From ce0a6af840b01991585f21a2de5f2e114592c944 Mon Sep 17 00:00:00 2001
From: Ranjit Rajan <ranjitr@nvidia.com>
Date: Wed, 1 Oct 2025 17:01:33 -0700
Subject: [PATCH 01/17] Add NemotronH VLM support for video captioning

Signed-off-by: Ranjit Rajan <ranjitr@nvidia.com>
---
 nemo_curator/models/nemotron_h_vl.py          |  225 ++
 .../models/nemotron_prompt_formatter.py       |  137 +
 nemo_curator/models/prompt_formatter.py       |   50 +-
 .../video/caption/caption_generation.py       |   41 +-
 .../video/caption/caption_preparation.py      |   26 +-
 nemo_curator/tasks/video.py                   |   14 +-
 pyproject.toml                                |    3 +-
 .../video_split_clip_example.py               |   27 +-
 uv.lock                                       | 2428 ++++++++++-------
 9 files changed, 1858 insertions(+), 1093 deletions(-)
 create mode 100644 nemo_curator/models/nemotron_h_vl.py
 create mode 100644 nemo_curator/models/nemotron_prompt_formatter.py

diff --git a/nemo_curator/models/nemotron_h_vl.py b/nemo_curator/models/nemotron_h_vl.py
new file mode 100644
index 0000000000..b587fa3150
--- /dev/null
+++ b/nemo_curator/models/nemotron_h_vl.py
@@ -0,0 +1,225 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+from pathlib import Path
+from typing import Any
+
+from loguru import logger
+
+from nemo_curator.models.base import ModelInterface
+from nemo_curator.utils import grouping
+from nemo_curator.utils.hf_download_utils import download_model_from_hf
+
+# Constants for prompt processing
+VIDEO_TAG_SPLIT_MAX = 1
+EXPECTED_VIDEO_TAG_PARTS = 2
+
+try:
+    from vllm import LLM, SamplingParams
+
+    VLLM_AVAILABLE = True
+except ImportError:
+    VLLM_AVAILABLE = False
+
+    class LLM:
+        pass
+
+    class SamplingParams:
+        pass
+
+
+# HuggingFace model IDs (will be updated when models are published)
+_NEMOTRON_H_NANO_MODEL_ID = None
+_NEMOTRON_H_NANO_MODEL_REVISION = None
+
+
+class NemotronHVL(ModelInterface):
+    """NemotronH hybrid Mamba-Attention VLM for video captioning."""
+
+    def __init__(  # noqa: PLR0913
+        self,
+        model_dir: str,
+        model_variant: str = "nemotron",
+        caption_batch_size: int = 8,
+        max_output_tokens: int = 512,
+        stage2_prompt_text: str | None = None,
+        verbose: bool = False,
+    ):
+        self.model_dir = model_dir
+        self.model_variant = model_variant
+        self.caption_batch_size = caption_batch_size
+        self.max_output_tokens = max_output_tokens
+        self.stage2_prompt = stage2_prompt_text if stage2_prompt_text else "Please refine this caption: "
+        self.verbose = verbose
+
+        if _NEMOTRON_H_NANO_MODEL_ID is not None:
+            self.weight_file = str(Path(model_dir) / _NEMOTRON_H_NANO_MODEL_ID)
+        else:
+            # Local checkpoint: model_dir is the checkpoint path itself
+            self.weight_file = str(Path(model_dir))
+
+    @property
+    def model_id_names(self) -> list[str]:
+        """Return model ID from config.json or HuggingFace ID."""
+        if _NEMOTRON_H_NANO_MODEL_ID is not None:
+            return [_NEMOTRON_H_NANO_MODEL_ID]
+
+        # Read from config.json if available
+        try:
+            config_path = Path(self.weight_file) / "config.json"
+            with open(config_path) as f:
+                config = json.load(f)
+            return [config.get("_name_or_path", Path(self.weight_file).name)]
+        except (FileNotFoundError, json.JSONDecodeError, KeyError):
+            return [Path(self.weight_file).name]
+
+    def setup(self) -> None:
+        if not VLLM_AVAILABLE:
+            msg = "vllm is required for NemotronHVL but is not installed. Please install vllm: pip install vllm"
+            raise ImportError(msg)
+
+        # Use V0 engine to avoid flashinfer issues for now
+        os.environ["VLLM_USE_V1"] = "0"
+        logger.info("Using vLLM V0 engine.")
+
+        os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+
+        self.model = LLM(
+            model=self.weight_file,
+            trust_remote_code=True,
+            tensor_parallel_size=1,
+            gpu_memory_utilization=0.9,
+            max_model_len=32768,
+            limit_mm_per_prompt={"video": 1},
+        )
+
+        self.sampling_params = SamplingParams(
+            temperature=0.6,
+            max_tokens=self.max_output_tokens,
+            top_p=0.95,
+            stop=["</s>", "<|endoftext|>", "<SPECIAL_12>", "</think>"],
+        )
+
+        logger.info(
+            f"NemotronHVL initialized: variant={self.model_variant}, "
+            f"TP=1, GPU_util=0.9, max_len=32768"
+        )
+
+    def _refine_caption_prompt(self, original_prompt: str, refinement_text: str) -> str:
+        """Create a refined prompt for stage 2 captioning."""
+        if "<video>" not in original_prompt:
+            return refinement_text
+
+        parts = original_prompt.split("<video>", VIDEO_TAG_SPLIT_MAX)
+        if len(parts) != EXPECTED_VIDEO_TAG_PARTS:
+            return refinement_text
+
+        prefix = parts[0] + "<video>"
+
+        # Find where the user message ends
+        suffix_markers = ["<SPECIAL_11>Assistant", "<|im_end|>", "</s>"]
+        suffix_start = len(parts[1])
+        for marker in suffix_markers:
+            if marker in parts[1]:
+                suffix_start = parts[1].index(marker)
+                break
+
+        suffix = parts[1][suffix_start:]
+        return prefix + "\n" + refinement_text + suffix
+
+    def generate(
+        self,
+        videos: list[dict[str, Any]],
+        generate_stage2_caption: bool = False,
+        batch_size: int = 16,
+    ) -> list[str]:
+        generated_text = []
+
+        for batch_videos in grouping.split_by_chunk_size(videos, batch_size):
+            model_inputs = list(batch_videos)
+            try:
+                # PASS 1: Generate initial captions
+                outputs = self.model.generate(
+                    model_inputs,
+                    sampling_params=self.sampling_params,
+                    use_tqdm=False,
+                )
+
+                # PASS 2: Refine captions if requested
+                if generate_stage2_caption:
+                    for i, out in enumerate(outputs):
+                        initial_caption = out.outputs[0].text
+                        refinement_text = self.stage2_prompt + initial_caption
+                        original_prompt = model_inputs[i]["prompt"]
+                        model_inputs[i]["prompt"] = self._refine_caption_prompt(
+                            original_prompt, refinement_text
+                        )
+
+                    outputs = self.model.generate(
+                        model_inputs,
+                        sampling_params=self.sampling_params,
+                        use_tqdm=False,
+                    )
+
+                generated_text.extend(out.outputs[0].text for out in outputs)
+
+                if self.verbose:
+                    for i, out in enumerate(outputs):
+                        logger.info(f"Generated caption {i}: {out.outputs[0].text[:100]}...")
+
+            except Exception as e:
+                logger.error(f"Error generating caption for batch: {e}")
+                raise
+
+        return generated_text
+
+    @classmethod
+    def download_weights_on_node(cls, model_dir: str) -> None:
+        """Download or verify NemotronH weights on the node (follows Qwen pattern)."""
+        # If HuggingFace model ID is configured, download from HF
+        if _NEMOTRON_H_NANO_MODEL_ID is not None:
+            model_dir_path = Path(model_dir) / _NEMOTRON_H_NANO_MODEL_ID
+            model_dir_path.mkdir(parents=True, exist_ok=True)
+
+            # Check if already downloaded
+            if model_dir_path.exists() and any(model_dir_path.glob("*.safetensors")):
+                logger.info(f"NemotronH checkpoint already exists at: {model_dir_path}")
+                return
+
+            # Download from HuggingFace
+            download_model_from_hf(
+                model_id=_NEMOTRON_H_NANO_MODEL_ID,
+                local_dir=model_dir_path,
+                revision=_NEMOTRON_H_NANO_MODEL_REVISION,
+            )
+            logger.info(f"NemotronH weights downloaded to: {model_dir_path}")
+        else:
+            # Local checkpoint mode: validate path exists
+            model_path = Path(model_dir)
+            if not model_path.exists():
+                msg = f"NemotronH checkpoint path does not exist: {model_dir}"
+                raise FileNotFoundError(msg)
+
+            # Verify it contains model files
+            if not any(model_path.glob("*.safetensors")) and not any(model_path.glob("*.bin")):
+                msg = (
+                    f"No model files (.safetensors or .bin) found in: {model_dir}\n"
+                    f"Please ensure this directory contains a valid NemotronH checkpoint."
+                )
+                raise FileNotFoundError(msg)
+
+            logger.info(f"Using local NemotronH checkpoint: {model_dir}")
+
diff --git a/nemo_curator/models/nemotron_prompt_formatter.py b/nemo_curator/models/nemotron_prompt_formatter.py
new file mode 100644
index 0000000000..ecc7fa55f2
--- /dev/null
+++ b/nemo_curator/models/nemotron_prompt_formatter.py
@@ -0,0 +1,137 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+import torch
+from jinja2 import Template
+from loguru import logger
+
+# Constants for tensor dimensions and channel counts
+EXPECTED_TENSOR_DIMENSIONS = 4
+EXPECTED_NUMPY_DIMENSIONS = 4
+EXPECTED_CHANNELS = 3
+
+
+class NemotronPromptFormatter:
+    """Prompt formatter for NemotronH models using Jinja2 chat templates."""
+
+    def __init__(self, model_path: str):
+        self.model_path = Path(model_path)
+
+        checkpoint_path = self._resolve_checkpoint_path(model_path)
+
+        template_path = checkpoint_path / "chat_template.jinja"
+        if not template_path.exists():
+            msg = (
+                f"chat_template.jinja not found at {template_path}.\n"
+                f"NemotronH models require this template file for proper prompt formatting.\n"
+                f"Please ensure your checkpoint directory contains chat_template.jinja."
+            )
+            raise FileNotFoundError(msg)
+
+        with open(template_path) as f:
+            self.chat_template = f.read()
+        logger.info(f"Loaded chat template from: {template_path}")
+
+    def _resolve_checkpoint_path(self, model_path: str) -> Path:
+        path = Path(model_path)
+
+        if (path / "config.json").exists():
+            return path
+
+        return path
+
+    def generate_inputs(
+        self,
+        prompt: str,
+        video_inputs: torch.Tensor | npt.NDArray[np.uint8] | None = None,
+        add_generation_prompt: bool = True,
+    ) -> dict[str, Any]:
+        video_np = self._convert_video_format(video_inputs)
+        formatted_prompt = self._apply_chat_template(prompt, add_generation_prompt)
+
+        return {
+            "prompt": formatted_prompt,
+            "multi_modal_data": {"video": video_np},
+        }
+
+    def _convert_tensor_to_numpy(self, tensor: torch.Tensor) -> npt.NDArray[np.uint8]:
+        """Convert torch.Tensor (T, C, H, W) to numpy (T, H, W, C)."""
+        if tensor.ndim != EXPECTED_TENSOR_DIMENSIONS:
+            msg = f"Expected 4D torch.Tensor (T, C, H, W), got shape {tensor.shape}"
+            raise ValueError(msg)
+
+        video_np = tensor.permute(0, 2, 3, 1).cpu().numpy()
+        return self._normalize_dtype(video_np)
+
+    def _validate_numpy_array(self, array: npt.NDArray[np.uint8]) -> npt.NDArray[np.uint8]:
+        """Validate and normalize numpy array format."""
+        if array.ndim != EXPECTED_NUMPY_DIMENSIONS:
+            msg = f"Expected 4D numpy array (T, H, W, C), got shape {array.shape}"
+            raise ValueError(msg)
+
+        if array.shape[-1] != EXPECTED_CHANNELS:
+            msg = f"Expected channels-last format (T, H, W, 3), got shape {array.shape}."
+            raise ValueError(msg)
+
+        return self._normalize_dtype(array)
+
+    def _normalize_dtype(self, array: npt.NDArray[np.uint8]) -> npt.NDArray[np.uint8]:
+        """Normalize array dtype to uint8."""
+        if array.dtype != np.uint8:
+            if array.dtype in (np.float32, np.float16) and array.max() <= 1.0:
+                return (array * 255).astype(np.uint8)
+            return array.astype(np.uint8)
+        return array
+
+    def _convert_video_format(
+        self,
+        video_inputs: torch.Tensor | npt.NDArray[np.uint8] | None,
+    ) -> npt.NDArray[np.uint8] | None:
+        """Convert torch.Tensor (T, C, H, W) or np.ndarray to vLLM format (T, H, W, C)."""
+        if video_inputs is None:
+            return None
+
+        if isinstance(video_inputs, torch.Tensor):
+            return self._convert_tensor_to_numpy(video_inputs)
+
+        if isinstance(video_inputs, np.ndarray):
+            return self._validate_numpy_array(video_inputs)
+
+        msg = f"Expected torch.Tensor or np.ndarray, got {type(video_inputs)}"
+        raise TypeError(msg)
+
+    def _apply_chat_template(
+        self,
+        prompt_text: str,
+        add_generation_prompt: bool = True,
+    ) -> str:
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": f"<video>\n{prompt_text}"}],
+            },
+        ]
+
+        template = Template(self.chat_template)
+        return template.render(
+            messages=messages,
+            add_generation_prompt=add_generation_prompt,
+        )
+
diff --git a/nemo_curator/models/prompt_formatter.py b/nemo_curator/models/prompt_formatter.py
index 3405f6b5a7..b657cc70c6 100644
--- a/nemo_curator/models/prompt_formatter.py
+++ b/nemo_curator/models/prompt_formatter.py
@@ -17,20 +17,34 @@
 import torch
 from transformers import AutoProcessor
 
+from nemo_curator.models.nemotron_prompt_formatter import NemotronPromptFormatter
+
 VARIANT_MAPPING = {
     "qwen": "Qwen/Qwen2.5-VL-7B-Instruct",
+    "nemotron": None,
 }
 
-
 class PromptFormatter:
-    def __init__(self, prompt_variant: str):
+    def __init__(self, prompt_variant: str, model_path: str | None = None):
         if prompt_variant not in VARIANT_MAPPING:
             msg = f"Invalid prompt variant: {prompt_variant}. Valid variants are: {', '.join(VARIANT_MAPPING.keys())}"
             raise ValueError(msg)
 
         self.prompt_variant = prompt_variant
         self.text_prompt = None
-        self.processor = AutoProcessor.from_pretrained(VARIANT_MAPPING[self.prompt_variant])
+
+        if prompt_variant == "qwen":
+            self.processor = AutoProcessor.from_pretrained(VARIANT_MAPPING[self.prompt_variant])
+            self.formatter = None
+        elif prompt_variant == "nemotron":
+            if model_path is None:
+                msg = f"model_path is required for nemotron models (variant: {prompt_variant})"
+                raise ValueError(msg)
+            self.processor = None
+            self.formatter = NemotronPromptFormatter(model_path)
+        else:
+            msg = f"Unsupported prompt variant: {prompt_variant}"
+            raise ValueError(msg)
 
     def generate_inputs(
         self,
@@ -61,17 +75,29 @@ def generate_inputs(
                 - "multi_modal_data": Dictionary containing processed "image" and/or "video" inputs
 
         """
-        message = self.create_message(prompt)
-        if self.text_prompt is None or override_text_prompt:
-            self.text_prompt = self.processor.apply_chat_template(
-                message,
-                tokenizer=False,
+        # Use model-specific formatter
+        if self.prompt_variant == "qwen":
+            message = self.create_message(prompt)
+            if self.text_prompt is None or override_text_prompt:
+                self.text_prompt = self.processor.apply_chat_template(
+                    message,
+                    tokenizer=False,
+                    add_generation_prompt=True,
+                )
+            return {
+                "prompt": self.text_prompt,
+                "multi_modal_data": {"video": video_inputs},
+            }
+
+        if self.prompt_variant == "nemotron":
+            return self.formatter.generate_inputs(
+                prompt=prompt,
+                video_inputs=video_inputs,
                 add_generation_prompt=True,
             )
-        return {
-            "prompt": self.text_prompt,
-            "multi_modal_data": {"video": video_inputs},
-        }
+
+        msg = f"Unsupported prompt variant: {self.prompt_variant}"
+        raise ValueError(msg)
 
     def create_message(self, prompt: str) -> list[dict[str, Any]]:
         """Create a message.
diff --git a/nemo_curator/stages/video/caption/caption_generation.py b/nemo_curator/stages/video/caption/caption_generation.py
index 43a851a286..ababa168c5 100644
--- a/nemo_curator/stages/video/caption/caption_generation.py
+++ b/nemo_curator/stages/video/caption/caption_generation.py
@@ -19,6 +19,7 @@
 from loguru import logger
 
 from nemo_curator.backends.base import NodeInfo, WorkerMetadata
+from nemo_curator.models.nemotron_h_vl import NemotronHVL
 from nemo_curator.models.qwen_vl import QwenVL
 from nemo_curator.stages.base import ProcessingStage
 from nemo_curator.stages.resources import Resources
@@ -62,14 +63,32 @@ def setup(self, worker_metadata: WorkerMetadata | None = None) -> None:  # noqa:
                 model_does_preprocess=self.model_does_preprocess,
                 disable_mmcache=self.disable_mmcache,
             )
+        elif self.model_variant == "nemotron":
+            if NemotronHVL is None:
+                msg = "NemotronHVL is not available. Please install required dependencies."
+                raise ImportError(msg)
+            self.model = NemotronHVL(
+                model_dir=self.model_dir,
+                model_variant=self.model_variant,
+                caption_batch_size=self.caption_batch_size,
+                max_output_tokens=self.max_output_tokens,
+                stage2_prompt_text=self.stage2_prompt_text,
+                verbose=self.verbose,
+            )
         else:
             msg = f"Unsupported model variant: {self.model_variant}"
             raise ValueError(msg)
         self.model.setup()
 
     def setup_on_node(self, node_info: NodeInfo, worker_metadata: WorkerMetadata) -> None:  # noqa: ARG002
-        """Download the weights for the QwenVL model on the node."""
-        QwenVL.download_weights_on_node(self.model_dir)
+        """Download/verify weights on the node."""
+        if self.model_variant == "qwen":
+            QwenVL.download_weights_on_node(self.model_dir)
+        elif self.model_variant == "nemotron":
+            if NemotronHVL is None:
+                msg = "NemotronHVL is not available. Please install required dependencies."
+                raise ImportError(msg)
+            NemotronHVL.download_weights_on_node(self.model_dir)
 
     def __post_init__(self) -> None:
         self.resources = Resources(gpus=1)
@@ -84,12 +103,20 @@ def process(self, task: VideoTask) -> VideoTask:
                 logger.warning(f"Clip {clip.uuid} has no windows")
                 clip.errors["windows"] = "empty"
             for window_idx, window in enumerate(clip.windows):
-                if window.qwen_llm_input is None:
-                    logger.error(f"Clip {clip.uuid} window {window_idx} has no prepared inputs.")
-                    clip.errors[f"window-{window_idx}"] = "empty"
+                llm_input = window.llm_inputs.get(self.model_variant)
+
+                if llm_input is None and self.model_variant == "qwen" and window.qwen_llm_input is not None:
+                    llm_input = window.qwen_llm_input
+
+                if llm_input is None:
+                    logger.error(
+                        f"Clip {clip.uuid} window {window_idx} has no prepared inputs for {self.model_variant}."
+                    )
+                    clip.errors[f"window-{window_idx}"] = f"no_{self.model_variant}_input"
                     continue
+
                 mapping[idx] = (clip_idx, window_idx)
-                inputs.append(window.qwen_llm_input)
+                inputs.append(llm_input)
                 idx += 1
 
         captions = self.model.generate(
@@ -123,5 +150,7 @@ def _assign_captions(
         # Clear caption inputs
         for clip in video.clips:
             for window in clip.windows:
+                if self.model_variant in window.llm_inputs:
+                    del window.llm_inputs[self.model_variant]
                 window.qwen_llm_input = None
                 window.mp4_bytes = None
diff --git a/nemo_curator/stages/video/caption/caption_preparation.py b/nemo_curator/stages/video/caption/caption_preparation.py
index 8a034631fd..579865fc87 100644
--- a/nemo_curator/stages/video/caption/caption_preparation.py
+++ b/nemo_curator/stages/video/caption/caption_preparation.py
@@ -78,6 +78,7 @@ class CaptionPreparationStage(ProcessingStage[VideoTask, VideoTask]):
     """Stage that prepares captions for video processing."""
 
     model_variant: str = "qwen"
+    model_path: str | None = None
     prompt_variant: str = "default"
     prompt_text: str | None = None
     verbose: bool = False
@@ -96,7 +97,13 @@ def outputs(self) -> tuple[list[str], list[str]]:
         return [], []
 
     def setup(self, worker_metadata: WorkerMetadata | None = None) -> None:  # noqa: ARG002
-        self.prompt_formatter = PromptFormatter(self.model_variant)
+        if self.model_variant == "nemotron":
+            if self.model_path is None:
+                msg = f"model_path is required for nemotron models (variant: {self.model_variant})"
+                raise ValueError(msg)
+            self.prompt_formatter = PromptFormatter(self.model_variant, model_path=self.model_path)
+        else:
+            self.prompt_formatter = PromptFormatter(self.model_variant)
 
     def process(self, task: VideoTask) -> VideoTask:
         video = task.data
@@ -133,13 +140,16 @@ def process(self, task: VideoTask) -> VideoTask:
                     clip.errors[f"{self.model_variant}_input"] = str(e)
                     continue
 
-                clip.windows.append(
-                    _Window(
-                        window_frame_info.start,
-                        window_frame_info.end,
-                        mp4_bytes=window_bytes,
-                        qwen_llm_input=llm_input,
-                    ),
+                window = _Window(
+                    window_frame_info.start,
+                    window_frame_info.end,
+                    mp4_bytes=window_bytes,
                 )
+                window.llm_inputs[self.model_variant] = llm_input
+
+                if self.model_variant == "qwen":
+                    window.qwen_llm_input = llm_input
+
+                clip.windows.append(window)
 
         return task
diff --git a/nemo_curator/tasks/video.py b/nemo_curator/tasks/video.py
index d50f5a6ce0..ae24e2350f 100644
--- a/nemo_curator/tasks/video.py
+++ b/nemo_curator/tasks/video.py
@@ -41,8 +41,9 @@ class _Window:
     end_frame: int
     # MP4 bytes for this window
     mp4_bytes: bytes | None = None
+    llm_inputs: dict[str, dict[str, Any]] = field(default_factory=dict)
     # Qwen LLM input for this window
-    qwen_llm_input: dict[str, Any] | None = None
+    qwen_llm_input: dict[str, Any] | None = None  # Backward compatibility
     # X1 model input for this window
     x1_input: Any | None = None
     # `caption: {model_name: caption}`
@@ -51,6 +52,13 @@ class _Window:
     # webp preview
     webp_bytes: bytes | None = None
 
+    def __post_init__(self):
+        """Sync qwen_llm_input with llm_inputs for backward compatibility."""
+        if self.qwen_llm_input is not None:
+            self.llm_inputs["qwen"] = self.qwen_llm_input
+        elif "qwen" in self.llm_inputs:
+            self.qwen_llm_input = self.llm_inputs["qwen"]
+
     def get_major_size(self) -> int:
         """Calculate total memory size of the window.
 
