Duplicate from nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8

Co-authored-by: Li Ding <dmax123@users.noreply.huggingface.co>

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README.md

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+---
+library_name: transformers
+license: other
+license_name: nvidia-nemotron-open-model-license
+license_link: >-
+  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/
+pipeline_tag: text-generation
+language:
+- en
+- fr
+- es
+- it
+- de
+- ja
+- zh
+tags:
+- nvidia
+- pytorch
+- nemotron-3
+- latent-moe
+- mtp
+datasets:
+- nvidia/nemotron-post-training-v3
+- nvidia/nemotron-pre-training-datasets
+track_downloads: true
+---
+
+# NVIDIA-Nemotron-3-Super-120B-A12B-FP8
+
+<div align="center" style="line-height: 1;">
+<a href="https://build.nvidia.com/nvidia/nemotron-3-super-120b-a12b" target="_blank" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/🤖Chat-Nemotron_3_Super-536af5?color=76B900&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+</a>
+<a href="https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Super-Technical-Report.pdf" target="_blank" style="margin: 2px;">
+    <img alt="Paper" src="https://img.shields.io/badge/📝Paper-Read Now!-536af5?color=76B900&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+</a>
+<a href="https://huggingface.co/collections/nvidia/nemotron-pre-training-datasets" target="_blank" style="margin: 2px;">
+    <img alt="Pre-Training Datasets" src="https://img.shields.io/badge/🗄️_Pre--Training_Datasets-Available_Here-76B900?logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+</a>
+<a href="https://huggingface.co/collections/nvidia/nemotron-post-training-v3" target="_blank" style="margin: 2px;">
+    <img alt="Post-Training Datasets" src="https://img.shields.io/badge/🗄️_Post--Training_Datasets-Available_Here-76B900?logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+</a>
+</div>
+<div align="center" style="line-height: 1;">
+  <a href="https://developer.nvidia.com/nemotron" target="_blank" style="margin: 2px;">
+    <img alt="Homepage" src="https://img.shields.io/badge/🏠Nemotron Developer Page-Learn More Here!-536af5?color=76B900&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+<a href="https://discord.gg/9xpKQtVvrk" target="_blank" style="margin: 2px;">
+    <img alt="Discord" src="https://img.shields.io/badge/Discord-NVIDIA%20AI%20Developer-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+<div align="center" style="line-height: 1;">
+  <a href="https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/" style="margin: 2px;">
+    <img alt="License" src="https://img.shields.io/badge/License-NVIDIA Nemotron Open Model License-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+
+![](./accuracy_chart.png)
+
+## Model Summary
+
+| | |
+|:---|:---|
+| **Total Parameters** | 120B (12B active) |
+| **Architecture** | LatentMoE - Mamba-2 + MoE + Attention hybrid with Multi-Token Prediction (MTP) |
+| **Context Length** | Up to 1M tokens |
+| **Minimum GPU Requirement** | 2× H100-80GB |
+| **Supported Languages** | English, French, German, Italian, Japanese, Spanish, Chinese |
+| **Best For** | Agentic workflows, long-context reasoning, high-volume workloads (e.g. IT ticket automation), tool use, RAG |
+| **Reasoning Mode** | Configurable on/off via chat template (`enable_thinking=True/False`) |
+| **License** | [NVIDIA Nemotron Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/) |
+| **Release Date** | March 11, 2026 |
+
+
+## Quick Start
+
+> Use `temperature=1.0` and `top_p=0.95` across **all tasks and serving backends** — reasoning, tool calling, and general chat alike.
+
+For more details on how to deploy and use the model - see the [Quick Start Guide](#quick-start-guide) below!
+
+## Model Overview
+
+**Model Developer:** NVIDIA Corporation
+
+**Model Dates:** December 2025 - March 2026
+
+**Data Freshness:**
+
+* The post-training data has a cutoff date of February 2026.
+* The pre-training data has a cutoff date of June 2025.
+
+### What is Nemotron?
+
+NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.
+
+## Description
+
+**Nemotron-3-Super-120B-A12B-FP8** is a large language model (LLM) trained by NVIDIA, designed to deliver strong agentic, reasoning, and conversational capabilities. It is optimized for collaborative agents and high-volume workloads such as IT ticket automation. Like other models in the family, it responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be configured through a flag in the chat template.
+
+The model employs a hybrid **Latent Mixture-of-Experts (LatentMoE)** architecture, utilizing interleaved Mamba-2 and MoE layers, along with select Attention layers. Distinct from the Nano model, the Super model incorporates **Multi-Token Prediction (MTP)** layers for faster text generation and improved quality, and it is trained using **NVFP4** quantization to maximize compute efficiency. The model has **12B active parameters** and **120B parameters in total**.
+
+The supported languages include: English, French, German, Italian, Japanese, Spanish, and Chinese
+
+This model is ready for commercial use.
+
+## License/Terms of Use
+
+**Governing Download Terms:** Use of this model is governed by the [NVIDIA Nemotron Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/).
+
+**Governing Download Terms with NIM:** The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product-Specific Terms for AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/). Use of this model is governed by the [NVIDIA Nemotron Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/).
+
+### Benchmarks 
+
+| Benchmark | Nemotron-3-Super | Nemotron-3-Super FP8 | Nemotron-3-Super NVFP4 |
+|---|---|---|---|
+| **General Knowledge** | | | |
+| MMLU-Pro | 83.73 | 83.63 | 83.33 |
+| **Reasoning** | | | |
+| HMMT Feb25 (with tools) | 94.73 | 94.38 | 95.36 |
+| GPQA (no tools) | 79.23 | 79.36 | 79.42 |
+| LiveCodeBench (v6 2024-08↔2025-05) | 78.69 | 78.44 | 78.44 |
+| LiveCodeBench (v5 2024-07↔2024-12) | 81.19 | 80.99 | 80.56 |
+| SciCode (subtask) | 42.05 | 41.38 | 40.83 |
+| HLE (no tools) | 18.26 | 17.42 | 17.42 |
+| **Agentic** | | | |
+| Terminal Bench (hard subset) | 25.78 | 26.04 | 24.48 |
+| **TauBench V2** | | | |
+|   Airline | 56.25 | 56.25 | 54.75 |
+|   Retail | 62.83 | 63.05 | 63.38 |
+|   Telecom | 64.36 | 63.93 | 63.27 |
+|   Average | 61.15 | 61.07 | 60.46 |
+| **Chat & Instruction Following** | | | |
+| IFBench (prompt) | 72.58 | 72.32 | 73.30 |
+| Scale AI Multi-Challenge | 55.23 | 54.35 | 52.8 |
+| Arena-Hard-V2 (Hard Prompt) | 73.88 | 76.06 | 76.00 |
+| **Long Context** | | | |
+| AA-LCR | 58.31 | 57.69 | 58.06 |
+| RULER-500 @ 128k (500 samples per task) | 96.79 | 96.85 | 95.99 |
+| RULER-500 @ 256k (500 samples per task) | 96.60 | 96.33 | 96.52 |
+| RULER-500 @ 512k (500 samples per task) | 96.09 | 95.66 | 96.23 |
+| **Multilingual** | | | |
+| MMLU-ProX (avg over languages) | 79.35 | 79.21 | 79.37 |
+
+
+All evaluation results were collected via [Nemo Evaluator SDK](https://github.com/NVIDIA-NeMo/Evaluator) and for most benchmarks, the [Nemo Skills Harness](https://github.com/NVIDIA-NeMo/Skills). For reproducibility purposes, more details on the evaluation settings can be found in the [Nemo Evaluator SDK configs folder](https://github.com/NVIDIA-NeMo/Evaluator/tree/main/packages/nemo-evaluator-launcher/examples/nemotron/nemotron-3-super) and the [reproducibility tutorial for Nemotron 3 Super](https://github.com/NVIDIA-NeMo/Evaluator/blob/main/packages/nemo-evaluator-launcher/examples/nemotron/nemotron-3-super/reproducibility.md). The open source container on Nemo Skills packaged via NVIDIA's Nemo Evaluator SDK used for evaluations can be found [here](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/eval-factory/containers/nemo_skills). In addition to Nemo Skills, the evaluations also used dedicated open-source packaged containers for Tau-2 Bench (default prompt), Terminal Bench Hard (48 tasks), ScaleAI Multi Challenge Multi-turn Instruction Following, and Ruler. 
+
+The following benchmarks are not onboarded yet in our open source tools and for these we used either their official open source implementation or otherwise an internal scaffolding that we plan to open source in the future: SWE Bench Verified (OpenHands), SWE Bench Multilingual (OpenHands), BrowseComp with Search (internal implementation with Serp API), Terminal Bench Core 2.0 (Harbor).
+
+### Deployment Geography: Global
+
+### Use Case
+
+NVIDIA-Nemotron-3-Super-120B-A12B-FP8 is a general purpose reasoning and chat model intended to be used in English, Code, and supported multilingual contexts. This model is optimized for collaborative agents and high-volume workloads. It is intended to be used by developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. This model is also suitable for complex instruction-following tasks and long-context reasoning.
+
+### Release Date
+
+Hugging Face - 03/11/2026 via [Hugging Face](https://huggingface.co/collections/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8)
+
+## Reference(s)
+
+* [NVIDIA Nemotron 3 model family on Hugging Face](https://huggingface.co/collections/nvidia/nvidia-nemotron-v3)
+* [NVIDIA Nemotron 3 Super Technical Report](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Super-Technical-Report.pdf)
+
+## Model Architecture
+
+- **Architecture Type:** Mamba2-Transformer Hybrid Latent Mixture of Experts (LatentMoE) with Multi-Token Prediction (MTP)
+- **Network Architecture:** Nemotron Hybrid LatentMoE
+- **Number of model parameters:** 120B Total / 12B Active
+
+## Model Design
+
+The model utilizes the **LatentMoE** architecture, where tokens are projected into a smaller latent dimension for expert routing and computation, improving accuracy per byte. The Super model is pre-trained using NVFP4 quantization — the first model in the Nemotron 3 family trained at this precision. The majority of linear layers use NVFP4 for weights, activations, and gradients, while select layers (including latent projections, MTP layers, QKV/attention projections, and embeddings) are maintained in BF16 or MXFP8 for training stability. The model includes **Multi-Token Prediction (MTP)** layers using a shared-weight design across prediction heads. This improves training signal quality, enables faster inference via native speculative decoding, and supports more stable autoregressive drafting at longer draft lengths compared to independently trained offset heads.
+
+## Training Methodology
+
+Stage 1: Pre-Training
+
+* [NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-Base-BF16) model was pre-trained for over 25T tokens using crawled and synthetic code, math, science, and general knowledge data. Training leveraged NVFP4 quantization for efficiency. All datasets are disclosed in the [Training and Evaluation Datasets](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8#training-and-evaluation-datasets) section of this document. Major portions of the pre-training corpus are released in the [Nemotron-Pre-Training-Datasets](https://huggingface.co/collections/nvidia/nemotron-pre-training-datasets) collection.
+* Software used for pre-training: [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
+
+Stage 2: Supervised Fine-Tuning
+
+* The model was further fine-tuned on synthetic code, math, science, tool calling, instruction following, structured outputs, and general knowledge data. This stage incorporated data designed to support long-range retrieval and multi-document aggregation. All datasets are disclosed in the [Training and Evaluation Datasets](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8#training-and-evaluation-datasets) section of this document. Major portions of the fine-tuning corpus are released in the [Nemotron-Post-Training-v3](https://huggingface.co/collections/nvidia/nemotron-post-training-v3) collection. [Data Designer](https://github.com/NVIDIA-NeMo/DataDesigner) is one of the libraries used to prepare these corpora.
+
+Stage 3: Reinforcement Learning
+
+* The model underwent multi-environment reinforcement learning using asynchronous GRPO (Group Relative Policy Optimization) across math, code, science, instruction following, multi-step tool use, multi-turn conversations, and structured output environments. It utilized an asynchronous RL architecture that fully decouples training from inference across separate GPU devices, leveraging in-flight weight updates and MTP to accelerate rollout generation. Conversational quality was further refined through RLHF. All datasets are disclosed in the *Training and Evaluation Datasets* section of this document. The RL environments and datasets are released as part of [NeMo Gym](https://github.com/NVIDIA-NeMo/Gym).
+* Software used for reinforcement learning: [NeMo RL](https://github.com/NVIDIA-NeMo/RL), [NeMo Gym](https://github.com/NVIDIA-NeMo/Gym)
+
+NVIDIA-Nemotron-3-Super-120B-A12B-FP8 model is a result of the above work.
+
+The end-to-end training recipe is available in the [NVIDIA Nemotron Developer Repository](https://github.com/NVIDIA-NeMo/Nemotron). Evaluation results can be replicated using the [NeMo Evaluator SDK](https://github.com/NVIDIA-NeMo/Evaluator). [Data Designer](https://github.com/NVIDIA-NeMo/DataDesigner) is one of the libraries used to prepare the pre and post training datasets. More details on the datasets and synthetic data generation methods can be found in the technical report [NVIDIA Nemotron 3 Super Technical Report](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Super-Technical-Report.pdf).
+
+## Input
+
+- **Input Type(s):** Text
+- **Input Format(s):** String
+- **Input Parameters:** One-Dimensional (1D): Sequences
+- **Other Properties Related to Input:** Maximum context length up to 1M tokens. Supported languages include: English, French, German, Italian, Japanese, Spanish, and Chinese
+
+## Output
+
+- **Output Type(s):** Text
+- **Output Format:** String
+- **Output Parameters:** One-Dimensional (1D): Sequences
+- **Other Properties Related to Output:** Maximum context length up to 1M tokens
+
+Our AI models are designed and optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
+
+## Software Integration
+
+- Runtime Engine(s): NeMo 25.11.01
+- Supported Hardware Microarchitecture Compatibility: NVIDIA Ampere - A100; NVIDIA Blackwell; NVIDIA Hopper - H100-80GB
+- Operating System(s): Linux
+
+The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
+
+## Model Version(s)
+
+* v1.0 - GA
+
+## Quick Start Guide
+
+For each inference backend, you'll need the custom `super_v3` reasoning parser. Download it with:
+```bash
+wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8/raw/main/super_v3_reasoning_parser.py
+```
+
+OR
+```bash
+curl -O https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8/raw/main/super_v3_reasoning_parser.py
+```
+
+For advanced deployment configurations, visit [this resource](https://github.com/NVIDIA-NeMo/Nemotron/tree/main/usage-cookbook/Nemotron-3-Super/AdvancedDeploymentGuide).
+
+---
+
+#### vLLM
+
+For more detailed information, please see [this cookbook](https://github.com/NVIDIA-NeMo/Nemotron/blob/main/usage-cookbook/Nemotron-3-Super/vllm_cookbook.ipynb).
+```bash
+pip install vllm==0.17.1
+
+export MODEL_CKPT=nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8
+```
+
+```bash
+# Optional: --enable-expert-parallel
+vllm serve $MODEL_CKPT \
+  --served-model-name nvidia/nemotron-3-super \
+  --async-scheduling \
+  --dtype auto \
+  --kv-cache-dtype fp8 \
+  --tensor-parallel-size 4 \
+  --max-model-len 262144 \
+  --enable-expert-parallel \
+  --swap-space 0 \
+  --trust-remote-code \
+  --gpu-memory-utilization 0.9 \
+  --max-cudagraph-capture-size 128 \
+  --enable-chunked-prefill \
+  --mamba-ssm-cache-dtype float16 \
+  --reasoning-parser nemotron_v3 \
+  --enable-auto-tool-choice \
+  --tool-call-parser qwen3_coder
+```
+
+> Context length defaults to 256k above. To use up to 1M, set `VLLM_ALLOW_LONG_MAX_MODEL_LEN=1` and `--max-model-len 1048576`.
+
+> **B200/B300 (FP8)**: The FP8 checkpoint fits on a single GPU. Set `--tensor-parallel-size 1` and remove `--enable-expert-parallel`. All other flags remain the same.
+
+---
+
+#### SGLang
+
+Container:
+```bash
+docker pull lmsysorg/sglang:nightly-dev-cu13-20260316-d852f26c
+```
+
+For more detailed information, please see [this cookbook](https://github.com/NVIDIA-NeMo/Nemotron/blob/main/usage-cookbook/Nemotron-3-Super/sglang_cookbook.ipynb).
+```bash
+sglang serve \
+  --model-path nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8 \
+  --served-model-name nvidia/nemotron-3-super \
+  --trust-remote-code \
+  --tp 4 \
+  --ep 4 \
+  --tool-call-parser qwen3_coder \
+  --reasoning-parser nemotron_3
+```
+
+> Context length defaults to 256k above. To use up to 1M, set `SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1` and `--context-length 1048576`.
+
+> **B200/B300 (FP8)**: The FP8 checkpoint fits on a single GPU. Set `--tp 1 --ep 1`. All other flags remain the same.
+
+---
+
+#### TRT-LLM
+
+Container:
+```bash
+docker pull nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc8
+```
+
+For more detailed information, please see [this cookbook](https://github.com/NVIDIA-NeMo/Nemotron/blob/main/usage-cookbook/Nemotron-3-Super/trtllm_cookbook.ipynb).
+```bash
+cat > ./extra-llm-api-config.yml << EOF
+kv_cache_config:
+  dtype: fp8
+  enable_block_reuse: false
+  free_gpu_memory_fraction: 0.8
+  mamba_ssm_cache_dtype: float16
+  mamba_ssm_stochastic_rounding: true
+  mamba_ssm_philox_rounds: 5
+moe_config:
+  backend: TRTLLM
+cuda_graph_config:
+  enable_padding: true
+  max_batch_size: 128
+enable_attention_dp: true
+enable_chunked_prefill: true
+num_postprocess_workers: 4
+stream_interval: 10
+EOF
+
+trtllm-serve nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8 \
+  --host 0.0.0.0 \
+  --port 8123 \
+  --max_batch_size 128 \
+  --tp_size 4 --ep_size 4 \
+  --max_num_tokens 8192 \
+  --trust_remote_code \
+  --reasoning_parser nano-v3 \
+  --tool_parser qwen3_coder \
+  --extra_llm_api_options extra-llm-api-config.yml
+```
+
+> **B200/B300 (FP8)**: The FP8 checkpoint fits on a single GPU. Set `--tp_size 1 --ep_size 1` and set `enable_attention_dp: false` in the config file. All other flags remain the same.
+
+### API Client
+
+The examples below use the OpenAI-compatible client and work with any of the serving backends above.
+
+> NOTE: For coding agents add the following to the API call - `extra_body={“chat_template_kwargs”: {“force_nonempty_content”: True}`
+
+```python
+from openai import OpenAI
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
+MODEL = "nvidia/nemotron-3-super"
+```
+
+**Reasoning ON (default)**
+
+```python
+response = client.chat.completions.create(
+    model=MODEL,
+    messages=[{"role": "user", "content": "Write a haiku about GPUs"}],
+    max_tokens=16000,
+    temperature=1.0,
+    top_p=0.95,
+    extra_body={"chat_template_kwargs": {"enable_thinking": True}}
+)
+print(response.choices[0].message.content)
+```
+
+**Reasoning OFF**
+
+```python
+response = client.chat.completions.create(
+    model=MODEL,
+    messages=[{"role": "user", "content": "What is the capital of Japan?"}],
+    max_tokens=16000,
+    temperature=1.0,
+    top_p=0.95,
+    extra_body={"chat_template_kwargs": {"enable_thinking": False}}
+)
+print(response.choices[0].message.content)
+```
+
+**Low-effort reasoning**
+
+Uses significantly fewer reasoning tokens than full thinking mode. Recommended as a starting point before tuning explicit token budgets.
+
+```python
+response = client.chat.completions.create(
+    model=MODEL,
+    messages=[{"role": "user", "content": "What is the capital of Japan?"}],
+    max_tokens=16000,
+    temperature=1.0,
+    top_p=0.95,
+    extra_body={"chat_template_kwargs": {"enable_thinking": True, "low_effort": True}}
+)
+print(response.choices[0].message.content)
+```
+
+### OpenCode
+
+[OpenCode](https://opencode.ai/docs) is an AI coding agent that runs in your terminal. It connects to any OpenAI-compatible endpoint, making it compatible with all three serving backends above (vLLM, SGLang, and TRT-LLM).
+
+Create or update your `~/.config/opencode/opencode.json`:
+
+```json
+{
+    "$schema": "https://opencode.ai/config.json",
+    "model": "local/nvidia-nemotron-3-super",
+    "provider": {
+        "local": {
+            "npm": "@ai-sdk/openai-compatible",
+            "name": "local_backend",
+            "options": {
+                "baseURL": "http://localhost:8000/v1",
+                "apiKey": "EMPTY"
+            },
+            "models": {
+                "nvidia-nemotron-3-super": {
+                    "name": "nvidia/nemotron-3-super",
+                    "limit": {
+                        "context": 1000000,
+                        "output": 32768
+                    }
+                }
+            }
+        }
+    },
+    "agent": {
+        "build": {
+            "temperature": 1.0,
+            "top_p": 0.95,
+            "max_tokens": 32000
+        },
+        "plan": {
+            "temperature": 1.0,
+            "top_p": 0.95,
+            "max_tokens": 32000
+        }
+    }
+}
+```
+
+> Update `baseURL` to match whichever backend you are running. The default port above (`8000`) matches the vLLM example; SGLang and TRT-LLM use `30000` and `8123` respectively.
+
+To learn more about other supported agent scaffolds - check out [this resource](https://github.com/NVIDIA-NeMo/Nemotron/tree/main/usage-cookbook/Nemotron-3-Super/OpenScaffoldingResources)
+
+<details>
+<summary><strong> Advanced: Budget-Controlled Reasoning</strong></summary>
+
+Set a hard token ceiling on the reasoning trace using `reasoning_budget`. The model will attempt to close the trace at the next newline before the budget is hit; if none is found within 500 tokens it closes abruptly at `reasoning_budget + 500`.
