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+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+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
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+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
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+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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+*This model was published in HF papers on 2025-04-04 and contributed to Hugging Face Transformers on 2026-03-03.*
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
+</div>
+
+# NemotronH
+
+[NemotronH](https://huggingface.co/papers/2504.03624) is a hybrid architecture combining attention and state-space layers for efficient long-context language modeling. It interleaves Mamba2 and transformer blocks, using a fixed ratio to balance expressiveness with linear-time sequence processing.
+
+The example below demonstrates how to generate text with [`Pipeline`] or the [`AutoModelForCausalLM`] class.
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```python
+from transformers import pipeline
+
+
+pipe = pipeline(
+    task="text-generation",
+    model="nvidia/Nemotron-H-8B-Reasoning-128K",
+)
+pipe("Plants create energy through a process known as")
+```
+
+</hfoption>
+<hfoption id="AutoModelForCausalLM">
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+tokenizer = AutoTokenizer.from_pretrained("nvidia/Nemotron-H-8B-Reasoning-128K")
+model = AutoModelForCausalLM.from_pretrained(
+    "nvidia/Nemotron-H-8B-Reasoning-128K",
+    device_map="auto",
+)
+input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)
+
+output = model.generate(**input_ids, max_new_tokens=50)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+</hfoption>
+</hfoptions>
+
+## NemotronHConfig
+
+[[autodoc]] NemotronHConfig
+
+## NemotronHModel
+
+[[autodoc]] NemotronHModel
+    - forward
+
+## NemotronHForCausalLM
+
+[[autodoc]] NemotronHForCausalLM
+    - forward