nvidia
/

DeepSeek-V4-Pro-NVFP4

@@ -1,240 +1,201 @@
 ---
 license: mit
-library_name: transformers
 ---
-# DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
-<!-- markdownlint-disable first-line-h1 -->
-<!-- markdownlint-disable html -->
-<!-- markdownlint-disable no-duplicate-header -->
-<div align="center">
-  <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V4" />
-</div>
-<hr>
-<div align="center" style="line-height: 1;">
-  <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
-    <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
-    <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V4-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-</div>
-<div align="center" style="line-height: 1;">
-  <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
-    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
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-  <a href="LICENSE" style="margin: 2px;">
-    <img alt="License" src="https://img.shields.io/badge/License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-</div>
-<p align="center">
-  <a href="https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/DeepSeek_V4.pdf"><b>Technical Report</b>👁️</a>
-</p>
-## Introduction
-We present a preview version of **DeepSeek-V4** series, including two strong Mixture-of-Experts (MoE) language models — **DeepSeek-V4-Pro** with 1.6T parameters (49B activated) and **DeepSeek-V4-Flash** with 284B parameters (13B activated) — both supporting a context length of **one million tokens**.
-DeepSeek-V4 series incorporate several key upgrades in architecture and optimization:
-1. **Hybrid Attention Architecture:** We design a hybrid attention mechanism combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) to dramatically improve long-context efficiency. In the 1M-token context setting, DeepSeek-V4-Pro requires only **27% of single-token inference FLOPs** and **10% of KV cache** compared with DeepSeek-V3.2.
-2. **Manifold-Constrained Hyper-Connections (mHC):** We incorporate mHC to strengthen conventional residual connections, enhancing stability of signal propagation across layers while preserving model expressivity.
-3. **Muon Optimizer:** We employ the Muon optimizer for faster convergence and greater training stability.
-We pre-train both models on more than **32T** diverse and high-quality tokens, followed by a comprehensive post-training pipeline. The post-training features a two-stage paradigm: independent cultivation of domain-specific experts (through SFT and RL with GRPO), followed by unified model consolidation via on-policy distillation, integrating distinct proficiencies across diverse domains into a single model.
-**DeepSeek-V4-Pro-Max**, the maximum reasoning effort mode of DeepSeek-V4-Pro, significantly advances the knowledge capabilities of open-source models, firmly establishing itself as the best open-source model available today. It achieves top-tier performance in coding benchmarks and significantly bridges the gap with leading closed-source models on reasoning and agentic tasks. Meanwhile, **DeepSeek-V4-Flash-Max** achieves comparable reasoning performance to the Pro version when given a larger thinking budget, though its smaller parameter scale naturally places it slightly behind on pure knowledge tasks and the most complex agentic workflows.