+
+```python
+from typing import Any, Dict, List
+import openai
+from transformers import AutoTokenizer
+
+
+class ThinkingBudgetClient:
+    def __init__(self, base_url: str, api_key: str, tokenizer_name_or_path: str):
+        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path)
+        self.client = openai.OpenAI(base_url=base_url, api_key=api_key)
+
+    def chat_completion(
+        self,
+        model: str,
+        messages: List[Dict[str, Any]],
+        reasoning_budget: int = 512,
+        max_tokens: int = 1024,
+        **kwargs,
+    ) -> Dict[str, Any]:
+        assert max_tokens > reasoning_budget, (
+            f"reasoning_budget must be less than max_tokens. "
+            f"Got {max_tokens=} and {reasoning_budget=}"
+        )
+
+        # Step 1: generate the reasoning trace up to the budget
+        response = self.client.chat.completions.create(
+            model=model, messages=messages, max_tokens=reasoning_budget, **kwargs
+        )
+        reasoning_content = response.choices[0].message.content
+        if "" not in reasoning_content:
+            reasoning_content = f"{reasoning_content}.\n\n\n"
+
+        reasoning_tokens_len = len(
+            self.tokenizer.encode(reasoning_content, add_special_tokens=False)
+        )
+        remaining_tokens = max_tokens - reasoning_tokens_len
+        assert remaining_tokens > 0, (
+            f"No tokens remaining for response ({remaining_tokens=}). "
+            "Increase max_tokens or lower reasoning_budget."
+        )
+
+        # Step 2: continue from the closed reasoning trace
+        messages.append({"role": "assistant", "content": reasoning_content})
+        prompt = self.tokenizer.apply_chat_template(
+            messages, tokenize=False, continue_final_message=True
+        )
+        response = self.client.completions.create(
+            model=model, prompt=prompt, max_tokens=remaining_tokens, **kwargs
+        )
+
+        return {
+            "reasoning_content": reasoning_content.strip().strip("").strip(),
+            "content": response.choices[0].text,
+            "finish_reason": response.choices[0].finish_reason,
+        }
+```
+
+**Example usage** (32-token reasoning budget):
+
+```python
+client = ThinkingBudgetClient(
+    base_url="http://localhost:8000/v1",
+    api_key="EMPTY",
+    tokenizer_name_or_path="nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8",
+)
+
+result = client.chat_completion(
+    model="nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant. /think"},
+        {"role": "user", "content": "What is 2+2?"},
+    ],
+    reasoning_budget=32,
+    max_tokens=512,
+    temperature=1.0,
+    top_p=0.95,
+)
+print(result)
+```
+
+</details>
+
+## Training and Evaluation Datasets
+
+# Training
+
+**Data Modality:** Text
+**The total size:** 15,573,172,908,990 Tokens
+**Total number of datasets:** 153
+**Dataset partition:** *Training [100%], testing [0%], validation [0%]*
+**Time period for training data collection:** 2013 to February 24, 2026
+**Time period for testing data collection:** 2013 to February 24, 2026
+**Time period for validation data collection:** 2013 to February 24, 2026
+**Data Collection Method by dataset:** Hybrid: Automated, Human, Synthetic
+**Labeling Method by dataset:** Hybrid: Automated, Human, Synthetic
+
+NVIDIA-Nemotron-3-Super-120B-A12B-FP8 is pre-trained on a large corpus of high-quality curated and synthetically-generated data. It is trained in the English language, as well as 19 other languages and 43 programming languages. Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including legal, math, science, finance, and more. We also include a small portion of question-answering, and alignment style data to improve model accuracy. The model was trained for approximately 25 trillion tokens.
+
+The post-training corpus for NVIDIA-Nemotron-3-Super-120B-A12B-FP8 of high-quality curated and synthetically-generated data. Primary languages used for post-training include English, French, German, Italian, Japanese, Spanish, and Chinese.
+
+These datasets, such as FinePDFs, EssentialWeb, HotpotQA, SQuAD, and HelpSteer3, do not collectively or exhaustively represent all demographic groups (and proportionally therein). For instance, these datasets do not contain explicit mentions of demographic classes such as age, gender, or ethnicity in 64-99% of samples, depending on the source. In the subset where such terms are present, document-based datasets (FinePDFs and EssentialWeb) contain representational skews, such as references to "male" outnumbering those to "female", and mentions of "White" as the most frequent among ethnic identifiers (comprising 43-44% of ethnicity mentions). To mitigate these imbalances, we recommend considering evaluation techniques such as bias audits, fine-tuning with demographically balanced datasets, and mitigation strategies like counterfactual data augmentation to align with the desired model behavior. This evaluation used a 3,000-sample subset per dataset, identified as the optimal threshold for maximizing embedder accuracy.
+
+During post-training, we generate synthetic data by distilling trajectories, solutions, and translations from strong teacher models and agent systems, often grounded in real tasks or documents and aggressively filtered for quality. For math, code, and science, we start from curated problem sets and use open source permissive models such as GPT-OSS-120B to produce step-by-step reasoning traces, candidate solutions, best-of-n selection traces, and verified CUDA kernels. For long-context and science, we build synthetic QA and reasoning data by retrieving passages from long documents, generating MCQ/OpenQA questions and answers, and paraphrasing them into multiple prompt/response formats to ensure diversity. Across all pipelines we stack automated verification—compilers, numerical checks, language identification—to ensure our data is high quality.
+
+For all domains, we apply a unified data filtering pipeline to ensure that only high-quality, license-compliant, and verifiable samples are used for post-training. We first discard malformed examples using structural checks (e.g., missing tool definitions when tool calls are present). We then aggressively filter reasoning traces exhibiting pathological repetition, such as repeated n-grams within a sliding window or across the entire trajectory, which we found to be a strong indicator of malformed or low-quality reasoning. Finally, based on internal audits of synthetically generated datasets, we observed that some teacher models occasionally produce reasoning traces and final responses that implicitly align with specific political entities or promote nationalistic narratives. To mitigate this, we apply targeted keyword- and regex-based filters and remove all trajectories matching such behavior.
+
+Alongside the model, we release our final pre-training and post-training data, as outlined in this section. For ease of analysis, there is a sample set that is ungated. For all remaining code, math and multilingual data, gating and approval is required, and the dataset is permissively licensed for model training purposes.
+
+More details on the datasets and synthetic data generation methods can be found in the technical report _[**_NVIDIA Nemotron 3 Super_**](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Super-Technical-Report.pdf)_.
+
+<details>
+<summary><strong>Click to explore the full dataset catalogue used for training</strong></summary>
+
+#### **Base Pre-Training Corpus (Nemotron 3 Foundation)**
+
+The foundation of the model is trained on the **Nemotron-3-Nano** corpus, comprising the following collections:
+
+| Dataset Collection | Token Counts | Description |
+| :--- | :--- | :--- |
+| **Nemotron-CC-v2** & **v2.1** | 9.13T | A massive collection of English web data filtered from Common Crawl, including 2.5T+ tokens of new organic, translated, and synthetically rephrased content. |
+| **Nemotron-CC-Code-v1** | 427.9B | High-quality code tokens extracted from Common Crawl using the Lynx + LLM pipeline to preserve structure and equations. |
+| **Nemotron-Pretraining-Code-v1** & **v2** | 1.09T | Curated GitHub code references with multi-stage filtering, deduplication, and large-scale synthetic code data. |
+| **Nemotron-CC-Math-v1** | 133.3B | High-quality math pre-training dataset preserving LaTeX formatting and mathematical structures. |
+| **Nemotron-Pretraining-Specialized-v1** | 336.4B | Synthetic datasets targeting specialized domains such as STEM reasoning and scientific coding. |
+
+### Public Datasets
+
+| Dataset | Collection Period |
+| :---- | :---- |
+| [GSM8K](https://github.com/openai/grade-school-math) | 4/23/2025 |
+| [CC-NEWS](https://commoncrawl.org/blog/news-dataset-available) | 4/23/2025 |
+| [Common Crawl](https://commoncrawl.org/) | 4/23/2025 |
+| [Wikimedia](https://dumps.wikimedia.org/) | 4/23/2025 |
+| [Bespoke-Stratos-17k](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k) | 4/23/2025 |
+| [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k) | 4/23/2025 |
+| [glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) | 4/23/2025 |
+| [APIGen Function-Calling](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) | 4/23/2025 |
+| [LMSYS-Chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) | 4/23/2025 |
+| [Open Textbook Library \- CC BY-SA & GNU subset](https://open.umn.edu/opentextbooks/textbooks/) and [OpenStax \- CC BY-SA subset](https://openstax.org/) | 4/23/2025 |
+| [Advanced Reasoning Benchmark](https://github.com/TheDuckAI/arb), [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k), [PRM800K](https://github.com/openai/prm800k), and [SciBench](https://github.com/mandyyyyii/scibench) | 4/23/2025 |
+| [FineWeb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) | 4/23/2025 |
+| [Court Listener](https://www.courtlistener.com/help/api/bulk-data/) | Legacy Download |
+| [peS2o](https://huggingface.co/datasets/allenai/peS2o) | Legacy Download |
+| [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math) | Legacy Download |
+| [BioRxiv](https://www.biorxiv.org/tdm) | Legacy Download |
+| [PMC Open Access Subset](https://pmc.ncbi.nlm.nih.gov/tools/openftlist/) | Legacy Download |
+| [OpenWebText2](https://openwebtext2.readthedocs.io/en/latest/) | Legacy Download |
+| [Stack Exchange Data Dump](https://archive.org/details/stackexchange) | Legacy Download |
+| [PubMed Abstracts](https://github.com/thoppe/The-Pile-PubMed) | Legacy Download |
+| [NIH ExPorter](https://exporter.nih.gov/ExPORTER_Catalog.aspx) | Legacy Download |
+| [arXiv](https://info.arxiv.org/help/bulk_data/index.html) | Legacy Download |
+| [BigScience Workshop Datasets](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#datasets) | Legacy Download |
+| [Reddit Dataset](https://files.pushshift.io/reddit/) | Legacy Download |
+| [SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR)](https://www.sec.gov/search-filings) | Legacy Download |
+| [Advanced Mathematical Problem Solving](https://github.com/hendrycks/math?tab=readme-ov-file) | Legacy Download |
+| [MathPile](https://github.com/GAIR-NLP/MathPile/) | Legacy Download |
+| [NuminaMath CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) | Legacy Download |
+| [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/) | Legacy Download |
+| [FLAN](https://github.com/google-research/FLAN) | Legacy Download |
+| [Advanced Reasoning Benchmark](https://github.com/TheDuckAI/arb) | Legacy Download |
+| [SciBench](https://github.com/mandyyyyii/scibench) | Legacy Download |
+| [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) | Legacy Download |
+| [FinQA](https://finqasite.github.io/) | Legacy Download |
+| [Riddles](https://github.com/crawsome/riddles) | Legacy Download |
+| [Problems in Elementary Mathematics for Home Study](https://archive.org/details/AntonovVygodskyNikitinSankinProblemsInElementaryMathematicsForHomeStudyMir1982) | Legacy Download |
+| [MedMCQA](https://huggingface.co/datasets/openlifescienceai/medmccqa) | Legacy Download |
+| [Cosmos QA](https://huggingface.co/datasets/allenai/cosmos_qa) | Legacy Download |
+| [MCTest](https://huggingface.co/datasets/sagnikrayc/mctest) | Legacy Download |
+| [AI2's Reasoning Challenge](https://huggingface.co/datasets/ai2_arc) | Legacy Download |
+| [OpenBookQA](https://github.com/allenai/OpenBookQA) | Legacy Download |
+| [MMLU Auxiliary Train](https://huggingface.co/datasets/cais/mmlu/viewer/all/auxiliary_train) | Legacy Download |
+| [social-chemestry-101](https://huggingface.co/datasets/tasksource/social-chemestry-101) | Legacy Download |
+| [Moral Stories](https://huggingface.co/datasets/demelin/moral_stories) | Legacy Download |
+| [The Common Pile v0.1](https://huggingface.co/common-pile) | Legacy Download |
+| [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath) | Legacy Download |
+| [MegaMath](https://huggingface.co/datasets/LLM360/MegaMath) | Legacy Download |
+| [MultiverseMathHard](https://huggingface.co/datasets/Nexusflow/MultiverseMathHard) | 10/2/2025 |
+| [News Commentary](https://opus.nlpl.eu/News-Commentary.php) | 10/2/2025 |
+| [Essential-Web](https://huggingface.co/datasets/EssentialAI/essential-web-v1.0) | 10/2/2025 |
+| [finepdfs](https://huggingface.co/datasets/HuggingFaceFW/finepdfs) | 10/2/2025 |
+| [HotpotQA](https://huggingface.co/hotpot_qa/datasets) | 10/2/2025 |
+| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | 10/2/2025 |
+| [NLTK Words Lists](https://www.nltk.org/nltk_data/) | 10/2/2025 |
+| Competitive Coding RL data from [Nemotron-Cascade-RL-SWE](https://huggingface.co/datasets/nvidia/Nemotron-Cascade-RL-SWE) | 01/10/2026 |
+| [NL2Bash](https://github.com/TellinaTool/nl2bash) | 01/10/2026 |
+| [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym) | 01/10/2026 |
+| [R2E-Gym-Subset](https://huggingface.co/datasets/R2E-Gym/R2E-Gym-Subset) | 01/10/2026 |
+| [SWE-bench_Verified](https://huggingface.co/datasets/princeton-nlp/SWE-bench_Verified) | 01/10/2026 |
+
+### **Crawled and Scraped from Online Sources by NVIDIA**
+
+The English Common Crawl data was downloaded from the Common Crawl Foundation (see their FAQ for details on their crawling) and includes the snapshots CC-MAIN-2013-20 through CC-MAIN-2025-13. The data was subsequently deduplicated and filtered in various ways described in the Nemotron-CC paper. Additionally, we extracted data for fifteen languages from the following three Common Crawl snapshots: CC-MAIN-2024-51, CC-MAIN-2025-08, CC-MAIN-2025-18. The fifteen languages included were Arabic, Chinese, Danish, Dutch, French, German, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Swedish, and Thai. As we did not have reliable multilingual model-based quality classifiers available, we applied just heuristic filtering instead—similar to what we did for lower quality English data in the Nemotron-CC pipeline, but selectively removing some filters for some languages that did not work well. Deduplication was done in the same way as for Nemotron-CC.
+
+The GitHub Crawl was collected using the GitHub REST API and the Amazon S3 API. Each crawl was operated in accordance with the rate limits set by its respective source, either GitHub or S3. We collect raw source code and subsequently remove any having a license which does not exist in our permissive-license set (for additional details, refer to the [technical report](https://arxiv.org/abs/2512.20848)).
+
+| Dataset | Modality | Dataset Size | Collection Period | Collecting Organisation |
+| :---- | :---- | :---- | :---- | :---- |
+| English Common Crawl | Text | 3.36T | 4/8/2025 | NVIDIA Advanced Deep Learning Research |
+| English Common Crawl 1.1 | Text | Not disclosed | 10/2/2025 | NVIDIA Advanced Deep Learning Research |
+| Multilingual Common Crawl | Text | 812.7B | 5/1/2025 | NVIDIA Advanced Deep Learning Research |
+| GitHub Crawl | Text | 747.4B | 4/29/2025 | NVIDIA Advanced Deep Learning Research |
+
+## Private Non-publicly Accessible Datasets of Third Parties
+
+| Dataset | Model(s) used |
+|---------|---------------|
+| Global Regulation | Unknown |
+| TAUS Translation Memory | Unknown |
+| Scale HLE | Unknown |
+| HackerRank Coding | Unknown |
+| RL data for Search | Gemini 3; GPT-5 * |
+
+* Models used for prompt generation only
+
+## Private Non-publicly Accessible Datasets by NVIDIA
+
+| Dataset | Model(s) used |
+|---------|---------------|
+| Simple Minesweeper | \- |
+| Simple Sudoku | \- |
+| Multitool Typewriter Hard | \- |
+| Machine Translation of News Commentary and TAUS Translation Memory | \- |
+| Machine Translation of STEM - | [Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) |
+| Competitive Coding RL data from Nemotron Cascade | \- |
+| Long context RL | \- |
+| Single-step SWE RL for patch generation | \- |
+| OpenHands SWE | \- |
+
+### NVIDIA-Sourced Synthetic Datasets
+
+| Dataset | Modality | Dataset Size | Seed Dataset | Model(s) used for generation |
+| :---- | :---- | :---- | :---- | :---- |
+| Nemotron-Pretraining-Formal-Logic | Text | 128,022,285 | [Nemotron Personas](https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA) | [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |
+| Nemotron-Pretraining-Economics | Text | 73,374,154 | - | [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |
+| Nemotron-Pretraining-Multiple-Choice | Text | 1,609,214,470 | [MMLU Auxiliary Train](https://huggingface.co/datasets/cais/mmlu/viewer/all/auxiliary_train) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) |
+| Nemotron-Pretraining-Code-Concepts | Text | 7,294,510,156 | - | [gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
+| Nemotron-Pretraining-Unconditional-Algorithmic | Text | 196,492,899 | - | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) |
+| Synthetic Tasks from DeepSeek-V3 and Qwen3-235B-A22B | Text | 6.7B | train splits of Into the Unknown; AI2 ARC (AI2 Reasoning Challenge); BLiMP (Benchmark of Linguistic Minimal Pairs); CommonSenseQA; GLUE; HeadQA; Hendrycks Ethics; Memo Trap; modus-tollens; NeQA; pattern-matching-suppression; mastermind_24_mcq_random; mastermind_24_mcq_close; quote-repetition; redefine-math; Repetitive Algebra; sig-figs; MMLU-Pro; MC-TACO; MedConceptsQA; MMLU_dataset; OpenbooksQA; PIQA (Physical Interaction Question Answering); SocialIQA; SuperGLUE; tinyAI2_arc; tinyMMLU; tinyWinogrande; TruthfulQA; WebQuestions; Winogrande; GPQA; MBPP | [DeepSeek v3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) |
+| Synthetic Art of Problem Solving from DeepSeek-R1 | Text | 40B | [Art of Problem Solving](https://artofproblemsolving.com/company); [American Mathematics Competitions 8](https://artofproblemsolving.com/wiki/index.php/AMC_8_Problems_and_Solutions); [American Mathematics Competitions 10](https://artofproblemsolving.com/wiki/index.php/AMC_10_Problems_and_Solutions); | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) |
+| Synthetic Moral Stories and Social Chemistry from Mixtral-8x22B-v0.1 | Text | 327M | [social-chemestry-101](https://huggingface.co/datasets/tasksource/social-chemestry-101); [Moral Stories](https://huggingface.co/datasets/demelin/moral_stories) | [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1) |
+| Synthetic Social Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B | Text | 83.6M | [OpenStax \- CC BY-SA subset](https://openstax.org/) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1); [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) |
+| Synthetic Health Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B | Text | 9.7M | [OpenStax \- CC BY-SA subset](https://openstax.org/) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1); [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) |
+| Synthetic STEM seeded with OpenStax, Open Textbook Library, and GSM8K from DeepSeek-R1, DeepSeek-V3, DeepSeek-V3-0324, and Qwen2.5-72B | Text | 175M | [OpenStax \- CC BY-SA subset](https://openstax.org/); [GSM8K](https://github.com/openai/grade-school-math); [Open Textbook Library \- CC BY-SA & GNU subset](https://open.umn.edu/opentextbooks/textbooks/) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1), [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324); [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) |
+| [Nemotron-PrismMath](https://huggingface.co/datasets/nvidia/Nemotron-PrismMath) | Text | 4.6B | [Big-Math-RL-Verified](https://huggingface.co/datasets/SynthLabsAI/Big-Math-RL-Verified); [OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) | [Qwen2.5-0.5B-instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct), [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct); [DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) |
+| Synthetic Question Answering Data from Papers and Permissible Books from Qwen2.5-72B-Instruct | Text | 350M | [arXiv](https://info.arxiv.org/help/bulk_data/index.html); [National Institutes of Health ExPorter](https://www.nih.gov/); [BioRxiv](https://www.biorxiv.org/tdm); [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/); [USPTO Backgrounds](https://data.uspto.gov/apis/transition-guide/bdss#pats); [peS2o](https://huggingface.co/datasets/allenai/peS2o); Global Regulation; [CORE](https://core.ac.uk/documentation/dataset); [PG-19](https://github.com/google-deepmind/pg19); [DOAB CC BY & CC BY-SA subset](https://www.doabooks.org/en); [NDLTD](https://ndltd.org/thesis-resources/global-etd-search/) | [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) |
+| Refreshed [Nemotron-MIND](https://huggingface.co/datasets/nvidia/Nemotron-MIND) from phi-4 | Text | 73B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
+| Nemotron-CC-Math-4plus | Text | 52.3B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
+| Nemotron-CC-Math-3 | Text | 80.9B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
+| Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from DeepSeek-V3 and DeepSeek-V3-0324 | Text | 4.0B | [AQUA-RAT](https://huggingface.co/datasets/deepmind/aqua_rat); [LogiQA](https://huggingface.co/datasets/lucasmccabe/logiqa); [AR-LSAT](https://github.com/zhongwanjun/AR-LSAT) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324) |
+| Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from Qwen3-30B-A3B | Text | 4.2B | [AQUA-RAT](https://huggingface.co/datasets/deepmind/aqua_rat); [LogiQA](https://huggingface.co/datasets/lucasmccabe/logiqa); [AR-LSAT](https://github.com/zhongwanjun/AR-LSAT) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
+| Synthetic Art of Problem Solving from Qwen2.5-32B-Instruct, Qwen2.5-Math-72B, Qwen2.5-Math-7B, and Qwen2.5-72B-Instruct | Text |  | [Art of Problem Solving](https://artofproblemsolving.com/company); [American Mathematics Competitions 8](https://artofproblemsolving.com/wiki/index.php/AMC_8_Problems_and_Solutions); [American Mathematics Competitions 10](https://artofproblemsolving.com/wiki/index.php/AMC_10_Problems_and_Solutions); [GSM8K](https://github.com/openai/grade-school-math); [PRM800K](https://github.com/openai/prm800k) | [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct); [Qwen2.5-Math-72B](https://huggingface.co/Qwen/Qwen2.5-Math-72B); [Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B); [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) |
+| Synthetic MMLU Auxiliary Train from DeepSeek-R1 | Text | 0.5B | [MMLU Auxiliary Train](https://huggingface.co/datasets/cais/mmlu/viewer/all/auxiliary_train) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) |
+| Synthetic Long Context Continued Post-Training Data from Papers and Permissible Books from Qwen2.5-72B-Instruct | Text |  | [arXiv](https://info.arxiv.org/help/bulk_data/index.html); [National Institutes of Health ExPorter](https://www.nih.gov/); [BioRxiv](https://www.biorxiv.org/tdm); [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/); [USPTO Backgrounds](https://data.uspto.gov/apis/transition-guide/bdss#pats); [peS2o](https://huggingface.co/datasets/allenai/peS2o); Global Regulation; [CORE](https://core.ac.uk/documentation/dataset); [PG-19](https://github.com/google-deepmind/pg19); [DOAB CC BY & CC BY-SA subset](https://www.doabooks.org/en); [NDLTD](https://ndltd.org/thesis-resources/global-etd-search/) | [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) |