@@ -61,7 +69,9 @@ def get_major_size(self) -> int:
         total_size = 0
         total_size += len(self.mp4_bytes) if self.mp4_bytes else 0
         # TODO: this is probably inaccurate
-        total_size += sys.getsizeof(self.qwen_llm_input) if self.qwen_llm_input else 0
+        total_size += sum(sys.getsizeof(v) for v in self.llm_inputs.values())
+        if self.qwen_llm_input is not None and "qwen" not in self.llm_inputs:
+            total_size += sys.getsizeof(self.qwen_llm_input)
         total_size += sys.getsizeof(self.caption)
         total_size += sys.getsizeof(self.enhanced_caption)
         total_size += len(self.webp_bytes) if self.webp_bytes else 0
diff --git a/pyproject.toml b/pyproject.toml
index c49b9eaf98..0e0d6c9c3a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -140,6 +140,7 @@ video_cpu = [
     "torchvision",
     "einops",
     "easydict",
+    "timm"
 ]
 
 video_cuda12 = [
@@ -147,7 +148,7 @@ video_cuda12 = [
     "nemo_curator[cuda12]",
     "nemo_curator[vllm]",
     "cvcuda_cu12",
-    "flash-attn<=2.8.3; (platform_machine == 'x86_64' and platform_system != 'Darwin')",
+    "flash-attn>=2.7.1,<=2.8.2; (platform_machine == 'x86_64' and platform_system != 'Darwin')",  
     "pycuda",
     "PyNvVideoCodec==2.0.2; (platform_machine == 'x86_64' and platform_system != 'Darwin')",
     "torch<=2.9.1",
diff --git a/tutorials/video/getting-started/video_split_clip_example.py b/tutorials/video/getting-started/video_split_clip_example.py
index f30c42d49f..11b91f30f0 100644
--- a/tutorials/video/getting-started/video_split_clip_example.py
+++ b/tutorials/video/getting-started/video_split_clip_example.py
@@ -192,9 +192,13 @@ def create_video_splitting_pipeline(args: argparse.Namespace) -> Pipeline:  # no
             raise ValueError(msg)
 
     if args.generate_captions:
+        # Determine model path for caption preparation
+        model_path = args.captioning_model_path if hasattr(args, "captioning_model_path") else None
+
         pipeline.add_stage(
             CaptionPreparationStage(
                 model_variant=args.captioning_algorithm,
+                model_path=model_path,  # Required for NemotronH
                 prompt_variant=args.captioning_prompt_variant,
                 prompt_text=args.captioning_prompt_text,
                 sampling_fps=args.captioning_sampling_fps,
@@ -215,9 +219,16 @@ def create_video_splitting_pipeline(args: argparse.Namespace) -> Pipeline:  # no
                 )
             )
 
+        # Determine model directory
+        model_dir = (
+            args.captioning_model_path
+            if (args.captioning_algorithm.startswith("nemotron") or args.captioning_algorithm.startswith("NemotronH_"))
+            else args.model_dir
+        )
+
         pipeline.add_stage(
             CaptionGenerationStage(
-                model_dir=args.model_dir,
+                model_dir=model_dir,
                 model_variant=args.captioning_algorithm,
                 caption_batch_size=args.captioning_batch_size,
                 fp8=args.captioning_use_fp8_weights,
@@ -586,8 +597,14 @@ def create_video_splitting_argparser() -> argparse.ArgumentParser:  # noqa: PLR0
         "--captioning-algorithm",
         type=str,
         default="qwen",
-        choices=["qwen"],
-        help="Captioning algorithm to use in annotation pipeline.",
+        choices=["qwen", "nemotron"],
+        help="Captioning algorithm to use (qwen or nemotron).",
+    )
+    parser.add_argument(
+        "--captioning-model-path",
+        type=str,
+        default=None,
+        help="Path to model checkpoint (required for NemotronH models)",
     )
     parser.add_argument(
         "--captioning-window-size",
@@ -648,13 +665,13 @@ def create_video_splitting_argparser() -> argparse.ArgumentParser:  # noqa: PLR0
         dest="captioning_stage2_caption",
         action="store_true",
         default=False,
-        help="If set, generated captions are used as input prompts again into QwenVL to refine them",
+        help="If set, generated captions are refined with a second model pass (works for both Qwen and Nemotron)",
     )
     parser.add_argument(
         "--captioning-stage2-prompt-text",
         type=str,
         default=None,
-        help="Specify the input prompt used to generate stage2 Qwen captions",
+        help="Specify the prompt used for stage2 caption refinement.",
     )
     parser.add_argument(
         "--captioning-batch-size",
diff --git a/uv.lock b/uv.lock
index cdb744de45..ebf3ae6cd1 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,33 +2,31 @@ version = 1
 revision = 3
 requires-python = ">=3.10, <3.13"
 resolution-markers = [
-    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'linux'",
-    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'linux'",
-    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
-    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'linux'",
-    "python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'linux'",
-    "python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'win32'",
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'emscripten'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform != 'darwin'",
     "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'darwin'",
     "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
     "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
     "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
     "python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
-    "python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'",
-    "python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
     "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'darwin'",
     "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
     "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
     "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
     "python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
-    "python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'",
-    "python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
     "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'darwin'",
     "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
     "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
     "python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
     "python_full_version < '3.11' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
-    "python_full_version < '3.11' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'",
-    "python_full_version < '3.11' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version < '3.11' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
 ]
 
 [manifest]
@@ -51,11 +49,11 @@ overrides = [
 
 [[package]]
 name = "absl-py"
-version = "2.3.1"
+version = "2.4.0"
 source = { registry = "https://pypi.org/simple" }
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+sdist = { url = "https://files.pythonhosted.org/packages/64/c7/8de93764ad66968d19329a7e0c147a2bb3c7054c554d4a119111b8f9440f/absl_py-2.4.0.tar.gz", hash = "sha256:8c6af82722b35cf71e0f4d1d47dcaebfff286e27110a99fc359349b247dfb5d4", size = 116543, upload-time = "2026-01-28T10:17:05.322Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl", hash = "sha256:88476fd881ca8aab94ffa78b7b6c632a782ab3ba1cd19c9bd423abc4fb4cd28d", size = 135750, upload-time = "2026-01-28T10:17:04.19Z" },
 ]
 
 [[package]]
@@ -69,7 +67,7 @@ dependencies = [
     { name = "psutil" },
     { name = "pyyaml" },
     { name = "safetensors" },
-    { name = "torch", version = "2.9.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'" },
     { name = "torch", version = "2.9.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
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 sdist = { url = "https://files.pythonhosted.org/packages/4a/8e/ac2a9566747a93f8be36ee08532eb0160558b07630a081a6056a9f89bf1d/accelerate-1.12.0.tar.gz", hash = "sha256:70988c352feb481887077d2ab845125024b2a137a5090d6d7a32b57d03a45df6", size = 398399, upload-time = "2025-11-21T11:27:46.973Z" }
@@ -209,7 +207,7 @@ wheels = [
 
 [[package]]
 name = "alembic"
-version = "1.17.2"
+version = "1.18.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "mako" },
@@ -217,9 +215,9 @@ dependencies = [
     { name = "tomli", marker = "python_full_version < '3.11'" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/02/a6/74c8cadc2882977d80ad756a13857857dbcf9bd405bc80b662eb10651282/alembic-1.17.2.tar.gz", hash = "sha256:bbe9751705c5e0f14877f02d46c53d10885e377e3d90eda810a016f9baa19e8e", size = 1988064, upload-time = "2025-11-14T20:35:04.057Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/49/cc/aca263693b2ece99fa99a09b6d092acb89973eb2bb575faef1777e04f8b4/alembic-1.18.1.tar.gz", hash = "sha256:83ac6b81359596816fb3b893099841a0862f2117b2963258e965d70dc62fb866", size = 2044319, upload-time = "2026-01-14T18:53:14.907Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/83/36/cd9cb6101e81e39076b2fbe303bfa3c85ca34e55142b0324fcbf22c5c6e2/alembic-1.18.1-py3-none-any.whl", hash = "sha256:f1c3b0920b87134e851c25f1f7f236d8a332c34b75416802d06971df5d1b7810", size = 260973, upload-time = "2026-01-14T18:53:17.533Z" },
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 [[package]]
@@ -276,36 +274,36 @@ sdist = { url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d
 
 [[package]]
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-version = "4.12.0"
+version = "4.12.1"
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 [[package]]
@@ -1212,31 +1213,91 @@ wheels = [
 name = "cuda-toolkit"
 version = "12.8.1"
 source = { registry = "https://pypi.nvidia.com/" }
+resolution-markers = [
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform != 'darwin'",
+]
 wheels = [
     { url = "https://pypi.nvidia.com/cuda-toolkit/cuda_toolkit-12.8.1-py2.py3-none-any.whl", hash = "sha256:adc7906af4ecbf9a352f9dca5734eceb21daec281ccfcf5675e1d2f724fc2cba" },
 ]
 
 [package.optional-dependencies]
 cublas = [
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 ]
 cufft = [
-    { name = "nvidia-cufft-cu12", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "nvidia-cufft-cu12", version = "11.3.3.83", source = { registry = "https://pypi.nvidia.com/" }, marker = "(python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'win32') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
 ]
 curand = [
-    { name = "nvidia-curand-cu12", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "nvidia-curand-cu12", version = "10.3.9.90", source = { registry = "https://pypi.nvidia.com/" }, marker = "(python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'win32') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
 ]
 cusolver = [
-    { name = "nvidia-cusolver-cu12", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "nvidia-cusolver-cu12", version = "11.7.3.90", source = { registry = "https://pypi.nvidia.com/" }, marker = "(python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'win32') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
 ]
 cusparse = [
-    { name = "nvidia-cusparse-cu12", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "nvidia-cusparse-cu12", version = "12.5.8.93", source = { registry = "https://pypi.nvidia.com/" }, marker = "(python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'win32') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
 ]
 nvcc = [
-    { name = "nvidia-cuda-nvcc-cu12", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "nvidia-cuda-nvcc-cu12", version = "12.8.93", source = { registry = "https://pypi.nvidia.com/" }, marker = "(python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'win32') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
 ]
 nvrtc = [
-    { name = "nvidia-cuda-nvrtc-cu12", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "nvidia-cuda-nvrtc-cu12", version = "12.8.93", source = { registry = "https://pypi.nvidia.com/" }, marker = "(python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'win32') or (platform_machine == 'x86_64' and sys_platform == 'linux')" },
+]
+
+[[package]]
+name = "cuda-toolkit"
+version = "12.9.1"
+source = { registry = "https://pypi.nvidia.com/" }
+resolution-markers = [
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'win32'",
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'emscripten'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
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-    { name = "nvidia-cuda-nvrtc-cu12" },
-    { name = "nvidia-cuda-runtime-cu12" },
-    { name = "nvidia-nvjitlink-cu12" },
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 [[package]]
 name = "nvidia-cuda-cccl-cu12"
 version = "12.9.27"
@@ -4797,38 +4858,152 @@ wheels = [
 name = "nvidia-cuda-nvcc-cu12"
 version = "12.8.93"
 source = { registry = "https://pypi.nvidia.com/" }
+resolution-markers = [
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform != 'darwin'",
+]
 wheels = [
     { url = "https://pypi.nvidia.com/nvidia-cuda-nvcc-cu12/nvidia_cuda_nvcc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:2d6dc36fb7cb5ac9c0b8825bc13d193c35487a315664007287d0126531238011" },
-    { url = "https://pypi.nvidia.com/nvidia-cuda-nvcc-cu12/nvidia_cuda_nvcc_cu12-12.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2b35ada240938d19398119ca596faaf626ba5e729511be9715842a29d112926d" },
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 ]
 
+[[package]]
+name = "nvidia-cuda-nvcc-cu12"
+version = "12.9.86"
+source = { registry = "https://pypi.nvidia.com/" }
+resolution-markers = [
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+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+]
+wheels = [
+    { url = "https://pypi.nvidia.com/nvidia-cuda-nvcc-cu12/nvidia_cuda_nvcc_cu12-12.9.86-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:5d6a0d32fdc7ea39917c20065614ae93add6f577d840233237ff08e9a38f58f0" },
+    { url = "https://pypi.nvidia.com/nvidia-cuda-nvcc-cu12/nvidia_cuda_nvcc_cu12-12.9.86-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:44e1eca4d08926193a558d2434b1bf83d57b4d5743e0c431c0c83d51da1df62b" },
+    { url = "https://pypi.nvidia.com/nvidia-cuda-nvcc-cu12/nvidia_cuda_nvcc_cu12-12.9.86-py3-none-win_amd64.whl", hash = "sha256:8ed7f0b17dea662755395be029376db3b94fed5cbb17c2d35cc866c5b1b84099" },
+]
+
 [[package]]
 name = "nvidia-cuda-nvrtc-cu12"
 version = "12.8.93"
 source = { registry = "https://pypi.nvidia.com/" }
+resolution-markers = [
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform != 'darwin'",
+]
 wheels = [
     { url = "https://pypi.nvidia.com/nvidia-cuda-nvrtc-cu12/nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:a7756528852ef889772a84c6cd89d41dfa74667e24cca16bb31f8f061e3e9994" },
-    { url = "https://pypi.nvidia.com/nvidia-cuda-nvrtc-cu12/nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:fc1fec1e1637854b4c0a65fb9a8346b51dd9ee69e61ebaccc82058441f15bce8" },
     { url = "https://pypi.nvidia.com/nvidia-cuda-nvrtc-cu12/nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-win_amd64.whl", hash = "sha256:7a4b6b2904850fe78e0bd179c4b655c404d4bb799ef03ddc60804247099ae909" },
 ]
 
+[[package]]
+name = "nvidia-cuda-nvrtc-cu12"
+version = "12.9.86"
+source = { registry = "https://pypi.nvidia.com/" }
+resolution-markers = [
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+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'emscripten'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+]
+wheels = [
+    { url = "https://pypi.nvidia.com/nvidia-cuda-nvrtc-cu12/nvidia_cuda_nvrtc_cu12-12.9.86-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:210cf05005a447e29214e9ce50851e83fc5f4358df8b453155d5e1918094dcb4" },
+    { url = "https://pypi.nvidia.com/nvidia-cuda-nvrtc-cu12/nvidia_cuda_nvrtc_cu12-12.9.86-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:096d4de6bda726415dfaf3198d4f5c522b8e70139c97feef5cd2ca6d4cd9cead" },
+    { url = "https://pypi.nvidia.com/nvidia-cuda-nvrtc-cu12/nvidia_cuda_nvrtc_cu12-12.9.86-py3-none-win_amd64.whl", hash = "sha256:72972ebdcf504d69462d3bcd67e7b81edd25d0fb85a2c46d3ea3517666636349" },
+]
+
 [[package]]
 name = "nvidia-cuda-runtime-cu12"
 version = "12.8.90"
 source = { registry = "https://pypi.nvidia.com/" }
+resolution-markers = [
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform != 'darwin'",
+]
 wheels = [
-    { url = "https://pypi.nvidia.com/nvidia-cuda-runtime-cu12/nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:52bf7bbee900262ffefe5e9d5a2a69a30d97e2bc5bb6cc866688caa976966e3d" },
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 ]
 
+[[package]]
+name = "nvidia-cuda-runtime-cu12"
+version = "12.9.79"
+source = { registry = "https://pypi.nvidia.com/" }
+resolution-markers = [
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'",
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+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'emscripten'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'darwin'",
+    "python_full_version < '3.11' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+]
+wheels = [
+    { url = "https://pypi.nvidia.com/nvidia-cuda-runtime-cu12/nvidia_cuda_runtime_cu12-12.9.79-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:83469a846206f2a733db0c42e223589ab62fd2fabac4432d2f8802de4bded0a4" },
+    { url = "https://pypi.nvidia.com/nvidia-cuda-runtime-cu12/nvidia_cuda_runtime_cu12-12.9.79-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:25bba2dfb01d48a9b59ca474a1ac43c6ebf7011f1b0b8cc44f54eb6ac48a96c3" },
+    { url = "https://pypi.nvidia.com/nvidia-cuda-runtime-cu12/nvidia_cuda_runtime_cu12-12.9.79-py3-none-win_amd64.whl", hash = "sha256:8e018af8fa02363876860388bd10ccb89eb9ab8fb0aa749aaf58430a9f7c4891" },
+]
+
 [[package]]
 name = "nvidia-cudnn-cu12"
 version = "9.10.2.21"
 source = { registry = "https://pypi.nvidia.com/" }
 dependencies = [
-    { name = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+    { name = "nvidia-cublas-cu12", version = "12.8.4.1", source = { registry = "https://pypi.nvidia.com/" }, marker = "(python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'emscripten') or (python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'win32') or (platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')" },
 ]
 wheels = [
     { url = "https://pypi.nvidia.com/nvidia-cudnn-cu12/nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:949452be657fa16687d0930933f032835951ef0892b37d2d53824d1a84dc97a8" },
@@ -4836,30 +5011,60 @@ wheels = [
 
 [[package]]
 name = "nvidia-cudnn-frontend"
-version = "1.17.0"
+version = "1.18.0"
 source = { registry = "https://pypi.nvidia.com/" }
 wheels = [
-    { url = "https://pypi.nvidia.com/nvidia-cudnn-frontend/nvidia_cudnn_frontend-1.17.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1da7e972dbba939ad21111f1208815b8c8024cbf72aa6c1eb223b14b2049d4b6" },
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-    { url = "https://pypi.nvidia.com/nvidia-cudnn-frontend/nvidia_cudnn_frontend-1.17.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6ea44a8f2c0cfd20868b239ea13a2e0f32895dab868f6ff2bee01caf3778d273" },
-    { url = "https://pypi.nvidia.com/nvidia-cudnn-frontend/nvidia_cudnn_frontend-1.17.0-cp311-cp311-win_amd64.whl", hash = "sha256:8dd6cc197a58d63da4d146a1febc1f99d425374d159f9b00628b140c65acb486" },
-    { url = "https://pypi.nvidia.com/nvidia-cudnn-frontend/nvidia_cudnn_frontend-1.17.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:c913c87fca691a91385287f2587575531933acfebc85c33dbcecb191886c7a53" },
-    { url = "https://pypi.nvidia.com/nvidia-cudnn-frontend/nvidia_cudnn_frontend-1.17.0-cp312-cp312-win_amd64.whl", hash = "sha256:a0d4cfd03961592108abd1ba246e43c8bb7540aed984df860256d0bff181de98" },
+    { url = "https://pypi.nvidia.com/nvidia-cudnn-frontend/nvidia_cudnn_frontend-1.18.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:2e4bcca42259e358002c8867e3624a558f66cd5dff2cc6c3aafd860ef2f41730" },
+    { url = "https://pypi.nvidia.com/nvidia-cudnn-frontend/nvidia_cudnn_frontend-1.18.0-cp310-cp310-win_amd64.whl", hash = "sha256:06252021ef1e5a7256f1e70429a426b01792636c05cc547fe8e64c6885a9652e" },
+    { url = "https://pypi.nvidia.com/nvidia-cudnn-frontend/nvidia_cudnn_frontend-1.18.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:382ea063b92cbfd5b442cb75ff8422932d78276aecf139e46713ed1ad3d07af4" },
+    { url = "https://pypi.nvidia.com/nvidia-cudnn-frontend/nvidia_cudnn_frontend-1.18.0-cp311-cp311-win_amd64.whl", hash = "sha256:baa509effc4d299d3f04e549d4188f88bca8a8b527f483cbd2f66bc18f13a8b1" },
+    { url = "https://pypi.nvidia.com/nvidia-cudnn-frontend/nvidia_cudnn_frontend-1.18.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:6c023539ca6de99234cf5102c3ec0d6af817f5396fc93028a22ba5b834a35b8a" },
+    { url = "https://pypi.nvidia.com/nvidia-cudnn-frontend/nvidia_cudnn_frontend-1.18.0-cp312-cp312-win_amd64.whl", hash = "sha256:e13f7dd46cdb4762dde87f181f06d1c5e15e9478bbdd547bfa74d9b11f415aae" },
 ]
 
 [[package]]
 name = "nvidia-cufft-cu12"
 version = "11.3.3.83"
 source = { registry = "https://pypi.nvidia.com/" }
+resolution-markers = [
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform != 'darwin'",
+]
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'darwin'" },
+    { name = "nvidia-nvjitlink-cu12", version = "12.8.93", source = { registry = "https://pypi.nvidia.com/" }, marker = "(python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'emscripten') or (python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'win32') or (platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')" },
 ]
 wheels = [
-    { url = "https://pypi.nvidia.com/nvidia-cufft-cu12/nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:848ef7224d6305cdb2a4df928759dca7b1201874787083b6e7550dd6765ce69a" },
     { url = "https://pypi.nvidia.com/nvidia-cufft-cu12/nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:4d2dd21ec0b88cf61b62e6b43564355e5222e4a3fb394cac0db101f2dd0d4f74" },
     { url = "https://pypi.nvidia.com/nvidia-cufft-cu12/nvidia_cufft_cu12-11.3.3.83-py3-none-win_amd64.whl", hash = "sha256:7a64a98ef2a7c47f905aaf8931b69a3a43f27c55530c698bb2ed7c75c0b42cb7" },
 ]
 
+[[package]]
+name = "nvidia-cufft-cu12"
+version = "11.4.1.4"
+source = { registry = "https://pypi.nvidia.com/" }
+resolution-markers = [
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'win32'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version < '3.11' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+]
+dependencies = [
+    { name = "nvidia-nvjitlink-cu12", version = "12.9.86", source = { registry = "https://pypi.nvidia.com/" }, marker = "(python_full_version >= '3.11' and sys_platform == 'win32') or (platform_machine != 'x86_64' and sys_platform != 'darwin') or (platform_machine != 'x86_64' and sys_platform == 'win32')" },
+]
+wheels = [
+    { url = "https://pypi.nvidia.com/nvidia-cufft-cu12/nvidia_cufft_cu12-11.4.1.4-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:1a28c9b12260a1aa7a8fd12f5ebd82d027963d635ba82ff39a1acfa7c4c0fbcf" },
+    { url = "https://pypi.nvidia.com/nvidia-cufft-cu12/nvidia_cufft_cu12-11.4.1.4-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:c67884f2a7d276b4b80eb56a79322a95df592ae5e765cf1243693365ccab4e28" },
+    { url = "https://pypi.nvidia.com/nvidia-cufft-cu12/nvidia_cufft_cu12-11.4.1.4-py3-none-win_amd64.whl", hash = "sha256:8e5bfaac795e93f80611f807d42844e8e27e340e0cde270dcb6c65386d795b80" },
+]
+
 [[package]]
 name = "nvidia-cufile-cu12"
 version = "1.13.1.3"
@@ -4872,40 +5077,129 @@ wheels = [
 name = "nvidia-curand-cu12"
 version = "10.3.9.90"
 source = { registry = "https://pypi.nvidia.com/" }
+resolution-markers = [
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform != 'darwin'",
+]
 wheels = [
-    { url = "https://pypi.nvidia.com/nvidia-curand-cu12/nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:dfab99248034673b779bc6decafdc3404a8a6f502462201f2f31f11354204acd" },
     { url = "https://pypi.nvidia.com/nvidia-curand-cu12/nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:b32331d4f4df5d6eefa0554c565b626c7216f87a06a4f56fab27c3b68a830ec9" },
     { url = "https://pypi.nvidia.com/nvidia-curand-cu12/nvidia_curand_cu12-10.3.9.90-py3-none-win_amd64.whl", hash = "sha256:f149a8ca457277da854f89cf282d6ef43176861926c7ac85b2a0fbd237c587ec" },
 ]
 
+[[package]]
+name = "nvidia-curand-cu12"
+version = "10.3.10.19"
+source = { registry = "https://pypi.nvidia.com/" }
+resolution-markers = [
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'win32'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version < '3.11' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+]
+wheels = [
+    { url = "https://pypi.nvidia.com/nvidia-curand-cu12/nvidia_curand_cu12-10.3.10.19-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:de663377feb1697e1d30ed587b07d5721fdd6d2015c738d7528a6002a6134d37" },
+    { url = "https://pypi.nvidia.com/nvidia-curand-cu12/nvidia_curand_cu12-10.3.10.19-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:49b274db4780d421bd2ccd362e1415c13887c53c214f0d4b761752b8f9f6aa1e" },
+    { url = "https://pypi.nvidia.com/nvidia-curand-cu12/nvidia_curand_cu12-10.3.10.19-py3-none-win_amd64.whl", hash = "sha256:e8129e6ac40dc123bd948e33d3e11b4aa617d87a583fa2f21b3210e90c743cde" },
+]
+
 [[package]]
 name = "nvidia-cusolver-cu12"
 version = "11.7.3.90"
 source = { registry = "https://pypi.nvidia.com/" }
+resolution-markers = [
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform != 'darwin'",
+]
 dependencies = [
-    { name = "nvidia-cublas-cu12", marker = "sys_platform != 'darwin'" },
-    { name = "nvidia-cusparse-cu12", marker = "sys_platform != 'darwin'" },
-    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'darwin'" },
+    { name = "nvidia-cublas-cu12", version = "12.8.4.1", source = { registry = "https://pypi.nvidia.com/" }, marker = "(python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'emscripten') or (python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'win32') or (platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')" },
+    { name = "nvidia-cusparse-cu12", version = "12.5.8.93", source = { registry = "https://pypi.nvidia.com/" }, marker = "(python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'emscripten') or (python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'win32') or (platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')" },
+    { name = "nvidia-nvjitlink-cu12", version = "12.8.93", source = { registry = "https://pypi.nvidia.com/" }, marker = "(python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'emscripten') or (python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'win32') or (platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')" },
 ]
 wheels = [
-    { url = "https://pypi.nvidia.com/nvidia-cusolver-cu12/nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:db9ed69dbef9715071232caa9b69c52ac7de3a95773c2db65bdba85916e4e5c0" },
     { url = "https://pypi.nvidia.com/nvidia-cusolver-cu12/nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:4376c11ad263152bd50ea295c05370360776f8c3427b30991df774f9fb26c450" },
     { url = "https://pypi.nvidia.com/nvidia-cusolver-cu12/nvidia_cusolver_cu12-11.7.3.90-py3-none-win_amd64.whl", hash = "sha256:4a550db115fcabc4d495eb7d39ac8b58d4ab5d8e63274d3754df1c0ad6a22d34" },
 ]
 
+[[package]]
+name = "nvidia-cusolver-cu12"
+version = "11.7.5.82"
+source = { registry = "https://pypi.nvidia.com/" }
+resolution-markers = [
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'win32'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version < '3.11' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+]
+dependencies = [
+    { name = "nvidia-cublas-cu12", version = "12.9.1.4", source = { registry = "https://pypi.nvidia.com/" }, marker = "(python_full_version >= '3.11' and sys_platform == 'win32') or (platform_machine != 'x86_64' and sys_platform != 'darwin') or (platform_machine != 'x86_64' and sys_platform == 'win32')" },
+    { name = "nvidia-cusparse-cu12", version = "12.5.10.65", source = { registry = "https://pypi.nvidia.com/" }, marker = "(python_full_version >= '3.11' and sys_platform == 'win32') or (platform_machine != 'x86_64' and sys_platform != 'darwin') or (platform_machine != 'x86_64' and sys_platform == 'win32')" },
+    { name = "nvidia-nvjitlink-cu12", version = "12.9.86", source = { registry = "https://pypi.nvidia.com/" }, marker = "(python_full_version >= '3.11' and sys_platform == 'win32') or (platform_machine != 'x86_64' and sys_platform != 'darwin') or (platform_machine != 'x86_64' and sys_platform == 'win32')" },
+]
+wheels = [
+    { url = "https://pypi.nvidia.com/nvidia-cusolver-cu12/nvidia_cusolver_cu12-11.7.5.82-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:62efa83e4ace59a4c734d052bb72158e888aa7b770e1a5f601682f16fe5b4fd2" },
+    { url = "https://pypi.nvidia.com/nvidia-cusolver-cu12/nvidia_cusolver_cu12-11.7.5.82-py3-none-manylinux_2_27_x86_64.whl", hash = "sha256:15da72d1340d29b5b3cf3fd100e3cd53421dde36002eda6ed93811af63c40d88" },
+    { url = "https://pypi.nvidia.com/nvidia-cusolver-cu12/nvidia_cusolver_cu12-11.7.5.82-py3-none-win_amd64.whl", hash = "sha256:77666337237716783c6269a658dea310195cddbd80a5b2919b1ba8735cec8efd" },
+]
+
 [[package]]
 name = "nvidia-cusparse-cu12"
 version = "12.5.8.93"
 source = { registry = "https://pypi.nvidia.com/" }
+resolution-markers = [
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform != 'darwin'",
+]
 dependencies = [
-    { name = "nvidia-nvjitlink-cu12", marker = "sys_platform != 'darwin'" },
+    { name = "nvidia-nvjitlink-cu12", version = "12.8.93", source = { registry = "https://pypi.nvidia.com/" }, marker = "(python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'emscripten') or (python_full_version < '3.11' and platform_machine == 'x86_64' and sys_platform == 'win32') or (platform_machine == 'x86_64' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'win32')" },
 ]
 wheels = [
-    { url = "https://pypi.nvidia.com/nvidia-cusparse-cu12/nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9b6c161cb130be1a07a27ea6923df8141f3c295852f4b260c65f18f3e0a091dc" },
     { url = "https://pypi.nvidia.com/nvidia-cusparse-cu12/nvidia_cusparse_cu12-12.5.8.93-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1ec05d76bbbd8b61b06a80e1eaf8cf4959c3d4ce8e711b65ebd0443bb0ebb13b" },
     { url = "https://pypi.nvidia.com/nvidia-cusparse-cu12/nvidia_cusparse_cu12-12.5.8.93-py3-none-win_amd64.whl", hash = "sha256:9a33604331cb2cac199f2e7f5104dfbb8a5a898c367a53dfda9ff2acb6b6b4dd" },
 ]
 
+[[package]]
+name = "nvidia-cusparse-cu12"
+version = "12.5.10.65"
+source = { registry = "https://pypi.nvidia.com/" }
+resolution-markers = [
+    "python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'",
+    "python_full_version == '3.11.*' and platform_machine == 'x86_64' and sys_platform == 'win32'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version == '3.11.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform != 'darwin' and sys_platform != 'linux'",
+    "python_full_version < '3.11' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'darwin'",
+]
+dependencies = [
+    { name = "nvidia-nvjitlink-cu12", version = "12.9.86", source = { registry = "https://pypi.nvidia.com/" }, marker = "(python_full_version >= '3.11' and sys_platform == 'win32') or (platform_machine != 'x86_64' and sys_platform != 'darwin') or (platform_machine != 'x86_64' and sys_platform == 'win32')" },
+]
+wheels = [
+    { url = "https://pypi.nvidia.com/nvidia-cusparse-cu12/nvidia_cusparse_cu12-12.5.10.65-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:221c73e7482dd93eda44e65ce567c031c07e2f93f6fa0ecd3ba876a195023e83" },
+    { url = "https://pypi.nvidia.com/nvidia-cusparse-cu12/nvidia_cusparse_cu12-12.5.10.65-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:73060ce019ac064a057267c585bf1fd5a353734151f87472ff02b2c5c9984e78" },
+    { url = "https://pypi.nvidia.com/nvidia-cusparse-cu12/nvidia_cusparse_cu12-12.5.10.65-py3-none-win_amd64.whl", hash = "sha256:9e487468a22a1eaf1fbd1d2035936a905feb79c4ce5c2f67626764ee4f90227c" },
+]
+
 [[package]]
 name = "nvidia-cusparselt-cu12"
 version = "0.7.1"
@@ -4916,7 +5210,7 @@ wheels = [
 