-<div align="center">
- <img src="assets/dsv4_performance.png" >
-</div>
-## Model Downloads
-<div align="center">
-| **Model** | **#Total Params** | **#Activated Params** | **Context Length** | **Precision** | **Download** |
-| :---: | :---: | :---: | :---: | :---: | :---: |
-| DeepSeek-V4-Flash-Base | 284B | 13B | 1M | FP8 Mixed | [HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Base) \| [ModelScope](https://modelscope.cn/models/deepseek-ai/DeepSeek-V4-Flash-Base) |
-| DeepSeek-V4-Flash | 284B | 13B | 1M | FP4 + FP8 Mixed* | [HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash) \| [ModelScope](https://modelscope.cn/models/deepseek-ai/DeepSeek-V4-Flash) |
-| DeepSeek-V4-Pro-Base | 1.6T | 49B | 1M | FP8 Mixed | [HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-Base) \| [ModelScope](https://modelscope.cn/models/deepseek-ai/DeepSeek-V4-Pro-Base) |
-| DeepSeek-V4-Pro | 1.6T | 49B | 1M | FP4 + FP8 Mixed* | [HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro) \| [ModelScope](https://modelscope.cn/models/deepseek-ai/DeepSeek-V4-Pro) |
-</div>
-*\*FP4 + FP8 Mixed: MoE expert parameters use FP4 precision; most other parameters use FP8.*
-## Evaluation Results
-### Base Model
-<div align="center">
-| Benchmark (Metric) | # Shots | DeepSeek-V3.2-Base | DeepSeek-V4-Flash-Base | DeepSeek-V4-Pro-Base |
-| :--- | :---: | :---: | :---: | :---: |
-| Architecture | - | MoE | MoE | MoE |
-| # Activated Params | - | 37B | 13B | 49B |
-| # Total Params | - | 671B | 284B | 1.6T |
-| **World Knowledge** | | | | |
-| AGIEval (EM) | 0-shot | 80.1 | 82.6 | **83.1** |
-| MMLU (EM) | 5-shot | 87.8 | 88.7 | **90.1** |
-| MMLU-Redux (EM) | 5-shot | 87.5 | 89.4 | **90.8** |
-| MMLU-Pro (EM) | 5-shot | 65.5 | 68.3 | **73.5** |
-| MMMLU (EM) | 5-shot | 87.9 | 88.8 | **90.3** |
-| C-Eval (EM) | 5-shot | 90.4 | 92.1 | **93.1** |
-| CMMLU (EM) | 5-shot | 88.9 | 90.4 | **90.8** |
-| MultiLoKo (EM) | 5-shot | 38.7 | 42.2 | **51.1** |
-| Simple-QA verified (EM) | 25-shot | 28.3 | 30.1 | **55.2** |
-| SuperGPQA (EM) | 5-shot | 45.0 | 46.5 | **53.9** |
-| FACTS Parametric (EM) | 25-shot | 27.1 | 33.9 | **62.6** |
-| TriviaQA (EM) | 5-shot | 83.3 | 82.8 | **85.6** |
-| **Language & Reasoning** | | | | |
-| BBH (EM) | 3-shot | **87.6** | 86.9 | 87.5 |
-| DROP (F1) | 1-shot | 88.2 | 88.6 | **88.7** |
-| HellaSwag (EM) | 0-shot | 86.4 | 85.7 | **88.0** |
-| WinoGrande (EM) | 0-shot | 78.9 | 79.5 | **81.5** |
-| CLUEWSC (EM) | 5-shot | 83.5 | 82.2 | **85.2** |
-| **Code & Math** | | | | |
-| BigCodeBench (Pass@1) | 3-shot | **63.9** | 56.8 | 59.2 |
-| HumanEval (Pass@1) | 0-shot | 62.8 | 69.5 | **76.8** |
-| GSM8K (EM) | 8-shot | 91.1 | 90.8 | **92.6** |
-| MATH (EM) | 4-shot | 60.5 | 57.4 | **64.5** |