+| Synthetic Common Crawl from Qwen3-30B-A3B and Mistral-Nemo-12B-Instruct | Text | 415.8B | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B); [Mistral-NeMo-12B-Instruct](https://huggingface.co/nvidia/Mistral-NeMo-12B-Instruct) |
+| Synthetic Multilingual Data from Common Crawl from Qwen3-30B-A3B | Text |  | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
+| Synthetic Multilingual Data from Wikimedia from Qwen3-30B-A3B | Text |  | [Wikimedia](https://dumps.wikimedia.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
+| Synthetic Math Data from Wikimedia from Nemotron-4-340B-Instruct | Text |  | \- | [Nemotron-4-340B-Instruct](https://huggingface.co/nvidia/Nemotron-4-340B-Instruct) |
+| Synthetic Common Crawl Code from phi-4 | Text | 427.9B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
+| Synthetic Scientific Coding from Qwen3-235B-A22B | Text | 1.2B | [Wikimedia](https://dumps.wikimedia.org/) | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) |
+| Tool Calling Data | Text | 26.2B |  | [Qwen3-235B-A22B-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
+| Synthetic Essential-Web from QwQ-32B | Text | 28.1B | [Essential-Web](https://huggingface.co/datasets/EssentialAI/essential-web-v1.0) | [QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) |
+| Translated Synthetic Crawl | Text | 389.9B | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
+| Translated Synthetic Wikipedia | Text | 7.9B | [Wikimedia](https://dumps.wikimedia.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
+| Synthetic Art of Problem Solving from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | [Art of Problem Solving](https://artofproblemsolving.com/company); [American Mathematics Competitions 8](https://artofproblemsolving.com/wiki/index.php/AMC_8_Problems_and_Solutions); [American Mathematics Competitions 10](https://artofproblemsolving.com/wiki/index.php/AMC_10_Problems_and_Solutions) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) |
+| Synthetic Stack Exchange from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | [Stack Exchange](https://archive.org/details/stackexchange) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) |
+| Synthetic OpenCodeReasoning from DeepSeek-R1-0528 | Text | Undisclosed | [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
+| Synthetic HackerRank Coding from DeepSeek-R1-0528 | Text | Undisclosed | HackerRank Coding Dataset | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
+| Synthetic SWE-Gym from Qwen3-Coder-480B-A35B-Instruct | Text | Undisclosed | [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym) | [Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct) |
+| Synthetic Art of Problem Solving and Stack Exchange from gpt-oss-120b, Qwen2.5-32B-Instruct, and Goedel-Prover-V2-32B | Text | Undisclosed | [Art of Problem Solving](https://artofproblemsolving.com/company); [American Mathematics Competitions 8](https://artofproblemsolving.com/wiki/index.php/AMC_8_Problems_and_Solutions); [American Mathematics Competitions 10](https://artofproblemsolving.com/wiki/index.php/AMC_10_Problems_and_Solutions); [Stack Exchange](https://archive.org/details/stackexchange) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct); [Goedel-Prover-V2-32B](https://huggingface.co/Goedel-LM/Goedel-Prover-V2-32B) |
+| Synthetic Multilingual Science and Code data from DeepSeek-R1, DeepSeek-R1-0528, Qwen2.5-32B-Instruct, and Qwen3-235B-A22B, translated with Qwen2.5-32B-Instruct and Qwen2.5-14B-Instruct | Text | Undisclosed | [Stack Exchange](https://archive.org/details/stackexchange); [SCP-116K](https://huggingface.co/datasets/EricLu/SCP-116K); [LIMO](https://huggingface.co/datasets/GAIR/LIMO); [TACO](https://huggingface.co/datasets/BAAI/TACO); Code Contest; Codeforces | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); |
+| Synthetic Safety from DeepSeek-R1-0528, gpt-oss-120b and Mixtral-8x7B-v0.1 | Text | Undisclosed | [Nemotron Content Safety Dataset V2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0); [Gretel Synthetic Safety Alignment Dataset](https://huggingface.co/datasets/gretelai/gretel-safety-alignment-en-v1); [RedTeam-2K](https://huggingface.co/datasets/JailbreakV-28K/JailBreakV-28k); [Malicious Tasks](https://github.com/CrystalEye42/eval-safety/blob/main/malicious_tasks_dataset.yaml); [Nemotron-Personas-USA](https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) |
+| Synthetic STEM from Qwen3-235B-A22B-Instruct-2507 and gpt-oss-120b | Text | Undisclosed | [arXiv](https://info.arxiv.org/help/bulk_data/index.html); [National Institutes of Health ExPorter](https://www.nih.gov/); [BioRxiv](https://www.biorxiv.org/tdm); [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/); [USPTO Backgrounds](https://data.uspto.gov/apis/transition-guide/bdss#pats); [peS2o](https://huggingface.co/datasets/allenai/peS2o); Global Regulation; [CORE](https://core.ac.uk/documentation/dataset); [PG-19](https://github.com/google-deepmind/pg19); [DOAB CC BY & CC BY-SA subset](https://www.doabooks.org/en); [NDLTD](https://ndltd.org/thesis-resources/global-etd-search/) | [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
+| Synthetic KernelBook from DeepSeek-R1-0528 | Text | Undisclosed | [KernelBook](https://huggingface.co/datasets/GPUMODE/KernelBook) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
+| Synthetic Tool Calling from Qwen3-235B-A22B-Thinking-2507 and Qwen3-Next-80B-A3B-Thinking | Text | Undisclosed | [ToolBench](https://github.com/OpenBMB/ToolBench/tree/master); [glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2); [APIGen Function-Calling](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k); [Nemotron-Personas-USA](https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA) | [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507); [Qwen3-Next-80B-A3B-Thinking](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking) |
+| Synthetic Chat from gpt-oss-120b, Mixtral-8x22B-Instruct-v0.1, Qwen3-235B-A22B-Instruct-2507 , and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | [C4](https://huggingface.co/datasets/allenai/c4); [LMSYS-Chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m); [ShareGPT](https://huggingface.co/datasets/RyokoAI/ShareGPT52K); [GSM8K](https://github.com/openai/grade-school-math); [PRM800K](https://github.com/openai/prm800k); [FinQA](https://finqasite.github.io/); [WikiTableQuestions](https://huggingface.co/wikitablequestions/datasets); [Riddles](https://github.com/crawsome/riddles); [glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2); [SciBench](https://huggingface.co/datasets/xw27/scibench); [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k); [OpenBookQA](https://github.com/allenai/OpenBookQA); [Advanced Reasoning Benchmark](https://github.com/TheDuckAI/arb); Software Heritage; [Khan Academy Math Keywords](https://www.khanacademy.org/math); [WildChat-1M](https://huggingface.co/datasets/allenai/WildChat-1M); [Nemotron-Personas-USA](https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Mixtral-8x22B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1); [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507); [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |
+| Synthetic Long Context from Qwen3-235B-A22B-Instruct-2507 | Text | Undisclosed | [CORE](https://core.ac.uk/documentation/dataset); [PG-19](https://github.com/google-deepmind/pg19); [DOAB CC BY & CC BY-SA subset](https://www.doabooks.org/en); [NDLTD](https://ndltd.org/thesis-resources/global-etd-search/) | [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) |
+| Synthetic Tool Use Interactive Agent from gpt-oss-120b, DeepSeek-R1-0528, Qwen3-32B, and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | NVIDIA Internal | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528); [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B); and [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |
+| Synthetic STEM from Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | [ICHO-IPH0](https://huggingface.co/datasets/II-Vietnam/IChO-IPhO-RL-v2-formated); [Physics Big](https://huggingface.co/datasets/Vikhrmodels/physics_big); Scale HLE; [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning); [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) | [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |
+| Synthetic DocFinQA and SWE-smith from Qwen3-Coder-480B-A35B-Instruct and Kimi-K2-Thinking | Text | Undisclosed | [DocFinQA](https://huggingface.co/datasets/kensho/DocFinQA); [SWE-smith](https://huggingface.co/datasets/SWE-bench/SWE-smith) | [Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct); [Kimi-K2-Thinking](https://huggingface.co/moonshotai/Kimi-K2-Thinking) |
+| Synthetic Math from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | \- | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) |
+| Synthetic Essential-Web from gpt-oss-120b | Text | Undisclosed | [Essential-Web](https://huggingface.co/datasets/EssentialAI/essential-web-v1.0) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
+| Synthetic Scale HLE from gpt-oss-120b | Text | Undisclosed | Scale HLE | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
+| Synthetic CDQuestions from gpt-oss-120b | Text | Undisclosed | [CDQuestions](https://cdquestions.com/) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
+| Synthetic Stack Exchange from gpt-oss-120b | Text | Undisclosed | [Stack Exchange](https://archive.org/details/stackexchange) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
+| Synthetic GPQA from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | [Stack Exchange](https://archive.org/details/stackexchange) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) |
+| Synthetic Vedantu from gpt-oss-120b | Text | Undisclosed | [Vedantu](https://www.vedantu.com/) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
+| Synthetic SWE-Gym and R2E-Gym-Subset from Qwen3-Coder-480B-A35B-Instruct | Text | Undisclosed | [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym); [R2E-Gym-Subset](https://huggingface.co/datasets/R2E-Gym/R2E-Gym-Subset) | [Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct) |
+| Synthetic SWE-Gym from Qwen3-Coder-480B-A35B-Instruct | Text | Undisclosed | [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym) | [Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct) |
+| Synthetic SWE-Gym and R2E-Gym-Subset from DeepSeek-R1-0528 | Text | Undisclosed | [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym); [R2E-Gym-Subset](https://huggingface.co/datasets/R2E-Gym/R2E-Gym-Subset) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
+| Synthetic HelpSteer, LMSYS-Chat-1M, and Nemotron-Personas-USA from gpt-oss-120b, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | [HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2); [HelpSteer3](https://huggingface.co/datasets/nvidia/HelpSteer3); [LMSYS-Chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m); [Nemotron-Personas-USA](https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507); [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |
+| Synthetic Structured Outputs from Qwen3-30B-A3B-Instruct-2507, Qwen3-30B-A3B-Thinking-2507, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | \- | [Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507); [Qwen3-30B-A3B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507); [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507); [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |
+| Synthetic Search STEM MCQ from Qwen3-235B-A22B and DeepSeek-R1-0528 | Text | Undisclosed | \- | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
+| Synthetic Search STEM OPENQ from DeepSeek-R1-0528 | Text | Undisclosed | \- | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
+| Synthetic OpenSTEM from Qwen2.5-32B-Instruct and DeepSeek-R1-0528 | Text | Undisclosed | \- | [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
+| Synthetic MCQ from Qwen2.5-32B-Instruct and DeepSeek-R1-0528 | Text | Undisclosed | \- | [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
+| Synthetic MCQ10 from DeepSeek-R1-0528 | Text | Undisclosed | \- | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
+| Synthetic MCQ4 from Qwen3-235B-A22B, DeepSeek-R1-0528, and Qwen3-235B-A22B-Instruct-2507 | Text | Undisclosed | \- | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528); [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) |
+| Synthetic OpenMathReasoning from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) |
+| Synthetic Offline Search MCQA HLE from DeepSeek-R1-0528 | Text | Undisclosed | \- | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
+| Synthetic Offline Search MCQA GPQA from Qwen3-235B-A22B and DeepSeek-R1-0528 | Text | Undisclosed | \- | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
+| Synthetic Human Preference from QwQ-32B, Qwen3-30B-A3B, Qwen3-235B-A22B, Qwen3-235B-A22B-Instruct-2507, Mistral-Small-3.1-24B-Instruct-2503, Mistral-Small-3.2-24B-Instruct-2506, MiniMax-M1-80k, MiniMax-M1-40k, Kimi-K2-Instruct, DeepSeek-V3-0324, DeepSeek-R1-0528 | Text | Undisclosed | \- | [QwQ-32B](https://huggingface.co/Qwen/QwQ-32B); [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507); [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503); [Mistral-Small-3.2-24B-Instruct-2506](https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506); [MiniMax-M1-80k](https://huggingface.co/MiniMaxAI/MiniMax-M1-80k); [MiniMax-M1-40k](https://huggingface.co/MiniMaxAI/MiniMax-M1-40k); [Kimi-K2-Instruct](https://huggingface.co/moonshotai/Kimi-K2-Instruct); [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
+| Synthetic WildChat-1M and arena-human-preference-140k from DeepSeek-R1, gemma-2-2b-it, gemma-3-27b-it, gpt-oss-20b, gpt-oss-120b, Mistral-7B-Instruct-v0.3, Mixtral-8x22B-Instruct-v0.1, Nemotron-4-340B-Instruct, NVIDIA-Nemotron-Nano-9B-v2, Phi-4-mini-instruct, Phi-3-small-8k-instruct, Phi-3-medium-4k-instruct, Qwen3-235B-A22B, QwQ-32B | Text | Undisclosed | [WildChat-1M](https://huggingface.co/datasets/allenai/WildChat-1M); [arena-human-preference-140k](https://huggingface.co/datasets/lmarena-ai/arena-human-preference-140k) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1); [gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it); [gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it); [gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3); [Mixtral-8x22B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1); [Nemotron-4-340B-Instruct](https://huggingface.co/nvidia/Nemotron-4-340B-Instruct); [NVIDIA-Nemotron-Nano-9B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2); [Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct); [Phi-3-small-8k-instruct](https://huggingface.co/microsoft/Phi-3-small-8k-instruct); [Phi-3-medium-4k-instruct](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) |
+| Synthetic Safety from DeepSeek-R1-0528, gpt-oss-120b, DeepSeek-R1-Distill-Qwen-7B, and Mixtral-8x7B-v0.1 | Text | Undisclosed | [Nemotron Content Safety Dataset V2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0); [Gretel Synthetic Safety Alignment Dataset](https://huggingface.co/datasets/gretelai/gretel-safety-alignment-en-v1); [RedTeam-2K](https://huggingface.co/datasets/JailbreakV-28K/JailBreakV-28k); [Malicious Tasks](https://github.com/CrystalEye42/eval-safety/blob/main/malicious_tasks_dataset.yaml); | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B); [Qwen3-30B-A3B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507); [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507); [Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) |
+| Synthetic Code from Qwen3-32B | Text | Undisclosed | English Common Crawl; English Common Crawl 1.1 | [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) |
+| Synthetic OpenCodeReasoning from DeepSeek-R1 | Text | Undisclosed | [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) |
+| Synthetic LIMO from DeepSeek-R1-0528 | Text | Undisclosed | [LIMO](https://huggingface.co/datasets/GAIR/LIMO) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
+| Synthetic SCP from DeepSeek-R1-0528 | Text | Undisclosed | [SCP-116K](https://huggingface.co/datasets/EricLu/SCP-116K) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
+| Synthetic Stack Exchange from DeepSeek-R1-0528 | Text | Undisclosed | [Stack Exchange](https://archive.org/details/stackexchange) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
+| Synthetic Common Crawl from Qwen3-30B-A3B | Text | Undisclosed | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
+| Synthetic Wikipedia from Qwen3-30B-A3B | Text | Undisclosed | [Wikimedia](https://dumps.wikimedia.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
+| Synthetic Essential-Web from Qwen3-30B-A3B and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | [Essential-Web](https://huggingface.co/datasets/EssentialAI/essential-web-v1.0) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B); [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |
+| Synthetic Textbook Math from Qwen3-30B-A3B, Qwen3-235B-A22B, phi-4 | Text | Undisclosed | [Common Crawl](https://commoncrawl.org/); [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [phi-4](https://huggingface.co/microsoft/phi-4) |
+| Synthetic Math and Code from DeepSeek-R1 and DeepSeek-R1-0528 | Text | Undisclosed | [Magicoder-Evol-Instruct-110K](https://huggingface.co/datasets/ise-uiuc/Magicoder-Evol-Instruct-110K); [opc-sft-stage2](https://huggingface.co/datasets/OpenCoder-LLM/opc-sft-stage2); [TACO](https://huggingface.co/datasets/BAAI/TACO); [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning); [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning); [NuminaMath CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
+| Synthetic Nemotron-Personas-USA from gpt-oss-120b and Qwen3-8B | Text | Undisclosed | [Nemotron-Personas-USA](https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) |
+| Synthetic Text-To-SQL | Text | Undisclosed | \- | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
+| Synthetic Agentless SWE | Text | Undisclosed | [SWE-Bench-Train](https://huggingface.co/datasets/princeton-nlp/SWE-bench/viewer/default/train); [SWE-Fixer-Train](https://huggingface.co/datasets/internlm/SWE-Fixer-Train-110K); [SWE-reBench](https://huggingface.co/datasets/nebius/SWE-rebench); [SWE-smith](https://huggingface.co/datasets/SWE-bench/SWE-smith) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
+| Synthetic Search Graph Walk | Text | Undisclosed | \- | [MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) |
+| Synthetic CUDA 100k | Text | Undisclosed | [KernelBook](https://huggingface.co/datasets/GPUMODE/KernelBook); [HuggingFace Transformers](https://github.com/huggingface/transformers); [FlashInfer](https://github.com/flashinfer-ai/flashinfer) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
+| Synthetic Safety | Text | Undisclosed | [Nemotron Content Safety Dataset V2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0); [Gretel Synthetic Safety Alignment Dataset](https://huggingface.co/datasets/gretelai/gretel-safety-alignment-en-v1); [RedTeam-2K](https://huggingface.co/datasets/Ericwang/gpt-oss-distilled-redteam2k); [HarmfulTasks](https://github.com/CrystalEye42/eval-safety/blob/main/malicious_tasks_dataset.yaml) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [NVIDIA-Nemotron-Nano-9B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2); [gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it) |
+| Synthetic Agentic Diverse Domains | Text | Undisclosed | \- | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528); [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507); [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507); [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [DeepSeek-V3.2](https://huggingface.co/deepseek-ai/DeepSeek-V3.2) |
+| Synthetic SWE Unverified | Text | Undisclosed | \- | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct); [GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash) |
+| Synthetic Scale HLE from Deepseek-V3 | Text | Undisclosed | Scale HLE | [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324)  |
+| Synthetic CDQuestions from Deepseek-V3 | Text | Undisclosed | [CDQuestions](https://cdquestions.com/) | [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324)  |
+| Synthetic Stack Exchange from Deepseek-V3 | Text | Undisclosed | [Stack Exchange](https://archive.org/details/stackexchange) | [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324)  |
+| Synthetic GPQA from Deepseek-V3 | Text | Undisclosed | [Stack Exchange](https://archive.org/details/stackexchange) | [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324)  |
+| Synthetic Vedantu from Deepseek-V3 | Text | Undisclosed | [Vedantu](https://www.vedantu.com/) | [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324) |
+| Synthetic Tool Call Schema for RL | Text | Undisclosed | [ToolBench](https://github.com/OpenBMB/ToolBench/tree/master); [glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2); [APIGen Function-Calling](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k); [Nemotron-Personas-USA](https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA) | [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507); [Qwen3-Next-80B-A3B-Thinking](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking) |
+| Synthetic Data for Search | Text | Undisclosed | [Wikimedia](https://dumps.wikimedia.org/) | [MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) |
+| Synthetic Instruction Following for RL | Text | Undisclosed | \- | [NVIDIA-Nemotron-Nano-9B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2); [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |
+| Synthetic Conversational Agentic Tool-Use RL | Text | Undisclosed | \- | [DeepSeek-V3.2](https://huggingface.co/deepseek-ai/DeepSeek-V3.2); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528); [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507); [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) |
+| Synthetic Terminal Pivot RL | Text | Undisclosed | [SWE-smith](https://huggingface.co/datasets/SWE-bench/SWE-smith); [Nemotron-Cascade-RL-SWE](https://huggingface.co/datasets/nvidia/Nemotron-Cascade-RL-SWE); Vendor supplied | [DeepSeek-V3.2](https://huggingface.co/deepseek-ai/DeepSeek-V3.2); [Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct); [Kimi-K2.5](https://huggingface.co/moonshotai/Kimi-K2.5); [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) |
+
+## Language Distribution in Post-Training
+
+For our post-training recipe, we focused on 9 main languages in addition to English: French, German, Italian, Japanese, Spanish, and Chinese
+
+Those languages were represented in the form of multilingual reasoning and translation tasks.
+
+The following table depicts our sample distribution for the 6 languages and 5 translation pairs.
+
+| Language | Size |
+| :--- | :--- |
+| English | 13.48M |
+| Italian | 53k |
+| German | 53k |
+| Spanish | 53k |
+| French | 53k |
+| Japanese | 53k |
+| Chinese | 53k |
+| English <-> Italian | 43.2k |
+| English <-> German | 43.2k |
+| English <-> Spanish | 43.2k |
+| English <-> French | 43.2k |
+| English <-> Japanese | 43.2k |
+
+</details>
+
+## Evaluation Dataset
+
+* **Data Collection Method by dataset**: Hybrid: Human, Synthetic
+* **Labeling Method by dataset**: Hybrid: Automated, Human, Synthetic
+
+## Inference
+
+* **Acceleration Engine:** PyTorch
+* **Test Hardware:**
+    * NVIDIA Hopper
+        * 1-8x H100
+        * 1-8x H200
+    * NVIDIA Grace Blackwell
+        * GB200
+
+## Ethical Considerations
+
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
+
+We advise against circumvention of any provided safety guardrails contained in the Model without a substantially similar guardrail appropriate for your use case. For more details: [Safety](./safety.md) and [Explainability](./explainability.md) Subcards.
+
+For more detailed information on ethical considerations for this model, please see the Model Card++ [Bias](./bias.md), and [Privacy](./privacy.md) Subcards.
