-from megatron.bridge.models.distillation_provider import DistillationProvider, convert_to_distillation_provider
+from transformers import LlamaConfig
+
+from megatron.bridge.models.conversion.auto_bridge import AutoBridge
+from megatron.bridge.models.distillation_provider import (
+    DistillationModelConfig,
+    DistillationProvider,
+    convert_to_distillation_provider,
+)
 from megatron.bridge.models.gpt_provider import GPTModelProvider
 from megatron.bridge.models.hybrid.hybrid_provider import HybridModelProvider
 from megatron.bridge.training.post_training.distillation import ModelOptDistillConfig
 
 
+def _llama_builder_config(hidden_size: int, num_hidden_layers: int):
+    """Create a builder-backed Llama config with non-default runtime fields."""
+    hf_config = LlamaConfig(
+        architectures=["LlamaForCausalLM"],
+        hidden_size=hidden_size,
+        intermediate_size=hidden_size * 4,
+        max_position_embeddings=16384,
+        num_attention_heads=8,
+        num_hidden_layers=num_hidden_layers,
+        num_key_value_heads=4,
+        rope_scaling={
+            "rope_type": "llama3",
+            "factor": 32.0,
+            "high_freq_factor": 4.0,
+            "low_freq_factor": 1.0,
+            "original_max_position_embeddings": 8192,
+        },
+        rope_theta=500000.0,
+        tie_word_embeddings=True,
+        vocab_size=128256,
+    )
+    return AutoBridge.from_hf_config(hf_config).get_model_config()
+
+
 class TestDistillationProvider:
     """Test cases for DistillationProvider class."""
 
+    def test_builder_config_preserves_runtime_fields(self):
+        """Builder distillation keeps the exact student and teacher configs."""
+        student = _llama_builder_config(hidden_size=1024, num_hidden_layers=8)
+        teacher = _llama_builder_config(hidden_size=2048, num_hidden_layers=16)
+        student_fields = {
+            "rotary_base": student.rotary_base,
+            "rope_scaling": student.rope_scaling,
+            "rope_scaling_factor": student.rope_scaling_factor,
+            "share_embeddings_and_output_weights": student.share_embeddings_and_output_weights,
+        }
+
+        converted = convert_to_distillation_provider(student, teacher)
+        converted.finalize()
+
+        assert converted is not student
+        assert isinstance(converted, DistillationModelConfig)
+        serialized = converted.as_dict()
+        assert serialized["_target_"] == f"{type(student).__module__}.{type(student).__qualname__}"
+        assert "teacher" not in serialized
+        assert converted.teacher is teacher
+        assert converted.rotary_base == student_fields["rotary_base"]
+        assert converted.rope_scaling == student_fields["rope_scaling"]
+        assert converted.rope_scaling_factor == student_fields["rope_scaling_factor"]
+        assert converted.share_embeddings_and_output_weights is student_fields["share_embeddings_and_output_weights"]
+        assert converted.pre_wrap_hooks[-1] == converted._convert_hook
+        converted.seq_length = 4096
+        assert converted.teacher.seq_length == 4096
+
+    def test_builder_config_converts_student_in_place(self):
+        """Builder conversion preserves the chunk identity expected by training."""
+        student = _llama_builder_config(hidden_size=1024, num_hidden_layers=8)
+        teacher = _llama_builder_config(hidden_size=2048, num_hidden_layers=16)
+        converted = convert_to_distillation_provider(student, teacher)
+        student_model = Mock()
+        teacher_model = Mock()
+        student_model.config = student.transformer
+        teacher_model.config = teacher.transformer
+        student_chunk = Mock()
+        teacher_chunk = Mock()
+        student_chunk.module = student_model
+        teacher_chunk.module = teacher_model
+        teacher_builder = Mock()
+        teacher_builder.build_distributed_models.return_value = [teacher_chunk]
+        kd_model = Mock()
+
+        with (
+            patch.object(type(teacher), "get_builder_cls", return_value=Mock(return_value=teacher_builder)),
+            patch("megatron.bridge.models.distillation_provider.ProcessGroupCollection.use_mpu_process_groups"),
+            patch("megatron.bridge.models.distillation_provider.mtd.convert", return_value=kd_model),
+            patch("megatron.bridge.models.distillation_provider.mtd_mcore.setup_distillation_config") as setup,
+            patch("megatron.bridge.models.distillation_provider.mtd_mcore.adjust_distillation_model_for_mcore"),
+        ):
+            setup.return_value = Mock(criterion=None, loss_balancer=None)
+            result = converted._convert_hook([student_chunk])
+
+        assert result == [kd_model]
+
+    def test_builder_config_rejects_mismatched_parallelism(self):
+        """Builder distillation validates settings shared by both models."""
+        student = _llama_builder_config(hidden_size=1024, num_hidden_layers=8)
+        teacher = _llama_builder_config(hidden_size=2048, num_hidden_layers=16)
+        teacher.tensor_model_parallel_size = 2
+
+        with pytest.raises(ValueError, match="tensor_model_parallel_size"):
+            converted = convert_to_distillation_provider(student, teacher)
+            converted.finalize()
+
     def test_initialization_with_teacher(self):
         """Test DistillationProvider can be initialized with a teacher."""
         teacher = GPTModelProvider(
@@ -130,6 +227,10 @@ def test_post_init_validates_seq_length(self):
         with pytest.raises(ValueError):
             convert_to_distillation_provider(student_base, teacher)
 