-| MGSM (EM) | 8-shot | 81.3 | **85.7** | 84.4 |
-| CMath (EM) | 3-shot | 92.6 | **93.6** | 90.9 |
-| **Long Context** | | | | |
-| LongBench-V2 (EM) | 1-shot | 40.2 | 44.7 | **51.5** |
-</div>
-### Instruct Model
-DeepSeek-V4-Pro and DeepSeek-V4-Flash both support three reasoning effort modes:
-| Reasoning Mode | Characteristics | Typical Use Cases | Response Format |
-| :--- | :--- | :--- | :--- |
-| Non-think | Fast, intuitive responses | Routine daily tasks, low-risk decisions | `</think>` summary |
-| Think High | Conscious logical analysis, slower but more accurate | Complex problem-solving, planning | `<think>` thinking `</think>` summary |
-| Think Max | Push reasoning to its fullest extent | Exploring the boundary of model reasoning capability | Special system prompt + `<think>` thinking `</think>` summary |
-#### DeepSeek-V4-Pro-Max vs Frontier Models
-<div align="center">
-| Benchmark (Metric) | Opus-4.6 Max | GPT-5.4 xHigh | Gemini-3.1-Pro High | K2.6 Thinking | GLM-5.1 Thinking | DS-V4-Pro Max |
-| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
-| **Knowledge & Reasoning** | | | | | | |
-| MMLU-Pro (EM) | 89.1 | 87.5 | **91.0** | 87.1 | 86.0 | 87.5 |
-| SimpleQA-Verified (Pass@1) | 46.2 | 45.3 | **75.6** | 36.9 | 38.1 | 57.9 |
-| Chinese-SimpleQA (Pass@1) | 76.4 | 76.8 | **85.9** | 75.9 | 75.0 | 84.4 |
-| GPQA Diamond (Pass@1) | 91.3 | 93.0 | **94.3** | 90.5 | 86.2 | 90.1 |
-| HLE (Pass@1) | 40.0 | 39.8 | **44.4** | 36.4 | 34.7 | 37.7 |
-| LiveCodeBench (Pass@1) | 88.8 | - | 91.7 | 89.6 | - | **93.5** |
-| Codeforces (Rating) | - | 3168 | 3052 | - | - | **3206** |
-| HMMT 2026 Feb (Pass@1) | 96.2 | **97.7** | 94.7 | 92.7 | 89.4 | 95.2 |
-| IMOAnswerBench (Pass@1) | 75.3 | **91.4** | 81.0 | 86.0 | 83.8 | 89.8 |
-| Apex (Pass@1) | 34.5 | 54.1 | **60.9** | 24.0 | 11.5 | 38.3 |
-| Apex Shortlist (Pass@1) | 85.9 | 78.1 | 89.1 | 75.5 | 72.4 | **90.2** |
-| **Long Context** | | | | | | |
-| MRCR 1M (MMR) | **92.9** | - | 76.3 | - | - | 83.5 |
-| CorpusQA 1M (ACC) | **71.7** | - | 53.8 | - | - | 62.0 |
-| **Agentic** | | | | | | |
-| Terminal Bench 2.0 (Acc) | 65.4 | **75.1** | 68.5 | 66.7 | 63.5 | 67.9 |
-| SWE Verified (Resolved) | **80.8** | - | 80.6 | 80.2 | - | 80.6 |
-| SWE Pro (Resolved) | 57.3 | 57.7 | 54.2 | **58.6** | 58.4 | 55.4 |
-| SWE Multilingual (Resolved) | **77.5** | - | - | 76.7 | 73.3 | 76.2 |
-| BrowseComp (Pass@1) | 83.7 | 82.7 | **85.9** | 83.2 | 79.3 | 83.4 |
-| HLE w/ tools (Pass@1) | 53.1 | 52.0 | 51.6 | **54.0** | 50.4 | 48.2 |
-| GDPval-AA (Elo) | 1619 | **1674** | 1314 | 1482 | 1535 | 1554 |
-| MCPAtlas Public (Pass@1) | **73.8** | 67.2 | 69.2 | 66.6 | 71.8 | 73.6 |
-| Toolathlon (Pass@1) | 47.2 | **54.6** | 48.8 | 50.0 | 40.7 | 51.8 |
-</div>
-#### Comparison across Modes
-<div align="center">
-| Benchmark (Metric) | V4-Flash Non-Think | V4-Flash High | V4-Flash Max | V4-Pro Non-Think | V4-Pro High | V4-Pro Max |
-| :--- | :---: | :---: | :---: | :---: | :---: | :---: |
-| **Knowledge & Reasoning** | | | | | | |
-| MMLU-Pro (EM) | 83.0 | 86.4 | 86.2 | 82.9 | 87.1 | **87.5** |