+
+Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+
+## Citation
+
+```bibtex
+@misc{nvidia_nemotron_3_2025,
+  title  = {NVIDIA Nemotron 3: Efficient and Open Intelligence},
+  author = {{NVIDIA}},
+  year   = {2025},
+  url    = {https://arxiv.org/abs/2512.20856},
+  note   = {White Paper}
+}
+```

bias.md

@@ -0,0 +1,11 @@
+| Field | Response |
+| :---- | :---- |
+| Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None |
+| Bias Metric (If Measured): | [BBQ Accuracy Scores in Ambiguous Contexts](https://github.com/nyu-mll/BBQ/) |
+| Which characteristic (feature) show(s) the greatest difference in performance?: | The model shows high variance in the characteristics when it is used with a high temperature. |
+| Which feature(s) have the worst performance overall? | Physical Appearance |
+| Measures taken to mitigate against unwanted bias: | Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) employed to calibrate the model’s reasoning capabilities to maintain logical consistency and appropriate complexity when interacting with or interpreting data from diverse age demographics. |
+| If using internal data, description of methods implemented in data acquisition or processing, if any, to address the prevalence of identifiable biases in the training, testing, and validation data: | The training datasets contain a large amount of synthetic data generated by LLMs. We manually curated prompts. |
+| Tools used to assess statistical imbalances and highlight patterns that may introduce bias into AI models: | [BBQ](https://github.com/nyu-mll/BBQ/) |
+| Tools used to assess statistical imbalances and highlight patterns that may introduce bias into AI models: | These datasets, such as web-scraped finance reasoning data, do not collectively or exhaustively represent all demographic groups (and proportionally therein). For instance, these datasets do not contain explicit mentions of the following classes: age, gender, or ethnicity in approximately 97% to 99% of samples. Finance reasoning data scraped from SEC EDGAR contained a notable representational skew where ethnicity mentions are dominated by Middle Eastern contexts (found in finance documents), while gender is explicitly mentioned in only 0.9% of samples (including Male-only, Female-only, and Both). To mitigate these imbalances, we recommend considering these evaluation techniques such as bias audits, fine-tuning with demographically balanced datasets, and mitigation strategies such as counterfactual data augmentation to align with the desired model behavior. This evaluation used a 3,000-sample subset per dataset, identified as the optimal threshold for maximizing embedder accuracy. |
+| Unwanted Bias Testing: | Constrained to English-language inputs. Multi-lingual parity is not currently claimed or guaranteed. |

chat_template.jinja

@@ -0,0 +1,209 @@
+{% macro render_extra_keys(json_dict, handled_keys) %}
+    {%- if json_dict is mapping %}
+        {%- for json_key in json_dict if json_key not in handled_keys %}
+            {%- if json_dict[json_key] is mapping or (json_dict[json_key] is sequence and json_dict[json_key] is not string) %}
+                {{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson | safe) ~ '</' ~ json_key ~ '>' }}
+            {%- else %}
+                {{-'\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '</' ~ json_key ~ '>' }}
+            {%- endif %}
+        {%- endfor %}
+    {%- endif %}
+{% endmacro %}
+{%- set enable_thinking = enable_thinking if enable_thinking is defined else True %}
+{%- set low_effort = low_effort if low_effort is defined else False %}
+{%- set truncate_history_thinking = truncate_history_thinking if truncate_history_thinking is defined else True %}
+
+{%- set ns = namespace(last_user_idx = -1) %}
+{%- set loop_messages = messages %}
+{%- for m in loop_messages %}
+  {%- if m["role"] == "user" %}
+    {%- set ns.last_user_idx = loop.index0 %}
+  {%- endif %}
+{%- endfor %}
+
+{%- if messages[0]["role"] == "system" %}
+    {%- set system_message = messages[0]["content"] %}
+    {%- set loop_messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+    {%- set loop_messages = messages %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = [] %}
+{%- endif %}
+{# Recompute last_user_idx relative to loop_messages after handling system #}
+{%- set ns = namespace(last_user_idx = -1) %}
+{%- for m in loop_messages %}
+  {%- if m["role"] == "user" %}
+    {%- set ns.last_user_idx = loop.index0 %}
+  {%- endif %}
+{%- endfor %}
+{%- if system_message is defined %}
+    {{- "<|im_start|>system\n" + system_message }}
+{%- else %}
+    {%- if tools is iterable and tools | length > 0 %}
+        {{- "<|im_start|>system\n" }}
+    {%- endif %}
+{%- endif %}
+{%- if tools is iterable and tools | length > 0 %}
+    {%- if system_message is defined and system_message | length > 0 %}
+        {{- "\n\n" }}
+    {%- endif %}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n" }}
+    {{- "<tools>" }}
+    {%- for tool in tools %}
+        {%- if tool.function is defined %}
+            {%- set tool = tool.function %}
+        {%- endif %}
+        {{- "\n<function>\n<name>" ~ tool.name ~ "</name>" }}
+        {%- if tool.description is defined %}
+            {{- '\n<description>' ~ (tool.description | trim) ~ '</description>' }}
+        {%- endif %}
+        {{- '\n<parameters>' }}
+        {%- if tool.parameters is defined and tool.parameters is mapping and tool.parameters.properties is defined and tool.parameters.properties is mapping %}
+            {%- for param_name, param_fields in tool.parameters.properties|items %}
+                {{- '\n<parameter>' }}
+                {{- '\n<name>' ~ param_name ~ '</name>' }}
+                {%- if param_fields.type is defined %}
+                    {{- '\n<type>' ~ (param_fields.type | string) ~ '</type>' }}
+                {%- endif %}
+                {%- if param_fields.description is defined %}
+                    {{- '\n<description>' ~ (param_fields.description | trim) ~ '</description>' }}
+                {%- endif %}
+                {%- if param_fields.enum is defined %}
+                    {{- '\n<enum>' ~ (param_fields.enum | tojson | safe) ~ '</enum>' }}
+                {%- endif %}
+                {%- set handled_keys = ['name', 'type', 'description', 'enum'] %}
+                {{- render_extra_keys(param_fields, handled_keys) }}
+                {{- '\n</parameter>' }}
+            {%- endfor %}
+        {%- endif %}
+        {% set handled_keys = ['type', 'properties', 'required'] %}
+        {{- render_extra_keys(tool.parameters, handled_keys) }}
+        {%- if tool.parameters is defined and tool.parameters.required is defined %}
+            {{- '\n<required>' ~ (tool.parameters.required | tojson | safe) ~ '</required>' }}
+        {%- endif %}
+        {{- '\n</parameters>' }}
+        {%- set handled_keys = ['type', 'name', 'description', 'parameters'] %}
+        {{- render_extra_keys(tool, handled_keys) }}
+        {{- '\n</function>' }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+{%- endif %}
+
+
+{%- if system_message is defined %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if tools is iterable and tools | length > 0 %}
+        {{- '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+
+{%- for message in loop_messages %}
+    {%- if message.role == "assistant" %}
+        {# Add reasoning content in to content field for unified processing below. #}
+        {%- if message.reasoning_content is defined and message.reasoning_content is string and message.reasoning_content | trim | length > 0 %}
+            {%- set content = "<think>\n" ~ message.reasoning_content ~ "\n</think>\n" ~ (message.content | default('', true)) %}
+        {%- else %}
+            {%- set content = message.content | default('', true) %}
+            {%- if content is string -%}
+                {# Allow downstream logic to to take care of broken thought, only handle coherent reasoning here. #}
+                {%- if '<think>' not in content and '</think>' not in content -%}
+                    {%- set content = "<think></think>" ~ content -%}
+                {%- endif -%}
+            {%- else -%}
+                {%- set content = content -%}
+            {%- endif -%}
+        {%- endif %}
+        {%- if message.tool_calls is defined and message.tool_calls is iterable and message.tool_calls | length > 0 %}
+            {# Assistant message has tool calls. #}
+            {{- '<|im_start|>assistant\n' }}
+                {%- set include_content = not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %}
+                {%- if content is string and content | trim | length > 0 %}
+                    {%- if include_content %}
+                        {{- (content | trim) ~ '\n' -}}
+                    {%- else %}
+                        {%- set c = (content | string) %}
+                        {%- if '</think>' in c %}
+                            {# Keep only content after the last closing think. Also generation prompt causes this. #}
+                            {%- set c = c.split('</think>')[-1] %}
+                        {%- elif '<think>' in c %}
+                            {# If <think> was opened but never closed, drop the trailing think segment #}
+                            {%- set c = c.split('<think>')[0] %}
+                        {%- endif %}
+                        {%- set c = "<think></think>" ~ c | trim %}
+                        {%- if c | length > 0 %}
+                            {{- c ~ '\n' -}}
+                        {%- endif %}
+                    {%- endif %}
+                {%- else %}
+                    {{- "<think></think>" -}}
+                {%- endif %}
+                {%- for tool_call in message.tool_calls %}
+                    {%- if tool_call.function is defined %}
+                        {%- set tool_call = tool_call.function %}
+                    {%- endif %}
+                    {{- '<tool_call>\n<function=' ~ tool_call.name ~ '>\n' -}}
+                        {%- if tool_call.arguments is defined %}
+                            {%- for args_name, args_value in tool_call.arguments|items %}
+                                {{- '<parameter=' ~ args_name ~ '>\n' -}}
+                                    {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
+                                {{- args_value ~ '\n</parameter>\n' -}}
+                            {%- endfor %}
+                        {%- endif %}
+                    {{- '</function>\n</tool_call>\n' -}}
+                {%- endfor %}
+                {{- '<|im_end|>\n' }}
+        {%- else %}
+            {# Assistant message doesn't have tool calls. #}
+            {%- if not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %}
+                {{- '<|im_start|>assistant\n' ~ (content | default('', true) | string | trim) ~ '<|im_end|>\n' }}
+            {%- else %}
+                {%- set c = (content | default('', true) | string) %}
+                {%- if '<think>' in c and '</think>' in c %}
+                    {%- set c = "<think></think>" ~ c.split('</think>')[-1] %}
+                {%- endif %}
+                {%- set c = c | trim %}
+                {%- if c | length > 0 %}
+                    {{- '<|im_start|>assistant\n' ~ c ~ '<|im_end|>\n' }}
+                {%- else %}
+                    {{- '<|im_start|>assistant\n<|im_end|>\n' }}
+                {%- endif %}
+            {%- endif %}
+        {%- endif %}
+    {%- elif message.role == "user" or message.role == "system" %}
+        {{- '<|im_start|>' + message.role + '\n' }}
+        {%- set content = message.content | string %}
+        {%- if message.role == "user" and loop.index0 == ns.last_user_idx and low_effort %}
+            {{- content + '\n\n{reasoning effort: low}' }}
+        {%- else %}
+            {{- content }}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user\n' }}
+        {%- endif %}
+        {{- '<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>\n' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endfor %}
+
+{%- if add_generation_prompt %}
+    {%- if enable_thinking %}
+        {{- '<|im_start|>assistant\n<think>\n' }}
+    {%- else %}
+        {{- '<|im_start|>assistant\n<think></think>' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,231 @@
+{
+  "architectures": [
+    "NemotronHForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_nemotron_h.NemotronHConfig",
+    "AutoModelForCausalLM": "modeling_nemotron_h.NemotronHForCausalLM"
+  },
+  "bos_token_id": 1,
+  "chunk_size": 128,
+  "conv_kernel": 4,
+  "dtype": "bfloat16",
+  "eos_token_id": 2,
+  "expand": 2,
+  "head_dim": 128,
+  "hidden_dropout": 0.0,
+  "hidden_size": 4096,
+  "hybrid_override_pattern": "MEMEMEM*EMEMEMEM*EMEMEMEM*EMEMEMEMEM*EMEMEMEMEM*EMEMEMEMEM*EMEMEMEMEM*EMEMEMEM*EMEMEMEME",
+  "initializer_range": 0.02,
+  "intermediate_size": 2688,
+  "layer_norm_epsilon": 1e-05,
+  "mamba_head_dim": 64,
+  "mamba_hidden_act": "silu",
+  "mamba_num_heads": 128,
+  "mamba_proj_bias": false,
+  "mamba_ssm_cache_dtype": "float32",
+  "max_position_embeddings": 262144,
+  "mlp_bias": false,
+  "mlp_hidden_act": "relu2",
+  "model_type": "nemotron_h",
+  "moe_intermediate_size": 2688,
+  "moe_latent_size": 1024,
+  "moe_shared_expert_intermediate_size": 5376,
+  "moe_shared_expert_overlap": false,
+  "mtp_hybrid_override_pattern": "*E",
+  "n_group": 1,
+  "n_groups": 8,
+  "n_routed_experts": 512,
+  "n_shared_experts": 1,
+  "norm_eps": 1e-05,
+  "norm_topk_prob": true,
+  "num_attention_heads": 32,
+  "num_experts_per_tok": 22,
+  "num_hidden_layers": 88,
+  "num_key_value_heads": 2,
+  "num_logits_to_keep": 1,
+  "num_nextn_predict_layers": 1,
+  "pad_token_id": 0,
+  "partial_rotary_factor": 1.0,
+  "rescale_prenorm_residual": true,
+  "residual_in_fp32": false,
+  "rope_theta": 10000,
+  "routed_scaling_factor": 5.0,
+  "sliding_window": null,
+  "ssm_state_size": 128,
+  "tie_word_embeddings": false,
+  "time_step_floor": 0.0001,
+  "time_step_max": 0.1,
+  "time_step_min": 0.001,
+  "topk_group": 1,
+  "transformers_version": "4.57.6",
+  "use_bias": false,
+  "use_cache": true,
+  "use_conv_bias": true,
+  "use_mamba_kernels": true,
+  "vocab_size": 131072,
+  "quantization_config": {
+    "config_groups": {
+      "group_0": {
+        "input_activations": {
+          "dynamic": false,
+          "num_bits": 8,
+          "type": "float"
+        },
+        "weights": {
+          "dynamic": false,
+          "num_bits": 8,
+          "type": "float"
+        },
+        "targets": [
+          "Linear"
+        ]
+      }
+    },
+    "ignore": [
+      "backbone.layers.0.mixer.conv1d",
+      "backbone.layers.1.mixer.fc1_latent_proj",
+      "backbone.layers.1.mixer.fc2_latent_proj",
+      "backbone.layers.10.mixer.fc1_latent_proj",
+      "backbone.layers.10.mixer.fc2_latent_proj",
+      "backbone.layers.11.mixer.conv1d",
+      "backbone.layers.12.mixer.fc1_latent_proj",
+      "backbone.layers.12.mixer.fc2_latent_proj",
+      "backbone.layers.13.mixer.conv1d",
+      "backbone.layers.14.mixer.fc1_latent_proj",
+      "backbone.layers.14.mixer.fc2_latent_proj",
+      "backbone.layers.15.mixer.conv1d",
+      "backbone.layers.16*",
+      "backbone.layers.17.mixer.fc1_latent_proj",
+      "backbone.layers.17.mixer.fc2_latent_proj",
+      "backbone.layers.18.mixer.conv1d",
+      "backbone.layers.19.mixer.fc1_latent_proj",
+      "backbone.layers.19.mixer.fc2_latent_proj",
+      "backbone.layers.2.mixer.conv1d",
+      "backbone.layers.20.mixer.conv1d",
+      "backbone.layers.21.mixer.fc1_latent_proj",
+      "backbone.layers.21.mixer.fc2_latent_proj",
+      "backbone.layers.22.mixer.conv1d",
+      "backbone.layers.23.mixer.fc1_latent_proj",
+      "backbone.layers.23.mixer.fc2_latent_proj",
+      "backbone.layers.24.mixer.conv1d",
+      "backbone.layers.25*",
+      "backbone.layers.26.mixer.fc1_latent_proj",
+      "backbone.layers.26.mixer.fc2_latent_proj",
+      "backbone.layers.27.mixer.conv1d",
+      "backbone.layers.28.mixer.fc1_latent_proj",
+      "backbone.layers.28.mixer.fc2_latent_proj",
+      "backbone.layers.29.mixer.conv1d",
+      "backbone.layers.3.mixer.fc1_latent_proj",
+      "backbone.layers.3.mixer.fc2_latent_proj",
+      "backbone.layers.30.mixer.fc1_latent_proj",
+      "backbone.layers.30.mixer.fc2_latent_proj",
+      "backbone.layers.31.mixer.conv1d",
+      "backbone.layers.32.mixer.fc1_latent_proj",
+      "backbone.layers.32.mixer.fc2_latent_proj",
+      "backbone.layers.33.mixer.conv1d",
+      "backbone.layers.34.mixer.fc1_latent_proj",
+      "backbone.layers.34.mixer.fc2_latent_proj",
+      "backbone.layers.35.mixer.conv1d",
+      "backbone.layers.36*",
+      "backbone.layers.37.mixer.fc1_latent_proj",
+      "backbone.layers.37.mixer.fc2_latent_proj",
+      "backbone.layers.38.mixer.conv1d",
+      "backbone.layers.39.mixer.fc1_latent_proj",
+      "backbone.layers.39.mixer.fc2_latent_proj",
+      "backbone.layers.4.mixer.conv1d",
+      "backbone.layers.40.mixer.conv1d",
+      "backbone.layers.41.mixer.fc1_latent_proj",
+      "backbone.layers.41.mixer.fc2_latent_proj",
+      "backbone.layers.42.mixer.conv1d",
+      "backbone.layers.43.mixer.fc1_latent_proj",
+      "backbone.layers.43.mixer.fc2_latent_proj",
+      "backbone.layers.44.mixer.conv1d",
+      "backbone.layers.45.mixer.fc1_latent_proj",
+      "backbone.layers.45.mixer.fc2_latent_proj",
+      "backbone.layers.46.mixer.conv1d",
+      "backbone.layers.47*",
+      "backbone.layers.48.mixer.fc1_latent_proj",
+      "backbone.layers.48.mixer.fc2_latent_proj",
+      "backbone.layers.49.mixer.conv1d",
+      "backbone.layers.5.mixer.fc1_latent_proj",
+      "backbone.layers.5.mixer.fc2_latent_proj",
+      "backbone.layers.50.mixer.fc1_latent_proj",
+      "backbone.layers.50.mixer.fc2_latent_proj",
+      "backbone.layers.51.mixer.conv1d",
+      "backbone.layers.52.mixer.fc1_latent_proj",
+      "backbone.layers.52.mixer.fc2_latent_proj",
+      "backbone.layers.53.mixer.conv1d",
+      "backbone.layers.54.mixer.fc1_latent_proj",
+      "backbone.layers.54.mixer.fc2_latent_proj",
+      "backbone.layers.55.mixer.conv1d",
+      "backbone.layers.56.mixer.fc1_latent_proj",
+      "backbone.layers.56.mixer.fc2_latent_proj",
+      "backbone.layers.57.mixer.conv1d",
+      "backbone.layers.58*",
+      "backbone.layers.59.mixer.fc1_latent_proj",
+      "backbone.layers.59.mixer.fc2_latent_proj",
+      "backbone.layers.6.mixer.conv1d",
+      "backbone.layers.60.mixer.conv1d",
+      "backbone.layers.61.mixer.fc1_latent_proj",
+      "backbone.layers.61.mixer.fc2_latent_proj",
+      "backbone.layers.62.mixer.conv1d",
+      "backbone.layers.63.mixer.fc1_latent_proj",
+      "backbone.layers.63.mixer.fc2_latent_proj",
+      "backbone.layers.64.mixer.conv1d",
+      "backbone.layers.65.mixer.fc1_latent_proj",
+      "backbone.layers.65.mixer.fc2_latent_proj",
+      "backbone.layers.66.mixer.conv1d",
+      "backbone.layers.67.mixer.fc1_latent_proj",
+      "backbone.layers.67.mixer.fc2_latent_proj",
+      "backbone.layers.68.mixer.conv1d",
+      "backbone.layers.69*",
+      "backbone.layers.7.*",
+      "backbone.layers.70.mixer.fc1_latent_proj",
+      "backbone.layers.70.mixer.fc2_latent_proj",
+      "backbone.layers.71.mixer.conv1d",
+      "backbone.layers.72.mixer.fc1_latent_proj",
+      "backbone.layers.72.mixer.fc2_latent_proj",
+      "backbone.layers.73.mixer.conv1d",
+      "backbone.layers.74.mixer.fc1_latent_proj",
+      "backbone.layers.74.mixer.fc2_latent_proj",
+      "backbone.layers.75.mixer.conv1d",
+      "backbone.layers.76.mixer.fc1_latent_proj",
+      "backbone.layers.76.mixer.fc2_latent_proj",
+      "backbone.layers.77.mixer.conv1d",
+      "backbone.layers.78*",
+      "backbone.layers.79.mixer.fc1_latent_proj",
+      "backbone.layers.79.mixer.fc2_latent_proj",
+      "backbone.layers.8.mixer.fc1_latent_proj",
+      "backbone.layers.8.mixer.fc2_latent_proj",
+      "backbone.layers.80.mixer.conv1d",
+      "backbone.layers.81.mixer.fc1_latent_proj",
+      "backbone.layers.81.mixer.fc2_latent_proj",
+      "backbone.layers.82.mixer.conv1d",
+      "backbone.layers.83.mixer.fc1_latent_proj",
+      "backbone.layers.83.mixer.fc2_latent_proj",
+      "backbone.layers.84.mixer.conv1d",
+      "backbone.layers.85.mixer.fc1_latent_proj",
+      "backbone.layers.85.mixer.fc2_latent_proj",
+      "backbone.layers.86.mixer.conv1d",
+      "backbone.layers.87.mixer.fc1_latent_proj",
+      "backbone.layers.87.mixer.fc2_latent_proj",
+      "backbone.layers.9.mixer.conv1d",
+      "lm_head",
+      "mtp*"
+    ],
+    "quant_algo": "FP8",
+    "kv_cache_scheme": {
+      "dynamic": false,
+      "num_bits": 8,
+      "type": "float"
+    },
+    "producer": {
+      "name": "modelopt",
+      "version": "0.41.0"
+    },
+    "quant_method": "modelopt"
+  }
+}

configuration_nemotron_h.py

@@ -0,0 +1,410 @@
+# Copyright 2024-2025 NVIDIA Corporation and The HuggingFace Inc. team. 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.
+"""NemotronH model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class NemotronHConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`NemotronHModel`]. It is used to instantiate a
+    NemotronH model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 [nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16).
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 131072):
+            Vocabulary size of the NemotronH model. Defines the number of different tokens that can be represented by
+            the `inputs_ids` passed when calling [`NemotronHModel`].