 [[package]]
 name = "nvidia-cutlass-dsl"
-version = "4.3.4"
+version = "4.3.5"
 source = { registry = "https://pypi.nvidia.com/" }
 dependencies = [
     { name = "cuda-python", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
@@ -4924,72 +5218,109 @@ dependencies = [
     { name = "typing-extensions", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
 ]
 wheels = [
-    { url = "https://pypi.nvidia.com/nvidia-cutlass-dsl/nvidia_cutlass_dsl-4.3.4-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:3fdf0603ab7ec1bf6a499fbf72cff65e73b597d6e1359286808317c69aeb7c3d" },
-    { url = "https://pypi.nvidia.com/nvidia-cutlass-dsl/nvidia_cutlass_dsl-4.3.4-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:671936f1df909e7de377d0cc00cb4287a3458c013d34947600423e9deb827e41" },
-    { url = "https://pypi.nvidia.com/nvidia-cutlass-dsl/nvidia_cutlass_dsl-4.3.4-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:a48fbff859e44dd548f8f26819d97d0595acea70e3b057c91dfdb47929015c72" },
+    { url = "https://pypi.nvidia.com/nvidia-cutlass-dsl/nvidia_cutlass_dsl-4.3.5-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:776f54fa72333bc8fca274e59b70552adbcd85aaef603c7d58a79ef284890046" },
+    { url = "https://pypi.nvidia.com/nvidia-cutlass-dsl/nvidia_cutlass_dsl-4.3.5-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:7a792f02ce548f311a3df313a7cdb4ac4ec1cccb6c7ff9cd68d5470b25a6daf6" },
+    { url = "https://pypi.nvidia.com/nvidia-cutlass-dsl/nvidia_cutlass_dsl-4.3.5-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:4687eef20c405023daa99dd4653a292fd875d6c9486f8d9a069ff6fcdb00834f" },
 ]
 
 [[package]]
 name = "nvidia-dali-cuda120"
-version = "1.52.0"
+version = "1.53.0"
 source = { registry = "https://pypi.nvidia.com/" }
 dependencies = [
     { name = "astunparse" },
     { name = "dm-tree" },
     { name = "gast" },
     { name = "makefun" },
-    { name = "nvidia-nvcomp-cu12" },
+    { name = "nvidia-libnvcomp-cu12" },
     { name = "nvidia-nvimgcodec-cu12", extra = ["all"] },
     { name = "nvtx" },
     { name = "packaging" },
     { name = "six" },
 ]
 wheels = [
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 name = "nvidia-nccl-cu12"
 version = "2.27.5"
 source = { registry = "https://pypi.nvidia.com/" }
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 [[package]]
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+version = "0.7.0.11"
 source = { registry = "https://pypi.nvidia.com/" }
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+    { url = "https://pypi.nvidia.com/nvidia-nvimgcodec-cu12/nvidia_nvimgcodec_cu12-0.7.0.11-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:32d3457859c5784e4c0f6a2f56b6a9afec8fe646cec1cbe4bb5c320948d92dfe" },
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 [package.optional-dependencies]
 all = [
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+    { name = "nvidia-libnvcomp-cu12" },
     { name = "nvidia-nvjpeg-cu12" },
     { name = "nvidia-nvjpeg2k-cu12" },
     { name = "nvidia-nvtiff-cu12" },
@@ -4999,12 +5330,50 @@ all = [
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 version = "12.8.93"
 source = { registry = "https://pypi.nvidia.com/" }
+resolution-markers = [
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 ]
 
+[[package]]
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+    { url = "https://pypi.nvidia.com/nvidia-nvjitlink-cu12/nvidia_nvjitlink_cu12-12.9.86-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:994a05ef08ef4b0b299829cde613a424382aff7efb08a7172c1fa616cc3af2ca" },
+    { url = "https://pypi.nvidia.com/nvidia-nvjitlink-cu12/nvidia_nvjitlink_cu12-12.9.86-py3-none-win_amd64.whl", hash = "sha256:cc6fcec260ca843c10e34c936921a1c426b351753587fdd638e8cff7b16bb9db" },
+]
+
 [[package]]
 name = "nvidia-nvjpeg-cu12"
 version = "12.4.0.76"
@@ -5083,7 +5452,7 @@ wheels = [
 
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@@ -5091,30 +5460,30 @@ dependencies = [
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-    { name = "nvidia-nvjitlink-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
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From 8bf8f345a5870cf98314630a3c49aff4f78b675a Mon Sep 17 00:00:00 2001
From: Ranjit Rajan <ranjitr@nvidia.com>
Date: Tue, 7 Oct 2025 09:19:09 -0700
Subject: [PATCH 02/17] Refactor caption generation and preparation to remove
 qwen_llm_input in favor of a unified llm_inputs structure. Update related
 tests and examples to reflect this change

Signed-off-by: Ranjit Rajan <ranjitr@nvidia.com>
---
 .../video/caption/caption_generation.py       |   4 -
 .../video/caption/caption_preparation.py      |   3 -
 nemo_curator/stages/video/io/clip_writer.py   |   2 +-
 nemo_curator/tasks/video.py                   |  11 -
 tests/models/test_nemotron_h_vl.py            | 218 ++++++++++++++++++
 .../video/caption/test_caption_generation.py  | 101 ++++++--
 .../video/caption/test_caption_preparation.py |  10 +-
 tests/stages/video/io/test_clip_writer.py     |   2 +-
 tests/tasks/test_video.py                     |   8 +-
 .../video_split_clip_example.py               |   8 +-
 10 files changed, 321 insertions(+), 46 deletions(-)
 create mode 100644 tests/models/test_nemotron_h_vl.py

diff --git a/nemo_curator/stages/video/caption/caption_generation.py b/nemo_curator/stages/video/caption/caption_generation.py
index ababa168c5..8395c4122a 100644
--- a/nemo_curator/stages/video/caption/caption_generation.py
+++ b/nemo_curator/stages/video/caption/caption_generation.py
@@ -105,9 +105,6 @@ def process(self, task: VideoTask) -> VideoTask:
             for window_idx, window in enumerate(clip.windows):
                 llm_input = window.llm_inputs.get(self.model_variant)
 
-                if llm_input is None and self.model_variant == "qwen" and window.qwen_llm_input is not None:
-                    llm_input = window.qwen_llm_input
-
                 if llm_input is None:
                     logger.error(
                         f"Clip {clip.uuid} window {window_idx} has no prepared inputs for {self.model_variant}."
@@ -152,5 +149,4 @@ def _assign_captions(
             for window in clip.windows:
                 if self.model_variant in window.llm_inputs:
                     del window.llm_inputs[self.model_variant]
-                window.qwen_llm_input = None
                 window.mp4_bytes = None
diff --git a/nemo_curator/stages/video/caption/caption_preparation.py b/nemo_curator/stages/video/caption/caption_preparation.py
index 579865fc87..5f04a1445d 100644
--- a/nemo_curator/stages/video/caption/caption_preparation.py
+++ b/nemo_curator/stages/video/caption/caption_preparation.py
@@ -147,9 +147,6 @@ def process(self, task: VideoTask) -> VideoTask:
                 )
                 window.llm_inputs[self.model_variant] = llm_input
 
-                if self.model_variant == "qwen":
-                    window.qwen_llm_input = llm_input
-
                 clip.windows.append(window)
 
         return task
diff --git a/nemo_curator/stages/video/io/clip_writer.py b/nemo_curator/stages/video/io/clip_writer.py
index 060e80cc4f..80ae674a7c 100644
--- a/nemo_curator/stages/video/io/clip_writer.py
+++ b/nemo_curator/stages/video/io/clip_writer.py
@@ -188,7 +188,7 @@ def process(self, task: VideoTask) -> VideoTask:
                 clip.cosmos_embed1_embedding = None
                 for window in clip.windows:
                     window.mp4_bytes = None
-                    window.qwen_llm_input = None
+                    window.llm_inputs.clear()
                     window.caption.clear()
                     window.enhanced_caption.clear()
                     window.webp_bytes = None
diff --git a/nemo_curator/tasks/video.py b/nemo_curator/tasks/video.py
index ae24e2350f..4c0c101039 100644
--- a/nemo_curator/tasks/video.py
+++ b/nemo_curator/tasks/video.py
@@ -42,8 +42,6 @@ class _Window:
     # MP4 bytes for this window
     mp4_bytes: bytes | None = None
     llm_inputs: dict[str, dict[str, Any]] = field(default_factory=dict)
-    # Qwen LLM input for this window
-    qwen_llm_input: dict[str, Any] | None = None  # Backward compatibility
     # X1 model input for this window
     x1_input: Any | None = None
     # `caption: {model_name: caption}`
@@ -52,13 +50,6 @@ class _Window:
     # webp preview
     webp_bytes: bytes | None = None
 
-    def __post_init__(self):
-        """Sync qwen_llm_input with llm_inputs for backward compatibility."""
-        if self.qwen_llm_input is not None:
-            self.llm_inputs["qwen"] = self.qwen_llm_input
-        elif "qwen" in self.llm_inputs:
-            self.qwen_llm_input = self.llm_inputs["qwen"]
-
     def get_major_size(self) -> int:
         """Calculate total memory size of the window.
 
@@ -70,8 +61,6 @@ def get_major_size(self) -> int:
         total_size += len(self.mp4_bytes) if self.mp4_bytes else 0
         # TODO: this is probably inaccurate
         total_size += sum(sys.getsizeof(v) for v in self.llm_inputs.values())
-        if self.qwen_llm_input is not None and "qwen" not in self.llm_inputs:
-            total_size += sys.getsizeof(self.qwen_llm_input)
         total_size += sys.getsizeof(self.caption)
         total_size += sys.getsizeof(self.enhanced_caption)
         total_size += len(self.webp_bytes) if self.webp_bytes else 0
diff --git a/tests/models/test_nemotron_h_vl.py b/tests/models/test_nemotron_h_vl.py
new file mode 100644
index 0000000000..2fd41fd264
--- /dev/null
+++ b/tests/models/test_nemotron_h_vl.py
@@ -0,0 +1,218 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import pathlib
+import tempfile
+from unittest.mock import Mock, patch
+
+import pytest
+
+from nemo_curator.models.nemotron_h_vl import NemotronHVL
+
+
+class TestNemotronHVL:
+    def setup_method(self) -> None:
+        """Set up model and mocks for each test."""
+        self.vllm_patcher = patch("nemo_curator.models.nemotron_h_vl.VLLM_AVAILABLE", True)
+        self.vllm_patcher.start()
+
+        self.model_dir = "/test/model/dir"
+        self.model_variant = "nemotron"
+        self.caption_batch_size = 4
+        self.model = NemotronHVL(
+            model_dir=self.model_dir,
+            model_variant=self.model_variant,
+            caption_batch_size=self.caption_batch_size,
+            max_output_tokens=512,
+            stage2_prompt_text="Test stage2: ",
+            verbose=False,
+        )
+
+    def teardown_method(self) -> None:
+        """Tear down mocks after each test."""
+        self.vllm_patcher.stop()
+
+    def test_init_defaults(self) -> None:
+        """Verify default initialization values."""
+        model = NemotronHVL(model_dir=self.model_dir)
+        assert model.model_dir == self.model_dir
+        assert model.model_variant == "nemotron"
+        assert model.caption_batch_size == 8
+        assert model.max_output_tokens == 512
+        assert model.stage2_prompt == "Please refine this caption: "
+        assert model.verbose is False
+        assert model.weight_file == self.model_dir
+
+    def test_model_id_names_local(self) -> None:
+        """Return local directory name when no HF id is set."""
+        local_dir = "/models/local_ckpt"
+        model = NemotronHVL(model_dir=local_dir)
+        assert model.model_id_names == [pathlib.Path(local_dir).name]
+
+    def test_model_id_names_config(self) -> None:
+        """Read name from config.json when present."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            cfg_path = pathlib.Path(tmpdir) / "config.json"
+            cfg_path.write_text('{"_name_or_path": "nemotron-hnano"}')
+            model = NemotronHVL(model_dir=tmpdir)
+            assert model.model_id_names == ["nemotron-hnano"]
+
+    def test_setup_no_vllm(self) -> None:
+        """Raise ImportError when vLLM is unavailable."""
+        with patch("nemo_curator.models.nemotron_h_vl.VLLM_AVAILABLE", False):
+            model = NemotronHVL(model_dir=self.model_dir)
+            with pytest.raises(ImportError, match="vllm is required for NemotronHVL"):
+                model.setup()
+
+    @patch("nemo_curator.models.nemotron_h_vl.logger")
+    @patch("nemo_curator.models.nemotron_h_vl.SamplingParams")
+    @patch("nemo_curator.models.nemotron_h_vl.LLM")
+    def test_setup(self, mock_llm: Mock, mock_sampling_params: Mock, mock_logger: Mock) -> None:
+        """Initialize LLM and SamplingParams with expected args."""
+        mock_llm_instance = Mock()
+        mock_llm.return_value = mock_llm_instance
+        mock_sampling_params_instance = Mock()
+        mock_sampling_params.return_value = mock_sampling_params_instance
+
+        self.model.setup()
+
+        mock_llm.assert_called_once_with(
+            model=self.model.weight_file,
+            trust_remote_code=True,
+            tensor_parallel_size=1,
+            gpu_memory_utilization=0.9,
+            max_model_len=32768,
+            limit_mm_per_prompt={"video": 1},
+        )
+        mock_sampling_params.assert_called_once_with(
+            temperature=0.6,
+            max_tokens=self.model.max_output_tokens,
+            top_p=0.95,
+            stop=["</s>", "<|endoftext|>", "<SPECIAL_12>", "</think>"],
+        )
+        assert self.model.model is mock_llm_instance
+        assert self.model.sampling_params is mock_sampling_params_instance
+        mock_logger.info.assert_any_call("Using vLLM V0 engine.")
+
+    @patch("nemo_curator.models.nemotron_h_vl.grouping.split_by_chunk_size")
+    def test_generate_basic(self, mock_split_by_chunk_size: Mock) -> None:
+        """Generate captions for a single batch without stage2."""
+        mock_model = Mock()
+        self.model.model = mock_model
+        self.model.sampling_params = Mock()
+
+        videos = [
+            {"prompt": "Describe this video", "multi_modal_data": {"video": "video1"}},
+            {"prompt": "What is happening?", "multi_modal_data": {"video": "video2"}},
+        ]
+        mock_split_by_chunk_size.return_value = [videos]
+
+        out1, out2 = Mock(), Mock()
+        out1.outputs = [Mock(text="Text 1")]
+        out2.outputs = [Mock(text="Text 2")]
+        mock_model.generate.return_value = [out1, out2]
+
+        result = self.model.generate(videos, batch_size=16)
+
+        assert result == ["Text 1", "Text 2"]
+        mock_split_by_chunk_size.assert_called_once_with(videos, 16)
+        assert mock_model.generate.call_count == 1
+        args, kwargs = mock_model.generate.call_args
+        assert args[0] == list(videos)
+        assert kwargs["sampling_params"] == self.model.sampling_params
+        assert kwargs["use_tqdm"] is False
+
+    @patch("nemo_curator.models.nemotron_h_vl.grouping.split_by_chunk_size")
+    def test_generate_batched(self, mock_split_by_chunk_size: Mock) -> None:
+        """Generate captions across multiple batches."""
+        mock_model = Mock()
+        self.model.model = mock_model
+        self.model.sampling_params = Mock()
+
+        videos = [{"prompt": f"Video {i}", "multi_modal_data": {"video": f"video{i}"}} for i in range(4)]
+        batch1, batch2 = videos[:2], videos[2:]
+        mock_split_by_chunk_size.return_value = [batch1, batch2]
+
+        def gen_outputs(batch_idx: int) -> list[Mock]:
+            return [Mock(outputs=[Mock(text=f"B{batch_idx} T{i}")]) for i in range(2)]
+        mock_model.generate.side_effect = [gen_outputs(1), gen_outputs(2)]
+
+        result = self.model.generate(videos, batch_size=2)
+        assert result == ["B1 T0", "B1 T1", "B2 T0", "B2 T1"]
+        assert mock_model.generate.call_count == 2
+
+    @patch("nemo_curator.models.nemotron_h_vl.grouping.split_by_chunk_size")
+    def test_generate_stage2(self, mock_split_by_chunk_size: Mock) -> None:
+        """Generate with stage2 refinement flow."""
+        mock_model = Mock()
+        self.model.model = mock_model
+        self.model.sampling_params = Mock()
+
+        videos = [{"prompt": "Human: <video>test<SPECIAL_11>Assistant", "multi_modal_data": {"video": "video1"}}]
+        mock_split_by_chunk_size.return_value = [videos]
+
+        out_stage1, out_stage2 = Mock(), Mock()
+        out_stage1.outputs = [Mock(text="Stage 1")]
+        out_stage2.outputs = [Mock(text="Stage 2")]
+        mock_model.generate.side_effect = [[out_stage1], [out_stage2]]
+
+        result = self.model.generate(videos, generate_stage2_caption=True, batch_size=16)
+        assert result == ["Stage 2"]
+
+        assert mock_model.generate.call_count == 2
+        second_args, _ = mock_model.generate.call_args
+        updated_inputs = second_args[0]
+        assert "Test stage2: Stage 1" in updated_inputs[0]["prompt"]
+        assert updated_inputs[0]["multi_modal_data"]["video"] == "video1"
+
+    def test_generate_empty(self) -> None:
+        """Return empty when no videos are provided."""
+        assert self.model.generate([]) == []
+
+    @patch("nemo_curator.models.nemotron_h_vl.logger")
+    @patch("nemo_curator.models.nemotron_h_vl.grouping.split_by_chunk_size")
+    def test_generate_error(self, mock_split_by_chunk_size: Mock, mock_logger: Mock) -> None:
+        """Log and raise on generation errors."""
+        mock_model = Mock()
+        self.model.model = mock_model
+        self.model.sampling_params = Mock()
+
+        videos = [{"prompt": "Test", "multi_modal_data": {"video": "video1"}}]
+        mock_split_by_chunk_size.return_value = [videos]
+
+        mock_model.generate.side_effect = Exception("Test error")
+
+        with pytest.raises(Exception, match="Test error"):
+            self.model.generate(videos)
+
+        mock_logger.error.assert_called_once()
+
+    def test_download_weights_missing_path(self) -> None:
+        """Raise when checkpoint path does not exist."""
+        with pytest.raises(FileNotFoundError):
+            NemotronHVL.download_weights_on_node("/non/existent/path")
+
+    def test_download_weights_no_files(self) -> None:
+        """Raise when checkpoint path has no model files."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            with pytest.raises(FileNotFoundError):
+                NemotronHVL.download_weights_on_node(tmpdir)
+
+    def test_download_weights_ok(self) -> None:
+        """Succeed when checkpoint path has model files."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            pathlib.Path(tmpdir, "model.safetensors").write_bytes(b"fake")
+            NemotronHVL.download_weights_on_node(tmpdir)
diff --git a/tests/stages/video/caption/test_caption_generation.py b/tests/stages/video/caption/test_caption_generation.py
index 6b50356389..c6ae346a89 100644
--- a/tests/stages/video/caption/test_caption_generation.py
+++ b/tests/stages/video/caption/test_caption_generation.py
@@ -105,26 +105,26 @@ def _create_test_video_task(self) -> VideoTask:
         # Create clips with windows
         clip1 = Clip(uuid=uuid4(), source_video="test1.mp4", span=(0.0, 10.0), buffer=b"test_buffer_1")
 
-        # Add windows with qwen_llm_input
+        # Add windows with llm_inputs
         window1 = _Window(
             start_frame=0,
             end_frame=10,
-            qwen_llm_input={"prompt": "test prompt 1", "multi_modal_data": {"video": "test_data_1"}},
+            llm_inputs={"qwen": {"prompt": "test prompt 1", "multi_modal_data": {"video": "test_data_1"}}},
         )
         window2 = _Window(
             start_frame=10,
             end_frame=20,
-            qwen_llm_input={"prompt": "test prompt 2", "multi_modal_data": {"video": "test_data_2"}},
+            llm_inputs={"qwen": {"prompt": "test prompt 2", "multi_modal_data": {"video": "test_data_2"}}},
         )
         clip1.windows = [window1, window2]
 
         clip2 = Clip(uuid=uuid4(), source_video="test2.mp4", span=(10.0, 20.0), buffer=b"test_buffer_2")
 
-        # Add window with qwen_llm_input
+        # Add window with llm_inputs
         window3 = _Window(
             start_frame=0,
             end_frame=15,
-            qwen_llm_input={"prompt": "test prompt 3", "multi_modal_data": {"video": "test_data_3"}},
+            llm_inputs={"qwen": {"prompt": "test prompt 3", "multi_modal_data": {"video": "test_data_3"}}},
         )
         clip2.windows = [window3]
 
@@ -160,7 +160,7 @@ def test_process_successful_generation(self):
         # Verify cleanup
         for clip in result.data.clips:
             for window in clip.windows:
-                assert window.qwen_llm_input is None
+                assert "qwen" not in window.llm_inputs
                 assert window.mp4_bytes is None
 
     def test_process_with_verbose_logging(self):
@@ -176,7 +176,9 @@ def test_process_with_verbose_logging(self):
         video = Video(input_video=pathlib.Path("test.mp4"))
         clip = Clip(uuid=uuid4(), source_video="test.mp4", span=(0.0, 5.0), buffer=b"test_buffer")
         window = _Window(
-            start_frame=0, end_frame=5, qwen_llm_input={"prompt": "test", "multi_modal_data": {"video": "data"}}
+            start_frame=0,
+            end_frame=5,
+            llm_inputs={"qwen": {"prompt": "test", "multi_modal_data": {"video": "data"}}},
         )
         clip.windows = [window]
         video.clips = [clip]
@@ -210,7 +212,7 @@ def test_process_empty_windows(self, mock_logger: Mock):
 
     @patch("nemo_curator.stages.video.caption.caption_generation.logger")
     def test_process_window_without_input(self, mock_logger: Mock):
-        """Test process method with windows that have no qwen_llm_input."""
+        """Test process method with windows that have no llm_inputs."""
         mock_model = Mock()
         mock_model.generate.return_value = []
         self.stage.model = mock_model
@@ -219,8 +221,8 @@ def test_process_window_without_input(self, mock_logger: Mock):
 
         video = Video(input_video=pathlib.Path("test.mp4"))
         clip = Clip(uuid=uuid4(), source_video="test.mp4", span=(0.0, 5.0), buffer=b"test_buffer")
-        # Window without qwen_llm_input
-        window = _Window(start_frame=0, end_frame=5, qwen_llm_input=None)
+        # Window without llm_inputs
+        window = _Window(start_frame=0, end_frame=5)
         clip.windows = [window]
         video.clips = [clip]
         task = VideoTask(task_id="test", dataset_name="test", data=video)
@@ -228,8 +230,8 @@ def test_process_window_without_input(self, mock_logger: Mock):
         self.stage.process(task)
 
         # Verify error was logged and set
-        mock_logger.error.assert_called_once_with(f"Clip {clip.uuid} window 0 has no prepared inputs.")
-        assert clip.errors["window-0"] == "empty"
+        mock_logger.error.assert_called_once_with(f"Clip {clip.uuid} window 0 has no prepared inputs for qwen.")
+        assert clip.errors["window-0"] == "no_qwen_input"
 
     def test_assign_captions(self):
         """Test _assign_captions method."""
@@ -293,7 +295,9 @@ def test_process_with_stage2_caption(self):
         video = Video(input_video=pathlib.Path("test.mp4"))
         clip = Clip(uuid=uuid4(), source_video="test.mp4", span=(0.0, 5.0), buffer=b"test_buffer")
         window = _Window(
-            start_frame=0, end_frame=5, qwen_llm_input={"prompt": "test", "multi_modal_data": {"video": "data"}}
+            start_frame=0,
+            end_frame=5,
+            llm_inputs={"qwen": {"prompt": "test", "multi_modal_data": {"video": "data"}}},
         )
         clip.windows = [window]
         video.clips = [clip]
@@ -328,3 +332,74 @@ def test_stage_with_worker_metadata(self):
             worker_metadata = WorkerMetadata(worker_id="test")
             self.stage.setup(worker_metadata)
             assert hasattr(self.stage, "model")
+
+    @patch("nemo_curator.stages.video.caption.caption_generation.NemotronHVL")
+    def test_setup_nemotron_variant(self, mock_nemotron_vl: Mock):
+        """Test setup method with nemotron variant."""
+        mock_model = Mock()
+        mock_nemotron_vl.return_value = mock_model
+
+        stage = CaptionGenerationStage(
+            model_dir="test/models",
+            model_variant="nemotron",
+            caption_batch_size=2,
+            max_output_tokens=256,
+            verbose=True,
+        )
+        stage.setup()
+
+        mock_nemotron_vl.assert_called_once_with(
+            model_dir="test/models",
+            model_variant="nemotron",
+            caption_batch_size=2,
+            max_output_tokens=256,
+            stage2_prompt_text=None,
+            verbose=True,
+        )
+        mock_model.setup.assert_called_once()
+        assert stage.model == mock_model
+
+    def test_process_nemotron_with_generic_llm_inputs(self):
+        """Test process method with nemotron using generic llm_inputs."""
+        mock_model = Mock()
+        mock_model.generate.return_value = ["Nemotron caption 1", "Nemotron caption 2"]
+
+        stage = CaptionGenerationStage(
+            model_dir="test/models",
+            model_variant="nemotron",
+            caption_batch_size=2,
+        )
+        stage.model = mock_model
+
+        import pathlib
+
+        video = Video(input_video=pathlib.Path("test.mp4"))
+        clip = Clip(uuid=uuid4(), source_video="test.mp4", span=(0.0, 10.0), buffer=b"test_buffer")
+
+        # Create windows with nemotron llm_inputs
+        window1 = _Window(
+            start_frame=0,
+            end_frame=10,
+            llm_inputs={"nemotron": {"prompt": "nemotron prompt 1", "multi_modal_data": {"video": "data1"}}},
+        )
+
+        window2 = _Window(
+            start_frame=10,
+            end_frame=20,
+            llm_inputs={"nemotron": {"prompt": "nemotron prompt 2", "multi_modal_data": {"video": "data2"}}},
+        )
+
+        clip.windows = [window1, window2]
+        video.clips = [clip]
+        task = VideoTask(task_id="test", dataset_name="test", data=video)
+
+        result = stage.process(task)
+
+        # Verify captions were assigned with nemotron key
+        assert result.data.clips[0].windows[0].caption["nemotron"] == "Nemotron caption 1"
+        assert result.data.clips[0].windows[1].caption["nemotron"] == "Nemotron caption 2"
+
+        # Verify cleanup - nemotron inputs should be deleted
+        assert "nemotron" not in result.data.clips[0].windows[0].llm_inputs
+        assert "nemotron" not in result.data.clips[0].windows[1].llm_inputs
+        
\ No newline at end of file
diff --git a/tests/stages/video/caption/test_caption_preparation.py b/tests/stages/video/caption/test_caption_preparation.py
index 0bce345b92..b3db53ebfd 100644
--- a/tests/stages/video/caption/test_caption_preparation.py
+++ b/tests/stages/video/caption/test_caption_preparation.py
@@ -216,11 +216,11 @@ def test_process_successful(self, mock_get_prompt: Mock, mock_split_video: Mock)
             assert window1.start_frame == 0
             assert window1.end_frame == 10
             assert window1.mp4_bytes == b"window1_bytes"
-            # Check structure of qwen_llm_input (can't compare tensors directly)
-            assert "prompt" in window1.qwen_llm_input
-            assert "multi_modal_data" in window1.qwen_llm_input
-            assert window1.qwen_llm_input["prompt"] == "test formatted prompt"
-            assert "video" in window1.qwen_llm_input["multi_modal_data"]
+            # Check structure of llm_inputs["qwen"] (can't compare tensors directly)
+            assert "prompt" in window1.llm_inputs["qwen"]
+            assert "multi_modal_data" in window1.llm_inputs["qwen"]
+            assert window1.llm_inputs["qwen"]["prompt"] == "test formatted prompt"
+            assert "video" in window1.llm_inputs["qwen"]["multi_modal_data"]
 