+    @patch("megatron.bridge.models.model_provider.ProcessGroupCollection.use_mpu_process_groups")
+    @patch("megatron.bridge.models.model_provider.parallel_state.is_initialized", return_value=True)
+    @patch("megatron.bridge.models.model_provider.torch.distributed.is_initialized", return_value=True)
+    @patch("megatron.bridge.models.model_provider.torch.cuda.set_device")
     @patch("modelopt.torch.distill.plugins.megatron.parallel_state")
     @patch("megatron.bridge.models.gpt_provider.calculate_padded_vocab_size", return_value=1024)
     @patch("megatron.bridge.models.gpt_provider.MCoreGPTModel")
@@ -138,6 +239,10 @@ def test_provide_method_creates_distillation_model(
         mock_mcore_gpt,
         mock_calc_vocab,
         mock_mtd_parallel_state,
+        mock_set_device,
+        mock_distributed_initialized,
+        mock_model_parallel_initialized,
+        mock_pg_collection,
     ):
         """Test the KD conversion runs (in the deferred pre-wrap hook) and yields a DistillationModel."""
         mock_mtd_parallel_state.is_pipeline_first_stage.return_value = True
@@ -168,9 +273,17 @@ def test_provide_method_creates_distillation_model(
         student = convert_to_distillation_provider(student_base, teacher, kd_config=ModelOptDistillConfig())
 
         # Attach minimal pg_collection needed by provider.provide
-        pg = type("PG", (), {"pp": object(), "tp": object(), "cp": object()})()
+        process_group = Mock()
+        process_group.size.return_value = 1
+        process_group.rank.return_value = 0
+        pg = type(
+            "PG",
+            (),
+            {"pp": process_group, "tp": process_group, "cp": process_group, "dp": process_group},
+        )()
         teacher._pg_collection = pg
         student._pg_collection = pg
+        mock_pg_collection.return_value = pg
 
         # Mock the provide method calls and modelopt functions
         mock_student_model = Mock()
diff --git a/tests/unit_tests/training/test_checkpointing.py b/tests/unit_tests/training/test_checkpointing.py
index 2483eb6d3b..79f273cb19 100644
--- a/tests/unit_tests/training/test_checkpointing.py
+++ b/tests/unit_tests/training/test_checkpointing.py
@@ -34,6 +34,7 @@
     _clear_auto_bridge_cache,
     _extract_megatron_lm_args_from_state_dict,
     _get_checkpoint_format,
+    _get_model_parallel_sizes,
     _get_non_persistent_iteration,
     _has_global_non_persistent_checkpoint,
     _load_base_checkpoint,
@@ -74,6 +75,23 @@ class _DummyClass:
 _dummy_obj = _DummyClass()
 