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        layers_block_type (`list`, *optional*):
+            Explicit list of layer types for each layer. Each element must be one of: "mamba", "attention", or "moe".
+            The number of layers is determined by the length of this list.
+        num_hidden_layers (`int`, *optional*):
+            Number of hidden layers in the Transformer encoder. This parameter is deprecated and only kept for
+            backward compatibility. The number of layers is now determined by the length of `layers_block_type`.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions.
+        num_logits_to_keep (`int`, *optional*, defaults to 1):
+            Number of prompt logits to calculate during generation. If `None`, all logits will be calculated.
+        pad_token_id (`int`, *optional*, defaults to 0):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention.
+        head_dim (`int`, *optional*, defaults to 128):
+            Dimension of each attention head.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used with.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in attention layers.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size.
+        intermediate_size (`int`, *optional*, defaults to 21504):
+            Dimension of the MLP representations.
+        mlp_hidden_act (`str`, *optional*, defaults to `"relu2"`):
+            The non-linear activation function in the MLP layers.
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in MLP layers.
+        use_mamba_kernels (`bool`, *optional*, defaults to `True`):
+            Flag indicating whether or not to use the fast mamba kernels.
+        ssm_state_size (`int`, *optional*, defaults to 128):
+            The dimension of the mamba state space latents.
+        mamba_num_heads (`int`, *optional*, defaults to 128):
+            Number of heads in Mamba layers.
+        mamba_n_groups (`int`, *optional*, defaults to 8):
+            Number of groups in Mamba layers.
+        mamba_head_dim (`int`, *optional*, defaults to 64):
+            Dimension of each Mamba head.
+        mamba_d_conv (`int`, *optional*, defaults to 4):
+            The size of the mamba convolution kernel.
+        mamba_expand (`int`, *optional*, defaults to 2):
+            Expanding factor used to determine the mamba intermediate size.
+        mamba_hidden_act (`str`, *optional*, defaults to `"silu"`):
+            The non-linear activation function in the Mamba layers.
+        mamba_dt_min (`float`, *optional*, defaults to 0.001):
+            Minimum value for the time step in Mamba.
+        mamba_dt_max (`float`, *optional*, defaults to 0.1):
+            Maximum value for the time step in Mamba.
+        mamba_dt_limit (`tuple`, *optional*, defaults to `(0.0, inf)`):
+            Limits for the time step in Mamba.
+        mamba_dt_init_floor (`float`, *optional*, defaults to 0.0001):
+            Floor value for time step initialization in Mamba.
+        mamba_conv_bias (`bool`, *optional*, defaults to `True`):
+            Whether to use bias in the convolution layer of the mamba mixer block.
+        mamba_proj_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in the input and output projections of the mamba mixer block.
+        mamba_chunk_size (`int`, *optional*, defaults to 128):
+            Size of chunks for Mamba processing.
+        mamba_ssm_cache_dtype (`str`, *optional*, defaults to `"float32"`):
+            Data type for Mamba SSM cache states.
+        n_routed_experts (`int`, *optional*, defaults to 8):
+            Number of routed experts in MoE layers.
+        n_shared_experts (`int`, *optional*, defaults to 1):
+            Number of shared experts that are always activated in MoE layers.
+        moe_intermediate_size (`int`, *optional*, defaults to 7688):
+            Dimension of the MLP representations in routed experts.
+        moe_shared_expert_intermediate_size (`int`, *optional*, defaults to 7688):
+            Dimension of the MLP representations in shared experts.
+        moe_latent_size (`int`, *optional*):
+            Latent size for MoE expert projections. If `None`, uses `hidden_size`.
+        moe_shared_expert_overlap (`bool`, *optional*, defaults to `True`):
+            Whether shared experts overlap with routed experts.
+        num_experts_per_tok (`int`, *optional*, defaults to 2):
+            The number of experts to route per token (top-k routing parameter).
+        routed_scaling_factor (`float`, *optional*, defaults to 1.0):
+            Scaling factor applied to routed expert outputs.
+        n_group (`int`, *optional*, defaults to 1):
+            Number of groups for expert routing.
+        topk_group (`int`, *optional*, defaults to 1):
+            Top-k group parameter for expert selection.
+        norm_topk_prob (`bool`, *optional*, defaults to `True`):
+            Whether to normalize top-k probabilities in expert routing.
+        num_nextn_predict_layers (`int`, *optional*, defaults to 0):
+            Number of additional layers for multi-token prediction. If 0, multi-token prediction is disabled.
+        mtp_layers_block_type (`list`, *optional*, defaults to `['attention', 'moe']`):
+            Explicit list of layer types for multi-token prediction layers when `num_nextn_predict_layers` > 0.
+        use_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in the model.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the layer normalization layers.
+        residual_in_fp32 (`bool`, *optional*, defaults to `False`):
+            Whether or not residuals should be in `float32`.
+        hidden_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the hidden states.
+        rescale_prenorm_residual (`bool`, *optional*, defaults to `True`):
+            Whether to rescale the pre-normalization residual connections.
+
+    ```python
+    >>> from transformers import NemotronHModel, NemotronHConfig
+
+    >>> # Initializing a NemotronH configuration
+    >>> configuration = NemotronHConfig()
+
+    >>> # Initializing a model (with random weights) from the configuration
+    >>> model = NemotronHModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "nemotron_h"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    @staticmethod
+    def _validate_layers_block_type(layers_block_type, expected_length=None, param_name="layers_block_type"):
+        """
+        Validate layers_block_type list.
+
+        Args:
+            layers_block_type: List of layer types to validate
+            expected_length: If provided, validate the list has this length
+            param_name: Parameter name for error messages
+
+        Raises:
+            ValueError: If validation fails
+        """
+        if not isinstance(layers_block_type, list):
+            raise ValueError(f"{param_name} must be a list of strings. Got type: {type(layers_block_type)}")
+
+        if expected_length is not None and len(layers_block_type) != expected_length:
+            raise ValueError(f"{param_name} must have length {expected_length}. Got length {len(layers_block_type)}.")
+
+        valid_types = {"mamba", "attention", "moe"}
+        if not all(block_type in valid_types for block_type in layers_block_type):
+            invalid = set(layers_block_type) - valid_types
+            raise ValueError(f"{param_name} contains invalid types: {invalid}. Must be one of: {valid_types}")
+
+    def __init__(
+        self,
+        # General model config
+        vocab_size=131072,
+        hidden_size=4096,
+        layers_block_type=None,
+        num_hidden_layers=None,  # Deprecated, only for backward compatibility
+        tie_word_embeddings=False,
+        use_cache=True,
+        num_logits_to_keep=1,
+        # Token IDs
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        # Attention layer config
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        head_dim=128,
+        max_position_embeddings=4096,
+        attention_bias=False,
+        attention_dropout=0.0,
+        sliding_window=None,
+        # MLP layer config
+        intermediate_size=21504,
+        mlp_hidden_act="relu2",
+        mlp_bias=False,
+        # Mamba layer config
+        use_mamba_kernels=True,
+        ssm_state_size=128,
+        mamba_num_heads=128,
+        mamba_n_groups=8,
+        mamba_head_dim=64,
+        mamba_d_conv=4,
+        mamba_expand=2,
+        mamba_hidden_act="silu",
+        mamba_dt_min=0.001,
+        mamba_dt_max=0.1,
+        mamba_dt_limit=(0.0, float("inf")),
+        mamba_dt_init_floor=1e-4,
+        mamba_conv_bias=True,
+        mamba_proj_bias=False,
+        mamba_chunk_size=128,
+        mamba_ssm_cache_dtype="float32",
+        # MoE config
+        n_routed_experts=8,
+        n_shared_experts=1,
+        moe_intermediate_size=7688,
+        moe_shared_expert_intermediate_size=7688,
+        moe_latent_size=None,
+        moe_shared_expert_overlap=True,
+        num_experts_per_tok=2,
+        routed_scaling_factor=1.0,
+        n_group=1,
+        topk_group=1,
+        norm_topk_prob=True,
+        # Multi-token prediction config
+        num_nextn_predict_layers=0,
+        mtp_layers_block_type=["attention", "moe"],
+        # General training config
+        use_bias=False,
+        initializer_range=0.02,
+        layer_norm_epsilon=1e-5,
+        residual_in_fp32=False,
+        hidden_dropout=0.0,
+        rescale_prenorm_residual=True,
+        **kwargs,
+    ):
+        # Backward compatibility: convert hybrid_override_pattern to layers_block_type
+        # Always pop hybrid_override_pattern from kwargs to prevent it from being set as an attribute
+        if "hybrid_override_pattern" in kwargs:
+            pattern = kwargs.pop("hybrid_override_pattern")
+            if layers_block_type is None:
+                layers_block_type = self._pattern_to_list(pattern)
+        elif layers_block_type is None:
+            # Default layers_block_type if not provided
+            layers_block_type = ["mamba", "moe", "attention", "moe"]
+
+        # Note: num_hidden_layers is deprecated and ignored if layers_block_type is explicitly provided
+        # It's only kept for backward compatibility when loading old configs
+        if num_hidden_layers is not None:
+            # Warn if num_hidden_layers is provided but doesn't match layers_block_type
+            if len(layers_block_type) != num_hidden_layers:
+                logger.warning(
+                    f"num_hidden_layers ({num_hidden_layers}) is deprecated and doesn't match "
+                    f"layers_block_type length ({len(layers_block_type)}). Using layers_block_type length."
+                )
+
+        # Backward compatibility: convert mtp_hybrid_override_pattern to mtp_layers_block_type
+        # Always pop mtp_hybrid_override_pattern from kwargs to prevent it from being set as an attribute
+        if "mtp_hybrid_override_pattern" in kwargs:
+            pattern = kwargs.pop("mtp_hybrid_override_pattern")
+            if mtp_layers_block_type is None or mtp_layers_block_type == ["attention", "moe"]:
+                mtp_layers_block_type = self._pattern_to_list(pattern)
+
+        self.vocab_size = vocab_size
+        self.tie_word_embeddings = tie_word_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_attention_heads = num_attention_heads
+        self.head_dim = head_dim
+        self.sliding_window = sliding_window
+        self.max_position_embeddings = max_position_embeddings
+        self.attention_dropout = attention_dropout
+        self.hidden_dropout = hidden_dropout
+
+        # Validate layers_block_type (no longer checking length against num_hidden_layers)
+        self._validate_layers_block_type(layers_block_type, expected_length=None, param_name="layers_block_type")
+        self.layers_block_type = layers_block_type
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.mlp_hidden_act = mlp_hidden_act
+        self.attention_bias = attention_bias
+        self.mlp_bias = mlp_bias
+        self.use_bias = use_bias
+        self.initializer_range = initializer_range
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.residual_in_fp32 = residual_in_fp32
+
+        self.use_cache = use_cache
+        self.num_logits_to_keep = num_logits_to_keep
+
+        self.use_mamba_kernels = use_mamba_kernels
+        self.n_groups = mamba_n_groups
+        self.mamba_head_dim = mamba_head_dim
+        self.ssm_state_size = ssm_state_size
+        self.mamba_num_heads = mamba_num_heads
+        self.conv_kernel = mamba_d_conv
+        self.expand = mamba_expand
+        self.mamba_hidden_act = mamba_hidden_act
+        self.time_step_min = mamba_dt_min
+        self.time_step_max = mamba_dt_max
+        self.time_step_limit = mamba_dt_limit
+        self.time_step_floor = mamba_dt_init_floor
+        self.use_conv_bias = mamba_conv_bias
+        self.mamba_proj_bias = mamba_proj_bias
+        self.chunk_size = mamba_chunk_size
+        self.rescale_prenorm_residual = rescale_prenorm_residual
+        self.n_routed_experts = n_routed_experts
+        self.n_shared_experts = n_shared_experts
+        self.moe_intermediate_size = moe_intermediate_size
+        self.moe_shared_expert_intermediate_size = moe_shared_expert_intermediate_size
+        self.moe_latent_size = moe_latent_size
+        self.moe_shared_expert_overlap = moe_shared_expert_overlap
+        self.num_experts_per_tok = num_experts_per_tok
+        self.routed_scaling_factor = routed_scaling_factor
+        self.n_group = n_group
+        self.topk_group = topk_group
+        self.norm_topk_prob = norm_topk_prob
+        self.mamba_ssm_cache_dtype = mamba_ssm_cache_dtype
+
+        # MTP config
+        self.num_nextn_predict_layers = num_nextn_predict_layers
+
+        # Validate mtp_layers_block_type is provided when MTP is enabled
+        if self.num_nextn_predict_layers > 0:
+            if mtp_layers_block_type is None:
+                raise ValueError(
+                    "mtp_layers_block_type is required when num_nextn_predict_layers > 0. "
+                    "Please provide an explicit list of layer types for MTP layers. "
+                    "Example: mtp_layers_block_type=['attention', 'moe']"
+                )
+            self._validate_layers_block_type(mtp_layers_block_type, None, "mtp_layers_block_type")
+        self.mtp_layers_block_type = mtp_layers_block_type
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    @property
+    def num_hidden_layers(self) -> int:
+        """
+        Number of hidden layers derived from the length of layers_block_type.
+        This property replaces the deprecated num_hidden_layers parameter.
+        """
+        return len(self.layers_block_type)
+
+    @num_hidden_layers.setter
+    def num_hidden_layers(self, value):
+        """
+        Setter for backward compatibility when loading configs.
+        The value is ignored since num_hidden_layers is computed from layers_block_type.
+        """
+        # Ignore the value - num_hidden_layers is always derived from layers_block_type
+        pass
+
+    @property
+    def hybrid_override_pattern(self) -> str:
+        """
+        Backward compatibility property.
+        Returns the pattern string representation of layers_block_type.
+        """
+        return self._list_to_pattern(self.layers_block_type)
+
+    @property
+    def mtp_hybrid_override_pattern(self) -> str:
+        """
+        Backward compatibility property.
+        Returns the pattern string representation of mtp_layers_block_type.
+        """
+        return self._list_to_pattern(self.mtp_layers_block_type)
+
+    @staticmethod
+    def _list_to_pattern(layers_list: list) -> str:
+        """Convert list of layer types back to pattern string (for backward compatibility)."""
+        reverse_mapping = {"mamba": "M", "moe": "E", "attention": "*"}
+        return "".join(reverse_mapping[layer_type] for layer_type in layers_list)
+
+    @staticmethod
+    def _pattern_to_list(pattern: str) -> list:
+        """Convert pattern string to list of layer types (for backward compatibility)."""
+        pattern_mapping = {"M": "mamba", "E": "moe", "*": "attention"}
+        return [pattern_mapping[char] for char in pattern]
+
+
+__all__ = ["NemotronHConfig"]

explainability.md

@@ -0,0 +1,14 @@
+| Field | Response |
+| :---- | :---- |
+| Intended Task/Domain: | Text generation, reasoning, and chat |
+| Model Type: | Text-to-text Mamba2-Transformer Hybrid |
+| Intended Users: | Generative AI creators working with conversational AI models and image content. |
+| Output: | Text |
+| Tools used to evaluate datasets to identify synthetic data and ensure data authenticity. | We used a Gemma-3 4B-based filtering model fine-tuned on [Nemotron Content Safety Dataset v2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) to ensure the quality of synthetic data. |
+| Describe how the model works: | Generates text by predicting the next word or token based on the context provided in the input sequence using multiple self-attention layers. |
+| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Age, Disability Status, Gender Identity, Nationality, Physical Appearance, Ethnicity, Socioeconomic Status, Sexual Orientation, Religion |
+| Technical Limitations & Mitigation: | This model performs particularly well in instruction following regimes, as such may be strongly influenced by untrusted inputs and should be paired with appropriate guardrails and data filtering to better align use-case behaviors when exposed to such data. |
+| Verified to have met prescribed NVIDIA quality standards: | Yes |
+| Performance Metrics: | Accuracy, Throughput, and User-side throughput |
+| Potential Known Risks: | The model was optimized explicitly for instruction following and as such may be influenced by untrusted inputs (prompt injection, indirect prompt injection, jailbreaking, web search, etc.) as a result of its instruction tuning that may degrade safety alignment and other training efforts. This model should be paired with additional guardrails and data filtering to limit exposure to instructions from malicious sources. Bypassing of safety alignment, system guardrails, and filters may allow harmful outcomes up to and including remote code execution in some agentic systems when effective security controls are not in place. The model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may generate and amplify harmful, biased, or otherwise unsafe content reinforcing these biases and return toxic responses especially when prompted with toxic prompts. The model may also generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.  The model may exhibit self-anthropomorphism (e.g., displaying human-like characteristics in dialogue, such as expressing preferences and emotions). In integrated system contexts, the model could potentially be exploited to access or disclose information beyond the model’s intended permissions or scope of operation. |
+| Licensing: | [NVIDIA Nemotron Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/) |

generation_config.json

@@ -0,0 +1,13 @@
+{
+  "_from_model_config": true,
+  "do_sample": true,
+  "bos_token_id": 1,
+  "eos_token_id": [
+    2,
+    11
+  ],
+  "pad_token_id": 0,
+  "temperature": 1.0,
+  "top_p": 0.95,
+  "transformers_version": "4.57.1"
+}

hf_quant_config.json

@@ -0,0 +1,142 @@
+{
+    "producer": {
+        "name": "modelopt",
+        "version": "0.41.0"
+    },
+    "quantization": {
+        "quant_algo": "FP8",
+        "kv_cache_quant_algo": "FP8",
+        "exclude_modules": [
+            "backbone.layers.0.mixer.conv1d",
+            "backbone.layers.1.mixer.fc1_latent_proj",
+            "backbone.layers.1.mixer.fc2_latent_proj",
+            "backbone.layers.10.mixer.fc1_latent_proj",
+            "backbone.layers.10.mixer.fc2_latent_proj",
+            "backbone.layers.11.mixer.conv1d",
+            "backbone.layers.12.mixer.fc1_latent_proj",
+            "backbone.layers.12.mixer.fc2_latent_proj",
+            "backbone.layers.13.mixer.conv1d",
+            "backbone.layers.14.mixer.fc1_latent_proj",
+            "backbone.layers.14.mixer.fc2_latent_proj",
+            "backbone.layers.15.mixer.conv1d",
+            "backbone.layers.16*",
+            "backbone.layers.17.mixer.fc1_latent_proj",
+            "backbone.layers.17.mixer.fc2_latent_proj",
+            "backbone.layers.18.mixer.conv1d",
+            "backbone.layers.19.mixer.fc1_latent_proj",
+            "backbone.layers.19.mixer.fc2_latent_proj",
+            "backbone.layers.2.mixer.conv1d",
+            "backbone.layers.20.mixer.conv1d",
+            "backbone.layers.21.mixer.fc1_latent_proj",
+            "backbone.layers.21.mixer.fc2_latent_proj",
+            "backbone.layers.22.mixer.conv1d",
+            "backbone.layers.23.mixer.fc1_latent_proj",
+            "backbone.layers.23.mixer.fc2_latent_proj",
+            "backbone.layers.24.mixer.conv1d",
+            "backbone.layers.25*",
+            "backbone.layers.26.mixer.fc1_latent_proj",
+            "backbone.layers.26.mixer.fc2_latent_proj",
+            "backbone.layers.27.mixer.conv1d",
+            "backbone.layers.28.mixer.fc1_latent_proj",
+            "backbone.layers.28.mixer.fc2_latent_proj",
+            "backbone.layers.29.mixer.conv1d",
+            "backbone.layers.3.mixer.fc1_latent_proj",
+            "backbone.layers.3.mixer.fc2_latent_proj",
+            "backbone.layers.30.mixer.fc1_latent_proj",
+            "backbone.layers.30.mixer.fc2_latent_proj",
+            "backbone.layers.31.mixer.conv1d",
+            "backbone.layers.32.mixer.fc1_latent_proj",
+            "backbone.layers.32.mixer.fc2_latent_proj",
+            "backbone.layers.33.mixer.conv1d",
+            "backbone.layers.34.mixer.fc1_latent_proj",
+            "backbone.layers.34.mixer.fc2_latent_proj",
+            "backbone.layers.35.mixer.conv1d",
+            "backbone.layers.36*",
+            "backbone.layers.37.mixer.fc1_latent_proj",
+            "backbone.layers.37.mixer.fc2_latent_proj",
+            "backbone.layers.38.mixer.conv1d",
+            "backbone.layers.39.mixer.fc1_latent_proj",
+            "backbone.layers.39.mixer.fc2_latent_proj",
+            "backbone.layers.4.mixer.conv1d",
+            "backbone.layers.40.mixer.conv1d",
+            "backbone.layers.41.mixer.fc1_latent_proj",
+            "backbone.layers.41.mixer.fc2_latent_proj",
+            "backbone.layers.42.mixer.conv1d",
+            "backbone.layers.43.mixer.fc1_latent_proj",
+            "backbone.layers.43.mixer.fc2_latent_proj",
+            "backbone.layers.44.mixer.conv1d",
+            "backbone.layers.45.mixer.fc1_latent_proj",
+            "backbone.layers.45.mixer.fc2_latent_proj",
+            "backbone.layers.46.mixer.conv1d",
+            "backbone.layers.47*",
+            "backbone.layers.48.mixer.fc1_latent_proj",
+            "backbone.layers.48.mixer.fc2_latent_proj",
+            "backbone.layers.49.mixer.conv1d",
+            "backbone.layers.5.mixer.fc1_latent_proj",
+            "backbone.layers.5.mixer.fc2_latent_proj",
+            "backbone.layers.50.mixer.fc1_latent_proj",
+            "backbone.layers.50.mixer.fc2_latent_proj",
+            "backbone.layers.51.mixer.conv1d",
+            "backbone.layers.52.mixer.fc1_latent_proj",
+            "backbone.layers.52.mixer.fc2_latent_proj",
+            "backbone.layers.53.mixer.conv1d",
+            "backbone.layers.54.mixer.fc1_latent_proj",
+            "backbone.layers.54.mixer.fc2_latent_proj",
+            "backbone.layers.55.mixer.conv1d",
+            "backbone.layers.56.mixer.fc1_latent_proj",
+            "backbone.layers.56.mixer.fc2_latent_proj",
+            "backbone.layers.57.mixer.conv1d",
+            "backbone.layers.58*",
+            "backbone.layers.59.mixer.fc1_latent_proj",
+            "backbone.layers.59.mixer.fc2_latent_proj",
+            "backbone.layers.6.mixer.conv1d",
+            "backbone.layers.60.mixer.conv1d",
+            "backbone.layers.61.mixer.fc1_latent_proj",
+            "backbone.layers.61.mixer.fc2_latent_proj",
+            "backbone.layers.62.mixer.conv1d",
+            "backbone.layers.63.mixer.fc1_latent_proj",
+            "backbone.layers.63.mixer.fc2_latent_proj",
+            "backbone.layers.64.mixer.conv1d",
+            "backbone.layers.65.mixer.fc1_latent_proj",
+            "backbone.layers.65.mixer.fc2_latent_proj",
+            "backbone.layers.66.mixer.conv1d",
+            "backbone.layers.67.mixer.fc1_latent_proj",
+            "backbone.layers.67.mixer.fc2_latent_proj",
+            "backbone.layers.68.mixer.conv1d",
+            "backbone.layers.69*",
+            "backbone.layers.7.*",
+            "backbone.layers.70.mixer.fc1_latent_proj",
+            "backbone.layers.70.mixer.fc2_latent_proj",
+            "backbone.layers.71.mixer.conv1d",
+            "backbone.layers.72.mixer.fc1_latent_proj",
+            "backbone.layers.72.mixer.fc2_latent_proj",
+            "backbone.layers.73.mixer.conv1d",
+            "backbone.layers.74.mixer.fc1_latent_proj",
+            "backbone.layers.74.mixer.fc2_latent_proj",
+            "backbone.layers.75.mixer.conv1d",
+            "backbone.layers.76.mixer.fc1_latent_proj",
+            "backbone.layers.76.mixer.fc2_latent_proj",
+            "backbone.layers.77.mixer.conv1d",
+            "backbone.layers.78*",
+            "backbone.layers.79.mixer.fc1_latent_proj",
+            "backbone.layers.79.mixer.fc2_latent_proj",
+            "backbone.layers.8.mixer.fc1_latent_proj",
+            "backbone.layers.8.mixer.fc2_latent_proj",
+            "backbone.layers.80.mixer.conv1d",
+            "backbone.layers.81.mixer.fc1_latent_proj",
+            "backbone.layers.81.mixer.fc2_latent_proj",
+            "backbone.layers.82.mixer.conv1d",
+            "backbone.layers.83.mixer.fc1_latent_proj",
+            "backbone.layers.83.mixer.fc2_latent_proj",
+            "backbone.layers.84.mixer.conv1d",
+            "backbone.layers.85.mixer.fc1_latent_proj",
+            "backbone.layers.85.mixer.fc2_latent_proj",
+            "backbone.layers.86.mixer.conv1d",
+            "backbone.layers.87.mixer.fc1_latent_proj",
+            "backbone.layers.87.mixer.fc2_latent_proj",
+            "backbone.layers.9.mixer.conv1d",
+            "lm_head",
+            "mtp*"
+        ]
+    }
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modeling_nemotron_h.py

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+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+# 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 contextlib
+import math
+from dataclasses import dataclass
+from typing import Any, Optional, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+
+from transformers.activations import ACT2FN
+from transformers.generation import GenerationMixin
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.utils import (
+    ModelOutput,
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+)
+from transformers.utils.import_utils import (
+    is_causal_conv1d_available,
+    is_flash_attn_2_available,
+    is_mamba_2_ssm_available,
+)
+
+from .configuration_nemotron_h import NemotronHConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+# Copied from transformers.models.mamba2.modeling_mamba2
+if is_mamba_2_ssm_available():
+    from mamba_ssm.ops.triton.selective_state_update import selective_state_update
+    from mamba_ssm.ops.triton.ssd_combined import mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined
+else:
+    mamba_chunk_scan_combined, mamba_split_conv1d_scan_combined, selective_state_update = None, None, None
+
+try:
+    from mamba_ssm.ops.triton.layernorm_gated import rmsnorm_fn
+except ImportError:
+    raise ImportError("mamba-ssm is required by the Mamba model but cannot be imported")
+
+if is_causal_conv1d_available():
+    from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
+else:
+    causal_conv1d_update, causal_conv1d_fn = None, None
+
+if is_flash_attn_2_available():
+    from transformers.modeling_flash_attention_utils import _flash_attention_forward
+
+is_fast_path_available = all(
+    (
+        selective_state_update,
+        mamba_chunk_scan_combined,
+        mamba_split_conv1d_scan_combined,
+        causal_conv1d_fn,
+        causal_conv1d_update,
+    )
+)
+
+# TODO: Update with correct checkpoint when model is published to HuggingFace Hub
+_CHECKPOINT_FOR_DOC = "nvidia/nemotron-h-placeholder"
+_CONFIG_FOR_DOC = "NemotronHConfig"
+
+
+# Helper methods for segment sum computation
+
+
+def pad_tensor_by_size(input_tensor: torch.Tensor, pad_size: int):
+    """
+    Padding x tensor with `pad_size` on the seq_len dim (dim=1)
+
+    Assumes that we only have tensors of either size 4 or 3
+    """
+    pad_shape = (0, 0, 0, 0, 0, pad_size, 0, 0) if len(input_tensor.shape) == 4 else (0, 0, 0, pad_size, 0, 0)
+
+    return torch.nn.functional.pad(input_tensor, pad_shape, mode="constant", value=0)
+
+
+def reshape_into_chunks(input_tensor, pad_size, chunk_size):
+    """
+    Padding input_tensor with `pad_size` on the seq_len dim (dim=1) and
+    simultaneously splitting it into chunk sequences.