     @patch("nemo_curator.stages.video.caption.caption_preparation.logger")
     def test_process_clip_without_buffer(self, mock_logger: Mock):
diff --git a/tests/stages/video/io/test_clip_writer.py b/tests/stages/video/io/test_clip_writer.py
index f5f5464686..9d039cf9f1 100644
--- a/tests/stages/video/io/test_clip_writer.py
+++ b/tests/stages/video/io/test_clip_writer.py
@@ -723,7 +723,7 @@ def test_process_success(self, mock_executor_class: MagicMock):
                 assert clip.cosmos_embed1_embedding is None
                 for window in clip.windows:
                     assert window.mp4_bytes is None
-                    assert window.qwen_llm_input is None
+                    assert window.llm_inputs == {}
                     assert window.caption == {}
                     assert window.enhanced_caption == {}
                     assert window.webp_bytes is None
diff --git a/tests/tasks/test_video.py b/tests/tasks/test_video.py
index 38dcddbfd8..c8d1ae18cd 100644
--- a/tests/tasks/test_video.py
+++ b/tests/tasks/test_video.py
@@ -47,7 +47,7 @@ def test_window_initialization(self) -> None:
         assert window.start_frame == 0
         assert window.end_frame == 10
         assert window.mp4_bytes is None
-        assert window.qwen_llm_input is None
+        assert window.llm_inputs == {}
         assert window.x1_input is None
         assert window.caption == {}
         assert window.enhanced_caption == {}
@@ -65,7 +65,7 @@ def test_window_initialization_with_data(self) -> None:
             start_frame=5,
             end_frame=15,
             mp4_bytes=mp4_data,
-            qwen_llm_input=qwen_input,
+            llm_inputs={"qwen": qwen_input},
             webp_bytes=webp_data,
             caption=caption,
             enhanced_caption=enhanced_caption,
@@ -74,7 +74,7 @@ def test_window_initialization_with_data(self) -> None:
         assert window.start_frame == 5
         assert window.end_frame == 15
         assert window.mp4_bytes == mp4_data
-        assert window.qwen_llm_input == qwen_input
+        assert window.llm_inputs["qwen"] == qwen_input
         assert window.webp_bytes == webp_data
         assert window.caption == caption
         assert window.enhanced_caption == enhanced_caption
@@ -99,7 +99,7 @@ def test_get_major_size_with_data(self) -> None:
             start_frame=0,
             end_frame=10,
             mp4_bytes=mp4_data,
-            qwen_llm_input=qwen_input,
+            llm_inputs={"qwen": qwen_input},
             webp_bytes=webp_data,
             caption=caption,
             enhanced_caption=enhanced_caption,
diff --git a/tutorials/video/getting-started/video_split_clip_example.py b/tutorials/video/getting-started/video_split_clip_example.py
index 11b91f30f0..f0604c7e28 100644
--- a/tutorials/video/getting-started/video_split_clip_example.py
+++ b/tutorials/video/getting-started/video_split_clip_example.py
@@ -193,7 +193,7 @@ def create_video_splitting_pipeline(args: argparse.Namespace) -> Pipeline:  # no
 
     if args.generate_captions:
         # Determine model path for caption preparation
-        model_path = args.captioning_model_path if hasattr(args, "captioning_model_path") else None
+        model_path = args.nemotronh_vl_model_path if hasattr(args, "nemotronh_vl_model_path") else None
 
         pipeline.add_stage(
             CaptionPreparationStage(
@@ -221,7 +221,7 @@ def create_video_splitting_pipeline(args: argparse.Namespace) -> Pipeline:  # no
 
         # Determine model directory
         model_dir = (
-            args.captioning_model_path
+            args.nemotronh_vl_model_path
             if (args.captioning_algorithm.startswith("nemotron") or args.captioning_algorithm.startswith("NemotronH_"))
             else args.model_dir
         )
@@ -601,10 +601,10 @@ def create_video_splitting_argparser() -> argparse.ArgumentParser:  # noqa: PLR0
         help="Captioning algorithm to use (qwen or nemotron).",
     )
     parser.add_argument(
-        "--captioning-model-path",
+        "--nemotronh-vl-model-path",
         type=str,
         default=None,
-        help="Path to model checkpoint (required for NemotronH models)",
+        help="Path to NemotronH VL model checkpoint (required for NemotronH models)",
     )
     parser.add_argument(
         "--captioning-window-size",

From 5efda69226152aaecc19a24c1ae6e551090040cd Mon Sep 17 00:00:00 2001
From: Ranjit Rajan <ranjitr@nvidia.com>
Date: Thu, 9 Oct 2025 14:07:07 -0700
Subject: [PATCH 03/17] Updated to use vLLM V1 engine for NemotronH Nano V2
 model

Signed-off-by: Ranjit Rajan <ranjitr@nvidia.com>
---
 nemo_curator/models/nemotron_h_vl.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/nemo_curator/models/nemotron_h_vl.py b/nemo_curator/models/nemotron_h_vl.py
index b587fa3150..56afb5c5be 100644
--- a/nemo_curator/models/nemotron_h_vl.py
+++ b/nemo_curator/models/nemotron_h_vl.py
@@ -91,9 +91,8 @@ def setup(self) -> None:
             msg = "vllm is required for NemotronHVL but is not installed. Please install vllm: pip install vllm"
             raise ImportError(msg)
 
-        # Use V0 engine to avoid flashinfer issues for now
-        os.environ["VLLM_USE_V1"] = "0"
-        logger.info("Using vLLM V0 engine.")
+        os.environ["VLLM_USE_V1"] = "1"
+        logger.info("Using vLLM V1 engine.")
 
         os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
 

From eea92488c9eaa035418fa224d4e29651fca993c3 Mon Sep 17 00:00:00 2001
From: Ranjit Rajan <ranjitr@nvidia.com>
Date: Fri, 10 Oct 2025 11:09:34 -0700
Subject: [PATCH 04/17] Reverted flash-attn back to 2.8.3 to work with latest
 upstream NemotronH VL fixes

Signed-off-by: Ranjit Rajan <ranjitr@nvidia.com>
---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 0e0d6c9c3a..10f8c3ed72 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -148,7 +148,7 @@ video_cuda12 = [
     "nemo_curator[cuda12]",
     "nemo_curator[vllm]",
     "cvcuda_cu12",
-    "flash-attn>=2.7.1,<=2.8.2; (platform_machine == 'x86_64' and platform_system != 'Darwin')",  
+    "flash-attn<=2.8.3; (platform_machine == 'x86_64' and platform_system != 'Darwin')",  
     "pycuda",
     "PyNvVideoCodec==2.0.2; (platform_machine == 'x86_64' and platform_system != 'Darwin')",
     "torch<=2.9.1",

From 78eb6b526316598b88a3d4fbbfa44a21d2a1837d Mon Sep 17 00:00:00 2001
From: Ranjit Rajan <ranjitr@nvidia.com>
Date: Fri, 9 Jan 2026 00:06:53 +0000
Subject: [PATCH 05/17] Enhance NemotronH VLM support for video captioning
 using HF model instead of local weights, introducing multiple model variants
 (BF16, FP8, NVFP4) and updating the prompt formatter. Remove the deprecated
 Nemotron prompt formatter and streamline model weight downloading. Update
 dependencies in pyproject.toml and uv.lock for compatibility with new model
 variants.

Signed-off-by: Ranjit Rajan <ranjitr@nvidia.com>
---
 nemo_curator/models/nemotron_h_vl.py          | 189 ++++++++-----
 .../models/nemotron_prompt_formatter.py       | 137 ----------
 nemo_curator/models/prompt_formatter.py       | 167 +++++++-----
 .../video/caption/caption_generation.py       |  12 +-
 .../video/caption/caption_preparation.py      |  11 +-
 pyproject.toml                                |   6 +-
 tests/models/test_nemotron_h_vl.py            | 256 +++++++++++++++---
 tests/models/test_prompt_formatter.py         | 227 +++++++++++++---
 .../video/caption/test_caption_preparation.py |  56 ++++
 .../video_split_clip_example.py               |  44 ++-
 uv.lock                                       |  32 +--
 11 files changed, 718 insertions(+), 419 deletions(-)
 delete mode 100644 nemo_curator/models/nemotron_prompt_formatter.py

diff --git a/nemo_curator/models/nemotron_h_vl.py b/nemo_curator/models/nemotron_h_vl.py
index 56afb5c5be..ff20613166 100644
--- a/nemo_curator/models/nemotron_h_vl.py
+++ b/nemo_curator/models/nemotron_h_vl.py
@@ -12,10 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import json
 import os
 from pathlib import Path
-from typing import Any
+from typing import Any, Final, Literal
 
 from loguru import logger
 
@@ -41,23 +40,70 @@ class SamplingParams:
         pass
 
 
-# HuggingFace model IDs (will be updated when models are published)
-_NEMOTRON_H_NANO_MODEL_ID = None
-_NEMOTRON_H_NANO_MODEL_REVISION = None
+# HuggingFace model IDs for Nemotron Nano V2 VL variants
+# Available variants: BF16 (default), FP8, NVFP4-QAD
+_NEMOTRON_VARIANTS_INFO: Final = {
+    "nemotron": "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16",  # Default BF16 variant
+    "nemotron-bf16": "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16",
+    "nemotron-fp8": "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-FP8",
+    "nemotron-nvfp4": "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-NVFP4-QAD",
+}
+
+_NEMOTRON_REVISION_INFO: Final = {
+    "nemotron": "5d250e2e111dc5e1434131bdf3d590c27a878ade",  # BF16 default
+    "nemotron-bf16": "5d250e2e111dc5e1434131bdf3d590c27a878ade",
+    "nemotron-fp8": "7394488badb786e1decc0e00e308de1cab9560e6",
+    "nemotron-nvfp4": "b8d3c170d9ee3a078917ef9bfd508eff988d6de7",
+}
+
+NemotronVariant = Literal["nemotron", "nemotron-bf16", "nemotron-fp8", "nemotron-nvfp4"]
 
 
 class NemotronHVL(ModelInterface):
-    """NemotronH hybrid Mamba-Attention VLM for video captioning."""
+    """NemotronH hybrid Mamba-Attention VLM for video captioning.
+
+    Supports multiple checkpoint variants from HuggingFace:
+    - nemotron / nemotron-bf16: BF16 precision (default)
+    - nemotron-fp8: FP8 quantized
+    - nemotron-nvfp4: NVFP4 quantized
+
+    Models are automatically downloaded from HuggingFace on first use.
+    """
 
     def __init__(  # noqa: PLR0913
         self,
         model_dir: str,
-        model_variant: str = "nemotron",
+        model_variant: NemotronVariant = "nemotron",
         caption_batch_size: int = 8,
         max_output_tokens: int = 512,
         stage2_prompt_text: str | None = None,
         verbose: bool = False,
     ):
+        """Initialize NemotronHVL model.
+
+        Args:
+            model_dir: Base directory for model weights. Models will be downloaded
+                to subdirectories named after the HuggingFace model ID.
+            model_variant: Model variant to use. Options:
+                - "nemotron" or "nemotron-bf16": BF16 precision (default)
+                - "nemotron-fp8": FP8 quantized
+                - "nemotron-nvfp4": NVFP4 quantized
+            caption_batch_size: Batch size for caption generation.
+            max_output_tokens: Maximum number of tokens to generate.
+            stage2_prompt_text: Optional prompt text for stage 2 caption refinement.
+            verbose: Whether to enable verbose logging.
+        """
+        # Normalize variant name - treat "nemotron" as "nemotron-bf16"
+        if model_variant == "nemotron":
+            self._normalized_variant: NemotronVariant = "nemotron-bf16"
+        else:
+            self._normalized_variant = model_variant
+
+        if self._normalized_variant not in _NEMOTRON_VARIANTS_INFO:
+            valid_variants = ", ".join(_NEMOTRON_VARIANTS_INFO.keys())
+            msg = f"Invalid model_variant: {model_variant}. Valid options are: {valid_variants}"
+            raise ValueError(msg)
+
         self.model_dir = model_dir
         self.model_variant = model_variant
         self.caption_batch_size = caption_batch_size
@@ -65,44 +111,44 @@ def __init__(  # noqa: PLR0913
         self.stage2_prompt = stage2_prompt_text if stage2_prompt_text else "Please refine this caption: "
         self.verbose = verbose
 
-        if _NEMOTRON_H_NANO_MODEL_ID is not None:
-            self.weight_file = str(Path(model_dir) / _NEMOTRON_H_NANO_MODEL_ID)
-        else:
-            # Local checkpoint: model_dir is the checkpoint path itself
-            self.weight_file = str(Path(model_dir))
+        # Set weight file path using HuggingFace model ID
+        self._hf_model_id = _NEMOTRON_VARIANTS_INFO[self._normalized_variant]
+        self.weight_file = str(Path(model_dir) / self._hf_model_id)
 
     @property
     def model_id_names(self) -> list[str]:
-        """Return model ID from config.json or HuggingFace ID."""
-        if _NEMOTRON_H_NANO_MODEL_ID is not None:
-            return [_NEMOTRON_H_NANO_MODEL_ID]
-
-        # Read from config.json if available
-        try:
-            config_path = Path(self.weight_file) / "config.json"
-            with open(config_path) as f:
-                config = json.load(f)
-            return [config.get("_name_or_path", Path(self.weight_file).name)]
-        except (FileNotFoundError, json.JSONDecodeError, KeyError):
-            return [Path(self.weight_file).name]
+        """Return HuggingFace model ID for the selected variant."""
+        return [self._hf_model_id]
 
     def setup(self) -> None:
         if not VLLM_AVAILABLE:
             msg = "vllm is required for NemotronHVL but is not installed. Please install vllm: pip install vllm"
             raise ImportError(msg)
 
-        os.environ["VLLM_USE_V1"] = "1"
-        logger.info("Using vLLM V1 engine.")
-
         os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
 
+        # Determine quantization and dtype based on variant
+        # See: https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-FP8
+        #      https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-NVFP4-QAD
+        quantization = None
+        dtype = "bfloat16"  # BF16 variant requires explicit dtype
+        if self._normalized_variant == "nemotron-fp8":
+            quantization = "modelopt"  # vllm serve uses: --quantization modelopt
+            dtype = "auto"  # FP8 determines dtype from quantization
+        elif self._normalized_variant == "nemotron-nvfp4":
+            quantization = "modelopt_fp4"  # vllm serve uses: --quantization modelopt_fp4
+            dtype = "auto"  # FP4 determines dtype from quantization
+
         self.model = LLM(
-            model=self.weight_file,
+            model=self._hf_model_id,
             trust_remote_code=True,
+            dtype=dtype,
             tensor_parallel_size=1,
             gpu_memory_utilization=0.9,
             max_model_len=32768,
             limit_mm_per_prompt={"video": 1},
+            quantization=quantization,
+            video_pruning_rate=0,  # Disable video pruning
         )
 
         self.sampling_params = SamplingParams(
@@ -114,7 +160,7 @@ def setup(self) -> None:
 
         logger.info(
             f"NemotronHVL initialized: variant={self.model_variant}, "
-            f"TP=1, GPU_util=0.9, max_len=32768"
+            f"quantization={quantization}, TP=1, GPU_util=0.9, max_len=32768"
         )
 
     def _refine_caption_prompt(self, original_prompt: str, refinement_text: str) -> str:
@@ -163,9 +209,7 @@ def generate(
                         initial_caption = out.outputs[0].text
                         refinement_text = self.stage2_prompt + initial_caption
                         original_prompt = model_inputs[i]["prompt"]
-                        model_inputs[i]["prompt"] = self._refine_caption_prompt(
-                            original_prompt, refinement_text
-                        )
+                        model_inputs[i]["prompt"] = self._refine_caption_prompt(original_prompt, refinement_text)
 
                     outputs = self.model.generate(
                         model_inputs,
@@ -186,39 +230,48 @@ def generate(
         return generated_text
 
     @classmethod
-    def download_weights_on_node(cls, model_dir: str) -> None:
-        """Download or verify NemotronH weights on the node (follows Qwen pattern)."""
-        # If HuggingFace model ID is configured, download from HF
-        if _NEMOTRON_H_NANO_MODEL_ID is not None:
-            model_dir_path = Path(model_dir) / _NEMOTRON_H_NANO_MODEL_ID
-            model_dir_path.mkdir(parents=True, exist_ok=True)
-
-            # Check if already downloaded
-            if model_dir_path.exists() and any(model_dir_path.glob("*.safetensors")):
-                logger.info(f"NemotronH checkpoint already exists at: {model_dir_path}")
-                return
-
-            # Download from HuggingFace
-            download_model_from_hf(
-                model_id=_NEMOTRON_H_NANO_MODEL_ID,
-                local_dir=model_dir_path,
-                revision=_NEMOTRON_H_NANO_MODEL_REVISION,
-            )
-            logger.info(f"NemotronH weights downloaded to: {model_dir_path}")
-        else:
-            # Local checkpoint mode: validate path exists
-            model_path = Path(model_dir)
-            if not model_path.exists():
-                msg = f"NemotronH checkpoint path does not exist: {model_dir}"
-                raise FileNotFoundError(msg)
-
-            # Verify it contains model files
-            if not any(model_path.glob("*.safetensors")) and not any(model_path.glob("*.bin")):
-                msg = (
-                    f"No model files (.safetensors or .bin) found in: {model_dir}\n"
-                    f"Please ensure this directory contains a valid NemotronH checkpoint."
-                )
-                raise FileNotFoundError(msg)
-
-            logger.info(f"Using local NemotronH checkpoint: {model_dir}")
-
+    def download_weights_on_node(
+        cls,
+        model_dir: str,
+        variant: NemotronVariant = "nemotron",
+    ) -> None:
+        """Download NemotronH VL weights from HuggingFace.
+
+        Models are automatically downloaded from HuggingFace Hub on first use.
+        Supports multiple quantization variants for different performance/memory tradeoffs.
+
+        Args:
+            model_dir: Base directory for model weights. The model will be downloaded
+                to a subdirectory named after the HuggingFace model ID.
+            variant: Model variant to download. Options:
+                - "nemotron" or "nemotron-bf16": BF16 precision (default)
+                - "nemotron-fp8": FP8 quantized
+                - "nemotron-nvfp4": NVFP4 quantized
+        """
+        # Normalize variant name
+        normalized_variant: NemotronVariant = "nemotron-bf16" if variant == "nemotron" else variant
+
+        if normalized_variant not in _NEMOTRON_VARIANTS_INFO:
+            valid_variants = ", ".join(_NEMOTRON_VARIANTS_INFO.keys())
+            msg = f"Invalid variant: {variant}. Valid options are: {valid_variants}"
+            raise ValueError(msg)
+
+        hf_model_id = _NEMOTRON_VARIANTS_INFO[normalized_variant]
+        revision = _NEMOTRON_REVISION_INFO.get(normalized_variant)
+
+        model_dir_path = Path(model_dir) / hf_model_id
+        model_dir_path.mkdir(parents=True, exist_ok=True)
+
+        # Check if already downloaded
+        if model_dir_path.exists() and any(model_dir_path.glob("*.safetensors")):
+            logger.info(f"NemotronH {variant} checkpoint already exists at: {model_dir_path}")
+            return
+
+        # Download from HuggingFace
+        logger.info(f"Downloading NemotronH {variant} from HuggingFace: {hf_model_id}")
+        download_model_from_hf(
+            model_id=hf_model_id,
+            local_dir=model_dir_path,
+            revision=revision,
+        )
+        logger.info(f"NemotronH {variant} weights downloaded to: {model_dir_path}")
diff --git a/nemo_curator/models/nemotron_prompt_formatter.py b/nemo_curator/models/nemotron_prompt_formatter.py
deleted file mode 100644
index ecc7fa55f2..0000000000
--- a/nemo_curator/models/nemotron_prompt_formatter.py
+++ /dev/null
@@ -1,137 +0,0 @@
-# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-import numpy.typing as npt
-import torch
-from jinja2 import Template
-from loguru import logger
-
-# Constants for tensor dimensions and channel counts
-EXPECTED_TENSOR_DIMENSIONS = 4
-EXPECTED_NUMPY_DIMENSIONS = 4
-EXPECTED_CHANNELS = 3
-
-
-class NemotronPromptFormatter:
-    """Prompt formatter for NemotronH models using Jinja2 chat templates."""
-
-    def __init__(self, model_path: str):
-        self.model_path = Path(model_path)
-
-        checkpoint_path = self._resolve_checkpoint_path(model_path)
-
-        template_path = checkpoint_path / "chat_template.jinja"
-        if not template_path.exists():
-            msg = (
-                f"chat_template.jinja not found at {template_path}.\n"
-                f"NemotronH models require this template file for proper prompt formatting.\n"
-                f"Please ensure your checkpoint directory contains chat_template.jinja."
-            )
-            raise FileNotFoundError(msg)
-
-        with open(template_path) as f:
-            self.chat_template = f.read()
-        logger.info(f"Loaded chat template from: {template_path}")
-
-    def _resolve_checkpoint_path(self, model_path: str) -> Path:
-        path = Path(model_path)
-
-        if (path / "config.json").exists():
-            return path
-
-        return path
-
-    def generate_inputs(
-        self,
-        prompt: str,
-        video_inputs: torch.Tensor | npt.NDArray[np.uint8] | None = None,
-        add_generation_prompt: bool = True,
-    ) -> dict[str, Any]:
-        video_np = self._convert_video_format(video_inputs)
-        formatted_prompt = self._apply_chat_template(prompt, add_generation_prompt)
-
-        return {
-            "prompt": formatted_prompt,
-            "multi_modal_data": {"video": video_np},
-        }
-
-    def _convert_tensor_to_numpy(self, tensor: torch.Tensor) -> npt.NDArray[np.uint8]:
-        """Convert torch.Tensor (T, C, H, W) to numpy (T, H, W, C)."""
-        if tensor.ndim != EXPECTED_TENSOR_DIMENSIONS:
-            msg = f"Expected 4D torch.Tensor (T, C, H, W), got shape {tensor.shape}"
-            raise ValueError(msg)
-
-        video_np = tensor.permute(0, 2, 3, 1).cpu().numpy()
-        return self._normalize_dtype(video_np)
-
-    def _validate_numpy_array(self, array: npt.NDArray[np.uint8]) -> npt.NDArray[np.uint8]:
-        """Validate and normalize numpy array format."""
-        if array.ndim != EXPECTED_NUMPY_DIMENSIONS:
-            msg = f"Expected 4D numpy array (T, H, W, C), got shape {array.shape}"
-            raise ValueError(msg)
-
-        if array.shape[-1] != EXPECTED_CHANNELS:
-            msg = f"Expected channels-last format (T, H, W, 3), got shape {array.shape}."
-            raise ValueError(msg)
-
-        return self._normalize_dtype(array)
-
-    def _normalize_dtype(self, array: npt.NDArray[np.uint8]) -> npt.NDArray[np.uint8]:
-        """Normalize array dtype to uint8."""
-        if array.dtype != np.uint8:
-            if array.dtype in (np.float32, np.float16) and array.max() <= 1.0:
-                return (array * 255).astype(np.uint8)
-            return array.astype(np.uint8)
-        return array
-
-    def _convert_video_format(
-        self,
-        video_inputs: torch.Tensor | npt.NDArray[np.uint8] | None,
-    ) -> npt.NDArray[np.uint8] | None:
-        """Convert torch.Tensor (T, C, H, W) or np.ndarray to vLLM format (T, H, W, C)."""
-        if video_inputs is None:
-            return None
-
-        if isinstance(video_inputs, torch.Tensor):
-            return self._convert_tensor_to_numpy(video_inputs)
-
-        if isinstance(video_inputs, np.ndarray):
-            return self._validate_numpy_array(video_inputs)
-
-        msg = f"Expected torch.Tensor or np.ndarray, got {type(video_inputs)}"
-        raise TypeError(msg)
-
-    def _apply_chat_template(
-        self,
-        prompt_text: str,
-        add_generation_prompt: bool = True,
-    ) -> str:
-        messages = [
-            {"role": "system", "content": "You are a helpful assistant."},
-            {
-                "role": "user",
-                "content": [{"type": "text", "text": f"<video>\n{prompt_text}"}],
-            },
-        ]
-
-        template = Template(self.chat_template)
-        return template.render(
-            messages=messages,
-            add_generation_prompt=add_generation_prompt,
-        )
-
diff --git a/nemo_curator/models/prompt_formatter.py b/nemo_curator/models/prompt_formatter.py
index b657cc70c6..845cbcb060 100644
--- a/nemo_curator/models/prompt_formatter.py
+++ b/nemo_curator/models/prompt_formatter.py
@@ -14,18 +14,32 @@
 
 from typing import Any
 
+import numpy as np
 import torch
 from transformers import AutoProcessor
 
-from nemo_curator.models.nemotron_prompt_formatter import NemotronPromptFormatter
+from nemo_curator.models.nemotron_h_vl import _NEMOTRON_VARIANTS_INFO
 
-VARIANT_MAPPING = {
+# Mapping of variants to their HuggingFace model IDs
+VARIANT_MAPPING: dict[str, str] = {
     "qwen": "Qwen/Qwen2.5-VL-7B-Instruct",
-    "nemotron": None,
+    **_NEMOTRON_VARIANTS_INFO,
 }
 
+
 class PromptFormatter:
-    def __init__(self, prompt_variant: str, model_path: str | None = None):
+    """Unified prompt formatter for VLM models using HuggingFace AutoProcessor.
+
+    Supports both Qwen and Nemotron model variants. Uses AutoProcessor.from_pretrained()
+    to load the appropriate tokenizer and chat template from HuggingFace Hub.
+    """
+
+    def __init__(self, prompt_variant: str):
+        """Initialize the prompt formatter.
+
+        Args:
+            prompt_variant: Model variant to use (e.g., "qwen", "nemotron", "nemotron-fp8").
+        """
         if prompt_variant not in VARIANT_MAPPING:
             msg = f"Invalid prompt variant: {prompt_variant}. Valid variants are: {', '.join(VARIANT_MAPPING.keys())}"
             raise ValueError(msg)
@@ -33,93 +47,122 @@ def __init__(self, prompt_variant: str, model_path: str | None = None):
         self.prompt_variant = prompt_variant
         self.text_prompt = None
 
-        if prompt_variant == "qwen":
-            self.processor = AutoProcessor.from_pretrained(VARIANT_MAPPING[self.prompt_variant])
-            self.formatter = None
-        elif prompt_variant == "nemotron":
-            if model_path is None:
-                msg = f"model_path is required for nemotron models (variant: {prompt_variant})"
-                raise ValueError(msg)
-            self.processor = None
-            self.formatter = NemotronPromptFormatter(model_path)
-        else:
-            msg = f"Unsupported prompt variant: {prompt_variant}"
-            raise ValueError(msg)
+        # Load processor from HuggingFace (auto-downloads and caches)
+        hf_model_id = VARIANT_MAPPING[prompt_variant]
+        self.processor = AutoProcessor.from_pretrained(hf_model_id, trust_remote_code=True)
 
     def generate_inputs(
         self,
         prompt: str,
-        video_inputs: torch.Tensor | None = None,
+        video_inputs: torch.Tensor | np.ndarray | None = None,
         *,
         override_text_prompt: bool = False,
+        fps: float = 2.0,
     ) -> dict[str, Any]:
         """Generate inputs for video and text data based on prompt_variant.
 
