[model, recipe, examples] feat: add Nemotron-3 Nano Omni support

examples/models/nemotron/nemotron_3_omni/README.md

@@ -96,7 +96,7 @@ the container), `uv` is resolving against a stale environment — re-run
 
 ## Checkpoint Conversion
 
-[conversion.sh](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/nemotron_3_omni/examples/models/vlm/nemotron_3_omni/conversion.sh) covers HF → Megatron import, Megatron → HF
+[conversion.sh](conversion.sh) covers HF → Megatron import, Megatron → HF
 export, and a multi-GPU HF↔Megatron round-trip verification.
 
 - **Import** writes `iter_0000000/`, `latest_train_state.pt`, and
@@ -107,6 +107,9 @@ export, and a multi-GPU HF↔Megatron round-trip verification.
   tensors (regenerated from config on the HF side):
   `sound_encoder.encoder.feature_extractor.featurizer.{fb,window}` and
   `vision_model.radio_model.input_conditioner.{norm_mean,norm_std}`.
+  `--trust-remote-code` is also required for export because the exporter
+  loads the HF config, which references the custom modeling module shipped
+  with `NemotronH_Nano_Omni_Reasoning_V3`.
 - **Round-trip** loads HF → Megatron (TP=2, EP=2) and re-exports back to HF,
   diffing every tensor; all weights should match (✅) and the same 4
   expected-missing tensors are reported on re-export. The re-exported HF
@@ -130,8 +133,8 @@ bash examples/models/nemotron/nemotron_3_omni/conversion.sh
 
 ## Inference
 
-[inference.sh](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/nemotron_3_omni/examples/models/vlm/nemotron_3_omni/inference.sh) drives
-`examples/conversion/hf_to_megatron_generate_nemotron_omni.py` over the four
+[inference.sh](inference.sh) drives
+`examples/models/nemotron/nemotron_3_omni/hf_to_megatron_generate_nemotron_omni.py` over the four
 modality combinations exercised by the model:
 
 | # | Modality | GPUs | Parallelism |
@@ -144,6 +147,14 @@ modality combinations exercised by the model:
 The default assets are pulled automatically from the public HF model card
 ([`nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16`](https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16/tree/main/media))
 on the first run — `curl` must be available.
+
+> **Video prerequisite:** the video paths (rows 2 and 4) sample frames via
+> [`decord`](https://github.com/dmlc/decord), which is not pulled in by any
+> pyproject extra. Install it before running those modes:
+>
+> ```bash
+> uv pip install decord
+> ```
 Override `IMAGE_PATH` / `VIDEO_PATH` / `AUDIO_PATH` with your own assets to
 use different inputs; omit `--megatron_model_path` (set `MEGATRON_PATH=""`)
 to convert HF → Megatron on the fly instead of reusing the imported
@@ -214,8 +225,8 @@ base config. Recipe base: `nemotron_omni_cord_v2_*_config` in
 
 | Mode | Script | Recipe |
 |---|---|---|
-| Full SFT | [slurm_sft_cord_v2.sh](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/nemotron_3_omni/examples/models/vlm/nemotron_3_omni/slurm_sft_cord_v2.sh) | `nemotron_omni_cord_v2_sft_config` |
-| LoRA | [slurm_peft_cord_v2.sh](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/nemotron_3_omni/examples/models/vlm/nemotron_3_omni/slurm_peft_cord_v2.sh) | `nemotron_omni_cord_v2_peft_config` |
+| Full SFT | [slurm_sft_cord_v2.sh](slurm_sft_cord_v2.sh) | `nemotron_omni_cord_v2_sft_config` |
+| LoRA | [slurm_peft_cord_v2.sh](slurm_peft_cord_v2.sh) | `nemotron_omni_cord_v2_peft_config` |
 
 Parallelism (both): TP=2, EP=8, CP=1, MBS=2, GBS=16, packed sequences,
 selective recompute. LoRA targets `linear_qkv`, `linear_proj`, `in_proj`,
@@ -235,17 +246,18 @@ embedder: frames are fused in pairs (`temporal_patch_dim=2`,
 `separate_video_embedder=True`) and audio is fed through the Parakeet
 encoder. Recipe base: `nemotron_omni_valor32k_*_config`.
 