+
+    Assumes that we only have tensors of either size 4 or 3
+    """
+    # [bsz, seq_len, ...] -> [bsz, seq_len multiple of chunk_size, ...]
+    input_tensor = pad_tensor_by_size(input_tensor, pad_size)
+
+    if len(input_tensor.shape) == 3:
+        # [bsz, seq_len multiple of chunk_size, num_heads] -> [bsz, -1, chunk_size, num_heads]
+        return input_tensor.reshape(input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2])
+    else:
+        # [bsz, seq_len multiple of chunk_size, num_heads, head_dim or state_size] -> [bsz, -1, chunk_size, num_heads, head_dim or state_size]
+        return input_tensor.reshape(
+            input_tensor.shape[0], -1, chunk_size, input_tensor.shape[2], input_tensor.shape[3]
+        )
+
+
+def segment_sum(input_tensor):
+    """
+    More stable segment sum calculation. Uses cumulative sums and masking instead of direct subtractions.
+    """
+    chunk_size = input_tensor.size(-1)
+    # 1. expand input tensor to have an additional dimension and repeat along that dimension
+    # [..., chunk_size] -> [..., chunk_size, chunk_size]
+    input_tensor = input_tensor[..., None].expand(*input_tensor.size(), chunk_size)
+    # 2. create a lower triangular mask with the diagonal set to 0 to 0 out elements above diag
+    mask = torch.tril(torch.ones(chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool), diagonal=-1)
+    input_tensor = input_tensor.masked_fill(~mask, 0)
+    # 3. compute actual cumsum
+    tensor_segsum = torch.cumsum(input_tensor, dim=-2)
+
+    # 4. apply mask to keep only the lower triangular part of the cumulative sum result (incl diagonal this time)
+    mask = torch.tril(torch.ones(chunk_size, chunk_size, device=input_tensor.device, dtype=torch.bool), diagonal=0)
+    tensor_segsum = tensor_segsum.masked_fill(~mask, -torch.inf)
+    return tensor_segsum
+
+
+def apply_mask_to_padding_states(hidden_states, attention_mask):
+    """
+    Tunes out the hidden states for padding tokens, see https://github.com/state-spaces/mamba/issues/66
+    """
+    if attention_mask is not None and not torch.all(attention_mask == 1):
+        dtype = hidden_states.dtype
+        hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
+
+    return hidden_states
+
+# Adapted from transformers.models.zamba2.modeling_zamba2.Zamba2HybridDynamicCache for the v2 mixer
+class NemotronHHybridDynamicCache:
+    """
+    A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
+    (which has a constant shape regardless of seq_len).
+
+    This cache has two sets of lists of tensors: `key_cache` and `value_cache` for attention cache and `conv_states`
+    and `ssm_states` for mamba cache. Each of these lists has `num_layers` tensors. The expected shape for each tensor
+    For attention layers, `key_cache` and `value_cache` have a shape of `(batch_size, num_heads, seq_len, head_dim)`,
+    while `conv_states` and `ssm_states` have a shape of `(batch_size, 0)` (empty tensors).
+    For mamba layers, `key_cache` and `value_cache` have a shape of `(batch_size, 0)` (empty tensors),
+    while `conv_states` represents the convolution state and has a shape of `(batch_size, d_inner, d_conv)`,
+    and `ssm_states` represents the ssm state and has a shape of `(batch_size, d_inner, d_state)`.
+    """
+
+    is_compileable = False
+
+    def __init__(
+        self, config: NemotronHConfig, batch_size: int, dtype: torch.dtype = torch.float16, device: str | None = None
+    ):
+        self.dtype = dtype
+        self.layers_block_type = config.layers_block_type
+        self.has_previous_state = False
+        self.intermediate_size = int(config.mamba_num_heads * config.mamba_head_dim)
+        self.ssm_state_size = config.ssm_state_size
+        self.conv_kernel_size = config.conv_kernel
+        self.n_mamba_heads = config.mamba_num_heads
+        self.transformer_layers = []
+        self._modules = {}
+        self._parameters = {}
+        self._buffers = {}
+        self.conv_states = {}
+        self.ssm_states = {}
+        for i in range(config.num_hidden_layers):
+            if self.layers_block_type[i] == "mamba":
+                # Only allocate mamba cache for mamba layers
+                self.conv_states[i] = torch.zeros(
+                    batch_size,
+                    self.intermediate_size + 2 * config.n_groups * self.ssm_state_size,
+                    self.conv_kernel_size,
+                    device=device,
+                    dtype=dtype,
+                )
+                self.ssm_states[i] = torch.zeros(
+                    batch_size,
+                    self.n_mamba_heads,
+                    config.mamba_head_dim,
+                    self.ssm_state_size,
+                    device=device,
+                    dtype=dtype,
+                )
+            else:
+                # For attention and moe layers, use empty tensors
+                self.conv_states[i] = torch.tensor([[]] * batch_size, device=device)
+                self.ssm_states[i] = torch.tensor([[]] * batch_size, device=device)
+
+            if self.layers_block_type[i] == "attention":
+                self.transformer_layers.append(i)
+        self.key_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
+        self.value_cache = [torch.tensor([[]] * batch_size, device=device) for _ in range(config.num_hidden_layers)]
+
+    def __len__(self):
+        return len(self.key_cache)
+
+    def update(
+        self,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        layer_idx: int,
+        cache_kwargs: dict[str, Any] | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Update the cache
+        if self.key_cache[layer_idx].shape[-1] == 0:
+            self.key_cache[layer_idx] = key_states
+            self.value_cache[layer_idx] = value_states
+        else:
+            self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=2)
+            self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=2)
+
+        return self.key_cache[layer_idx], self.value_cache[layer_idx]
+
+    def reorder_cache(self, beam_idx: torch.LongTensor):
+        """Reorders the cache for beam search, given the selected beam indices."""
+        if self.get_seq_length() > 0:
+            for layer_idx in range(len(self.key_cache)):
+                device = self.key_cache[layer_idx].device
+                self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
+                device = self.value_cache[layer_idx].device
+                self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))
+
+                device = self.conv_states[layer_idx].device
+                self.conv_states[layer_idx] = self.conv_states[layer_idx].index_select(0, beam_idx.to(device))
+                device = self.ssm_states[layer_idx].device
+                self.ssm_states[layer_idx] = self.ssm_states[layer_idx].index_select(0, beam_idx.to(device))
+
+    def get_seq_length(self, layer_idx: int | None = 0) -> int:
+        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
+        # take any layer that contains cache and not empty tensor
+        layer_idx = self.transformer_layers[0] if layer_idx not in self.transformer_layers else layer_idx
+        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].numel() == 0:
+            return 0
+        return self.key_cache[layer_idx].shape[-2]
+
+    def get_mask_sizes(self, cache_position: torch.Tensor, layer_idx: int) -> tuple[int, int]:
+        """Return the length and offset of the cache, used to generate the mask"""
+        kv_offset = 0
+        query_length = cache_position.shape[0]
+        kv_length = self.get_seq_length(layer_idx) + query_length
+        return kv_length, kv_offset
+
+    def update_conv_state(
+        self, layer_idx: int, new_conv_state: torch.Tensor, cache_position: torch.LongTensor
+    ) -> torch.Tensor:
+        conv_state = self.conv_states[layer_idx]
+        cache_position = cache_position.clamp(0, self.conv_kernel_size - 1)
+
+        conv_state = conv_state.roll(shifts=-1, dims=-1)
+        conv_state[:, :, cache_position] = new_conv_state.to(conv_state.device)
+        self.conv_states[layer_idx].zero_()
+        self.conv_states[layer_idx] += conv_state
+        return self.conv_states[layer_idx]
+
+    def reset(self):
+        self.conv_states.zero_()
+        self.ssm_states.zero_()
+
+class MambaRMSNormGated(torch.nn.Module):
+    """
+    Gated Root Mean Square Normalization for Mamba layers.
+
+    This normalization variant supports gating, allowing the normalization to be
+    modulated by a gating signal. It is specifically designed for use in Mamba blocks
+    and supports grouped normalization.
+
+    Args:
+        hidden_size (`int`):
+            The dimension of the hidden states to normalize.
+        group_size (`int`):
+            Size of each group for grouped normalization.
+        eps (`float`, *optional*, defaults to 1e-5):
+            A small value added to the variance for numerical stability.
+    """
+    def __init__(self, hidden_size, group_size, eps=1e-5):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+        self.group_size = group_size
+
+    def forward(self, hidden_states, gate=None):
+        return rmsnorm_fn(x=hidden_states,
+                          weight=self.weight,
+                          bias=None,
+                          z=gate,
+                          eps=self.variance_epsilon,
+                          group_size=self.group_size,
+                          norm_before_gate=False
+        )
+
+# Adapted from transformers.models.zamba2.modeling_zamba2.Zamba2MambaMixer
+class NemotronHMamba2Mixer(nn.Module):
+    """
+    Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
+    A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
+    ∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4,
+    and is why Mamba is called **selective** state spaces)
+    """
+
+    def __init__(self, config: NemotronHConfig, layer_idx: int | None = None):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.ssm_state_size = config.ssm_state_size
+        self.conv_kernel_size = config.conv_kernel
+        self.intermediate_size = config.mamba_num_heads * config.mamba_head_dim
+        self.layer_idx = layer_idx
+        self.use_conv_bias = config.use_conv_bias
+        self.activation = config.mamba_hidden_act
+        self.act = ACT2FN[config.mamba_hidden_act]
+        self.use_mem_eff_path = True
+
+        self.n_groups = config.n_groups
+        self.head_dim = config.mamba_head_dim
+        self.num_heads = config.mamba_num_heads
+        self.chunk_size = config.chunk_size
+
+        self.time_step_limit = config.time_step_limit
+        self.time_step_min = config.time_step_min
+        self.time_step_max = config.time_step_max
+
+        self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.ssm_state_size
+
+        self.conv1d = nn.Conv1d(
+            in_channels=self.conv_dim,
+            out_channels=self.conv_dim,
+            bias=config.use_conv_bias,
+            kernel_size=self.conv_kernel_size,
+            groups=self.conv_dim,
+            padding=self.conv_kernel_size - 1,
+        )
+
+        # projection of the input hidden states
+        projection_size = self.intermediate_size + self.conv_dim + self.num_heads
+
+        self.in_proj = nn.Linear(
+            self.hidden_size,
+            projection_size,
+            bias=config.use_bias,
+        )
+        # selective projection used to make dt, B and C input dependent
+
+        # time step projection (discretization)
+        # instantiate once and copy inv_dt in init_weights of PretrainedModel
+        self.dt_bias = nn.Parameter(torch.ones(self.num_heads))
+
+        # S4D real initialization. These are not discretized!
+        # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
+        A = torch.arange(1, self.num_heads + 1)
+        self.A_log = nn.Parameter(torch.log(A))
+
+        self.norm = MambaRMSNormGated(self.intermediate_size, eps=config.layer_norm_epsilon, group_size=self.intermediate_size // self.n_groups)
+        self.D = nn.Parameter(torch.ones(self.num_heads))
+
+        self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.use_bias)
+
+        if not is_fast_path_available:
+            logger.warning_once(
+                "The fast path is not available because one of `(selective_state_update, causal_conv1d_fn, causal_conv1d_update)`"
+                " is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and"
+                " https://github.com/Dao-AILab/causal-conv1d"
+            )
+
+
+    def cuda_kernels_forward(
+        self,
+        hidden_states: torch.Tensor,
+        cache_params: Optional[NemotronHHybridDynamicCache] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+    ):
+        # set up dimensions for reshapes later
+
+        batch_size, seq_len, _ = hidden_states.shape
+        groups_time_state_size = self.n_groups * self.ssm_state_size
+        d_to_remove = 2 * self.intermediate_size + 2 * self.n_groups * self.ssm_state_size + self.num_heads
+
+        # getting projected states from cache if it exists
+        if cache_params is not None and cache_params.has_previous_state:
+            in_projected_states = self.in_proj(hidden_states.squeeze(1))  # (B 2D)
+            d_mlp = (in_projected_states.shape[-1] - d_to_remove) // 2
+            split_projection_dim = [d_mlp, d_mlp, self.intermediate_size, self.conv_dim, self.num_heads]
+            _, _, gate, hidden_states_B_C, dt = torch.split(in_projected_states, split_projection_dim, dim=-1)
+
+            hidden_states_B_C = causal_conv1d_update(
+                hidden_states_B_C,
+                cache_params.conv_states[self.layer_idx],
+                self.conv1d.weight.squeeze(1),
+                self.conv1d.bias,
+                self.activation,
+            )
+
+            hidden_states, B, C = torch.split(
+                hidden_states_B_C,
+                [self.intermediate_size, groups_time_state_size, groups_time_state_size],
+                dim=-1,
+            )
+            A = -torch.exp(self.A_log.float())  # (nheads,)
+
+            A = A[:, None, ...][:, :, None].expand(-1, self.head_dim, self.ssm_state_size).to(dtype=torch.float32)
+            dt = dt[:, :, None].expand(-1, -1, self.head_dim)
+            dt_bias = self.dt_bias[:, None, ...].expand(-1, self.head_dim)
+            D = self.D[:, None, ...].expand(-1, self.head_dim)
+            B = B.view(batch_size, self.n_groups, B.shape[1] // self.n_groups)
+            C = C.view(batch_size, self.n_groups, C.shape[1] // self.n_groups)
+            hidden_states_reshaped = hidden_states.view(batch_size, self.num_heads, self.head_dim)
+            hidden_states = selective_state_update(
+                cache_params.ssm_states[self.layer_idx],
+                hidden_states_reshaped,
+                dt,
+                A,
+                B,
+                C,
+                D,
+                z=None,
+                dt_bias=dt_bias,
+                dt_softplus=True,
+            )
+            hidden_states = hidden_states.view(batch_size, self.num_heads * self.head_dim)
+            hidden_states = self.norm(hidden_states, gate)
+            out = self.out_proj(hidden_states)[:, None, ...]