-        Processes video and text inputs to create the input for the model. It handles both video and
-        image inputs, decoding video and applying preprocessing if needed, and creates a structured
-        input dictionary containing the processed prompt and multimodal data.
-
         Args:
             prompt: Text prompt to be included with the input.
-            fps: Frames per second of the input video.
-            preprocess_dtype: Data type to use for preprocessing the video/image inputs.
-            num_frames_to_use: Number of frames to extract from the video. If 0, uses all frames.
-            flip_input: Whether to flip the input video/image horizontally.
-            video_inputs: Pre-processed video inputs. If None, and video data is to be passed to
-                          the model, then video cannot be None.
-            override_text_prompt: whether the text prompt should be overridden
+            video_inputs: Pre-processed video inputs (tensor or numpy array).
+            override_text_prompt: Whether to regenerate the text prompt even if cached.
+            fps: Frames per second of the input video (used for Nemotron metadata).
 
         Returns:
             dict containing:
                 - "prompt": The processed text prompt with chat template applied
-                - "multi_modal_data": Dictionary containing processed "image" and/or "video" inputs
+                - "multi_modal_data": Dictionary containing processed "video" inputs
 
         """
-        # Use model-specific formatter
         if self.prompt_variant == "qwen":
-            message = self.create_message(prompt)
-            if self.text_prompt is None or override_text_prompt:
-                self.text_prompt = self.processor.apply_chat_template(
-                    message,
-                    tokenizer=False,
-                    add_generation_prompt=True,
-                )
-            return {
-                "prompt": self.text_prompt,
-                "multi_modal_data": {"video": video_inputs},
-            }
-
-        if self.prompt_variant == "nemotron":
-            return self.formatter.generate_inputs(
-                prompt=prompt,
-                video_inputs=video_inputs,
-                add_generation_prompt=True,
-            )
+            return self._generate_qwen_inputs(prompt, video_inputs, override_text_prompt)
+
+        if self.prompt_variant.startswith("nemotron"):
+            return self._generate_nemotron_inputs(prompt, video_inputs, fps)
 
         msg = f"Unsupported prompt variant: {self.prompt_variant}"
         raise ValueError(msg)
 
-    def create_message(self, prompt: str) -> list[dict[str, Any]]:
-        """Create a message.
-
-        Args:
-            text_input: The text input to create a message for.
+    def _generate_qwen_inputs(
+        self,
+        prompt: str,
+        video_inputs: torch.Tensor | None,
+        override_text_prompt: bool,
+    ) -> dict[str, Any]:
+        """Generate inputs for Qwen models."""
+        message = self._create_qwen_message(prompt)
+        if self.text_prompt is None or override_text_prompt:
+            self.text_prompt = self.processor.apply_chat_template(
+                message,
+                tokenize=False,
+                add_generation_prompt=True,
+            )
+        return {
+            "prompt": self.text_prompt,
+            "multi_modal_data": {"video": video_inputs},
+        }
 
-        Returns:
-            List of messages for the VLM model including the text prompt and video.
+    def _generate_nemotron_inputs(
+        self,
+        prompt: str,
+        video_inputs: torch.Tensor | np.ndarray | None,
+        fps: float,
+    ) -> dict[str, Any]:
+        """Generate inputs for Nemotron models.
 
+        Nemotron requires video metadata (fps, frames_indices) for vLLM processing.
         """
+        # Format messages for Nemotron
+        messages = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": [{"type": "text", "text": f"<video>\n{prompt}"}]},
+        ]
+
+        formatted_prompt = self.processor.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True,
+        )
+
+        # Handle video metadata (vLLM's Nemotron processor requires this tuple format)
+        video_with_metadata = None
+        if video_inputs is not None:
+            video_np = self._convert_to_numpy(video_inputs)
+            num_frames = video_np.shape[0]
+            video_with_metadata = (
+                video_np,
+                {"fps": fps, "frames_indices": list(range(num_frames))},
+            )
+
+        return {
+            "prompt": formatted_prompt,
+            "multi_modal_data": {"video": video_with_metadata},
+        }
+
+    def _convert_to_numpy(self, video_inputs: torch.Tensor | np.ndarray) -> np.ndarray:
+        """Convert video inputs to numpy array in (T, H, W, C) format."""
+        if isinstance(video_inputs, torch.Tensor):
+            # Assume tensor is (T, C, H, W), convert to (T, H, W, C)
+            video_np = video_inputs.permute(0, 2, 3, 1).cpu().numpy()
+        else:
+            video_np = video_inputs
+
+        # Normalize to uint8 if needed
+        if video_np.dtype != np.uint8:
+            if video_np.dtype in (np.float32, np.float16) and video_np.max() <= 1.0:
+                video_np = (video_np * 255).astype(np.uint8)
+            else:
+                video_np = video_np.astype(np.uint8)
+
+        return video_np
+
+    def _create_qwen_message(self, prompt: str) -> list[dict[str, Any]]:
+        """Create a message for Qwen models."""
         return [
             {
                 "role": "user",
                 "content": [
-                    {
-                        "type": "video",
-                    },
-                    {
-                        "type": "text",
-                        "text": prompt,
-                    },
+                    {"type": "video"},
+                    {"type": "text", "text": prompt},
                 ],
             },
         ]
diff --git a/nemo_curator/stages/video/caption/caption_generation.py b/nemo_curator/stages/video/caption/caption_generation.py
index 8395c4122a..3938a63701 100644
--- a/nemo_curator/stages/video/caption/caption_generation.py
+++ b/nemo_curator/stages/video/caption/caption_generation.py
@@ -63,10 +63,7 @@ def setup(self, worker_metadata: WorkerMetadata | None = None) -> None:  # noqa:
                 model_does_preprocess=self.model_does_preprocess,
                 disable_mmcache=self.disable_mmcache,
             )
-        elif self.model_variant == "nemotron":
-            if NemotronHVL is None:
-                msg = "NemotronHVL is not available. Please install required dependencies."
-                raise ImportError(msg)
+        elif self.model_variant.startswith("nemotron"):
             self.model = NemotronHVL(
                 model_dir=self.model_dir,
                 model_variant=self.model_variant,
@@ -84,11 +81,8 @@ def setup_on_node(self, node_info: NodeInfo, worker_metadata: WorkerMetadata) ->
         """Download/verify weights on the node."""
         if self.model_variant == "qwen":
             QwenVL.download_weights_on_node(self.model_dir)
-        elif self.model_variant == "nemotron":
-            if NemotronHVL is None:
-                msg = "NemotronHVL is not available. Please install required dependencies."
-                raise ImportError(msg)
-            NemotronHVL.download_weights_on_node(self.model_dir)
+        elif self.model_variant.startswith("nemotron"):
+            NemotronHVL.download_weights_on_node(self.model_dir, variant=self.model_variant)
 
     def __post_init__(self) -> None:
         self.resources = Resources(gpus=1)
diff --git a/nemo_curator/stages/video/caption/caption_preparation.py b/nemo_curator/stages/video/caption/caption_preparation.py
index 5f04a1445d..344a3d5076 100644
--- a/nemo_curator/stages/video/caption/caption_preparation.py
+++ b/nemo_curator/stages/video/caption/caption_preparation.py
@@ -78,7 +78,6 @@ class CaptionPreparationStage(ProcessingStage[VideoTask, VideoTask]):
     """Stage that prepares captions for video processing."""
 
     model_variant: str = "qwen"
-    model_path: str | None = None
     prompt_variant: str = "default"
     prompt_text: str | None = None
     verbose: bool = False
@@ -97,13 +96,8 @@ def outputs(self) -> tuple[list[str], list[str]]:
         return [], []
 
     def setup(self, worker_metadata: WorkerMetadata | None = None) -> None:  # noqa: ARG002
-        if self.model_variant == "nemotron":
-            if self.model_path is None:
-                msg = f"model_path is required for nemotron models (variant: {self.model_variant})"
-                raise ValueError(msg)
-            self.prompt_formatter = PromptFormatter(self.model_variant, model_path=self.model_path)
-        else:
-            self.prompt_formatter = PromptFormatter(self.model_variant)
+        # PromptFormatter uses AutoProcessor from HuggingFace (auto-downloads/caches)
+        self.prompt_formatter = PromptFormatter(self.model_variant)
 
     def process(self, task: VideoTask) -> VideoTask:
         video = task.data
@@ -134,6 +128,7 @@ def process(self, task: VideoTask) -> VideoTask:
                     llm_input = self.prompt_formatter.generate_inputs(
                         prompt=prompt,
                         video_inputs=window_frames,
+                        fps=self.sampling_fps,
                     )
                 except Exception as e:  # noqa: BLE001
                     logger.error(f"Error in Caption preparation: {e}")
diff --git a/pyproject.toml b/pyproject.toml
index 10f8c3ed72..4621cfea07 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -140,7 +140,6 @@ video_cpu = [
     "torchvision",
     "einops",
     "easydict",
-    "timm"
 ]
 
 video_cuda12 = [
@@ -148,11 +147,14 @@ video_cuda12 = [
     "nemo_curator[cuda12]",
     "nemo_curator[vllm]",
     "cvcuda_cu12",
-    "flash-attn<=2.8.3; (platform_machine == 'x86_64' and platform_system != 'Darwin')",  
+    "flash-attn<=2.8.3; (platform_machine == 'x86_64' and platform_system != 'Darwin')",
     "pycuda",
     "PyNvVideoCodec==2.0.2; (platform_machine == 'x86_64' and platform_system != 'Darwin')",
     "torch<=2.9.1",
     "torchaudio",
+    # Nemotron VL support
+    "transformers>=4.56.0",
+    "vllm>=0.13; (platform_machine == 'x86_64' and platform_system != 'Darwin')",
 ]
 
 # All dependencies
diff --git a/tests/models/test_nemotron_h_vl.py b/tests/models/test_nemotron_h_vl.py
index 2fd41fd264..bb1daeba3a 100644
--- a/tests/models/test_nemotron_h_vl.py
+++ b/tests/models/test_nemotron_h_vl.py
@@ -20,7 +20,33 @@
 
 import pytest
 
-from nemo_curator.models.nemotron_h_vl import NemotronHVL
+from nemo_curator.models.nemotron_h_vl import (
+    _NEMOTRON_REVISION_INFO,
+    _NEMOTRON_VARIANTS_INFO,
+    NemotronHVL,
+    NemotronVariant,
+)
+
+
+class TestNemotronHVLVariantInfo:
+    """Test cases for variant info constants."""
+
+    def test_variants_info_contains_all_variants(self) -> None:
+        """Verify all expected variants are defined."""
+        expected_variants = {"nemotron", "nemotron-bf16", "nemotron-fp8", "nemotron-nvfp4"}
+        assert set(_NEMOTRON_VARIANTS_INFO.keys()) == expected_variants
+
+    def test_variants_info_hf_ids(self) -> None:
+        """Verify HuggingFace model IDs are correct."""
+        assert _NEMOTRON_VARIANTS_INFO["nemotron"] == "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16"
+        assert _NEMOTRON_VARIANTS_INFO["nemotron-bf16"] == "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16"
+        assert _NEMOTRON_VARIANTS_INFO["nemotron-fp8"] == "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-FP8"
+        assert _NEMOTRON_VARIANTS_INFO["nemotron-nvfp4"] == "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-NVFP4-QAD"
+
+    def test_revision_info_contains_all_variants(self) -> None:
+        """Verify revision info has entries for all variants."""
+        expected_variants = {"nemotron", "nemotron-bf16", "nemotron-fp8", "nemotron-nvfp4"}
+        assert set(_NEMOTRON_REVISION_INFO.keys()) == expected_variants
 
 
 class TestNemotronHVL:
@@ -30,7 +56,7 @@ def setup_method(self) -> None:
         self.vllm_patcher.start()
 
         self.model_dir = "/test/model/dir"
-        self.model_variant = "nemotron"
+        self.model_variant: NemotronVariant = "nemotron"
         self.caption_batch_size = 4
         self.model = NemotronHVL(
             model_dir=self.model_dir,
@@ -50,25 +76,49 @@ def test_init_defaults(self) -> None:
         model = NemotronHVL(model_dir=self.model_dir)
         assert model.model_dir == self.model_dir
         assert model.model_variant == "nemotron"
+        assert model._normalized_variant == "nemotron-bf16"
         assert model.caption_batch_size == 8
         assert model.max_output_tokens == 512
         assert model.stage2_prompt == "Please refine this caption: "
         assert model.verbose is False
-        assert model.weight_file == self.model_dir
-
-    def test_model_id_names_local(self) -> None:
-        """Return local directory name when no HF id is set."""
-        local_dir = "/models/local_ckpt"
-        model = NemotronHVL(model_dir=local_dir)
-        assert model.model_id_names == [pathlib.Path(local_dir).name]
-
-    def test_model_id_names_config(self) -> None:
-        """Read name from config.json when present."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            cfg_path = pathlib.Path(tmpdir) / "config.json"
-            cfg_path.write_text('{"_name_or_path": "nemotron-hnano"}')
-            model = NemotronHVL(model_dir=tmpdir)
-            assert model.model_id_names == ["nemotron-hnano"]
+        # Weight file should include HF model ID path
+        expected_hf_id = _NEMOTRON_VARIANTS_INFO["nemotron-bf16"]
+        assert model.weight_file == str(pathlib.Path(self.model_dir) / expected_hf_id)
+
+    def test_init_bf16_variant(self) -> None:
+        """Verify BF16 variant initialization."""
+        model = NemotronHVL(model_dir=self.model_dir, model_variant="nemotron-bf16")
+        assert model.model_variant == "nemotron-bf16"
+        assert model._normalized_variant == "nemotron-bf16"
+        expected_hf_id = _NEMOTRON_VARIANTS_INFO["nemotron-bf16"]
+        assert model.weight_file == str(pathlib.Path(self.model_dir) / expected_hf_id)
+
+    def test_init_fp8_variant(self) -> None:
+        """Verify FP8 variant initialization."""
+        model = NemotronHVL(model_dir=self.model_dir, model_variant="nemotron-fp8")
+        assert model.model_variant == "nemotron-fp8"
+        assert model._normalized_variant == "nemotron-fp8"
+        expected_hf_id = _NEMOTRON_VARIANTS_INFO["nemotron-fp8"]
+        assert model.weight_file == str(pathlib.Path(self.model_dir) / expected_hf_id)
+
+    def test_init_nvfp4_variant(self) -> None:
+        """Verify NVFP4 variant initialization."""
+        model = NemotronHVL(model_dir=self.model_dir, model_variant="nemotron-nvfp4")
+        assert model.model_variant == "nemotron-nvfp4"
+        assert model._normalized_variant == "nemotron-nvfp4"
+        expected_hf_id = _NEMOTRON_VARIANTS_INFO["nemotron-nvfp4"]
+        assert model.weight_file == str(pathlib.Path(self.model_dir) / expected_hf_id)
+
+    def test_init_invalid_variant(self) -> None:
+        """Raise ValueError for invalid variant."""
+        with pytest.raises(ValueError, match="Invalid model_variant: invalid"):
+            NemotronHVL(model_dir=self.model_dir, model_variant="invalid")
+
+    def test_model_id_names(self) -> None:
+        """Return HuggingFace model ID for selected variant."""
+        model = NemotronHVL(model_dir=self.model_dir, model_variant="nemotron-fp8")
+        expected_hf_id = _NEMOTRON_VARIANTS_INFO["nemotron-fp8"]
+        assert model.model_id_names == [expected_hf_id]
 
     def test_setup_no_vllm(self) -> None:
         """Raise ImportError when vLLM is unavailable."""
@@ -77,35 +127,83 @@ def test_setup_no_vllm(self) -> None:
             with pytest.raises(ImportError, match="vllm is required for NemotronHVL"):
                 model.setup()
 
-    @patch("nemo_curator.models.nemotron_h_vl.logger")
     @patch("nemo_curator.models.nemotron_h_vl.SamplingParams")
     @patch("nemo_curator.models.nemotron_h_vl.LLM")
-    def test_setup(self, mock_llm: Mock, mock_sampling_params: Mock, mock_logger: Mock) -> None:
-        """Initialize LLM and SamplingParams with expected args."""
+    def test_setup_bf16(self, mock_llm: Mock, mock_sampling_params: Mock) -> None:
+        """Initialize LLM with BF16 settings (no quantization)."""
         mock_llm_instance = Mock()
         mock_llm.return_value = mock_llm_instance
         mock_sampling_params_instance = Mock()
         mock_sampling_params.return_value = mock_sampling_params_instance
 
-        self.model.setup()
+        model = NemotronHVL(model_dir=self.model_dir, model_variant="nemotron-bf16")
+        model.setup()
 
         mock_llm.assert_called_once_with(
-            model=self.model.weight_file,
+            model=model.weight_file,
             trust_remote_code=True,
+            dtype="bfloat16",
             tensor_parallel_size=1,
             gpu_memory_utilization=0.9,
             max_model_len=32768,
             limit_mm_per_prompt={"video": 1},
+            quantization=None,
+            video_pruning_rate=0,
         )
         mock_sampling_params.assert_called_once_with(
             temperature=0.6,
-            max_tokens=self.model.max_output_tokens,
+            max_tokens=model.max_output_tokens,
             top_p=0.95,
             stop=["</s>", "<|endoftext|>", "<SPECIAL_12>", "</think>"],
         )
-        assert self.model.model is mock_llm_instance
-        assert self.model.sampling_params is mock_sampling_params_instance
-        mock_logger.info.assert_any_call("Using vLLM V0 engine.")
+        assert model.model is mock_llm_instance
+        assert model.sampling_params is mock_sampling_params_instance
+
+    @patch("nemo_curator.models.nemotron_h_vl.SamplingParams")
+    @patch("nemo_curator.models.nemotron_h_vl.LLM")
+    def test_setup_fp8(self, mock_llm: Mock, mock_sampling_params: Mock) -> None:
+        """Initialize LLM with FP8 quantization settings."""
+        mock_llm_instance = Mock()
+        mock_llm.return_value = mock_llm_instance
+        mock_sampling_params.return_value = Mock()
+
+        model = NemotronHVL(model_dir=self.model_dir, model_variant="nemotron-fp8")
+        model.setup()
+
+        mock_llm.assert_called_once_with(
+            model=model.weight_file,
+            trust_remote_code=True,
+            dtype="auto",
+            tensor_parallel_size=1,
+            gpu_memory_utilization=0.9,
+            max_model_len=32768,
+            limit_mm_per_prompt={"video": 1},
+            quantization="modelopt",
+            video_pruning_rate=0,
+        )
+
+    @patch("nemo_curator.models.nemotron_h_vl.SamplingParams")
+    @patch("nemo_curator.models.nemotron_h_vl.LLM")
+    def test_setup_nvfp4(self, mock_llm: Mock, mock_sampling_params: Mock) -> None:
+        """Initialize LLM with NVFP4 quantization settings."""
+        mock_llm_instance = Mock()
+        mock_llm.return_value = mock_llm_instance
+        mock_sampling_params.return_value = Mock()
+
+        model = NemotronHVL(model_dir=self.model_dir, model_variant="nemotron-nvfp4")
+        model.setup()
+
+        mock_llm.assert_called_once_with(
+            model=model.weight_file,
+            trust_remote_code=True,
+            dtype="auto",
+            tensor_parallel_size=1,
+            gpu_memory_utilization=0.9,
+            max_model_len=32768,
+            limit_mm_per_prompt={"video": 1},
+            quantization="modelopt_fp4",
+            video_pruning_rate=0,
+        )
 
     @patch("nemo_curator.models.nemotron_h_vl.grouping.split_by_chunk_size")
     def test_generate_basic(self, mock_split_by_chunk_size: Mock) -> None:
@@ -148,6 +246,7 @@ def test_generate_batched(self, mock_split_by_chunk_size: Mock) -> None:
 
         def gen_outputs(batch_idx: int) -> list[Mock]:
             return [Mock(outputs=[Mock(text=f"B{batch_idx} T{i}")]) for i in range(2)]
+
         mock_model.generate.side_effect = [gen_outputs(1), gen_outputs(2)]
 
         result = self.model.generate(videos, batch_size=2)
@@ -200,19 +299,100 @@ def test_generate_error(self, mock_split_by_chunk_size: Mock, mock_logger: Mock)
 
         mock_logger.error.assert_called_once()
 
-    def test_download_weights_missing_path(self) -> None:
-        """Raise when checkpoint path does not exist."""
-        with pytest.raises(FileNotFoundError):
-            NemotronHVL.download_weights_on_node("/non/existent/path")
+    @patch("nemo_curator.models.nemotron_h_vl.download_model_from_hf")
+    def test_download_weights_bf16(self, mock_download: Mock) -> None:
+        """Download BF16 weights from HuggingFace."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            NemotronHVL.download_weights_on_node(tmpdir, variant="nemotron-bf16")
+
+            expected_hf_id = _NEMOTRON_VARIANTS_INFO["nemotron-bf16"]
+            expected_path = pathlib.Path(tmpdir) / expected_hf_id
 
-    def test_download_weights_no_files(self) -> None:
-        """Raise when checkpoint path has no model files."""
+            mock_download.assert_called_once_with(
+                model_id=expected_hf_id,
+                local_dir=expected_path,
+                revision=_NEMOTRON_REVISION_INFO["nemotron-bf16"],
+            )
+
+    @patch("nemo_curator.models.nemotron_h_vl.download_model_from_hf")
+    def test_download_weights_fp8(self, mock_download: Mock) -> None:
+        """Download FP8 weights from HuggingFace."""
         with tempfile.TemporaryDirectory() as tmpdir:
-            with pytest.raises(FileNotFoundError):
-                NemotronHVL.download_weights_on_node(tmpdir)
+            NemotronHVL.download_weights_on_node(tmpdir, variant="nemotron-fp8")
+
+            expected_hf_id = _NEMOTRON_VARIANTS_INFO["nemotron-fp8"]
+            expected_path = pathlib.Path(tmpdir) / expected_hf_id
 
-    def test_download_weights_ok(self) -> None:
-        """Succeed when checkpoint path has model files."""
+            mock_download.assert_called_once_with(
+                model_id=expected_hf_id,
+                local_dir=expected_path,
+                revision=_NEMOTRON_REVISION_INFO["nemotron-fp8"],
+            )
+
+    @patch("nemo_curator.models.nemotron_h_vl.download_model_from_hf")
+    def test_download_weights_nvfp4(self, mock_download: Mock) -> None:
+        """Download NVFP4 weights from HuggingFace."""
         with tempfile.TemporaryDirectory() as tmpdir:
-            pathlib.Path(tmpdir, "model.safetensors").write_bytes(b"fake")
-            NemotronHVL.download_weights_on_node(tmpdir)
+            NemotronHVL.download_weights_on_node(tmpdir, variant="nemotron-nvfp4")
+
+            expected_hf_id = _NEMOTRON_VARIANTS_INFO["nemotron-nvfp4"]
+            expected_path = pathlib.Path(tmpdir) / expected_hf_id
+
+            mock_download.assert_called_once_with(
+                model_id=expected_hf_id,
+                local_dir=expected_path,
+                revision=_NEMOTRON_REVISION_INFO["nemotron-nvfp4"],
+            )
+
+    @patch("nemo_curator.models.nemotron_h_vl.download_model_from_hf")
+    def test_download_weights_default_variant(self, mock_download: Mock) -> None:
+        """Download default variant (nemotron -> bf16) from HuggingFace."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            NemotronHVL.download_weights_on_node(tmpdir, variant="nemotron")
+
+            expected_hf_id = _NEMOTRON_VARIANTS_INFO["nemotron"]
+            expected_path = pathlib.Path(tmpdir) / expected_hf_id
+
+            mock_download.assert_called_once_with(
+                model_id=expected_hf_id,
+                local_dir=expected_path,
+                revision=_NEMOTRON_REVISION_INFO["nemotron"],
+            )
+
+    def test_download_weights_already_exists(self) -> None:
+        """Skip download when weights already exist."""
+        with tempfile.TemporaryDirectory() as tmpdir:
+            # Create the expected directory with a safetensors file
+            expected_hf_id = _NEMOTRON_VARIANTS_INFO["nemotron-bf16"]
+            model_path = pathlib.Path(tmpdir) / expected_hf_id
+            model_path.mkdir(parents=True)
+            (model_path / "model.safetensors").write_bytes(b"fake")
+
+            with patch("nemo_curator.models.nemotron_h_vl.download_model_from_hf") as mock_download:
+                NemotronHVL.download_weights_on_node(tmpdir, variant="nemotron-bf16")
+                mock_download.assert_not_called()
+
+    def test_download_weights_invalid_variant(self) -> None:
+        """Raise ValueError for invalid variant."""
+        with tempfile.TemporaryDirectory() as tmpdir, pytest.raises(ValueError, match="Invalid variant: invalid"):
+            NemotronHVL.download_weights_on_node(tmpdir, variant="invalid")
+
+    def test_refine_caption_prompt_with_video_tag(self) -> None:
+        """Test _refine_caption_prompt with video tag in prompt."""
+        original_prompt = "Human: <video>original question<SPECIAL_11>Assistant"
+        refinement_text = "Please refine: initial caption"
+
+        result = self.model._refine_caption_prompt(original_prompt, refinement_text)
+
+        assert "<video>" in result
+        assert "Please refine: initial caption" in result
+        assert "<SPECIAL_11>Assistant" in result
+
+    def test_refine_caption_prompt_without_video_tag(self) -> None:
+        """Test _refine_caption_prompt without video tag returns refinement text."""
+        original_prompt = "Human: original question"
+        refinement_text = "Please refine: initial caption"
+
+        result = self.model._refine_caption_prompt(original_prompt, refinement_text)
+
+        assert result == refinement_text
diff --git a/tests/models/test_prompt_formatter.py b/tests/models/test_prompt_formatter.py
index ba2ff974b3..f48d98e795 100644
--- a/tests/models/test_prompt_formatter.py
+++ b/tests/models/test_prompt_formatter.py
@@ -16,14 +16,35 @@
 
 from unittest.mock import Mock, patch
 
+import numpy as np
 import pytest
 import torch
 
 from nemo_curator.models.prompt_formatter import VARIANT_MAPPING, PromptFormatter
 