-| SimpleQA-Verified (Pass@1) | 23.1 | 28.9 | 34.1 | 45.0 | 46.2 | **57.9** |
-| Chinese-SimpleQA (Pass@1) | 71.5 | 73.2 | 78.9 | 75.8 | 77.7 | **84.4** |
-| GPQA Diamond (Pass@1) | 71.2 | 87.4 | 88.1 | 72.9 | 89.1 | **90.1** |
-| HLE (Pass@1) | 8.1 | 29.4 | 34.8 | 7.7 | 34.5 | **37.7** |
-| LiveCodeBench (Pass@1) | 55.2 | 88.4 | 91.6 | 56.8 | 89.8 | **93.5** |
-| Codeforces (Rating) | - | 2816 | 3052 | - | 2919 | **3206** |
-| HMMT 2026 Feb (Pass@1) | 40.8 | 91.9 | 94.8 | 31.7 | 94.0 | **95.2** |
-| IMOAnswerBench (Pass@1) | 41.9 | 85.1 | 88.4 | 35.3 | 88.0 | **89.8** |
-| Apex (Pass@1) | 1.0 | 19.1 | 33.0 | 0.4 | 27.4 | **38.3** |
-| Apex Shortlist (Pass@1) | 9.3 | 72.1 | 85.7 | 9.2 | 85.5 | **90.2** |
-| **Long Context** | | | | | | |
-| MRCR 1M (MMR) | 37.5 | 76.9 | 78.7 | 44.7 | 83.3 | **83.5** |
-| CorpusQA 1M (ACC) | 15.5 | 59.3 | 60.5 | 35.6 | 56.5 | **62.0** |
-| **Agentic** | | | | | | |
-| Terminal Bench 2.0 (Acc) | 49.1 | 56.6 | 56.9 | 59.1 | 63.3 | **67.9** |
-| SWE Verified (Resolved) | 73.7 | 78.6 | 79.0 | 73.6 | 79.4 | **80.6** |
-| SWE Pro (Resolved) | 49.1 | 52.3 | 52.6 | 52.1 | 54.4 | **55.4** |
-| SWE Multilingual (Resolved) | 69.7 | 70.2 | 73.3 | 69.8 | 74.1 | **76.2** |
-| BrowseComp (Pass@1) | - | 53.5 | 73.2 | - | 80.4 | **83.4** |
-| HLE w/ tools (Pass@1) | - | 40.3 | 45.1 | - | 44.7 | **48.2** |
-| MCPAtlas (Pass@1) | 64.0 | 67.4 | 69.0 | 69.4 | **74.2** | 73.6 |
-| GDPval-AA (Elo) | - | - | 1395 | - | - | **1554** |
-| Toolathlon (Pass@1) | 40.7 | 43.5 | 47.8 | 46.3 | 49.0 | **51.8** |
-</div>
-## Chat Template
-This release does not include a Jinja-format chat template. Instead, we provide a dedicated `encoding` folder with Python scripts and test cases demonstrating how to encode messages in OpenAI-compatible format into input strings for the model, and how to parse the model's text output. Please refer to the [`encoding`](encoding/README.md) folder for full documentation.
-A brief example:
-```python
-from encoding_dsv4 import encode_messages, parse_message_from_completion_text
-messages = [
-    {"role": "user", "content": "hello"},
-    {"role": "assistant", "content": "Hello! I am DeepSeek.", "reasoning_content": "thinking..."},
-    {"role": "user", "content": "1+1=?"}
-]
-# messages -> string
-prompt = encode_messages(messages, thinking_mode="thinking")
-# string -> tokens
-import transformers
-tokenizer = transformers.AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V4-Pro")
-tokens = tokenizer.encode(prompt)
-```
-## How to Run Locally
-Please refer to the [inference](inference/README.md) folder for detailed instructions on running DeepSeek-V4 locally, including model weight conversion and interactive chat demos.
-For local deployment, we recommend setting the sampling parameters to `temperature = 1.0, top_p = 1.0`. For the Think Max reasoning mode, we recommend setting the context window to at least **384K** tokens.
-## License
-This repository and the model weights are licensed under the [MIT License](LICENSE).