-Prepare the Energon shards once:
+Prepare the Energon shards once. For the full walkthrough, see
+[`tutorials/data/valor32k-avqa/data-preparation.md`](../../../../tutorials/data/valor32k-avqa/data-preparation.md).
 
 ```bash
-uv run python examples/models/nemotron/nemotron_3_omni/data/build_valor32k_avqa_shards.py \
+uv run python tutorials/data/valor32k-avqa/build_valor32k_avqa_shards.py \
   --output_dir ${WORKSPACE}/datasets/valor32k_avqa
 ```
 
 | Mode | Script | Recipe |
 |---|---|---|
-| Full SFT | [slurm_sft_valor32k_avqa.sh](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/nemotron_3_omni/examples/models/vlm/nemotron_3_omni/slurm_sft_valor32k_avqa.sh) | `nemotron_omni_valor32k_sft_config` |
-| LoRA | [slurm_peft_valor32k_avqa.sh](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/nemotron_3_omni/examples/models/vlm/nemotron_3_omni/slurm_peft_valor32k_avqa.sh) | `nemotron_omni_valor32k_peft_config` |
+| Full SFT | [slurm_sft_valor32k_avqa.sh](slurm_sft_valor32k_avqa.sh) | `nemotron_omni_valor32k_sft_config` |
+| LoRA | [slurm_peft_valor32k_avqa.sh](slurm_peft_valor32k_avqa.sh) | `nemotron_omni_valor32k_peft_config` |
 
 Parallelism (both): TP=2, EP=8, CP=1, MBS=2, packed sequences, selective
 recompute. SFT uses GBS=16 and the recipe-default LR; LoRA uses GBS=64 and
@@ -268,8 +280,8 @@ finetuned Megatron checkpoint on the same datasets used for training:
 
 | Dataset | Script | Output |
 |---|---|---|
-| CORD-V2 | [cord_v2_inference.py](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/nemotron_3_omni/examples/models/vlm/nemotron_3_omni/cord_v2_inference.py) | JSON of `{prompt, gold, prediction}` per sample plus image bytes for eyeballing |
-| VALOR32K-AVQA | [valor32k_avqa_inference.py](https://github.com/NVIDIA-NeMo/Megatron-Bridge/blob/nemotron_3_omni/examples/models/vlm/nemotron_3_omni/valor32k_avqa_inference.py) | Per-sample predictions and an aggregate multiple-choice accuracy |
+| CORD-V2 | [cord_v2_inference.py](cord_v2_inference.py) | JSON of `{prompt, gold, prediction}` per sample plus image bytes for eyeballing |
+| VALOR32K-AVQA | [valor32k_avqa_inference.py](valor32k_avqa_inference.py) | Per-sample predictions and an aggregate multiple-choice accuracy |
 
 Example invocations (8 GPUs, single node). The slurm scripts tag
 `OUTPUT_DIR` with the run config (`<recipe>_<sft|lora>_<RUN_TAG>`, where

examples/models/nemotron/nemotron_3_omni/conversion.sh

@@ -0,0 +1,49 @@
+#!/usr/bin/env bash
+# Copyright (c) 2026, 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.
+
+# ==============================================================================
+# Nemotron-3 Nano Omni - Checkpoint conversion (HF <-> Megatron) + roundtrip
+# ==============================================================================
+
+set -xeuo pipefail
+
+WORKSPACE=${WORKSPACE:-/workspace}
+HF_MODEL_ID=${HF_MODEL_ID:-nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16}
+MODEL_NAME=$(basename "$HF_MODEL_ID")
+
+MEGATRON_PATH=${WORKSPACE}/models/${MODEL_NAME}
+HF_EXPORT_PATH=${WORKSPACE}/models/${MODEL_NAME}-hf-export
+
+# Import HF -> Megatron
+uv run python examples/conversion/convert_checkpoints.py import \
+    --hf-model "$HF_MODEL_ID" \
+    --megatron-path "$MEGATRON_PATH" \
+    --trust-remote-code
+
+# Export Megatron -> HF (--not-strict allows 4 expected-missing tensors that
+# are regenerated from config on the HF side: sound_encoder featurizer fb/window
+# and vision_model input_conditioner norm_mean/norm_std)
+uv run python examples/conversion/convert_checkpoints.py export \
+    --hf-model "$HF_MODEL_ID" \
+    --megatron-path "$MEGATRON_PATH" \
+    --hf-path "$HF_EXPORT_PATH" \
+    --not-strict
+
+# Round-trip validation (multi-GPU): HF -> Megatron -> HF and compare weights
+uv run python -m torch.distributed.run --nproc_per_node=4 \
+    examples/conversion/hf_megatron_roundtrip_multi_gpu.py \
+    --hf-model-id "$HF_MODEL_ID" \
+    --trust-remote-code \
+    --tp 2 --ep 2 --not-strict