 
-class TestPromptFormatter:
-    """Test cases for PromptFormatter class."""
+class TestPromptFormatterVariantMapping:
+    """Test cases for variant mapping constants."""
+
+    def test_variant_mapping_contains_all_variants(self) -> None:
+        """Test that all expected variants are in mapping."""
+        expected_variants = {"qwen", "nemotron", "nemotron-bf16", "nemotron-fp8", "nemotron-nvfp4"}
+        assert set(VARIANT_MAPPING.keys()) == expected_variants
+
+    def test_variant_mapping_qwen_hf_id(self) -> None:
+        """Test that Qwen variant has correct HuggingFace ID."""
+        assert VARIANT_MAPPING["qwen"] == "Qwen/Qwen2.5-VL-7B-Instruct"
+
+    def test_variant_mapping_nemotron_hf_ids(self) -> None:
+        """Test that Nemotron variants have correct HuggingFace IDs."""
+        assert VARIANT_MAPPING["nemotron"] == "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16"
+        assert VARIANT_MAPPING["nemotron-bf16"] == "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16"
+        assert VARIANT_MAPPING["nemotron-fp8"] == "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-FP8"
+        assert VARIANT_MAPPING["nemotron-nvfp4"] == "nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-NVFP4-QAD"
+
+
+class TestPromptFormatterQwen:
+    """Test cases for PromptFormatter with Qwen variant."""
 
     def setup_method(self) -> None:
         """Set up test fixtures."""
@@ -33,11 +54,6 @@ def setup_method(self) -> None:
             self.formatter = PromptFormatter(prompt_variant="qwen")
             self.mock_processor = mock_processor_instance
 
-    def test_variant_mapping_constants(self) -> None:
-        """Test that variant mapping constants are correctly defined."""
-        assert "qwen" in VARIANT_MAPPING
-        assert VARIANT_MAPPING["qwen"] == "Qwen/Qwen2.5-VL-7B-Instruct"
-
     def test_initialization_valid_variant(self) -> None:
         """Test initialization with valid prompt variant."""
         with patch("nemo_curator.models.prompt_formatter.AutoProcessor") as mock_processor:
@@ -49,7 +65,7 @@ def test_initialization_valid_variant(self) -> None:
             assert formatter.prompt_variant == "qwen"
             assert formatter.text_prompt is None
             assert formatter.processor == mock_processor_instance
-            mock_processor.from_pretrained.assert_called_once_with(VARIANT_MAPPING["qwen"])
+            mock_processor.from_pretrained.assert_called_once_with(VARIANT_MAPPING["qwen"], trust_remote_code=True)
 
     def test_initialization_invalid_variant(self) -> None:
         """Test initialization with invalid prompt variant raises ValueError."""
@@ -59,19 +75,16 @@ def test_initialization_invalid_variant(self) -> None:
     @patch("nemo_curator.models.prompt_formatter.AutoProcessor")
     def test_generate_inputs_first_time(self, mock_processor_class: Mock) -> None:
         """Test generate_inputs method when text_prompt is None (first time)."""
-        # Setup mock processor
         mock_processor_instance = Mock()
         mock_processor_class.from_pretrained.return_value = mock_processor_instance
         mock_processor_instance.apply_chat_template.return_value = "formatted_prompt"
 
         formatter = PromptFormatter(prompt_variant="qwen")
 
-        # Create mock video tensor
         video_tensor = torch.randn(1, 3, 224, 224)
 
         result = formatter.generate_inputs(prompt="Test prompt", video_inputs=video_tensor)
 
-        # Verify the result structure
         assert isinstance(result, dict)
         assert "prompt" in result
         assert "multi_modal_data" in result
@@ -81,7 +94,7 @@ def test_generate_inputs_first_time(self, mock_processor_class: Mock) -> None:
         # Verify processor was called correctly
         expected_message = [{"role": "user", "content": [{"type": "video"}, {"type": "text", "text": "Test prompt"}]}]
         mock_processor_instance.apply_chat_template.assert_called_once_with(
-            expected_message, tokenizer=False, add_generation_prompt=True
+            expected_message, tokenize=False, add_generation_prompt=True
         )
 
         # Verify text_prompt was cached
@@ -90,44 +103,36 @@ def test_generate_inputs_first_time(self, mock_processor_class: Mock) -> None:
     @patch("nemo_curator.models.prompt_formatter.AutoProcessor")
     def test_generate_inputs_cached_prompt(self, mock_processor_class: Mock) -> None:
         """Test generate_inputs method when text_prompt is already cached."""
-        # Setup mock processor
         mock_processor_instance = Mock()
         mock_processor_class.from_pretrained.return_value = mock_processor_instance
 
         formatter = PromptFormatter(prompt_variant="qwen")
-        formatter.text_prompt = "cached_prompt"  # Pre-set cached prompt
+        formatter.text_prompt = "cached_prompt"
 
         video_tensor = torch.randn(1, 3, 224, 224)
 
         result = formatter.generate_inputs(prompt="Test prompt", video_inputs=video_tensor)
 
-        # Verify cached prompt is used
         assert result["prompt"] == "cached_prompt"
         assert result["multi_modal_data"]["video"] is video_tensor
-
-        # Verify processor was NOT called since prompt is cached
         mock_processor_instance.apply_chat_template.assert_not_called()
 
     @patch("nemo_curator.models.prompt_formatter.AutoProcessor")
     def test_generate_inputs_override_text_prompt(self, mock_processor_class: Mock) -> None:
         """Test generate_inputs method with override_text_prompt=True."""
-        # Setup mock processor
         mock_processor_instance = Mock()
         mock_processor_class.from_pretrained.return_value = mock_processor_instance
         mock_processor_instance.apply_chat_template.return_value = "new_formatted_prompt"
 
         formatter = PromptFormatter(prompt_variant="qwen")
-        formatter.text_prompt = "old_cached_prompt"  # Pre-set cached prompt
+        formatter.text_prompt = "old_cached_prompt"
 
         video_tensor = torch.randn(1, 3, 224, 224)
 
         result = formatter.generate_inputs(prompt="Test prompt", video_inputs=video_tensor, override_text_prompt=True)
 
-        # Verify new prompt is generated and cached
         assert result["prompt"] == "new_formatted_prompt"
         assert formatter.text_prompt == "new_formatted_prompt"
-
-        # Verify processor was called even though prompt was cached
         mock_processor_instance.apply_chat_template.assert_called_once()
 
     @patch("nemo_curator.models.prompt_formatter.AutoProcessor")
@@ -144,9 +149,9 @@ def test_generate_inputs_no_video_inputs(self, mock_processor_class: Mock) -> No
         assert result["prompt"] == "formatted_prompt"
         assert result["multi_modal_data"]["video"] is None
 
-    def test_create_message(self) -> None:
-        """Test create_message method creates correct message structure."""
-        result = self.formatter.create_message("Test prompt text")
+    def test_create_qwen_message(self) -> None:
+        """Test _create_qwen_message method creates correct message structure."""
+        result = self.formatter._create_qwen_message("Test prompt text")
 
         expected_message = [
             {"role": "user", "content": [{"type": "video"}, {"type": "text", "text": "Test prompt text"}]}
@@ -159,36 +164,168 @@ def test_create_message(self) -> None:
         assert len(result[0]["content"]) == 2
         assert result[0]["content"][0]["type"] == "video"
         assert result[0]["content"][1]["type"] == "text"
-        assert result[0]["content"][1]["text"] == "Test prompt text"
-
-    def test_create_message_empty_prompt(self) -> None:
-        """Test create_message method with empty prompt."""
-        result = self.formatter.create_message("")
 
+    def test_create_qwen_message_empty_prompt(self) -> None:
+        """Test _create_qwen_message method with empty prompt."""
+        result = self.formatter._create_qwen_message("")
         assert result[0]["content"][1]["text"] == ""
 
-    def test_create_message_special_characters(self) -> None:
-        """Test create_message method with special characters in prompt."""
+    def test_create_qwen_message_special_characters(self) -> None:
+        """Test _create_qwen_message method with special characters in prompt."""
         special_prompt = "Test with 特殊字符 and émojis 🎉"
-        result = self.formatter.create_message(special_prompt)
-
+        result = self.formatter._create_qwen_message(special_prompt)
         assert result[0]["content"][1]["text"] == special_prompt
 
+
+class TestPromptFormatterNemotron:
+    """Test cases for PromptFormatter with Nemotron variants."""
+
     @patch("nemo_curator.models.prompt_formatter.AutoProcessor")
-    def test_processor_interaction(self, mock_processor_class: Mock) -> None:
-        """Test that the processor is correctly initialized and used."""
+    def test_initialization_nemotron(self, mock_processor_class: Mock) -> None:
+        """Test Nemotron initialization uses AutoProcessor from HuggingFace."""
         mock_processor_instance = Mock()
         mock_processor_class.from_pretrained.return_value = mock_processor_instance
 
-        # Create formatter and verify processor initialization
-        formatter = PromptFormatter(prompt_variant="qwen")
-        mock_processor_class.from_pretrained.assert_called_once_with(VARIANT_MAPPING["qwen"])
+        formatter = PromptFormatter(prompt_variant="nemotron")
 
-        # Test processor method call
-        mock_processor_instance.apply_chat_template.return_value = "test_output"
-        formatter.generate_inputs("test prompt")
+        assert formatter.prompt_variant == "nemotron"
+        assert formatter.processor == mock_processor_instance
+        mock_processor_class.from_pretrained.assert_called_once_with(
+            VARIANT_MAPPING["nemotron"], trust_remote_code=True
+        )
 
-        # Verify apply_chat_template was called with correct parameters
-        mock_processor_instance.apply_chat_template.assert_called_once_with(
-            formatter.create_message("test prompt"), tokenizer=False, add_generation_prompt=True
+    @patch("nemo_curator.models.prompt_formatter.AutoProcessor")
+    def test_initialization_nemotron_fp8(self, mock_processor_class: Mock) -> None:
+        """Test nemotron-fp8 initialization uses correct HuggingFace model ID."""
+        mock_processor_instance = Mock()
+        mock_processor_class.from_pretrained.return_value = mock_processor_instance
+
+        formatter = PromptFormatter(prompt_variant="nemotron-fp8")
+
+        assert formatter.prompt_variant == "nemotron-fp8"
+        mock_processor_class.from_pretrained.assert_called_once_with(
+            VARIANT_MAPPING["nemotron-fp8"], trust_remote_code=True
         )
+
+    @patch("nemo_curator.models.prompt_formatter.AutoProcessor")
+    def test_generate_inputs_nemotron(self, mock_processor_class: Mock) -> None:
+        """Test generate_inputs for Nemotron variant with video metadata."""
+        mock_processor_instance = Mock()
+        mock_processor_class.from_pretrained.return_value = mock_processor_instance
+        mock_processor_instance.apply_chat_template.return_value = "nemotron_formatted_prompt"
+
+        formatter = PromptFormatter(prompt_variant="nemotron")
+
+        # Create video tensor (T, C, H, W)
+        video_tensor = torch.randint(0, 255, (10, 3, 224, 224), dtype=torch.uint8)
+
+        result = formatter.generate_inputs(prompt="Test prompt", video_inputs=video_tensor, fps=2.0)
+
+        assert result["prompt"] == "nemotron_formatted_prompt"
+
+        # Verify video is returned as tuple with metadata
+        video_data = result["multi_modal_data"]["video"]
+        assert isinstance(video_data, tuple)
+        assert len(video_data) == 2
+
+        video_np, metadata = video_data
+        assert isinstance(video_np, np.ndarray)
+        assert video_np.shape == (10, 224, 224, 3)  # Converted to (T, H, W, C)
+        assert metadata["fps"] == 2.0
+        assert metadata["frames_indices"] == list(range(10))
+
+    @patch("nemo_curator.models.prompt_formatter.AutoProcessor")
+    def test_generate_inputs_nemotron_no_video(self, mock_processor_class: Mock) -> None:
+        """Test generate_inputs for Nemotron variant without video."""
+        mock_processor_instance = Mock()
+        mock_processor_class.from_pretrained.return_value = mock_processor_instance
+        mock_processor_instance.apply_chat_template.return_value = "prompt"
+
+        formatter = PromptFormatter(prompt_variant="nemotron")
+
+        result = formatter.generate_inputs(prompt="Test prompt", video_inputs=None)
+
+        assert result["multi_modal_data"]["video"] is None
+
+    @patch("nemo_curator.models.prompt_formatter.AutoProcessor")
+    def test_generate_inputs_nemotron_numpy_video(self, mock_processor_class: Mock) -> None:
+        """Test generate_inputs for Nemotron variant with numpy video."""
+        mock_processor_instance = Mock()
+        mock_processor_class.from_pretrained.return_value = mock_processor_instance
+        mock_processor_instance.apply_chat_template.return_value = "prompt"
+
+        formatter = PromptFormatter(prompt_variant="nemotron")
+
+        # Create numpy video (T, H, W, C) - already in correct format
+        rng = np.random.default_rng(42)
+        video_np = rng.integers(0, 255, (10, 224, 224, 3), dtype=np.uint8)
+
+        result = formatter.generate_inputs(prompt="Test", video_inputs=video_np, fps=4.0)
+
+        video_data = result["multi_modal_data"]["video"]
+        assert isinstance(video_data, tuple)
+        assert video_data[1]["fps"] == 4.0
+
+    @patch("nemo_curator.models.prompt_formatter.AutoProcessor")
+    def test_generate_inputs_nemotron_message_format(self, mock_processor_class: Mock) -> None:
+        """Test that Nemotron uses correct message format with system prompt."""
+        mock_processor_instance = Mock()
+        mock_processor_class.from_pretrained.return_value = mock_processor_instance
+        mock_processor_instance.apply_chat_template.return_value = "prompt"
+
+        formatter = PromptFormatter(prompt_variant="nemotron")
+        formatter.generate_inputs(prompt="Describe this video")
+
+        # Verify message format passed to apply_chat_template
+        call_args = mock_processor_instance.apply_chat_template.call_args
+        messages = call_args[0][0]
+
+        assert len(messages) == 2
+        assert messages[0]["role"] == "system"
+        assert messages[0]["content"] == "You are a helpful assistant."
+        assert messages[1]["role"] == "user"
+        assert "<video>\nDescribe this video" in messages[1]["content"][0]["text"]
+
+
+class TestPromptFormatterConvertToNumpy:
+    """Test cases for _convert_to_numpy method."""
+
+    @patch("nemo_curator.models.prompt_formatter.AutoProcessor")
+    def test_convert_tensor_to_numpy(self, mock_processor_class: Mock) -> None:
+        """Test converting torch tensor to numpy array."""
+        mock_processor_class.from_pretrained.return_value = Mock()
+        formatter = PromptFormatter(prompt_variant="nemotron")
+
+        # Tensor in (T, C, H, W) format
+        tensor = torch.randint(0, 255, (10, 3, 224, 224), dtype=torch.uint8)
+        result = formatter._convert_to_numpy(tensor)
+
+        assert isinstance(result, np.ndarray)
+        assert result.shape == (10, 224, 224, 3)  # Converted to (T, H, W, C)
+        assert result.dtype == np.uint8
+
+    @patch("nemo_curator.models.prompt_formatter.AutoProcessor")
+    def test_convert_float_tensor_to_uint8(self, mock_processor_class: Mock) -> None:
+        """Test converting float tensor (0-1 range) to uint8."""
+        mock_processor_class.from_pretrained.return_value = Mock()
+        formatter = PromptFormatter(prompt_variant="nemotron")
+
+        # Float tensor in 0-1 range
+        tensor = torch.rand(10, 3, 224, 224, dtype=torch.float32)
+        result = formatter._convert_to_numpy(tensor)
+
+        assert result.dtype == np.uint8
+        assert result.max() <= 255
+
+    @patch("nemo_curator.models.prompt_formatter.AutoProcessor")
+    def test_passthrough_numpy_array(self, mock_processor_class: Mock) -> None:
+        """Test passing through numpy array."""
+        mock_processor_class.from_pretrained.return_value = Mock()
+        formatter = PromptFormatter(prompt_variant="nemotron")
+
+        rng = np.random.default_rng(42)
+        arr = rng.integers(0, 255, (10, 224, 224, 3), dtype=np.uint8)
+        result = formatter._convert_to_numpy(arr)
+
+        assert isinstance(result, np.ndarray)
+        assert result.dtype == np.uint8
diff --git a/tests/stages/video/caption/test_caption_preparation.py b/tests/stages/video/caption/test_caption_preparation.py
index b3db53ebfd..e158af74dc 100644
--- a/tests/stages/video/caption/test_caption_preparation.py
+++ b/tests/stages/video/caption/test_caption_preparation.py
@@ -417,3 +417,59 @@ def test_process_empty_windowing_result(self, mock_split_video: Mock):
 
             # Verify formatter was not called
             mock_formatter.generate_inputs.assert_not_called()
+
+
+class TestCaptionPreparationStageNemotron:
+    """Test cases for CaptionPreparationStage with Nemotron variants.
+
+    Note: Nemotron now uses AutoProcessor from HuggingFace, same as Qwen.
+    """
+
+    def test_init_nemotron_variant(self):
+        """Test initialization with nemotron variant."""
+        stage = CaptionPreparationStage(model_variant="nemotron")
+        assert stage.model_variant == "nemotron"
+
+    def test_init_nemotron_fp8_variant(self):
+        """Test initialization with nemotron-fp8 variant."""
+        stage = CaptionPreparationStage(model_variant="nemotron-fp8")
+        assert stage.model_variant == "nemotron-fp8"
+
+    def test_init_nemotron_nvfp4_variant(self):
+        """Test initialization with nemotron-nvfp4 variant."""
+        stage = CaptionPreparationStage(model_variant="nemotron-nvfp4")
+        assert stage.model_variant == "nemotron-nvfp4"
+
+    @patch("nemo_curator.stages.video.caption.caption_preparation.PromptFormatter")
+    def test_setup_nemotron_variant(self, mock_prompt_formatter: Mock):
+        """Test setup method with nemotron variant uses PromptFormatter."""
+        mock_formatter = Mock()
+        mock_prompt_formatter.return_value = mock_formatter
+
+        stage = CaptionPreparationStage(model_variant="nemotron")
+        stage.setup()
+
+        mock_prompt_formatter.assert_called_once_with("nemotron")
+        assert stage.prompt_formatter == mock_formatter
+
+    @patch("nemo_curator.stages.video.caption.caption_preparation.PromptFormatter")
+    def test_setup_nemotron_fp8_variant(self, mock_prompt_formatter: Mock):
+        """Test setup method with nemotron-fp8 variant."""
+        mock_formatter = Mock()
+        mock_prompt_formatter.return_value = mock_formatter
+
+        stage = CaptionPreparationStage(model_variant="nemotron-fp8")
+        stage.setup()
+
+        mock_prompt_formatter.assert_called_once_with("nemotron-fp8")
+
+    @patch("nemo_curator.stages.video.caption.caption_preparation.PromptFormatter")
+    def test_setup_nemotron_nvfp4_variant(self, mock_prompt_formatter: Mock):
+        """Test setup method with nemotron-nvfp4 variant."""
+        mock_formatter = Mock()
+        mock_prompt_formatter.return_value = mock_formatter
+
+        stage = CaptionPreparationStage(model_variant="nemotron-nvfp4")
+        stage.setup()
+
+        mock_prompt_formatter.assert_called_once_with("nemotron-nvfp4")
diff --git a/tutorials/video/getting-started/video_split_clip_example.py b/tutorials/video/getting-started/video_split_clip_example.py
index f0604c7e28..5215fe258e 100644
--- a/tutorials/video/getting-started/video_split_clip_example.py
+++ b/tutorials/video/getting-started/video_split_clip_example.py
@@ -192,13 +192,9 @@ def create_video_splitting_pipeline(args: argparse.Namespace) -> Pipeline:  # no
             raise ValueError(msg)
 
     if args.generate_captions:
-        # Determine model path for caption preparation
-        model_path = args.nemotronh_vl_model_path if hasattr(args, "nemotronh_vl_model_path") else None
-
         pipeline.add_stage(
             CaptionPreparationStage(
                 model_variant=args.captioning_algorithm,
-                model_path=model_path,  # Required for NemotronH
                 prompt_variant=args.captioning_prompt_variant,
                 prompt_text=args.captioning_prompt_text,
                 sampling_fps=args.captioning_sampling_fps,
@@ -219,16 +215,10 @@ def create_video_splitting_pipeline(args: argparse.Namespace) -> Pipeline:  # no
                 )
             )
 
-        # Determine model directory
-        model_dir = (
-            args.nemotronh_vl_model_path
-            if (args.captioning_algorithm.startswith("nemotron") or args.captioning_algorithm.startswith("NemotronH_"))
-            else args.model_dir
-        )
-
+        # All models now use the standard model_dir (auto-downloaded from HuggingFace)
         pipeline.add_stage(
             CaptionGenerationStage(
-                model_dir=model_dir,
+                model_dir=args.model_dir,
                 model_variant=args.captioning_algorithm,
                 caption_batch_size=args.captioning_batch_size,
                 fp8=args.captioning_use_fp8_weights,
@@ -308,16 +298,17 @@ def create_video_splitting_argparser() -> argparse.ArgumentParser:  # noqa: PLR0
         default="./models",
         help=(
             "Path to model directory containing required model weights. "
-            "Models will be automatically downloaded on first use if not present. "
+            "Models will be automatically downloaded from HuggingFace on first use if not present. "
             "Required models depend on selected algorithms:\n"
             "  - TransNetV2: For scene detection (--splitting-algorithm transnetv2)\n"
             "  - InternVideo2: For embeddings (--embedding-algorithm internvideo2)\n"
             "  - Cosmos-Embed1: For embeddings (--embedding-algorithm cosmos-embed1-*)\n"
-            "  - Qwen: For captioning (--generate-captions)\n"
+            "  - Qwen2.5-VL: For captioning (--captioning-algorithm qwen)\n"
+            "  - Nemotron Nano VL: For captioning (--captioning-algorithm nemotron[-bf16|-fp8|-nvfp4])\n"
             "  - Aesthetic models: For filtering (--aesthetic-threshold)\n"
             "Default: ./models\n"
             "Example: --model-dir /path/to/models or --model-dir ./models"
-        )
+        ),
     )
     parser.add_argument("--video-limit", type=int, default=None, help="Limit the number of videos to read")
     parser.add_argument("--verbose", action="store_true", default=False)
@@ -597,14 +588,14 @@ def create_video_splitting_argparser() -> argparse.ArgumentParser:  # noqa: PLR0
         "--captioning-algorithm",
         type=str,
         default="qwen",
-        choices=["qwen", "nemotron"],
-        help="Captioning algorithm to use (qwen or nemotron).",
-    )
-    parser.add_argument(
-        "--nemotronh-vl-model-path",
-        type=str,
-        default=None,
-        help="Path to NemotronH VL model checkpoint (required for NemotronH models)",
+        choices=["qwen", "nemotron", "nemotron-bf16", "nemotron-fp8", "nemotron-nvfp4"],
+        help=(
+            "Captioning algorithm to use. Options:\n"
+            "  - qwen: Qwen2.5-VL-7B-Instruct (default)\n"
+            "  - nemotron / nemotron-bf16: Nemotron Nano 12B v2 VL BF16 (auto-downloaded from HF)\n"
+            "  - nemotron-fp8: Nemotron Nano 12B v2 VL FP8 quantized\n"
+            "  - nemotron-nvfp4: Nemotron Nano 12B v2 VL NVFP4-QAD quantized"
+        ),
     )
     parser.add_argument(
         "--captioning-window-size",
@@ -677,13 +668,16 @@ def create_video_splitting_argparser() -> argparse.ArgumentParser:  # noqa: PLR0
         "--captioning-batch-size",
         type=int,
         default=8,
-        help="Batch size for Qwen captioning stage.",
+        help="Batch size for captioning stage (applies to both Qwen and Nemotron).",
     )
     parser.add_argument(
         "--captioning-use-fp8-weights",
         action="store_true",
         default=False,
-        help="Whether to use fp8 weights for Qwen VL model or not.",
+        help=(
+            "Whether to use fp8 weights for Qwen VL model. "
+            "Note: For Nemotron, use --captioning-algorithm nemotron-fp8 instead."
+        ),
     )
     parser.add_argument(
         "--captioning-max-output-tokens",
diff --git a/uv.lock b/uv.lock
index ebf3ae6cd1..b2c04ebd14 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2050,13 +2050,13 @@ wheels = [
 
 [[package]]
 name = "flash-attn"
-version = "2.8.2"
+version = "2.8.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "einops", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
     { name = "torch", version = "2.9.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/63/91/91bcbd7424877fa8da4e7cc04b8a777247a33037b9c1fd976a5ad426c047/flash_attn-2.8.2.tar.gz", hash = "sha256:740a5370f406cbe16155cc6d078ec543a97a137501f32cba89198295e6b80e54", size = 8167111, upload-time = "2025-07-24T16:06:45.953Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/3b/b2/8d76c41ad7974ee264754709c22963447f7f8134613fd9ce80984ed0dab7/flash_attn-2.8.3.tar.gz", hash = "sha256:1e71dd64a9e0280e0447b8a0c2541bad4bf6ac65bdeaa2f90e51a9e57de0370d", size = 8447812, upload-time = "2025-08-15T08:28:12.911Z" }
 