+        # if no cache is found, calling the kernel
+        else:
+            if attention_mask is not None and not torch.all(attention_mask == 1):
+                # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
+                dtype = hidden_states.dtype
+                hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
+            # 1. Gated MLP's linear projection
+            projected_states = self.in_proj(hidden_states)
+            A = -torch.exp(self.A_log.float())  # (num_heads) or (intermediate_size, state_size)
+            dt_limit_kwargs = {} if self.time_step_limit is None else {"dt_limit": self.time_step_limit}
+            if attention_mask is not None:
+                input_not_masked = torch.all(attention_mask == 1)
+            else:
+                input_not_masked = True
+
+            if self.use_mem_eff_path and self.training and cache_params is None and input_not_masked:
+                out, ssm_state = mamba_split_conv1d_scan_combined(
+                    projected_states,
+                    self.conv1d.weight.squeeze(1),
+                    self.conv1d.bias,
+                    self.dt_bias,
+                    A,
+                    D=self.D,
+                    chunk_size=self.chunk_size,
+                    seq_idx=None,
+                    activation=self.activation,
+                    rmsnorm_weight=self.norm.weight,
+                    rmsnorm_eps=self.norm.variance_epsilon,
+                    outproj_weight=self.out_proj.weight,
+                    outproj_bias=self.out_proj.bias,
+                    headdim=self.head_dim,
+                    ngroups=self.n_groups,
+                    norm_before_gate=False,
+                    return_final_states=True,
+                    **dt_limit_kwargs,
+                )
+
+            else:
+                gate, hidden_states_B_C, time_step = torch.split(
+                    projected_states,
+                    [self.intermediate_size, self.conv_dim, self.num_heads],
+                    dim=-1,
+                )
+
+                # 1D Convolution
+                if cache_params is not None:
+                    hidden_states_B_C_t = hidden_states_B_C.transpose(1, 2)
+                    conv_state = nn.functional.pad(
+                        hidden_states_B_C_t, (self.conv_kernel_size - hidden_states_B_C_t.shape[-1], 0)
+                    )
+                    cache_params.conv_states[self.layer_idx].copy_(conv_state)
+                if causal_conv1d_fn is None or self.activation not in ["silu", "swish"]:
+                    hidden_states_B_C = self.act(
+                        self.conv1d(hidden_states_B_C.transpose(1, 2)).transpose(1, 2)[:, :seq_len]
+                    )  # (B, L, self.d_inner + 2 * ngroups * d_state)
+                else:
+                    hidden_states_B_C = causal_conv1d_fn(
+                        x=hidden_states_B_C.transpose(1, 2),
+                        weight=self.conv1d.weight.squeeze(1),
+                        bias=self.conv1d.bias,
+                        activation=self.activation,
+                    ).transpose(1, 2)[:, :seq_len]
+                hidden_states, B, C = torch.split(
+                    hidden_states_B_C,
+                    [self.intermediate_size, groups_time_state_size, groups_time_state_size],
+                    dim=-1,
+                )
+                if attention_mask is not None and not torch.all(attention_mask == 1):
+                    # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
+                    dtype = hidden_states.dtype
+                    hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
+                scan_output, ssm_state = mamba_chunk_scan_combined(
+                    hidden_states.view(batch_size, seq_len, -1, self.head_dim),
+                    time_step,
+                    A,
+                    B.view(batch_size, seq_len, self.n_groups, -1),
+                    C.view(batch_size, seq_len, self.n_groups, -1),
+                    chunk_size=self.chunk_size,
+                    D=self.D,
+                    z=None,
+                    seq_idx=None,
+                    return_final_states=True,
+                    dt_bias=self.dt_bias,
+                    dt_softplus=True,
+                    **dt_limit_kwargs,
+                )
+                if ssm_state is not None and cache_params is not None:
+                    cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+                scan_output = scan_output.view(batch_size, seq_len, -1)
+                # Multiply "gate" branch and apply extra normalization layer
+                scan_output = self.norm(scan_output, gate)
+                out = self.out_proj(scan_output)
+        return out
+
+    # fmt: off
+    def torch_forward(self, input_states, cache_params: Optional[NemotronHHybridDynamicCache]=None, attention_mask: Optional[torch.Tensor]=None):
+        batch_size, seq_len, _ = input_states.shape
+        dtype = input_states.dtype
+        # Gated MLP's linear projection
+        if cache_params is not None and cache_params.has_previous_state:
+            projected_states = self.in_proj(input_states.squeeze(1))
+        else:
+            if attention_mask is not None and not torch.all(attention_mask==1):
+                # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
+                input_states = (input_states * attention_mask[:, :, None]).to(dtype)
+            projected_states = self.in_proj(input_states)
+        d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - 2 * self.n_groups * self.ssm_state_size- self.num_heads) // 2
+        _, _, gate, hidden_states, dt = projected_states.split(
+                [d_mlp, d_mlp, self.intermediate_size,  self.conv_dim, self.num_heads], dim=-1
+        )
+
+        # Convolution sequence transformation
+        if cache_params is not None:
+            ssm_state = cache_params.ssm_states[self.layer_idx].clone()
+            ssm_state = ssm_state.to(hidden_states.device)
+            if cache_params.has_previous_state:
+                gate = gate.unsqueeze(1)
+                conv_state = cache_params.conv_states[self.layer_idx]                   # [batch, intermediate_size, conv_kernel_size]
+                conv_state = torch.roll(conv_state, shifts=-1, dims=-1)
+                # handle batched generation - states are copied through
+                conv_state[:, :, -1] = hidden_states[:, 0, :] if hidden_states.ndim == 3 else hidden_states
+                cache_params.conv_states[self.layer_idx].copy_(conv_state)
+                hidden_states = torch.sum(conv_state.to(projected_states.device) * self.conv1d.weight[:, 0, :], dim=-1)
+                if self.use_conv_bias:
+                    hidden_states += self.conv1d.bias
+                hidden_states = self.act(hidden_states).to(dtype)[:, None, ...]         # [batch, 1, intermediate_size] : decoding
+            else:
+                hidden_states = hidden_states.transpose(1,2)
+                conv_state = nn.functional.pad(
+                    hidden_states,
+                    (self.conv_kernel_size - hidden_states.shape[-1], 0)
+                )
+                cache_params.conv_states[self.layer_idx].copy_(conv_state)
+                hidden_states = self.act(self.conv1d(hidden_states).transpose(1,2))[:, :seq_len, :]     # [batch, intermediate_size, seq_len]
+                if attention_mask is not None and not torch.all(attention_mask==1):
+                    dtype = hidden_states.dtype
+                    # tune out hidden states for pad tokens, see https://github.com/state-spaces/mamba/issues/66
+                    hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)
+        else:
+            ssm_state = torch.zeros(
+                (batch_size, self.num_heads, self.head_dim, self.ssm_state_size),
+                device=hidden_states.device, dtype=dtype
+            )
+            hidden_states = self.act(self.conv1d(hidden_states.transpose(1, 2))[..., :seq_len].transpose(1, 2))
+        hidden_states, B, C = torch.split(hidden_states, [self.intermediate_size, self.n_groups * self.ssm_state_size, self.n_groups * self.ssm_state_size], dim=-1)
+        A = -torch.exp(self.A_log.float())                            # [num_heads]
+        if cache_params is not None and cache_params.has_previous_state:
+            # Note: there is no need to pad parameter matrices here, as there is just one new token
+            # for batched generation
+            dt = dt[:, None, ...] if dt.ndim == 2 else dt[:, 0, :][:, None, ...]
+            dt = dt.transpose(1, 2).expand(batch_size, dt.shape[-1], self.head_dim)
+            # [num_heads] -> [num_heads, head_dim]
+            dt_bias = self.dt_bias[..., None].expand(self.dt_bias.shape[0], self.head_dim)
+
+            dt = torch.nn.functional.softplus(dt + dt_bias.to(dt.dtype))
+            dt = torch.clamp(dt, self.time_step_min) #, self.time_step_max)
+            A = A[..., None, None].expand(self.num_heads, self.head_dim, self.ssm_state_size).to(dtype=torch.float32)
+            # [bsz, num_heads, head_dim, state_size]
+            dA = torch.exp(dt[..., None] * A)
+
+            # Discretize B
+            # [bsz, n_groups * state_size] -> [bsz, n_groups, 1, state_size] ->
+            # -> [bsz, n_groups, group to head repetition factor, state_size] -> [bsz, num_heads, state_size]
+            B = B.reshape(batch_size, self.n_groups, -1)[..., None, :]
+            B = B.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, B.shape[-1]).contiguous()
+            B = B.reshape(batch_size, -1, B.shape[-1])
+            # [bsz, num_heads, head_dim, state_size]
+            dB = dt[..., None] * B[..., None, :]
+
+            # Discretize x into dB
+            # [bsz, intermediate_size] -> [bsz, num_heads, head_dim]
+            hidden_states = hidden_states.reshape(batch_size, -1, self.head_dim)
+            dBx = dB * hidden_states[..., None]
+
+            # State calculation
+            cache_params.ssm_states[self.layer_idx].copy_(
+                cache_params.ssm_states[self.layer_idx] * dA + dBx
+            )
+
+            # Subsequent output
+            # [bsz, n_groups * state_size] -> [bsz, num_heads, state_size]
+            C = C.reshape(batch_size, self.n_groups, -1)[..., None, :]
+            C = C.expand(batch_size, self.n_groups, self.num_heads // self.n_groups, C.shape[-1]).contiguous()
+            C = C.reshape(batch_size, -1, C.shape[-1])
+            # [bsz, num_heads, head_dim]
+
+            ssm_states = cache_params.ssm_states[self.layer_idx].to(C.dtype)  # Shape: [b, h, d, n]
+            # Reshape ssm_states to merge the first two dimensions
+            ssm_states_reshaped = ssm_states.view(batch_size * self.num_heads, self.head_dim, self.ssm_state_size)  # Shape: [b*h, d, n]
+            C_reshaped = C.view(batch_size * self.num_heads, self.ssm_state_size, 1)  # Shape: [b*h, n, 1]
+            y = torch.bmm(ssm_states_reshaped, C_reshaped)
+            y = y.view(batch_size, self.num_heads, self.head_dim)
+
+            # D skip connection
+            # [num_heads] -> [num_heads, head_dim]
+            D = self.D[..., None].expand(self.D.shape[0], self.head_dim)
+            y = (y + hidden_states * D).to(y.dtype)
+
+            # [bsz, num_heads, head_dim] -> [bsz, 1, intermediate_size]
+            y = y.reshape(batch_size, -1)[:, None, ...]
+        else:
+            # begin ssd naive implementation without einsums
+            dt = nn.functional.softplus(dt + self.dt_bias)
+            dt = torch.clamp(dt, self.time_step_min)
+            hidden_states = hidden_states.reshape(batch_size, seq_len, -1, self.head_dim).float()
+            B = B.reshape(batch_size, seq_len,  -1, self.ssm_state_size).float()
+            C = C.reshape(batch_size, seq_len, -1, self.ssm_state_size).float()
+            B = B.repeat_interleave(self.num_heads // self.n_groups, dim=2, output_size=self.num_heads)
+            C = C.repeat_interleave(self.num_heads // self.n_groups, dim=2, output_size=self.num_heads)
+            pad_size = (self.chunk_size - seq_len % self.chunk_size) % self.chunk_size
+
+            D_residual = self.D[..., None] * pad_tensor_by_size(hidden_states, pad_size)
+
+            # Discretize x and A
+            hidden_states = hidden_states * dt[..., None]
+            A = A.to(hidden_states.dtype) * dt
+
+            # Rearrange into blocks/chunks
+            hidden_states, A, B, C = [reshape_into_chunks(t, pad_size, self.chunk_size) for t in (hidden_states, A, B, C)]
+
+
+            # [bsz, -1, chunk_size, num_heads] -> [bsz, num_heads, -1, chunk_size]
+            A = A.permute(0, 3, 1, 2)
+            A_cumsum = torch.cumsum(A, dim=-1)
+
+            # 1. Compute the output for each intra-chunk (diagonal blocks)
+            # This is the analog of a causal mask
+            L = torch.exp(segment_sum(A))
+
+            # First, contraction of C and B to get G (attention-weights like)
+            G_intermediate = C[:, :, :, None, :, :] * B[:, :, None, :, : ,:]  # shape: (b, c, l, s, h, n)
+            G = G_intermediate.sum(dim=-1)  # shape: (b, c, l, s, h)
+
+
+            # Step 2: Compute M, equivalent to applying attention mask to weights
+            M_intermediate = G[..., None] * L.permute(0, 2, 3, 4, 1)[..., None]
+            M = M_intermediate.sum(dim=-1)
+
+            # Step 3: Compute Y_diag (apply to values)
+            Y_diag = (M[..., None] * hidden_states[:, :, None]).sum(3)
+
+            # (right term of low-rank factorization of off-diagonal blocks; B terms)
+
+            decay_states = torch.exp(A_cumsum[:, :, :, -1:] - A_cumsum)
+            B_decay_contraction = B * decay_states.permute(0, 2, 3, 1)[..., None]
+            # permute back B * decay states
+            states = (B_decay_contraction.permute(0, 1, 3, 2, 4)[..., None]  * hidden_states.permute(0, 1, 3, 2, 4)[..., None, :]).sum(dim=3).permute(0, 1, 2, 4, 3)
+            if cache_params is not None and cache_params.has_previous_state:
+                previous_states = cache_params.ssm_states[self.layer_idx][:, None, ...]
+            else:
+                previous_states = torch.zeros_like(states[:, :1])
+            states = torch.cat([previous_states, states], dim=1)
+            decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
+
+            states_permuted = states.permute(0, 2, 1, 3, 4)
+            result = (decay_chunk[..., None, None] * states_permuted[:, :, None, ...]).sum(dim=2)
+            new_states = result.permute(0, 2, 1, 3, 4)
+            states, ssm_state = new_states[:, :-1], new_states[:, -1]
+
+            # Compute state -> output conversion per chunk
+            # (left term of low-rank factorization of off-diagonal blocks; C terms)
+            state_decay_out = torch.exp(A_cumsum)
+            # compute Yoff
+            C_times_states = (C[..., None, :] * states[:, :, None, ...])
+            state_decay_out_permuted = state_decay_out.permute(0, 2, 3, 1)
+            Y_off = (C_times_states.sum(-1) * state_decay_out_permuted[..., None])
+            # Add output of intra-chunk and inter-chunk terms (diagonal and off-diagonal blocks)
+
+            y = Y_diag + Y_off
+            # [bsz, -1, self.chunk_size, num_heads, head_dim] -> [bsz, (padded) seq_len, num_heads, head_dim]
+            y = y.reshape(batch_size, -1, self.num_heads, self.head_dim)
+
+            y = y + D_residual
+            # Cutting off padded chunks
+            if pad_size > 0:
+                y = y[:, :seq_len, :, :]
+            y = y.reshape(batch_size, seq_len, -1)
+            if ssm_state is not None and cache_params is not None:
+                cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+
+        scan_output = self.norm(y, gate)
+
+        # end ssd naive
+
+        # 4. Final linear projection
+        contextualized_states = self.out_proj(scan_output.to(dtype))  # [batch, seq_len, hidden_size]
+        return contextualized_states
+    # fmt: on
+
+    def forward(
+        self,
+        hidden_states,
+        cache_params: Optional[NemotronHHybridDynamicCache] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+    ):
+        if is_fast_path_available and "cuda" in self.in_proj.weight.device.type:
+            return self.cuda_kernels_forward(hidden_states, cache_params, attention_mask)
+
+        return self.torch_forward(hidden_states, cache_params, attention_mask)
+
+
+class NemotronHRMSNorm(nn.Module):
+    """
+    Root Mean Square Layer Normalization for NemotronH.
+
+    NemotronHRMSNorm is equivalent to T5LayerNorm and LlamaRMSNorm. It normalizes
+    the input using the root mean square of the hidden dimensions, then scales by
+    a learned weight parameter.
+
+    Args:
+        hidden_size (`int`):
+            The dimension of the hidden states to normalize.
+        eps (`float`, *optional*, defaults to 1e-6):
+            A small value added to the variance for numerical stability.
+    """
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return (self.weight.to(torch.float32) * hidden_states).to(input_dtype)
+
+class NemotronHBlock(nn.Module):
+    """
+    A single transformer block in the NemotronH model.
+
+    This block can contain different types of mixers (Mamba, Attention, MLP, or MoE)
+    depending on the configuration. Each block applies pre-normalization followed by
+    the mixer, then adds a residual connection.
+
+    Args:
+        config (`NemotronHConfig`):
+            Model configuration specifying the block architecture.
+        layer_idx (`int`):
+            Index of this block in the model. Used to determine the block type from
+            `config.layers_block_type[layer_idx]`.
+    """
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.residual_in_fp32 = config.residual_in_fp32
+        self.norm = NemotronHRMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+
+        # M: Mamba2, *: Attention, -: MLP
+        self.block_type = config.layers_block_type[layer_idx]
+        if self.block_type == "mamba":
+            self.mixer = NemotronHMamba2Mixer(config, layer_idx=layer_idx)
+        elif self.block_type == "attention":
+            self.mixer = NemotronHAttention(config, layer_idx=layer_idx)
+        elif self.block_type == "mlp":
+            self.mixer = NemotronHMLP(config, layer_idx=layer_idx)
+        elif self.block_type == "moe":
+            self.mixer = NemotronHMoE(config, layer_idx=layer_idx)
+        else:
+            raise ValueError(f"Invalid layer pattern {config.hybrid_override_pattern[layer_idx]}")
+
+    def forward(
+        self,
+        hidden_states,
+        past_key_values: Optional[NemotronHHybridDynamicCache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: bool = False,
+    ):
+        if hidden_states.device.type == "cuda":
+            stream_context = torch.cuda.stream(torch.cuda.default_stream(hidden_states.device))
+        else:
+            stream_context = contextlib.nullcontext()
+
+        with stream_context:
+            residual = hidden_states
+            hidden_states = self.norm(hidden_states.to(dtype=self.norm.weight.dtype))
+            if self.residual_in_fp32:
+                residual = residual.to(torch.float32)
+
+            if self.block_type == "mamba":
+                hidden_states = self.mixer(
+                    hidden_states, cache_params=past_key_values, attention_mask=attention_mask
+                )
+            elif self.block_type == "attention":
+                hidden_states, _, _ = self.mixer(
+                    hidden_states=hidden_states,
+                    past_key_values=past_key_values,
+                    attention_mask=attention_mask,
+                    output_attentions=output_attentions,
+                )
+            elif self.block_type in ["mlp", "moe"]:
+                hidden_states = self.mixer(
+                    hidden_states
+                )
+            else:
+                raise ValueError(f"Invalid block_type: {self.block_type}")
+
+            hidden_states = residual + hidden_states
+            return hidden_states
+
+
+# Copied from transformers.models.nemotron.modeling_nemotron Nemotron->NemotronH
+class NemotronHMLP(nn.Module):
+    """
+    Multi-Layer Perceptron (MLP) module for NemotronH.
+
+    This module implements a standard feed-forward network with one hidden layer,
+    applying an activation function between the up and down projections.
+
+    Args:
+        config (`NemotronHConfig`):
+            Model configuration containing hyperparameters.
+        intermediate_size (`int`, *optional*):
+            Dimension of the intermediate hidden layer. If not provided, uses `config.intermediate_size`.
+        layer_idx (`int`, *optional*):
+            Index of the layer in the model. Used for proper cache management.
+        is_expert (`bool`, *optional*, defaults to `False`):
+            Whether this MLP is used as an expert in a Mixture-of-Experts layer.
+    """
+    def __init__(self, config, intermediate_size=None, layer_idx: Optional[int] = None, is_expert=False):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+        use_latent_size = (self.config.moe_latent_size is not None) and is_expert
+        self.hidden_size = config.hidden_size
+        input_size = self.hidden_size if not use_latent_size else config.moe_latent_size
+
+        self.intermediate_size = intermediate_size or config.intermediate_size
+        self.up_proj = nn.Linear(input_size, self.intermediate_size, bias=config.mlp_bias)
+        self.down_proj = nn.Linear(self.intermediate_size, input_size, bias=config.mlp_bias)
+        self.act_fn = ACT2FN[config.mlp_hidden_act]
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.up_proj(x)))
+
+
+class NemotronHMoE(nn.Module):
+    """
+    Mixture-of-Experts (MoE) module for NemotronH.
+
+    This module implements a sparse MoE layer with both routed experts and shared experts.
+    Tokens are routed to a subset of experts based on learned routing weights, while all
+    tokens are processed by shared experts. The architecture supports optional latent
+    dimension projection for computational efficiency.
+
+    Args:
+        config (`NemotronHConfig`):
+            Model configuration containing MoE-specific hyperparameters including:
+            - `n_routed_experts`: Number of routed expert MLPs
+            - `num_experts_per_tok`: Number of experts each token is routed to
+            - `moe_intermediate_size`: Hidden dimension for routed experts
+            - `moe_shared_expert_intermediate_size`: Hidden dimension for shared experts
+            - `moe_latent_size`: Optional latent dimension for dimensionality reduction
+        layer_idx (`int`, *optional*):
+            Index of the layer in the model.
+    """
+    def __init__(self, config, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.experts = nn.ModuleList(
+            [
+                NemotronHMLP(config, intermediate_size=config.moe_intermediate_size, layer_idx=layer_idx, is_expert=True)
+                for _ in range(config.n_routed_experts)
+            ]
+        )
+        self.gate = NemotronHTopkRouter(config)
+        self.shared_experts = NemotronHMLP(
+            config=config, intermediate_size=config.moe_shared_expert_intermediate_size, layer_idx=layer_idx, is_expert=False
+        )
+
+        if config.moe_latent_size is not None:
+            self.fc1_latent_proj = nn.Linear(config.hidden_size, config.moe_latent_size, bias=config.mlp_bias)
+            self.fc2_latent_proj = nn.Linear(config.moe_latent_size, config.hidden_size, bias=config.mlp_bias)
+        else:
+            self.fc1_latent_proj = nn.Identity()
+            self.fc2_latent_proj = nn.Identity()
+
+    def moe(self, hidden_states: torch.Tensor, topk_indices: torch.Tensor, topk_weights: torch.Tensor):
+        final_hidden_states = torch.zeros_like(hidden_states, dtype=topk_weights.dtype)
+        expert_mask = torch.nn.functional.one_hot(topk_indices, num_classes=len(self.experts))
+        expert_mask = expert_mask.permute(2, 0, 1)
+
+        for expert_idx in range(len(self.experts)):
+            expert = self.experts[expert_idx]
+            mask = expert_mask[expert_idx]
+            token_indices, weight_indices = torch.where(mask)
+
+            if token_indices.numel() > 0:
+                expert_weights = topk_weights[token_indices, weight_indices]
+                expert_input = hidden_states[token_indices]
+                expert_output = expert(expert_input)
+                weighted_output = expert_output * expert_weights.unsqueeze(-1)
+                final_hidden_states.index_add_(0, token_indices, weighted_output)
+            else:
+                # Local empty expert: no-op compute that still marks params as used.
+                expert_dtype = expert.down_proj.weight.dtype
+                dummy_out = expert(torch.zeros_like(hidden_states[0]).unsqueeze(0).to(expert_dtype))
+                final_hidden_states = final_hidden_states + dummy_out
+
+        # in original deepseek, the output of the experts are gathered once we leave this module
+        # thus the moe module is itself an IsolatedParallel module
+        # and all expert are "local" meaning we shard but we don't gather
+        return final_hidden_states.type(hidden_states.dtype)
+
+    def forward(self, hidden_states):
+        residuals = hidden_states
+        orig_shape = hidden_states.shape
+        topk_indices, topk_weights = self.gate(hidden_states)
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+
+        hidden_states = self.fc1_latent_proj(hidden_states)
+        hidden_states = self.moe(hidden_states, topk_indices, topk_weights)
+        hidden_states = self.fc2_latent_proj(hidden_states)
+
+        hidden_states = hidden_states.view(*orig_shape)
+
+        hidden_states = hidden_states + self.shared_experts(residuals)
+        return hidden_states
+
+
+class NemotronHTopkRouter(nn.Module):
+    """
+    Top-K routing module for Mixture-of-Experts.
+
+    This router determines which experts should process each token by computing routing
+    logits and selecting the top-K experts based on grouped scoring. It implements
+    group-based expert selection with score correction for load balancing.