-## Citation
-```
-@misc{deepseekai2026deepseekv4,
-      title={DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence},
-      author={DeepSeek-AI},
-      year={2026},
-}
 ```
-## Contact
-If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).

 ---
+pipeline_tag: text-generation
+base_model:
+- deepseek-ai/DeepSeek-V4-Pro
 license: mit
+library_name: Model Optimizer
+tags:
+- nvidia
+- ModelOpt
+- DeepSeekV4
+- quantized
+- NVFP4
+- nvfp4
 ---
+# Model Overview
+## Description:
+The NVIDIA DeepSeek-V4-Pro-NVFP4 model is the quantized version of the DeepSeek-V4-Pro model, which is a Mixture-of-Experts (MoE) language model with 1.6 trillion total parameters and 49 billion activated parameters. For more information, please check [here](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro). The NVIDIA DeepSeek V4 Pro NVFP4 model is quantized with [TensorRT Model Optimizer](https://github.com/NVIDIA/TensorRT-Model-Optimizer).
+This model is ready for commercial/non-commercial use.  <br>
+## Third-Party Community Consideration
+This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA [(DeepSeek V4 Pro) Model Card](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro).
+### License/Terms of Use:
+[MIT](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/LICENSE)
+### Deployment Geography:
+Global <br>
+### Use Case:
+DeepSeek V4 is well-suited for advanced reasoning, agentic AI applications, tool use scenarios, and complex problem-solving in domains such as mathematics, software engineering, and enterprise AI assistants. <br>
+### Release Date:
+Huggingface 04/27/2026/25/2025 via https://huggingface.co/nvidia/DeepSeek-V4-Pro-NVFP4 <br>
+## Model Architecture:
+**Architecture Type:** Transformers  <br>
+**Network Architecture:** Mixture-of-Experts (MoE) with Hybrid Attention (Compressed Sparse Attention + Heavily Compressed Attention) <br>
+**Total Parameters:** 1.6 Trillion (49 Billion activated) <br>
+## Input:
+**Input Type(s):** Text <br>
+**Input Format(s):** String <br>
+**Input Parameters:** 1D (One Dimensional) <br>
+**Other Properties Related to Input:** Supports multi-turn conversations with system prompts, user messages, and assistant responses. Maximum context length of 1 million tokens. Uses a custom encoding pipeline (encoding_dsv4) with three reasoning modes: Non-think (fast), Think High (logical analysis), and Think Max (full reasoning extent). <br>
+## Output:
+**Output Type(s):** Text <br>
+**Output Format:** String <br>
+**Output Parameters:** 1D (One Dimensional) <br>
+**Other Properties Related to Output:** Supports structured JSON output, function/tool calling, and reasoning content when enabled. <br>
+Our AI models are designed and/or 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. <br>
+## Software Integration:
+**Supported Runtime Engine(s):** <br>
+* SGLang <br>
+* vLLM <br>
+**Supported Hardware Microarchitecture Compatibility:** <br>
+* NVIDIA Blackwell <br>
+**Preferred Operating System(s):** <br>
+* Linux <br>
+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):
+** The model is quantized DeepSeek-V4-Pro-NVFP4 with nvidia-modelopt **v0.43.0**  <br>
+## Training and Evaluation Datasets:
+## Calibration Dataset:
+** Link: [cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail), [Nemotron-Post-Training-Dataset-v2](https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2) <br>
+** Data Collection Method by dataset: Automated. <br>
+** Labeling Method by dataset: Automated. <br>
+** Properties: The cnn_dailymail dataset is an English-language dataset containing just over 300k unique news articles as written by journalists at CNN and the Daily Mail. The Nemotron-Post-Training-Dataset-v2 is a post-training dataset curated by NVIDIA containing multi-turn conversations across diverse topics. <br>