 [[package]]
 name = "flashinfer-python"
@@ -4272,7 +4272,6 @@ all = [
     { name = "scikit-learn" },
     { name = "sentence-transformers" },
     { name = "sentencepiece" },
-    { name = "timm" },
     { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'" },
     { name = "torch", version = "2.9.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
     { name = "torchaudio", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'" },
@@ -4280,6 +4279,7 @@ all = [
     { name = "torchvision", version = "0.24.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'" },
     { name = "torchvision", version = "0.24.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
     { name = "trafilatura" },
+    { name = "transformers" },
     { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
     { name = "warcio" },
 ]
@@ -4369,7 +4369,6 @@ video-cpu = [
     { name = "easydict" },
     { name = "einops" },
     { name = "opencv-python" },
-    { name = "timm" },
     { name = "torchvision", version = "0.24.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'" },
     { name = "torchvision", version = "0.24.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
 ]
@@ -4384,13 +4383,13 @@ video-cuda12 = [
     { name = "opencv-python" },
     { name = "pycuda" },
     { name = "pynvvideocodec", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
-    { name = "timm" },
     { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'" },
     { name = "torch", version = "2.9.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
     { name = "torchaudio", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'" },
     { name = "torchaudio", version = "2.9.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
     { name = "torchvision", version = "0.24.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'" },
     { name = "torchvision", version = "0.24.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
+    { name = "transformers" },
     { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
 ]
 vllm = [
@@ -4434,7 +4433,7 @@ requires-dist = [
     { name = "easydict", marker = "extra == 'video-cpu'" },
     { name = "einops", marker = "extra == 'video-cpu'" },
     { name = "fasttext", marker = "extra == 'text-cpu'", specifier = "==0.9.3" },
-    { name = "flash-attn", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin' and extra == 'video-cuda12'", specifier = ">=2.7.1,<=2.8.2" },
+    { name = "flash-attn", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin' and extra == 'video-cuda12'", specifier = "<=2.8.3" },
     { name = "fsspec" },
     { name = "ftfy", marker = "extra == 'text-cpu'" },
     { name = "gpustat", marker = "extra == 'cuda12'" },
@@ -4483,7 +4482,6 @@ requires-dist = [
     { name = "scikit-learn", marker = "extra == 'deduplication-cuda12'", specifier = "<1.8.0" },
     { name = "sentence-transformers", marker = "extra == 'text-cpu'" },
     { name = "sentencepiece", marker = "extra == 'text-cpu'" },
-    { name = "timm", marker = "extra == 'video-cpu'" },
     { name = "torch", marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'", index = "https://pypi.org/simple" },
     { name = "torch", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'", index = "https://download.pytorch.org/whl/cu128" },
     { name = "torch", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin' and extra == 'video-cuda12'", specifier = "<=2.9.1", index = "https://download.pytorch.org/whl/cu128" },
@@ -4496,6 +4494,8 @@ requires-dist = [
     { name = "torchvision", marker = "(platform_machine != 'x86_64' and extra == 'video-cpu') or (sys_platform == 'darwin' and extra == 'video-cpu')", index = "https://pypi.org/simple" },
     { name = "trafilatura", marker = "extra == 'text-cpu'", specifier = "==2.0.0" },
     { name = "transformers" },
+    { name = "transformers", marker = "extra == 'video-cuda12'", specifier = ">=4.56.0" },
+    { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin' and extra == 'video-cuda12'", specifier = ">=0.13" },
     { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin' and extra == 'vllm'", specifier = ">=0.14.0" },
     { name = "warcio", marker = "extra == 'text-cpu'" },
 ]
@@ -7960,24 +7960,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/81/10/b8523105c590c5b8349f2587e2fdfe51a69544bd5a76295fc20f2374f470/tiktoken-0.12.0-cp312-cp312-win_amd64.whl", hash = "sha256:ffc5288f34a8bc02e1ea7047b8d041104791d2ddbf42d1e5fa07822cbffe16bd", size = 878694, upload-time = "2025-10-06T20:21:59.876Z" },
 ]
 
-[[package]]
-name = "timm"
-version = "1.0.24"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "huggingface-hub" },
-    { name = "pyyaml" },
-    { name = "safetensors" },
-    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'" },
-    { name = "torch", version = "2.9.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
-    { name = "torchvision", version = "0.24.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'" },
-    { name = "torchvision", version = "0.24.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/f4/9d/0ea45640be447445c8664ce2b10c74f763b0b0b9ed11620d41a4d4baa10c/timm-1.0.24.tar.gz", hash = "sha256:c7b909f43fe2ef8fe62c505e270cd4f1af230dfbc37f2ee93e3608492b9d9a40", size = 2412239, upload-time = "2026-01-07T00:26:17.541Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/92/dd/c1f5b0890f7b5db661bde0864b41cb0275be76851047e5f7e085fe0b455a/timm-1.0.24-py3-none-any.whl", hash = "sha256:8301ac783410c6ad72c73c49326af6d71a9e4d1558238552796e825c2464913f", size = 2560563, upload-time = "2026-01-07T00:26:13.956Z" },
-]
-
 [[package]]
 name = "tinycss2"
 version = "1.4.0"

From bea3f958835748065569822eb97ab8b66686cb50 Mon Sep 17 00:00:00 2001
From: Ranjit Rajan <ranjitr@nvidia.com>
Date: Fri, 9 Jan 2026 18:46:29 +0000
Subject: [PATCH 06/17] Fix unit tests and address review comments

Signed-off-by: Ranjit Rajan <ranjitr@nvidia.com>

Signed-off-by: Ranjit Rajan <ranjitr@nvidia.com>
---
 nemo_curator/models/nemotron_h_vl.py | 2 +-
 tests/models/test_nemotron_h_vl.py   | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/nemo_curator/models/nemotron_h_vl.py b/nemo_curator/models/nemotron_h_vl.py
index ff20613166..37ad08c96f 100644
--- a/nemo_curator/models/nemotron_h_vl.py
+++ b/nemo_curator/models/nemotron_h_vl.py
@@ -140,7 +140,7 @@ def setup(self) -> None:
             dtype = "auto"  # FP4 determines dtype from quantization
 
         self.model = LLM(
-            model=self._hf_model_id,
+            model=self.weight_file,
             trust_remote_code=True,
             dtype=dtype,
             tensor_parallel_size=1,
diff --git a/tests/models/test_nemotron_h_vl.py b/tests/models/test_nemotron_h_vl.py
index bb1daeba3a..ba413acc87 100644
--- a/tests/models/test_nemotron_h_vl.py
+++ b/tests/models/test_nemotron_h_vl.py
@@ -112,7 +112,7 @@ def test_init_nvfp4_variant(self) -> None:
     def test_init_invalid_variant(self) -> None:
         """Raise ValueError for invalid variant."""
         with pytest.raises(ValueError, match="Invalid model_variant: invalid"):
-            NemotronHVL(model_dir=self.model_dir, model_variant="invalid")
+            NemotronHVL(model_dir=self.model_dir, model_variant="invalid")  # type: ignore[arg-type]
 
     def test_model_id_names(self) -> None:
         """Return HuggingFace model ID for selected variant."""
@@ -375,7 +375,7 @@ def test_download_weights_already_exists(self) -> None:
     def test_download_weights_invalid_variant(self) -> None:
         """Raise ValueError for invalid variant."""
         with tempfile.TemporaryDirectory() as tmpdir, pytest.raises(ValueError, match="Invalid variant: invalid"):
-            NemotronHVL.download_weights_on_node(tmpdir, variant="invalid")
+            NemotronHVL.download_weights_on_node(tmpdir, variant="invalid")  # type: ignore[arg-type]
 
     def test_refine_caption_prompt_with_video_tag(self) -> None:
         """Test _refine_caption_prompt with video tag in prompt."""

From 7ecab7753df051f8f82add2ccd9ecc15c657e387 Mon Sep 17 00:00:00 2001
From: Ranjit Rajan <ranjitr@nvidia.com>
Date: Fri, 9 Jan 2026 20:15:05 +0000
Subject: [PATCH 07/17] Fix linting errors: remove trailing whitespace and add
 newline at EOF

Signed-off-by: Ranjit Rajan <ranjitr@nvidia.com>
---
 tests/stages/video/caption/test_caption_generation.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/tests/stages/video/caption/test_caption_generation.py b/tests/stages/video/caption/test_caption_generation.py
index c6ae346a89..e6b0b9dcdb 100644
--- a/tests/stages/video/caption/test_caption_generation.py
+++ b/tests/stages/video/caption/test_caption_generation.py
@@ -402,4 +402,3 @@ def test_process_nemotron_with_generic_llm_inputs(self):
         # Verify cleanup - nemotron inputs should be deleted
         assert "nemotron" not in result.data.clips[0].windows[0].llm_inputs
         assert "nemotron" not in result.data.clips[0].windows[1].llm_inputs
-        
\ No newline at end of file

From ccca9d728b3b841d96316701495059e0ba3c387e Mon Sep 17 00:00:00 2001
From: Ranjit Rajan <ranjitr@nvidia.com>
Date: Mon, 12 Jan 2026 18:34:37 +0000
Subject: [PATCH 08/17] Improve dtype check in prompt_formatter to handle all
 floating types

Signed-off-by: Ranjit Rajan <ranjitr@nvidia.com>
---
 nemo_curator/models/prompt_formatter.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nemo_curator/models/prompt_formatter.py b/nemo_curator/models/prompt_formatter.py
index 845cbcb060..d41589785c 100644
--- a/nemo_curator/models/prompt_formatter.py
+++ b/nemo_curator/models/prompt_formatter.py
@@ -148,7 +148,7 @@ def _convert_to_numpy(self, video_inputs: torch.Tensor | np.ndarray) -> np.ndarr
 
         # Normalize to uint8 if needed
         if video_np.dtype != np.uint8:
-            if video_np.dtype in (np.float32, np.float16) and video_np.max() <= 1.0:
+            if np.issubdtype(video_np.dtype, np.floating) and video_np.max() <= 1.0:
                 video_np = (video_np * 255).astype(np.uint8)
             else:
                 video_np = video_np.astype(np.uint8)

From 76c365b7d580f8469aeaecb8ca35c5912854f8d7 Mon Sep 17 00:00:00 2001
From: weijiac <weijiac@NVIDIA.com>
Date: Fri, 27 Feb 2026 03:00:55 +0000
Subject: [PATCH 09/17] patch

Signed-off-by: weijiac <weijiac@NVIDIA.com>
---
 nemo_curator/models/cosmos_embed1.py | 195 ++++++++++++++++++++++++++-
 1 file changed, 194 insertions(+), 1 deletion(-)

diff --git a/nemo_curator/models/cosmos_embed1.py b/nemo_curator/models/cosmos_embed1.py
index 758c0021f3..5004f1aaa1 100644
--- a/nemo_curator/models/cosmos_embed1.py
+++ b/nemo_curator/models/cosmos_embed1.py
@@ -12,6 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import importlib
+import importlib.util
+from collections.abc import Callable
 from pathlib import Path
 from typing import Final, Literal, cast
 
@@ -38,6 +41,191 @@
 }
 
 
+_HEAD_MASK_VECTOR_DIM: Final[int] = 1
+_HEAD_MASK_MATRIX_DIM: Final[int] = 2
+_HEAD_MASK_TARGET_DIM: Final[int] = 5
+
+
+def _resolve_symbol(name: str) -> object | None:
+    for module_name in ("transformers.pytorch_utils", "transformers.modeling_utils"):
+        if importlib.util.find_spec(module_name) is None:
+            continue
+        module = importlib.import_module(module_name)
+        if hasattr(module, name):
+            return getattr(module, name)
+    return None
+
+
+def _fallback_apply_chunking_to_forward(
+    forward_fn: Callable[..., object], chunk_size: int, chunk_dim: int, *input_tensors: torch.Tensor
+) -> object:
+    if len(input_tensors) == 0:
+        msg = "input_tensors cannot be empty"
+        raise ValueError(msg)
+
+    if chunk_size <= 0:
+        return forward_fn(*input_tensors)
+
+    tensor_shape = input_tensors[0].shape[chunk_dim]
+    if any(t.shape[chunk_dim] != tensor_shape for t in input_tensors):
+        msg = "All input tensors must have the same shape on chunk_dim"
+        raise ValueError(msg)
+    if tensor_shape % chunk_size != 0:
+        msg = "The dimension to be chunked must be a multiple of chunk_size"
+        raise ValueError(msg)
+
+    num_chunks = tensor_shape // chunk_size
+    input_chunks = tuple(t.chunk(num_chunks, dim=chunk_dim) for t in input_tensors)
+    output_chunks = tuple(forward_fn(*chunk_inputs) for chunk_inputs in zip(*input_chunks, strict=False))
+    return torch.cat(output_chunks, dim=chunk_dim)
+
+
+def _fallback_find_pruneable_heads_and_indices(
+    heads: list[int] | set[int], n_heads: int, head_size: int, already_pruned_heads: set[int]
+) -> tuple[set[int], torch.Tensor]:
+    heads_to_prune = set(heads) - already_pruned_heads
+    mask = torch.ones(n_heads, head_size)
+    for head in heads_to_prune:
+        adjusted_head = head - sum(1 for h in already_pruned_heads if h < head)
+        mask[adjusted_head] = 0
+    mask = mask.view(-1).contiguous().eq(1)
+    index = torch.arange(mask.numel())[mask].long()
+    return heads_to_prune, index
+
+
+def _fallback_prune_linear_layer(layer: torch.nn.Linear, index: torch.Tensor, dim: int = 0) -> torch.nn.Linear:
+    index = index.to(layer.weight.device)
+    weight = layer.weight.index_select(dim, index).clone().detach()
+    if layer.bias is not None:
+        bias = layer.bias.clone().detach() if dim == 1 else layer.bias.index_select(0, index).clone().detach()
+    new_size = list(layer.weight.size())
+    new_size[dim] = index.size(0)
+    new_layer = torch.nn.Linear(new_size[1], new_size[0], bias=layer.bias is not None).to(layer.weight.device)
+    new_layer.weight.requires_grad = False
+    new_layer.weight.copy_(weight.contiguous())
+    new_layer.weight.requires_grad = True
+    if layer.bias is not None:
+        new_layer.bias.requires_grad = False
+        new_layer.bias.copy_(bias.contiguous())
+        new_layer.bias.requires_grad = True
+    return new_layer
+
+
+def _ensure_all_tied_weights_keys(model: object) -> None:
+    if hasattr(model, "all_tied_weights_keys"):
+        return
+    tied = getattr(model, "_tied_weights_keys", None)
+    if tied is None:
+        model.all_tied_weights_keys = {}
+        return
+    if isinstance(tied, dict):
+        model.all_tied_weights_keys = tied
+        return
+    try:
+        model.all_tied_weights_keys = dict.fromkeys(tied)
+    except TypeError:
+        model.all_tied_weights_keys = {}
+
+
+def _convert_head_mask_to_5d(self: object, head_mask: torch.Tensor, num_hidden_layers: int) -> torch.Tensor:
+    if head_mask.dim() == _HEAD_MASK_VECTOR_DIM:
+        head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+        head_mask = head_mask.expand(num_hidden_layers, -1, -1, -1, -1)
+    elif head_mask.dim() == _HEAD_MASK_MATRIX_DIM:
+        head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
+    if head_mask.dim() != _HEAD_MASK_TARGET_DIM:
+        msg = f"head_mask.dim != {_HEAD_MASK_TARGET_DIM}, got {head_mask.dim()}"
+        raise ValueError(msg)
+    try:
+        dtype = self.dtype
+    except Exception:  # noqa: BLE001
+        dtype = next(self.parameters()).dtype
+    return head_mask.to(dtype=dtype)
+
+
+def _get_head_mask(
+    self: object, head_mask: torch.Tensor | None, num_hidden_layers: int, is_attention_chunked: bool = False
+) -> torch.Tensor | list[None]:
+    if head_mask is not None:
+        head_mask = self._convert_head_mask_to_5d(head_mask, num_hidden_layers)
+        if is_attention_chunked:
+            head_mask = head_mask.unsqueeze(-1)
+        return head_mask
+    return [None] * num_hidden_layers
+
+
+def _patch_modeling_utils_symbols(modeling_utils_module: object) -> None:
+    if not hasattr(modeling_utils_module, "apply_chunking_to_forward"):
+        symbol = _resolve_symbol("apply_chunking_to_forward") or _fallback_apply_chunking_to_forward
+        modeling_utils_module.apply_chunking_to_forward = symbol
+    if not hasattr(modeling_utils_module, "find_pruneable_heads_and_indices"):
+        symbol = _resolve_symbol("find_pruneable_heads_and_indices") or _fallback_find_pruneable_heads_and_indices
+        modeling_utils_module.find_pruneable_heads_and_indices = symbol
+    if not hasattr(modeling_utils_module, "prune_linear_layer"):
+        symbol = _resolve_symbol("prune_linear_layer") or _fallback_prune_linear_layer
+        modeling_utils_module.prune_linear_layer = symbol
+
+
+def _patch_pretrained_model_compat(pre_trained_model_cls: object) -> None:
+    if not getattr(pre_trained_model_cls, "_nemo_curator_tied_weights_compat_patched", False):
+        original_mark_tied = getattr(pre_trained_model_cls, "mark_tied_weights_as_initialized", None)
+        if original_mark_tied is not None:
+
+            def _patched_mark_tied_weights_as_initialized(self: object, *args: object, **kwargs: object) -> object:
+                _ensure_all_tied_weights_keys(self)
+                return original_mark_tied(self, *args, **kwargs)
+
+            pre_trained_model_cls.mark_tied_weights_as_initialized = _patched_mark_tied_weights_as_initialized
+
+        original_adjust_tied = getattr(pre_trained_model_cls, "_adjust_tied_keys_with_tied_pointers", None)
+        if original_adjust_tied is not None:
+
+            def _patched_adjust_tied_keys_with_tied_pointers(self: object, *args: object, **kwargs: object) -> object:
+                _ensure_all_tied_weights_keys(self)
+                return original_adjust_tied(self, *args, **kwargs)
+
+            pre_trained_model_cls._adjust_tied_keys_with_tied_pointers = _patched_adjust_tied_keys_with_tied_pointers
+        pre_trained_model_cls._nemo_curator_tied_weights_compat_patched = True
+
+    if not hasattr(pre_trained_model_cls, "_convert_head_mask_to_5d"):
+        pre_trained_model_cls._convert_head_mask_to_5d = _convert_head_mask_to_5d
+    if not hasattr(pre_trained_model_cls, "get_head_mask"):
+        pre_trained_model_cls.get_head_mask = _get_head_mask
+
+
+def _patch_transformers_compat() -> None:
+    """Patch moved Transformers symbols expected by older Cosmos remote code."""
+    from transformers import modeling_utils
+    from transformers.modeling_utils import PreTrainedModel
+
+    _patch_modeling_utils_symbols(modeling_utils)
+    _patch_pretrained_model_compat(PreTrainedModel)
+
+
+def _patch_cosmos_embed1_modeling_vit(*, weights_dir: str, variant: Literal["224p", "336p", "448p"]) -> None:
+    """Patch Cosmos-Embed1 ViT code to avoid meta-tensor .item() failures."""
+    old_line = "dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule"
+    new_line = 'dpr = torch.linspace(0, drop_path_rate, depth, device="cpu", dtype=torch.float32).tolist()'
+    candidates = [
+        Path(weights_dir) / "modeling_vit.py",
+        Path.home()
+        / ".cache/huggingface/modules/transformers_modules"
+        / f"Cosmos_hyphen_Embed1_hyphen_{variant}"
+        / "modeling_vit.py",
+    ]
+
+    for path in candidates:
+        try:
+            if not path.exists():
+                continue
+            text = path.read_text(encoding="utf-8")
+            if old_line not in text:
+                continue
+            path.write_text(text.replace(old_line, new_line), encoding="utf-8")
+        except Exception as e:  # noqa: BLE001
+            logger.warning(f"Could not patch {path}: {e}")
+
+
 class CosmosEmbed1(ModelInterface):
     """Cosmos-Embed1 embedding model."""
 
@@ -78,6 +266,8 @@ def setup(self) -> None:
         This method initializes the model and its configuration for processing video and text data.
         """
         logger.info("Setting up Cosmos-Embed1 model")
+        _patch_transformers_compat()
+        _patch_cosmos_embed1_modeling_vit(weights_dir=self._weights_dir, variant=self.variant)
         if not Path(self._weights_dir).exists():
             exception = f"Weights directory {self._weights_dir} not found!"
             raise FileNotFoundError(exception)
@@ -86,6 +276,7 @@ def setup(self) -> None:
                 self._weights_dir,
                 trust_remote_code=True,
                 local_files_only=True,
+                low_cpu_mem_usage=False,
             ).to("cuda", dtype=torch.bfloat16)
             if self._model is None:
                 msg = "Model failed to load"
@@ -201,7 +392,9 @@ def download_weights_on_node(cls, model_dir: str, variant: Literal["224p", "336p
         logger.info(f"CosmosEmbed1 {variant} weights downloaded to: {model_dir_path}")
 
     @classmethod
-    def download_processor_config_on_node(cls, model_dir: str, variant: Literal["224p", "336p", "448p"] = "336p") -> None:
+    def download_processor_config_on_node(
+        cls, model_dir: str, variant: Literal["224p", "336p", "448p"] = "336p"
+    ) -> None:
         """Download the processor config for the CosmosEmbed1 model on the node."""
         model_dir_path = Path(model_dir) / _COSMOS_EMBED1_VARIANTS_INFO[variant]
         model_dir_path.mkdir(parents=True, exist_ok=True)

From 1c374439758dc3dccfc228c202ce9d8d8f475d11 Mon Sep 17 00:00:00 2001
From: weijiac <weijiac@NVIDIA.com>
Date: Fri, 27 Feb 2026 03:16:14 +0000
Subject: [PATCH 10/17] clean

Signed-off-by: weijiac <weijiac@NVIDIA.com>
---
 nemo_curator/models/cosmos_embed1.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/nemo_curator/models/cosmos_embed1.py b/nemo_curator/models/cosmos_embed1.py
index 5004f1aaa1..86bdcda158 100644
--- a/nemo_curator/models/cosmos_embed1.py
+++ b/nemo_curator/models/cosmos_embed1.py
@@ -276,7 +276,6 @@ def setup(self) -> None:
                 self._weights_dir,
                 trust_remote_code=True,
                 local_files_only=True,
-                low_cpu_mem_usage=False,
             ).to("cuda", dtype=torch.bfloat16)
             if self._model is None:
                 msg = "Model failed to load"

From f60dab8396c0ff266580423f7f0979c998e9a498 Mon Sep 17 00:00:00 2001
From: weijiac <weijiac@NVIDIA.com>
Date: Tue, 24 Mar 2026 19:46:44 +0000
Subject: [PATCH 11/17] nemotron preprocessing

Signed-off-by: weijiac <weijiac@NVIDIA.com>
---
 .../video/caption/caption_preparation.py      | 12 ++++++
 nemo_curator/utils/windowing_utils.py         | 41 +++++++++++--------
 2 files changed, 35 insertions(+), 18 deletions(-)

diff --git a/nemo_curator/stages/video/caption/caption_preparation.py b/nemo_curator/stages/video/caption/caption_preparation.py
index 344a3d5076..91777e3a5a 100644
--- a/nemo_curator/stages/video/caption/caption_preparation.py
+++ b/nemo_curator/stages/video/caption/caption_preparation.py
@@ -95,6 +95,17 @@ def inputs(self) -> tuple[list[str], list[str]]:
     def outputs(self) -> tuple[list[str], list[str]]:
         return [], []
 
+    def __post_init__(self) -> None:
+        self._skip_intermediate_resize = False
+        if self.model_variant.startswith("nemotron"):
+            if not self.model_does_preprocess:
+                logger.warning(
+                    f"model_variant={self.model_variant!r}: overriding model_does_preprocess=True. "
+                    "Nemotron uses vLLM's internal preprocessing; CLIP normalization must not be applied beforehand."
+                )
+                self.model_does_preprocess = True
+            self._skip_intermediate_resize = True
+
     def setup(self, worker_metadata: WorkerMetadata | None = None) -> None:  # noqa: ARG002
         # PromptFormatter uses AutoProcessor from HuggingFace (auto-downloads/caches)
         self.prompt_formatter = PromptFormatter(self.model_variant)
@@ -116,6 +127,7 @@ def process(self, task: VideoTask) -> VideoTask:
                     sampling_fps=self.sampling_fps,
                     model_does_preprocess=self.model_does_preprocess,
                     preprocess_dtype=self.preprocess_dtype,
+                    skip_resize=self._skip_intermediate_resize,
                     return_bytes=self.generate_previews,
                     num_threads=max(int(self.resources.cpus), 1),
                 ),
diff --git a/nemo_curator/utils/windowing_utils.py b/nemo_curator/utils/windowing_utils.py
index 1666bd5a75..dce926f823 100644
--- a/nemo_curator/utils/windowing_utils.py
+++ b/nemo_curator/utils/windowing_utils.py
@@ -84,6 +84,7 @@ def split_video_into_windows(  # noqa: PLR0913
     *,
     model_does_preprocess: bool = False,
     preprocess_dtype: str = "uint8",
+    skip_resize: bool = False,
     flip_input: bool = False,
     num_frames_to_use: int = 0,
     return_bytes: bool = False,
@@ -135,6 +136,7 @@ def split_video_into_windows(  # noqa: PLR0913
                 preprocess_dtype=preprocess_dtype,
                 num_frames_to_use=num_frames_to_use,
                 flip_input=flip_input,
+                skip_resize=skip_resize,
             )
 
             index = 0
@@ -293,7 +295,7 @@ def read_video_cpu(
     return video, frame_counts
 
 
-def fetch_video(  # noqa: C901, PLR0911, PLR0913
+def fetch_video(  # noqa: C901, PLR0911, PLR0912, PLR0913
     video_path: str,
     sampling_fps: float = 2.0,
     window_range: list[WindowFrameInfo] | None = None,
@@ -302,6 +304,7 @@ def fetch_video(  # noqa: C901, PLR0911, PLR0913
     preprocess_dtype: str = "float32",
     num_frames_to_use: int = 0,
     flip_input: bool = False,
+    skip_resize: bool = False,
 ) -> tuple[torch.Tensor, list[int]]:
     """Load and preprocess video frames from a file.
 