+
+    Args:
+        config (`NemotronHConfig`):
+            Model configuration containing routing hyperparameters including:
+            - `num_experts_per_tok`: Number of experts to route each token to (K)
+            - `n_routed_experts`: Total number of available experts
+            - `routed_scaling_factor`: Scaling factor applied to routing weights
+            - `n_group`: Number of expert groups for grouped routing
+            - `topk_group`: Number of groups to select from
+            - `norm_topk_prob`: Whether to normalize the top-K routing probabilities
+    """
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.top_k = config.num_experts_per_tok
+        self.n_routed_experts = config.n_routed_experts
+        self.routed_scaling_factor = config.routed_scaling_factor
+        self.n_group = config.n_group
+        self.topk_group = config.topk_group
+        self.norm_topk_prob = config.norm_topk_prob
+
+        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, config.hidden_size)))
+        self.register_buffer("e_score_correction_bias", torch.zeros(self.n_routed_experts, dtype=torch.float32))
+
+    @torch.no_grad()
+    def get_topk_indices(self, scores):
+        scores_for_choice = scores.view(-1, self.n_routed_experts) + self.e_score_correction_bias.unsqueeze(0)
+        group_scores = (
+            scores_for_choice.view(-1, self.n_group, self.n_routed_experts // self.n_group)
+            .topk(2, dim=-1)[0]
+            .sum(dim=-1)
+        )
+        group_idx = torch.topk(group_scores, k=self.topk_group, dim=-1, sorted=False)[1]
+        group_mask = torch.zeros_like(group_scores)
+        group_mask.scatter_(1, group_idx, 1)
+        score_mask = (
+            group_mask.unsqueeze(-1)
+            .expand(-1, self.n_group, self.n_routed_experts // self.n_group)
+            .reshape(-1, self.n_routed_experts)
+        )
+        scores_for_choice = scores_for_choice.masked_fill(~score_mask.bool(), 0.0)
+        topk_indices = torch.topk(scores_for_choice, k=self.top_k, dim=-1, sorted=False)[1]
+        return topk_indices
+
+    def forward(self, hidden_states):
+        """
+        Compute expert routing for each token in the input.
+
+        This method performs the following steps:
+        1. Compute routing logits using a linear projection
+        2. Apply sigmoid activation to get routing scores
+        3. Select top-K experts using grouped selection strategy
+        4. Gather and optionally normalize the routing weights
+        5. Apply scaling factor to final weights
+
+        Args:
+            hidden_states (`torch.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
+                Input hidden states to be routed to experts.
+
+        Returns:
+            `tuple` containing:
+                - topk_indices (`torch.Tensor` of shape `(batch_size * sequence_length, num_experts_per_tok)`):
+                    Indices of the selected experts for each token.
+                - topk_weights (`torch.Tensor` of shape `(batch_size * sequence_length, num_experts_per_tok)`):
+                    Normalized routing weights for each selected expert, scaled by routed_scaling_factor.
+        """
+        self._maintain_float32_expert_bias()
+
+        hidden_states = hidden_states.view(-1, self.config.hidden_size)
+        router_logits = F.linear(hidden_states.type(torch.float32), self.weight.type(torch.float32))
+        scores = router_logits.sigmoid()
+        topk_indices = self.get_topk_indices(scores)
+        topk_weights = scores.gather(1, topk_indices)
+        if self.norm_topk_prob:
+            denominator = topk_weights.sum(dim=-1, keepdim=True) + 1e-20
+            topk_weights /= denominator
+        topk_weights = topk_weights * self.routed_scaling_factor
+        return topk_indices, topk_weights
+
+    def _maintain_float32_expert_bias(self):
+        """
+        Ensure e_score_correction_bias stays in float32 for numerical stability.
+
+        This method is called at the start of forward() to revert the bias back to
+        float32 if the model was cast to a lower precision dtype (e.g., via model.to(torch.bfloat16)).
+
+        """
+        if self.e_score_correction_bias.dtype != torch.float32:
+            self.e_score_correction_bias.data = self.e_score_correction_bias.data.to(torch.float32)
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs,
+):
+    """Eager attention forward pass - computes attention weights explicitly."""
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = F.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+class NemotronHAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper
+
+    Args:
+        config (`NemotronHConfig`):
+            Model configuration containing attention parameters like num_attention_heads, num_key_value_heads,
+            hidden_size, head_dim, attention_dropout, and attention_bias.
+        layer_idx (`int`, *optional*):
+            Index of the layer in the model. Required for proper caching during generation. If not provided,
+            a warning is emitted and caching may fail.
+    """
+
+    def __init__(self, config: NemotronHConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        if config.head_dim is not None:
+            self.head_dim = config.head_dim
+        else:
+            self.head_dim = config.hidden_size // config.num_attention_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.scaling = self.head_dim ** -0.5
+        self.is_causal = True
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.o_proj = nn.Linear(self.head_dim * self.num_heads, self.hidden_size, bias=config.attention_bias)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[NemotronHHybridDynamicCache] = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        if past_key_values is not None:
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)
+
+        # Select attention implementation based on config
+        attention_interface = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        if attention_mask is None and q_len > 1:
+            mask = torch.triu(torch.full((q_len, q_len), float("-inf"), device=hidden_states.device), diagonal=1)
+            attention_mask = mask.view(1, 1, q_len, q_len)
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, attn_weights, past_key_values
+
+
+# Copied from transformers.models.mamba2.modeling_mamba2.Mamba2PreTrainedModel
+class NemotronHPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = NemotronHConfig
+    base_model_prefix = "model"
+    _no_split_modules = ["NemotronHBlock"]
+    supports_gradient_checkpointing = True
+    _is_stateful = True
+    _supports_sdpa = True
+    _supports_flash_attn_2 = True
+    _checkpoint_conversion_mapping = {"backbone": "model"}
+
+    def _init_weights(self, module):
+        """Initialize the weights."""
+        if isinstance(module, NemotronHMamba2Mixer):
+            if getattr(module.dt_bias, "_is_hf_initialized", False):
+                return
+            module.A_log._no_weight_decay = True
+            module.D._no_weight_decay = True
+
+            dt = torch.exp(
+                torch.rand(self.config.mamba_num_heads)
+                * (math.log(self.config.time_step_max) - math.log(self.config.time_step_min))
+                + math.log(self.config.time_step_min)
+            ).clamp(min=self.config.time_step_floor)
+
+            # # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
+            inv_dt = dt + torch.log(-torch.expm1(-dt))
+            with torch.no_grad():
+                module.dt_bias.copy_(inv_dt)
+            module.dt_bias._no_reinit = True
+        elif isinstance(module, NemotronHTopkRouter):
+            nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
+            nn.init.zeros_(module.e_score_correction_bias)
+
+        if isinstance(module, nn.Linear):
+            if module.bias is not None:
+                if not getattr(module.bias, "_no_reinit", False):
+                    nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            nn.init.normal_(module.weight, std=self.config.initializer_range)
+
+        if self.config.rescale_prenorm_residual:
+            # Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
+            #   > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
+            #   > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
+            #   >   -- GPT-2 :: https://openai.com/blog/better-language-models/
+            #
+            # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
+            for name, p in module.named_parameters():
+                if getattr(p, "_is_hf_initialized", False):
+                    continue
+                if name in ["out_proj.weight"]:
+                    # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
+                    # Following Pytorch init, except scale by 1/sqrt(2 * n_layer)
+                    # We need to reinit p since this code could be called multiple times
+                    # Having just p *= scale would repeatedly scale it down
+                    nn.init.kaiming_uniform_(p, a=math.sqrt(5))
+                    with torch.no_grad():
+                        p /= math.sqrt(self.config.num_hidden_layers)
+
+
+@dataclass
+# Copied from transformers.models.mamba2.modeling_mamba2.Mamba2Output with MAMBA2->NemotronH,Mamba2->NemotronH
+class NemotronHOutput(ModelOutput):
+    """
+    Class for the NemotronH model outputs.
+
+    Args:
+        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+        past_key_values (`NemotronHHybridDynamicCache`):
+            The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
+            avoid providing the old `input_ids`.
+
+            Includes both the State space model state matrices after the selective scan, and the Convolutional states
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+    """
+
+    last_hidden_state: Optional[torch.FloatTensor] = None
+    past_key_values: Optional[NemotronHHybridDynamicCache] = None
+    hidden_states: Optional[tuple[torch.FloatTensor]] = None
+    attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+@dataclass
+# Copied from transformers.models.mamba2.modeling_mamba2.MambaCausalLMOutput with Mamba2->NemotronH
+class NemotronHCausalLMOutput(ModelOutput):
+    """
+    Base class for causal language model (or autoregressive) outputs.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Language modeling loss (for next-token prediction).
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        past_key_values (`NemotronHHybridDynamicCache`):
+            The state of the model at the last time step. Can be used in a forward method with the next `input_ids` to
+            avoid providing the old `input_ids`.
+
+            Includes both the State space model state matrices after the selective scan, and the Convolutional states
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+    """
+
+    loss: Optional[torch.FloatTensor] = None
+    logits: Optional[torch.FloatTensor] = None
+    past_key_values: Optional[NemotronHHybridDynamicCache] = None
+    hidden_states: Optional[tuple[torch.FloatTensor]] = None
+    attentions: Optional[tuple[torch.FloatTensor]] = None
+
+
+NEMOTRONH_START_DOCSTRING = r"""
+
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`NemotronHConfig`]): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+NEMOTRONH_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*):
+            Indices of input sequence tokens in the vocabulary.
+
+            If `past_key_values.seqlen_offset>0`, only `input_ids` that do not have their past calculated should be passed as
+            `input_ids`.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        position_ids (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings.
+        past_key_values (`NemotronHHybridDynamicCache`, *optional*):
+            If passed along, the model uses the previous state in all the blocks (which will give the output for the
+            `input_ids` provided as if the model add `state_input_ids + input_ids` as context).
+        use_cache (`bool`, *optional*):
+            If set to `True`, the `past_key_values` is returned and can be used to quickly generate the next logits.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            The position of the current input in the cache. This is used to ensure that the cache is correctly updated.
+            If `past_key_values` is passed, `cache_position` should also be passed.
+        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+"""
+
+
+@add_start_docstrings(
+    "The bare NemotronH Model transformer outputting raw hidden-states without any specific head on top.",
+    NEMOTRONH_START_DOCSTRING,
+)
+class NemotronHModel(NemotronHPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList([NemotronHBlock(config, layer_idx=idx) for idx in range(config.num_hidden_layers)])
+
+        self.gradient_checkpointing = False
+        self.norm_f = NemotronHRMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+        # Initialize weights and apply final processing
+        self._register_load_state_dict_pre_hook(self.load_hook)
+        self.post_init()
+
+    def load_hook(self, state_dict, prefix, *args):
+        for k in state_dict:
+            if "embedding." in k:
+                state_dict[k.replace("embedding.", "embeddings.")] = state_dict.pop(k)
+                break
+
+    def get_input_embeddings(self):
+        return self.embeddings
+
+    def set_input_embeddings(self, new_embeddings):
+        self.embeddings = new_embeddings
+
+    @add_start_docstrings_to_model_forward(NEMOTRONH_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=NemotronHOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[NemotronHHybridDynamicCache] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> Union[tuple, NemotronHOutput]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False)
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):  # ^ is python for xor
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embeddings(input_ids)
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+
+        # From zamba_modeling.py
+        if use_cache and past_key_values is None:
+            logger.warning_once(
+                "NemotronH requires an initialized `NemotronHHybridDynamicCache` to return a cache. None was "
+                "provided, so no cache will be returned."
+            )
+
+        hidden_states = inputs_embeds
+
+        if cache_position is None:
+            past_seen_tokens = (
+                past_key_values.get_seq_length()
+                if past_key_values is not None
+                else 0
+            )
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + hidden_states.shape[1], device=hidden_states.device
+            )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position)
+        mamba_mask = self._update_mamba_mask(attention_mask, cache_position)
+
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        # Until HERE
+
+        for layer_idx, mixer_block in enumerate(self.layers):
+            # Depending on the layer type we opt for 2D base attention mask (Mamba) or 4D causal mask (Attention)
+            if mixer_block.block_type == "mamba":
+                layer_mask = mamba_mask
+            elif mixer_block.block_type == "attention":
+                layer_mask = causal_mask
+            elif mixer_block.block_type in ["mlp", "moe"]:
+                layer_mask = None
+            else:
+                raise ValueError(f"Invalid block_type: {self.block_type}")
+
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                hidden_states = self._gradient_checkpointing_func(
+                    mixer_block.__call__, hidden_states, past_key_values, cache_position, layer_mask
+                )
+            else:
+                hidden_states = mixer_block(
+                    hidden_states,
+                    past_key_values=past_key_values,
+                    cache_position=cache_position,
+                    attention_mask=layer_mask,
+                    output_attentions=output_attentions,
+                )
+
+        hidden_states = self.norm_f(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if past_key_values is not None and not past_key_values.has_previous_state:
+            past_key_values.has_previous_state = True
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, past_key_values, all_hidden_states] if v is not None)
+
+        return NemotronHOutput(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    def _update_causal_mask(self, attention_mask, input_tensor, cache_position):
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+
+        dtype, device = input_tensor.dtype, input_tensor.device
+        min_dtype = torch.finfo(dtype).min
+        sequence_length = input_tensor.shape[1]
+        if cache_position is None:
+            target_length = sequence_length
+        else:
+            target_length = cache_position[-1] + 1
+
+        causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
+        if sequence_length != 1:
+            causal_mask = torch.triu(causal_mask, diagonal=1)
+        if cache_position is not None:
+            causal_mask *= (torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)).to(torch.bool)
+        causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
+        if attention_mask is not None:
+            causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+            if attention_mask.dim() == 2:
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)
+                causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype)
+
+        if (
+            self.config._attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type in ["cuda", "xpu", "npu"]
+        ):
+            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+            # Details: https://github.com/pytorch/pytorch/issues/110213
+            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+        return causal_mask
+
+    def _update_mamba_mask(self, attention_mask, cache_position):
+        """
+        No need for zeroing states when
+            1. Cached forward
+            2. Attending to all inputs
+        """
+        mamba_mask = attention_mask
+        if cache_position[0] > 0 or (attention_mask is not None and torch.all(attention_mask == 1)):
+            mamba_mask = None
+        return mamba_mask
+
+
+def register_nemotron_h_conversion_mapping():
+    try:
+        from transformers.conversion_mapping import WeightRenaming, register_checkpoint_conversion_mapping
+        has_conversion_mapping = True
+    except ImportError:
+        has_conversion_mapping = False
+
+    if not has_conversion_mapping:
+        return
+
+    register_checkpoint_conversion_mapping(
+        "nemotron_h",
+        [
+            WeightRenaming("backbone.", "model."),
+            WeightRenaming("embedding.weight", "embeddings.weight"),
+        ],
+        overwrite=True,
+    )
+
+
+
+@add_start_docstrings(
+    """
+    The NEMOTRONH Model transformer with a language modeling head on top (linear layer with weights not tied to the input
+    embeddings).
+    """,
+    NEMOTRONH_START_DOCSTRING,
+)
+class NemotronHForCausalLM(NemotronHPreTrainedModel, GenerationMixin):
+    _keys_to_ignore_on_load_unexpected = [r"mtp.*"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = NemotronHModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        register_nemotron_h_conversion_mapping()
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def _get_key_renaming_mapping(
+        self,
+        checkpoint_keys: list[str],
+        key_mapping: Optional[dict[str, str]] = None,
+        loading_base_model_from_task_state_dict: bool = False,
+        loading_task_model_from_base_state_dict: bool = False,
+    ):
+        """Convert backbone.* keys to model.* keys for backward compatibility."""
+        if key_mapping is None:
+            key_mapping = {"^backbone": "model"}
+        else:
+            key_mapping = {"^backbone": "model", **key_mapping}
+
+        has_prefix_module = any(s.startswith("backbone") for s in checkpoint_keys)
+        if has_prefix_module:
+            loading_task_model_from_base_state_dict = False
+
+        return super()._get_key_renaming_mapping(
+            checkpoint_keys,
+            key_mapping,
+            loading_base_model_from_task_state_dict=loading_base_model_from_task_state_dict,
+            loading_task_model_from_base_state_dict=loading_task_model_from_base_state_dict,
+        )
+
+    def get_input_embeddings(self):
+        return self.model.get_input_embeddings()
+
+    def set_input_embeddings(self, new_embeddings):
+        return self.model.set_input_embeddings(new_embeddings)
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def get_decoder(self):
+        return self.model
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        cache_position=None,
+        position_ids=None,
+        use_cache=True,
+        is_first_iteration=False,
+        **kwargs,
+    ):
+        # Overwritten -- has a unique cache type, `NemotronHHybridDynamicCache`
+
+        if past_key_values is None:
+            past_key_values = NemotronHHybridDynamicCache(
+                self.config, input_ids.shape[0], dtype=self.dtype, device=self.device
+            )
+
+        kwargs["logits_to_keep"] = self.config.num_logits_to_keep
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            cache_position=cache_position,
+            position_ids=position_ids,
+            use_cache=use_cache,
+            is_first_iteration=is_first_iteration,
+            **kwargs,
+        )
+
+        return model_inputs
+
+    @add_start_docstrings_to_model_forward(NEMOTRONH_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=NemotronHCausalLMOutput,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[NemotronHHybridDynamicCache] = None,
+        labels: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        use_cache: Optional[bool] = None,
+        cache_position: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        **kwargs,  # for now we need this for generation
+    ) -> Union[tuple, NemotronHCausalLMOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
+            `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
+            are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        nemotron_h_outputs = self.model(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            attention_mask=attention_mask,
+        )
+        hidden_states = nemotron_h_outputs[0]
+
+        logits = self.lm_head(hidden_states.to(self.lm_head.weight.dtype)).float()
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + nemotron_h_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return NemotronHCausalLMOutput(
+            loss=loss,
+            logits=logits,
+            past_key_values=nemotron_h_outputs.past_key_values,
+            hidden_states=nemotron_h_outputs.hidden_states,
+            attentions=nemotron_h_outputs.attentions,
+        )

privacy.md

@@ -0,0 +1,14 @@
+| Field | Response |
+| :---- | :---- |
+| Generatable or reverse engineerable personal data? | No |
+| Personal data used to create this model? | No |
+| Was consent obtained for any personal data used? | Not Applicable |
+| A description of any methods implemented in data acquisition or processing, if any, to address the prevalence of personal data in the training data, where relevant and applicable. | We used only prompts that do not contain any personal data for synthetic data generation. |
+| How often is the dataset reviewed? | Before Release |
+| Is there provenance for all datasets used in training? | Yes |
+| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |
+| Is data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data. |
+| Applicable Privacy Policy | [NVIDIA Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) |
+| During AI model development, strict adherence to copyright policy ensured compliance through risk mitigation and legal reviews. Post-data collection, reserved rights content is identified and removed, with verified opt-out processes for rightsholders. Detailed records document due diligence and transparency. | True |
+| We employ automated tools and data processing techniques during data preparation to identify and filter certain categories of personal information. Scans of training datasets detected no PII. | True. We employ automated tools and data processing techniques to scan for Personally Identifiable Information (PII) during data preparation to identify and filter certain categories of personal information, including phone numbers, email addresses, credit card numbers, and public-facing contact details. Scans of Common Crawl, CC-News, and Wikimedia datasets did not detect PII in the majority of samples; however, Microsoft Presidio indicated potential findings including business contact information embedded in natural language, such as email addresses and phone numbers. These were removed using verified instances of PII through a combination of automated filtering and human-in-the-loop validation. In contrast, scans of financial reasoning datasets, including NVIDIA-created and web-scraped datasets, via Presidio Analyzer, indicated false positives such as numerical sequences, and did not indicate any verified instances of PII. This evaluation used a 3,000-sample subset per dataset, identified as the optimal threshold for maximizing embedder accuracy. |
+| Privacy Testing: | Constrained to English-language inputs. Multi-lingual parity is not currently claimed or guaranteed. |

safety.md

@@ -0,0 +1,9 @@
+| Field | Response |
+| :---- | :---- |
+| Model Application Field(s): | Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning, Customer Service |
+| Describe the life critical impact (if present). | Not Applicable |
+| Description of methods implemented in data acquisition or processing, if any, to address other types of potentially harmful data in the training, testing, and validation data: | We used a guard model for content safety to exclude potentially harmful data from training.  |
+| Description of any methods implemented in data acquisition or processing, if any, to address illegal or harmful content in the training data, including, but not limited to, child sexual abuse material (CSAM) and non-consensual intimate imagery (NCII) | We used a Gemma-3 4B-based guard model trained on [Nemotron Content Safety Dataset v2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) for content safety to exclude potentially illegal or harmful content from the training. |
+| Use Case Restrictions: | Abide by the [NVIDIA Nemotron Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/). |
+| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development.  Restrictions enforce dataset access during training, and dataset license constraints adhered to. |
+| This AI model was developed based on our policies to ensure responsible data handling and risk mitigation. The datasets used for training have been scanned for harmful content and illegal content, consistent with our policies including scanning for Child Sexual Abuse Material (CSAM). Ongoing review and monitoring mechanisms are in place based on our policies and to maintain data integrity. | True. We use [Nemotron Content Safety Dataset V2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) and an internal safety dataset specialized for minority sexuality for content safety evaluation to ensure the safety of this model. |

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

super_v3_reasoning_parser.py

@@ -0,0 +1,28 @@
+from vllm.reasoning.abs_reasoning_parsers import ReasoningParserManager
+from vllm.reasoning.deepseek_r1_reasoning_parser import DeepSeekR1ReasoningParser
+
+
+@ReasoningParserManager.register_module("super_v3")
+class SuperV3ReasoningParser(DeepSeekR1ReasoningParser):
+    def extract_reasoning(self, model_output, request):
+        reasoning_content, final_content = super().extract_reasoning(
+            model_output, request
+        )
+        if (
+            hasattr(request, "chat_template_kwargs")
+            and request.chat_template_kwargs
+            and (
+                request.chat_template_kwargs.get("enable_thinking") is False
+                or request.chat_template_kwargs.get("force_nonempty_content") is True
+            )
+            and final_content is None
+        ):
+            """
+            The original `deepseek_r1` reasoning parser this inherits from will automatically put everything in the reasoning content when it cannot parse out reasoning. This was fine for the DeepSeek R1 model that was not intended to be used without reasoning.
+            1. Since the Nemotron 3 Nano and Super both have thinking off modes modulated by "enable_thinking=false" in the chat template kwargs, this change instead which will properly place the content in cases where there is no thinking enabled via config.
+            2. There are rare cases where the model will output only reasoning without an end-think token `</think>` (e.g. reasoning exceeds max length), which results in empty content returned. End users may want to unilaterally avoid such cases and always have a content response even if the model does not finish its reasoning.
+            """
+            # Put all nonempty content into the content, rather than return content
+            reasoning_content, final_content = None, reasoning_content
+
+        return reasoning_content, final_content

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:623c34567aebb18582765289fbe23d901c62704d6518d71866e0e58db892b5b7
+size 17077484