Create README.md

README.md

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+---
+library_name: transformers
+license: other
+license_name: nvidia-open-model-license
+license_link: >-
+  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
+pipeline_tag: text-generation
+language:
+  - en
+tags:
+  - nvidia
+  - Nemotron-Cascade
+  - reasoning
+  - general-purpose
+  - SFT
+  - RL
+  - pytorch
+---
+
+# Nemotron-Cascade-8B Intermediate ckpts
+
+<p align="center">
+
+[![Technical Report](https://img.shields.io/badge/2512.13607-Technical_Report-blue)](https://arxiv.org/abs/2512.13607)
+[![SFT Dataset](https://img.shields.io/badge/🤗-SFT_Datset-blue)](https://huggingface.co/collections/nvidia/nemotron-cascade)
+[![RL Dataset](https://img.shields.io/badge/🤗-RL_Datset-blue)](https://huggingface.co/collections/nvidia/nemotron-cascade)
+[![Models](https://img.shields.io/badge/🤗-Models-blue)](https://huggingface.co/collections/nvidia/nemotron-cascade)
+</p>
+
+
+## Introduction
+
+This repository releases the intermediate checkpoints produced during the development of [Nemotron-Cascade-8B](https://huggingface.co/nvidia/Nemotron-Cascade-8B). Nemotron-Cascade-8B is a general-purpose model trained using a sequential, domain-wise reinforcement learning pipeline, illustrated in the figure below.
+
+<img src="fig/pipeline.png" alt="train_pipeline_fig" style="width: 1000px; max-width: 100%;" />
+
+We release checkpoints corresponding to each major stage of training:
+
+- **Nemotron-Cascade-8B-SFT** (completed multi-stage SFT)
+- **Nemotron-Cascade-8B-RLHF** (completed RLHF)
+- **Nemotron-Cascade-8B-IFRL** (completed instruction following RL)
+- **Nemotron-Cascade-8B-MathRL** (completed Math RL)
+- **Nemotron-Cascade-8B-CodeRL** (completed Code RL)
+
+The final model, [Nemotron-Cascade-8B](https://huggingface.co/nvidia/Nemotron-Cascade-8B), is obtained after the concluding SWE RL stage.
+
+
+## Usage Recommendations
+
+We recommend using RoPE scaling with the [YaRN](https://arxiv.org/abs/2309.00071) method to better support contexts longer than 32K. This can be enabled by updating the model’s `config.json` as shown below:
+```json
+  {
+    ...,
+    "rope_scaling": {
+        "rope_type": "yarn",
+        "factor": 2.0,
+        "original_max_position_embeddings": 32768
+    }
+  }
+```
+
+
+## Results
+
+Same as [Nemotron-Cascade-8B](https://huggingface.co/nvidia/Nemotron-Cascade-8B), we use a maximum output length of 64K tokens for evaluation, with the temperature set to 0.6 and top-p to 0.95. We also apply RoPE scaling using the YaRN method with a scaling factor of 2.0.
+
+| **Benchmark<br>Metric: Pass@1** | **Nemotron-<br>Cascade-8B-SFT** | **Nemotron-<br>Cascade-8B-RLHF** | **Nemotron-<br>Cascade-8B-IFRL** | **Nemotron-<br>Cascade-8B-MathRL** | **Nemotron-<br>Cascade-8B-CodeRL** | **Nemotron-<br>Cascade-8B** |
+| :---- | :---: | :---: | :---: | :---: | :---: | :---: |
+| ***Knowledge Reasoning*** |
+| MMLU | 83.0 | 83.1 | 83.4 | 83.4 | 83.7 | 83.7 |
+| MMLU Pro | 74.4 | 77.8 | 74.5 | 75.0 | 75.3 | 75.7 |
+| GPQA-Diamond | 63.5 | 66.8 | 66.1 | 65.7 | 67.4 | 66.5 |
+| ***Alignment*** |
+| ArenaHard | 70.0 | 90.1 | 88.0 | 87.0 | 87.8 | 87.9 |
+| IFEval (Strict Prompt) | 70.8 | 50.1 | 90.4 | 92.1 | 90.7 | 90.2 |
+| IFBench | 21.2 | 24.5 | 40.5 | 40.4 | 38.1 | 40.8 |
+| ***Math*** |
+| AIME 2024 | 83.6 | 86.1 | 86.2 | 90.2 | 89.1 | 89.5 |
+| AIME 2025 | 72.8 | 75.0 | 75.2 | 81.9 | 80.5 | 80.1 |
+| ***Code*** |
+| LCB v5 (08/24-02/25) | 59.2 | 70.2 | 70.2 | 70.6 | 75.3 | 74.3 |
+| LCB v6 (08/24-05/25) | 56.7 | 67.2 | 66.7 | 67.4 | 71.5 | 71.1 |
+| SWE Verified (Agentless) | 26.1 | 28.2 | 28.3 | 30.6 | 31.6 | 37.2 |
+
+
+## Chat Template
+
+All intermediate checkpoints use the same chat template as [Nemotron-Cascade-8B](https://huggingface.co/nvidia/Nemotron-Cascade-8B). Each is a unified model supporting both ***thinking*** and ***instruct*** (non-reasoning) modes. To switch between these two modes, simply append the `" /think"` (for ***thinking***) or the `" /no_think"` (for ***instruct***) tag to the end of the user input. See [Nemotron-Cascade-8B](https://huggingface.co/nvidia/Nemotron-Cascade-8B) for additional details.
+
+
+## Release Date
+Dec 19, 2025
+
+
+## License
+Your use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).
+
+
+## Citation
+```
+@article{Nemotron_Cascade_Scaling_Cascaded_Reinforcement_Learning,
+  title={Nemotron-Cascade: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models},
+  author={Wang, Boxin and Lee, Chankyu and Lee, Nayeon and Lin, Sheng-Chieh and Dai, Wenliang and Chen, Yang and Chen, Yangyi and Yang, Zhuolin and Liu, Zihan and Shoeybi, Mohammad and Catanzaro, Bryan and Ping, Wei},
+  year={2025}
+}
+```
+