@@ -328,17 +331,18 @@ def fetch_video(  # noqa: C901, PLR0911, PLR0913
     )
     nframes, _, height, width = video.shape
 
-    max_pixels = max(
-        min(VIDEO_MAX_PIXELS, int(VIDEO_TOTAL_PIXELS / nframes * FRAME_FACTOR)),
-        int(VIDEO_MIN_PIXELS * 1.05),
-    )
-    resized_height, resized_width = smart_resize(
-        height,
-        width,
-        factor=IMAGE_FACTOR,
-        min_pixels=VIDEO_MIN_PIXELS,
-        max_pixels=max_pixels,
-    )
+    if do_preprocess or not skip_resize:
+        max_pixels = max(
+            min(VIDEO_MAX_PIXELS, int(VIDEO_TOTAL_PIXELS / nframes * FRAME_FACTOR)),
+            int(VIDEO_MIN_PIXELS * 1.05),
+        )
+        resized_height, resized_width = smart_resize(
+            height,
+            width,
+            factor=IMAGE_FACTOR,
+            min_pixels=VIDEO_MIN_PIXELS,
+            max_pixels=max_pixels,
+        )
 
     if do_preprocess:
         try:
@@ -368,12 +372,13 @@ def fetch_video(  # noqa: C901, PLR0911, PLR0913
         if preprocess_dtype == "uint8":
             return video.to(torch.uint8), frame_counts
         return video, frame_counts
-    video = transforms.functional.resize(
-        video,
-        [resized_height, resized_width],
-        interpolation=InterpolationMode.BICUBIC,
-        antialias=True,
-    )
+    if not skip_resize:
+        video = transforms.functional.resize(
+            video,
+            [resized_height, resized_width],
+            interpolation=InterpolationMode.BICUBIC,
+            antialias=True,
+        )
     if flip_input:
         video = [torch.flip(frame, dims=[1, 2]) for frame in video]  # type: ignore[assignment]
     if preprocess_dtype == "float32":

From d1064a4c28ebc7eacec97abe3e0290145518c2d8 Mon Sep 17 00:00:00 2001
From: weijiac <weijiac@NVIDIA.com>
Date: Tue, 24 Mar 2026 21:16:04 +0000
Subject: [PATCH 12/17] stage2 fallback for qwen

Signed-off-by: weijiac <weijiac@NVIDIA.com>
---
 nemo_curator/models/qwen_vl.py | 2 +-
 tests/models/test_qwen_vl.py   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/nemo_curator/models/qwen_vl.py b/nemo_curator/models/qwen_vl.py
index 0605216b53..6b683405dd 100644
--- a/nemo_curator/models/qwen_vl.py
+++ b/nemo_curator/models/qwen_vl.py
@@ -67,7 +67,7 @@ def __init__(  # noqa: PLR0913
         self.max_output_tokens = max_output_tokens
         self.model_does_preprocess = model_does_preprocess
         self.disable_mmcache = disable_mmcache
-        self.stage2_prompt = stage2_prompt_text
+        self.stage2_prompt = stage2_prompt_text if stage2_prompt_text else "Please refine this caption: "
         self.verbose = verbose
         self.weight_file = str(pathlib.Path(model_dir) / _QWEN_VARIANTS_INFO[model_variant])
         # Default pattern for stage2 caption generation - matches (.*)(user_prompt)(.*)
diff --git a/tests/models/test_qwen_vl.py b/tests/models/test_qwen_vl.py
index f7c93fcc4e..0fcce7e975 100644
--- a/tests/models/test_qwen_vl.py
+++ b/tests/models/test_qwen_vl.py
@@ -71,7 +71,7 @@ def test_initialization_default_parameters(self) -> None:
         assert qwen_vl.max_output_tokens == 512
         assert qwen_vl.model_does_preprocess is False
         assert qwen_vl.disable_mmcache is False
-        assert qwen_vl.stage2_prompt is None
+        assert qwen_vl.stage2_prompt == "Please refine this caption: "
         assert qwen_vl.verbose is False
 
         expected_weight_file = str(pathlib.Path(self.model_dir) / _QWEN_VARIANTS_INFO[self.model_variant])

From 9ec33e8279670247f2e314db72fb75de591a9b00 Mon Sep 17 00:00:00 2001
From: weijiac <weijiac@NVIDIA.com>
Date: Wed, 1 Apr 2026 03:00:18 +0000
Subject: [PATCH 13/17] clean up

Signed-off-by: weijiac <weijiac@NVIDIA.com>
---
 nemo_curator/models/cosmos_embed1.py | 198 +--------------------------
 nemo_curator/models/nemotron_h_vl.py |   3 -
 2 files changed, 4 insertions(+), 197 deletions(-)

diff --git a/nemo_curator/models/cosmos_embed1.py b/nemo_curator/models/cosmos_embed1.py
index 86bdcda158..7eb6d7f93b 100644
--- a/nemo_curator/models/cosmos_embed1.py
+++ b/nemo_curator/models/cosmos_embed1.py
@@ -12,9 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import importlib
-import importlib.util
-from collections.abc import Callable
 from pathlib import Path
 from typing import Final, Literal, cast
 
@@ -35,197 +32,12 @@
 }
 
 COSMOS_EMBED1_MODEL_REVISION_INFO: Final = {
-    "224p": "85f5627",
-    "336p": "5d8309d",
-    "448p": "9f4ff4d",
+    "224p": "787e0b9",
+    "336p": "0e8a28f",
+    "448p": "f60ec73",
 }
 
 
-_HEAD_MASK_VECTOR_DIM: Final[int] = 1
-_HEAD_MASK_MATRIX_DIM: Final[int] = 2
-_HEAD_MASK_TARGET_DIM: Final[int] = 5
-
-
-def _resolve_symbol(name: str) -> object | None:
-    for module_name in ("transformers.pytorch_utils", "transformers.modeling_utils"):
-        if importlib.util.find_spec(module_name) is None:
-            continue
-        module = importlib.import_module(module_name)
-        if hasattr(module, name):
-            return getattr(module, name)
-    return None
-
-
-def _fallback_apply_chunking_to_forward(
-    forward_fn: Callable[..., object], chunk_size: int, chunk_dim: int, *input_tensors: torch.Tensor
-) -> object:
-    if len(input_tensors) == 0:
-        msg = "input_tensors cannot be empty"
-        raise ValueError(msg)
-
-    if chunk_size <= 0:
-        return forward_fn(*input_tensors)
-
-    tensor_shape = input_tensors[0].shape[chunk_dim]
-    if any(t.shape[chunk_dim] != tensor_shape for t in input_tensors):
-        msg = "All input tensors must have the same shape on chunk_dim"
-        raise ValueError(msg)
-    if tensor_shape % chunk_size != 0:
-        msg = "The dimension to be chunked must be a multiple of chunk_size"
-        raise ValueError(msg)
-
-    num_chunks = tensor_shape // chunk_size
-    input_chunks = tuple(t.chunk(num_chunks, dim=chunk_dim) for t in input_tensors)
-    output_chunks = tuple(forward_fn(*chunk_inputs) for chunk_inputs in zip(*input_chunks, strict=False))
-    return torch.cat(output_chunks, dim=chunk_dim)
-
-
-def _fallback_find_pruneable_heads_and_indices(
-    heads: list[int] | set[int], n_heads: int, head_size: int, already_pruned_heads: set[int]
-) -> tuple[set[int], torch.Tensor]:
-    heads_to_prune = set(heads) - already_pruned_heads
-    mask = torch.ones(n_heads, head_size)
-    for head in heads_to_prune:
-        adjusted_head = head - sum(1 for h in already_pruned_heads if h < head)
-        mask[adjusted_head] = 0
-    mask = mask.view(-1).contiguous().eq(1)
-    index = torch.arange(mask.numel())[mask].long()
-    return heads_to_prune, index
-
-
-def _fallback_prune_linear_layer(layer: torch.nn.Linear, index: torch.Tensor, dim: int = 0) -> torch.nn.Linear:
-    index = index.to(layer.weight.device)
-    weight = layer.weight.index_select(dim, index).clone().detach()
-    if layer.bias is not None:
-        bias = layer.bias.clone().detach() if dim == 1 else layer.bias.index_select(0, index).clone().detach()
-    new_size = list(layer.weight.size())
-    new_size[dim] = index.size(0)
-    new_layer = torch.nn.Linear(new_size[1], new_size[0], bias=layer.bias is not None).to(layer.weight.device)
-    new_layer.weight.requires_grad = False
-    new_layer.weight.copy_(weight.contiguous())
-    new_layer.weight.requires_grad = True
-    if layer.bias is not None:
-        new_layer.bias.requires_grad = False
-        new_layer.bias.copy_(bias.contiguous())
-        new_layer.bias.requires_grad = True
-    return new_layer
-
-
-def _ensure_all_tied_weights_keys(model: object) -> None:
-    if hasattr(model, "all_tied_weights_keys"):
-        return
-    tied = getattr(model, "_tied_weights_keys", None)
-    if tied is None:
-        model.all_tied_weights_keys = {}
-        return
-    if isinstance(tied, dict):
-        model.all_tied_weights_keys = tied
-        return
-    try:
-        model.all_tied_weights_keys = dict.fromkeys(tied)
-    except TypeError:
-        model.all_tied_weights_keys = {}
-
-
-def _convert_head_mask_to_5d(self: object, head_mask: torch.Tensor, num_hidden_layers: int) -> torch.Tensor:
-    if head_mask.dim() == _HEAD_MASK_VECTOR_DIM:
-        head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
-        head_mask = head_mask.expand(num_hidden_layers, -1, -1, -1, -1)
-    elif head_mask.dim() == _HEAD_MASK_MATRIX_DIM:
-        head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
-    if head_mask.dim() != _HEAD_MASK_TARGET_DIM:
-        msg = f"head_mask.dim != {_HEAD_MASK_TARGET_DIM}, got {head_mask.dim()}"
-        raise ValueError(msg)
-    try:
-        dtype = self.dtype
-    except Exception:  # noqa: BLE001
-        dtype = next(self.parameters()).dtype
-    return head_mask.to(dtype=dtype)
-
-
-def _get_head_mask(
-    self: object, head_mask: torch.Tensor | None, num_hidden_layers: int, is_attention_chunked: bool = False
-) -> torch.Tensor | list[None]:
-    if head_mask is not None:
-        head_mask = self._convert_head_mask_to_5d(head_mask, num_hidden_layers)
-        if is_attention_chunked:
-            head_mask = head_mask.unsqueeze(-1)
-        return head_mask
-    return [None] * num_hidden_layers
-
-
-def _patch_modeling_utils_symbols(modeling_utils_module: object) -> None:
-    if not hasattr(modeling_utils_module, "apply_chunking_to_forward"):
-        symbol = _resolve_symbol("apply_chunking_to_forward") or _fallback_apply_chunking_to_forward
-        modeling_utils_module.apply_chunking_to_forward = symbol
-    if not hasattr(modeling_utils_module, "find_pruneable_heads_and_indices"):
-        symbol = _resolve_symbol("find_pruneable_heads_and_indices") or _fallback_find_pruneable_heads_and_indices
-        modeling_utils_module.find_pruneable_heads_and_indices = symbol
-    if not hasattr(modeling_utils_module, "prune_linear_layer"):
-        symbol = _resolve_symbol("prune_linear_layer") or _fallback_prune_linear_layer
-        modeling_utils_module.prune_linear_layer = symbol
-
-
-def _patch_pretrained_model_compat(pre_trained_model_cls: object) -> None:
-    if not getattr(pre_trained_model_cls, "_nemo_curator_tied_weights_compat_patched", False):
-        original_mark_tied = getattr(pre_trained_model_cls, "mark_tied_weights_as_initialized", None)
-        if original_mark_tied is not None:
-
-            def _patched_mark_tied_weights_as_initialized(self: object, *args: object, **kwargs: object) -> object:
-                _ensure_all_tied_weights_keys(self)
-                return original_mark_tied(self, *args, **kwargs)
-
-            pre_trained_model_cls.mark_tied_weights_as_initialized = _patched_mark_tied_weights_as_initialized
-
-        original_adjust_tied = getattr(pre_trained_model_cls, "_adjust_tied_keys_with_tied_pointers", None)
-        if original_adjust_tied is not None:
-
-            def _patched_adjust_tied_keys_with_tied_pointers(self: object, *args: object, **kwargs: object) -> object:
-                _ensure_all_tied_weights_keys(self)
-                return original_adjust_tied(self, *args, **kwargs)
-
-            pre_trained_model_cls._adjust_tied_keys_with_tied_pointers = _patched_adjust_tied_keys_with_tied_pointers
-        pre_trained_model_cls._nemo_curator_tied_weights_compat_patched = True
-
-    if not hasattr(pre_trained_model_cls, "_convert_head_mask_to_5d"):
-        pre_trained_model_cls._convert_head_mask_to_5d = _convert_head_mask_to_5d
-    if not hasattr(pre_trained_model_cls, "get_head_mask"):
-        pre_trained_model_cls.get_head_mask = _get_head_mask
-
-
-def _patch_transformers_compat() -> None:
-    """Patch moved Transformers symbols expected by older Cosmos remote code."""
-    from transformers import modeling_utils
-    from transformers.modeling_utils import PreTrainedModel
-
-    _patch_modeling_utils_symbols(modeling_utils)
-    _patch_pretrained_model_compat(PreTrainedModel)
-
-
-def _patch_cosmos_embed1_modeling_vit(*, weights_dir: str, variant: Literal["224p", "336p", "448p"]) -> None:
-    """Patch Cosmos-Embed1 ViT code to avoid meta-tensor .item() failures."""
-    old_line = "dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule"
-    new_line = 'dpr = torch.linspace(0, drop_path_rate, depth, device="cpu", dtype=torch.float32).tolist()'
-    candidates = [
-        Path(weights_dir) / "modeling_vit.py",
-        Path.home()
-        / ".cache/huggingface/modules/transformers_modules"
-        / f"Cosmos_hyphen_Embed1_hyphen_{variant}"
-        / "modeling_vit.py",
-    ]
-
-    for path in candidates:
-        try:
-            if not path.exists():
-                continue
-            text = path.read_text(encoding="utf-8")
-            if old_line not in text:
-                continue
-            path.write_text(text.replace(old_line, new_line), encoding="utf-8")
-        except Exception as e:  # noqa: BLE001
-            logger.warning(f"Could not patch {path}: {e}")
-
-
 class CosmosEmbed1(ModelInterface):
     """Cosmos-Embed1 embedding model."""
 
@@ -266,10 +78,8 @@ def setup(self) -> None:
         This method initializes the model and its configuration for processing video and text data.
         """
         logger.info("Setting up Cosmos-Embed1 model")
-        _patch_transformers_compat()
-        _patch_cosmos_embed1_modeling_vit(weights_dir=self._weights_dir, variant=self.variant)
         if not Path(self._weights_dir).exists():
-            exception = f"Weights directory {self._weights_dir} not found!"
+            exception = rf"Weights directory {self._weights_dir} not found\!"
             raise FileNotFoundError(exception)
         if not self._utils_only:
             self._model = AutoModel.from_pretrained(
diff --git a/nemo_curator/models/nemotron_h_vl.py b/nemo_curator/models/nemotron_h_vl.py
index 37ad08c96f..32259839c4 100644
--- a/nemo_curator/models/nemotron_h_vl.py
+++ b/nemo_curator/models/nemotron_h_vl.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
 from pathlib import Path
 from typing import Any, Final, Literal
 
@@ -125,8 +124,6 @@ def setup(self) -> None:
             msg = "vllm is required for NemotronHVL but is not installed. Please install vllm: pip install vllm"
             raise ImportError(msg)
 
-        os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
-
         # Determine quantization and dtype based on variant
         # See: https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-FP8
         #      https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-NVFP4-QAD

From d30889662a827943e22184f72428ee98fc67c88a Mon Sep 17 00:00:00 2001
From: weijiac <weijiac@NVIDIA.com>
Date: Wed, 1 Apr 2026 03:30:25 +0000
Subject: [PATCH 14/17] resolved conflicts

Signed-off-by: weijiac <weijiac@NVIDIA.com>
---
 .github/workflows/config/.secrets.baseline  | 23 +++++++++++++++++++++
 nemo_curator/models/nemotron_h_vl.py        |  2 +-
 nemo_curator/stages/video/io/clip_writer.py |  8 +++----
 3 files changed, 28 insertions(+), 5 deletions(-)

diff --git a/.github/workflows/config/.secrets.baseline b/.github/workflows/config/.secrets.baseline
index bef9c287af..3cac371d2b 100644
--- a/.github/workflows/config/.secrets.baseline
+++ b/.github/workflows/config/.secrets.baseline
@@ -306,6 +306,29 @@
         "is_verified": false,
         "line_number": 670
       }
+    ],
+    "nemo_curator/models/nemotron_h_vl.py": [
+      {
+        "type": "Hex High Entropy String",
+        "filename": "nemo_curator/models/nemotron_h_vl.py",
+        "hashed_secret": "7e44041c8ec081054af6ccf0758efa3771e99123",
+        "is_verified": false,
+        "line_number": 52
+      },
+      {
+        "type": "Hex High Entropy String",
+        "filename": "nemo_curator/models/nemotron_h_vl.py",
+        "hashed_secret": "f1a89dd2b19e1d30ac4fc1a8d1f4993796c9d1e6",
+        "is_verified": false,
+        "line_number": 54
+      },
+      {
+        "type": "Hex High Entropy String",
+        "filename": "nemo_curator/models/nemotron_h_vl.py",
+        "hashed_secret": "d7f11e2581dd9d25522592e7cfb9346a749f51f3",
+        "is_verified": false,
+        "line_number": 55
+      }
     ]
   },
   "generated_at": "2026-03-17T21:09:47Z"
diff --git a/nemo_curator/models/nemotron_h_vl.py b/nemo_curator/models/nemotron_h_vl.py
index 32259839c4..fb9d5bced1 100644
--- a/nemo_curator/models/nemotron_h_vl.py
+++ b/nemo_curator/models/nemotron_h_vl.py
@@ -96,7 +96,7 @@ def __init__(  # noqa: PLR0913
         if model_variant == "nemotron":
             self._normalized_variant: NemotronVariant = "nemotron-bf16"
         else:
-            self._normalized_variant = model_variant
+            self._normalized_variant = model_variant  # type: ignore[assignment]
 
         if self._normalized_variant not in _NEMOTRON_VARIANTS_INFO:
             valid_variants = ", ".join(_NEMOTRON_VARIANTS_INFO.keys())
diff --git a/nemo_curator/stages/video/io/clip_writer.py b/nemo_curator/stages/video/io/clip_writer.py
index eb62d17774..e21914e26a 100644
--- a/nemo_curator/stages/video/io/clip_writer.py
+++ b/nemo_curator/stages/video/io/clip_writer.py
@@ -352,11 +352,11 @@ def _write_clip_metadata(self, clip: Clip, video_metadata: VideoMetadata, *, fil
                 "start_frame": window.start_frame,
                 "end_frame": window.end_frame,
             }
-            for model in self.caption_models:
+            for model in self.caption_models or []:
                 if model in window.caption:
                     curr_window[f"{model}_caption"] = window.caption[model]
                     has_caption = True
-            for model in self.enhanced_caption_models:
+            for model in self.enhanced_caption_models or []:
                 if model in window.enhanced_caption:
                     curr_window[f"{model}_enhanced_caption"] = window.enhanced_caption[model]
             data["windows"].append(curr_window)
@@ -415,12 +415,12 @@ def _write_video_metadata(self, video: Video) -> None:
             for window in clip.windows:
                 window_key = f"{window.start_frame}_{window.end_frame}"
                 # Try each caption model in order, using the first one available.
-                for model in self.caption_models:
+                for model in self.caption_models or []:
                     if model in window.caption:
                         data["all_windows"][clip_uuid][window_key] = window.caption[model]
                         break
                 # Try each enhanced caption model in order, using the first one found.
-                for model in self.enhanced_caption_models:
+                for model in self.enhanced_caption_models or []:
                     if model in window.enhanced_caption:
                         data["all_windows_enhanced_caption"][clip_uuid][window_key] = window.enhanced_caption[model]
                         break

From e13ece315c617290283837874a312894f530d576 Mon Sep 17 00:00:00 2001
From: weijiac <weijiac@NVIDIA.com>
Date: Wed, 1 Apr 2026 05:00:55 +0000
Subject: [PATCH 15/17] clean

Signed-off-by: weijiac <weijiac@NVIDIA.com>
---
 nemo_curator/models/cosmos_embed1.py        | 2 +-
 nemo_curator/stages/video/io/clip_writer.py | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/nemo_curator/models/cosmos_embed1.py b/nemo_curator/models/cosmos_embed1.py
index 7eb6d7f93b..c3771944bc 100644
--- a/nemo_curator/models/cosmos_embed1.py
+++ b/nemo_curator/models/cosmos_embed1.py
@@ -79,7 +79,7 @@ def setup(self) -> None:
         """
         logger.info("Setting up Cosmos-Embed1 model")
         if not Path(self._weights_dir).exists():
-            exception = rf"Weights directory {self._weights_dir} not found\!"
+            exception = f"Weights directory {self._weights_dir} not found!"
             raise FileNotFoundError(exception)
         if not self._utils_only:
             self._model = AutoModel.from_pretrained(
diff --git a/nemo_curator/stages/video/io/clip_writer.py b/nemo_curator/stages/video/io/clip_writer.py
index e21914e26a..eb62d17774 100644
--- a/nemo_curator/stages/video/io/clip_writer.py
+++ b/nemo_curator/stages/video/io/clip_writer.py
@@ -352,11 +352,11 @@ def _write_clip_metadata(self, clip: Clip, video_metadata: VideoMetadata, *, fil
                 "start_frame": window.start_frame,
                 "end_frame": window.end_frame,
             }
-            for model in self.caption_models or []:
+            for model in self.caption_models:
                 if model in window.caption:
                     curr_window[f"{model}_caption"] = window.caption[model]
                     has_caption = True
-            for model in self.enhanced_caption_models or []:
+            for model in self.enhanced_caption_models:
                 if model in window.enhanced_caption:
                     curr_window[f"{model}_enhanced_caption"] = window.enhanced_caption[model]
             data["windows"].append(curr_window)
@@ -415,12 +415,12 @@ def _write_video_metadata(self, video: Video) -> None:
             for window in clip.windows:
                 window_key = f"{window.start_frame}_{window.end_frame}"
                 # Try each caption model in order, using the first one available.
-                for model in self.caption_models or []:
+                for model in self.caption_models:
                     if model in window.caption:
                         data["all_windows"][clip_uuid][window_key] = window.caption[model]
                         break
                 # Try each enhanced caption model in order, using the first one found.
-                for model in self.enhanced_caption_models or []:
+                for model in self.enhanced_caption_models:
                     if model in window.enhanced_caption:
                         data["all_windows_enhanced_caption"][clip_uuid][window_key] = window.enhanced_caption[model]
                         break

From c44c94dcd120d4fbda31e8790af2b7cd361396dc Mon Sep 17 00:00:00 2001
From: weijiac <weijiac@NVIDIA.com>
Date: Wed, 1 Apr 2026 18:58:08 +0000
Subject: [PATCH 16/17] comments

Signed-off-by: weijiac <weijiac@NVIDIA.com>
---
 pyproject.toml |  5 +----
 uv.lock        | 54 +++++++++++++++++++++++++-------------------------
 2 files changed, 28 insertions(+), 31 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index e66c7cf3c0..28e7b7fb6e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -167,9 +167,6 @@ video_cuda12 = [
     "PyNvVideoCodec==2.0.2; (platform_machine == 'x86_64' and platform_system != 'Darwin')",
     "torch<=2.9.1",
     "torchaudio",
-    # Nemotron VL support
-    "transformers>=4.56.0",
-    "vllm>=0.13; (platform_machine == 'x86_64' and platform_system != 'Darwin')",
 ]
 
 # Math Curation Dependencies
@@ -252,12 +249,12 @@ override-dependencies = [
     "apex; sys_platform == 'never'",
     "distance; sys_platform == 'never'",
     "huggingface-hub>=0.34,<1.0", # Override huggingface-hub, transformers and data-designer require two different versions of hugging-face hub
+    "transformers>=4.56.0", # Override nemo-toolkit[asr]'s <=4.52.0 constraint; required for Nemotron VL support
     "kaldiio;  sys_platform == 'never'",
     "levenshtein;  sys_platform == 'never'",
     "numpy>=2.0.0,<=2.2.0", # Override nemo-toolkits constraint of <2.0.0, upperbounds for Numba compatibility
     "protobuf>=5.29.5",  # Override nemo-toolkits constraint of ~=5.29.5
     "setuptools>=80.10.1", # Override setuptools range in other dependencies to address CVE GHSA-58pv-8j8x-9vj2
-    "transformers<=4.55.2", # Else Cosmos Embed imports fail
     "nixl-cu12>=0.10.0; (platform_machine == 'x86_64' and platform_system != 'Darwin')",  # Override ray[llm]'s unconditional nixl dep for ARM
 ]
 
diff --git a/uv.lock b/uv.lock
index 0f8f9e56a3..f307da82df 100644
--- a/uv.lock
+++ b/uv.lock
@@ -55,7 +55,7 @@ overrides = [
     { name = "numpy", specifier = ">=2.0.0,<=2.2.0" },
     { name = "protobuf", specifier = ">=5.29.5" },
     { name = "setuptools", specifier = ">=80.10.1" },
-    { name = "transformers", specifier = "<=4.55.2" },
+    { name = "transformers", specifier = ">=4.56.0" },
 ]
 
 [[package]]
@@ -4866,7 +4866,6 @@ all = [
     { name = "torchvision", version = "0.24.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'" },
     { name = "torchvision", version = "0.24.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
     { name = "trafilatura" },
-    { name = "transformers" },
     { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
     { name = "warcio" },
 ]
@@ -5050,7 +5049,6 @@ video-cuda12 = [
     { name = "torchaudio", version = "2.9.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
     { name = "torchvision", version = "0.24.1", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'x86_64' or sys_platform == 'darwin'" },
     { name = "torchvision", version = "0.24.1+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
-    { name = "transformers" },
     { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin'" },
 ]
 vllm = [
@@ -5181,9 +5179,7 @@ requires-dist = [
     { name = "torchvision", marker = "(platform_machine != 'x86_64' and extra == 'video-cpu') or (sys_platform == 'darwin' and extra == 'video-cpu')", index = "https://pypi.org/simple" },
     { name = "trafilatura", marker = "extra == 'text-cpu'", specifier = "==2.0.0" },
     { name = "transformers" },
-    { name = "transformers", marker = "extra == 'video-cuda12'", specifier = ">=4.56.0" },
     { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin' and extra == 'math-cuda12'", specifier = ">=0.13" },
-    { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin' and extra == 'video-cuda12'", specifier = ">=0.13" },
     { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform != 'darwin' and extra == 'vllm'", specifier = ">=0.14.1" },
     { name = "warcio", marker = "extra == 'text-cpu'" },
 ]
@@ -8871,27 +8867,32 @@ wheels = [
 
 [[package]]
 name = "tokenizers"
-version = "0.21.4"
+version = "0.22.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
 ]
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@@ -9245,23 +9246,22 @@ wheels = [
 
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     { name = "huggingface-hub" },
     { name = "numpy" },
     { name = "packaging" },
     { name = "pyyaml" },
     { name = "regex" },
-    { name = "requests" },
     { name = "safetensors" },
     { name = "tokenizers" },
     { name = "tqdm" },
+    { name = "typer" },
 ]
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 [[package]]

From c9a2c1c184ba88d35e5c769c99cb3df2ca51e706 Mon Sep 17 00:00:00 2001
From: weijiac <weijiac@NVIDIA.com>
Date: Wed, 1 Apr 2026 20:49:03 +0000
Subject: [PATCH 17/17] cap transformers <5.0

Signed-off-by: weijiac <weijiac@NVIDIA.com>
---
 pyproject.toml |  2 +-
 uv.lock        | 11 ++++++-----
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 28e7b7fb6e..a0b6dd3580 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -249,7 +249,7 @@ override-dependencies = [
     "apex; sys_platform == 'never'",
     "distance; sys_platform == 'never'",
     "huggingface-hub>=0.34,<1.0", # Override huggingface-hub, transformers and data-designer require two different versions of hugging-face hub
-    "transformers>=4.56.0", # Override nemo-toolkit[asr]'s <=4.52.0 constraint; required for Nemotron VL support
+    "transformers>=4.56.0,<5.0", # Override nemo-toolkit[asr]'s <=4.52.0 constraint; required for Nemotron VL support
     "kaldiio;  sys_platform == 'never'",
     "levenshtein;  sys_platform == 'never'",
     "numpy>=2.0.0,<=2.2.0", # Override nemo-toolkits constraint of <2.0.0, upperbounds for Numba compatibility
diff --git a/uv.lock b/uv.lock
index f307da82df..0b8dbae5f0 100644
--- a/uv.lock
+++ b/uv.lock
@@ -55,7 +55,7 @@ overrides = [
     { name = "numpy", specifier = ">=2.0.0,<=2.2.0" },
     { name = "protobuf", specifier = ">=5.29.5" },
     { name = "setuptools", specifier = ">=80.10.1" },
-    { name = "transformers", specifier = ">=4.56.0" },
+    { name = "transformers", specifier = ">=4.56.0,<5.0" },
 ]
 
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@@ -9246,22 +9246,23 @@ wheels = [
 
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-version = "5.4.0"
+version = "4.57.6"
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     { name = "huggingface-hub" },
     { name = "numpy" },
     { name = "packaging" },
     { name = "pyyaml" },
     { name = "regex" },
+    { name = "requests" },
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