 
+class TestModelParallelSizes:
+    """Tests for provider and builder run-config parallelism layouts."""
+
+    def test_reads_legacy_provider_layout(self):
+        model_config = {"tensor_model_parallel_size": 2, "pipeline_model_parallel_size": 4}
+
+        assert _get_model_parallel_sizes(model_config) == (2, 4)
+
+    def test_reads_builder_transformer_layout(self):
+        model_config = {
+            "_builder_": "megatron.training.models.gpt.GPTModelBuilder",
+            "transformer": {"tensor_model_parallel_size": 2, "pipeline_model_parallel_size": 4},
+        }
+
+        assert _get_model_parallel_sizes(model_config) == (2, 4)
+
+
 class TestCheckpointUtilities:
     """Test utility functions for checkpoint management."""
 
diff --git a/uv.lock b/uv.lock
index 6f64ebce58..6f5d7b31d4 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1444,11 +1444,11 @@ wheels = [
 
 [[package]]
 name = "huey"
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+version = "3.3.1"
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+    { url = "https://files.pythonhosted.org/packages/82/1f/f03f2dc13d3d92abf0d0c0d8be44bffdd86e3e17f1fe6f8c657a34672645/huey-3.3.1-py3-none-any.whl", hash = "sha256:b0ab6bd65fa39774e601429c1224e3c345374b06056345161132cdff19d83ebb", size = 123189, upload-time = "2026-07-31T13:46:49.802Z" },
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 [[package]]
@@ -2034,6 +2034,7 @@ docs = [
     { name = "sphinxcontrib-mermaid" },
 ]
 no-pypi-wheels = [
+    { name = "fast-hadamard-transform" },
     { name = "flash-mla" },
 ]
 test = [
@@ -2119,7 +2120,10 @@ docs = [
     { name = "sphinx-copybutton", specifier = ">=0.5.2" },
     { name = "sphinxcontrib-mermaid" },
 ]
-no-pypi-wheels = [{ name = "flash-mla", git = "https://github.com/deepseek-ai/FlashMLA?rev=nv_dev" }]
+no-pypi-wheels = [
+    { name = "fast-hadamard-transform" },
+    { name = "flash-mla", git = "https://github.com/deepseek-ai/FlashMLA?rev=nv_dev" },
+]
 test = [
     { name = "click" },
     { name = "coverage", specifier = ">=7.8.1" },
@@ -2352,7 +2356,7 @@ wheels = [
 
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-version = "3.14.0"
+version = "3.15.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohttp" },
@@ -2376,14 +2380,14 @@ dependencies = [
     { name = "sqlalchemy" },
     { name = "waitress", marker = "sys_platform == 'win32'" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/af/9f/249b80849aae0919aee3f590003419c394f0efe92ae98546ea626be86e1e/mlflow-3.15.0.tar.gz", hash = "sha256:c53d7933391fefda2ac0f6b942d1744a02b5b65da74a93cc125247f4c4da57c9", size = 10395873, upload-time = "2026-07-31T07:06:00.211Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/0e/7a/3da40be70e7ca0c4d8c2f98edfcb275ef2898d8f4da565f55a9fff95fc78/mlflow-3.15.0-py3-none-any.whl", hash = "sha256:3ae54c7f91a6b98ae9360aaeda9b31eb7930571c2690491a7b550c84f795a709", size = 11188317, upload-time = "2026-07-31T07:05:57.492Z" },
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 [[package]]
 name = "mlflow-skinny"
-version = "3.14.0"
+version = "3.15.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cachetools" },
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 [[package]]
 name = "mlflow-tracing"
-version = "3.14.0"
+version = "3.15.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
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@@ -2426,9 +2430,9 @@ dependencies = [
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+sdist = { url = "https://files.pythonhosted.org/packages/7b/47/8a3c4f56199aadce644256684060df09f0af6ecaec460860657f2cb13b10/mlflow_tracing-3.15.0.tar.gz", hash = "sha256:e4e73243eed04d0771954b6fa17014dd21dabc67af17d4937fac2b62ec476e95", size = 1500398, upload-time = "2026-07-31T07:05:54.445Z" }
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 [[package]]
@@ -2910,7 +2914,7 @@ wheels = [
 
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-version = "0.7.2.dev20260725"
+version = "0.7.2.dev20260731"
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@@ -2920,9 +2924,9 @@ dependencies = [
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 [[package]]
@@ -2946,7 +2950,7 @@ wheels = [
 
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+version = "2.52.0"
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@@ -2958,9 +2962,9 @@ dependencies = [
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 [package.optional-dependencies]
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