+## Training Dataset: <br>
+**Data Modality:** Undisclosed <br>
+**Data Collection Method by dataset:** Hybrid: Human, Automated <br>
+**Labeling Method by dataset:** Hybrid: Human, Automated <br>
+## Testing Dataset: <br>
+**Data Collection Method by dataset:** Hybrid: Human, Automated <br>
+**Labeling Method by dataset:** Hybrid: Human, Automated <br>
+**Dataset Properties:** Undisclosed <br>
+## Evaluation Dataset: <br>
+**Data Collection Method by dataset:** Hybrid: Human, Automated <br>
+**Labeling Method by dataset: Hybrid:** Human, Automated <br>
+**Dataset Properties:** We evaluated the model on text-based reasoning, coding, and agentic tool-use benchmarks: GPQA Diamond contains 448 graduate-level multiple-choice questions written by domain experts in biology, physics, and chemistry; AA-LCR (Artificial Analysis Long Context Recall) evaluates a model's ability to accurately retrieve and recall information from long input contexts; τ²-Bench Telecom evaluates agentic tool-use and policy-adherence capabilities in dual-control telecom customer-service scenarios where the model interacts with a simulated user and external tools to resolve account issues; SciCode evaluates scientific coding capabilities; IFBench is a benchmark for evaluating instruction-following capabilities across diverse and structured task constraints. <br>
+## Inference:
+**Acceleration Engine:** SGLang, vLLM <br>
+**Test Hardware:** NVIDIA Blackwell B200 <br>
+## Post Training Quantization
+This model was obtained by quantizing the weights and activations of DeepSeek-V4-Pro to NVFP4 data type, ready for inference with SGLang and vLLM. Only the weights and activations of the linear operators within transformer blocks in MoE are quantized.
+## Usage
+### Deploy with vLLM
+Requires [vLLM PR #42209](https://github.com/vllm-project/vllm/pull/42209):
+```sh
+VLLM_USE_FLASHINFER_MOE_FP4=1 \
+VLLM_FLASHINFER_MOE_BACKEND=latency \
+python -m vllm.entrypoints.cli.main serve \
+    nvidia/DeepSeek-V4-Pro-NVFP4 \
+    --tensor-parallel-size 8 \
+    --trust-remote-code \
+    --kv-cache-dtype fp8 \
+    --served-model-name nvfp4
 ```
+### Deploy with SGLang
+Requires [SGLang PR #25820](https://github.com/sgl-project/sglang/pull/25820). The integration auto-detects NVFP4 from the checkpoint's `hf_quant_config.json` (weights are stored in FP8 with `"moe_quant_algo": "NVFP4"`):
+```sh
+python3 -m sglang.launch_server --model nvidia/DeepSeek-V4-Pro-NVFP4 --tensor-parallel-size 8 --trust-remote-code
+```
+### Evaluation
+The accuracy benchmark results are presented in the table below:
+<table>
+  <tr>
+   <td><strong>Precision</strong>
+   </td>
+   <td><strong>GPQA Diamond</strong>
+   </td>
+   <td><strong>AA-LCR</strong>
+   </td>
+   <td><strong>τ²-Bench Telecom</strong>
+   </td>
+   <td><strong>SciCode</strong>
+   </td>
+   <td><strong>IFBench</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>FP8 (AA Ref)
+   </td>
+   <td>89.00
+   </td>
+   <td>66.00
+   </td>
+   <td>96.00
+   </td>
+   <td>50.00
+   </td>
+   <td>76.00
+   </td>
+  </tr>
+  <tr>
+   <td>FP8 (Ours)
+   </td>
+   <td>89.49
+   </td>
+   <td>66.89
+   </td>
+   <td>94.25
+   </td>
+   <td>51.08
+   </td>
+   <td>77.82
+   </td>
+  </tr>
+  <tr>
+   <td>NVFP4
+   </td>
+   <td>89.33
+   </td>
+   <td>66.33
+   </td>
+   <td>94.83
+   </td>
+   <td>53.45
+   </td>
+   <td>77.21
+   </td>
+  </tr>
+</table>
+*Max OSL for evals can be as high as 64K.
+## Model Limitations:
+The base model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may 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.
+## 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.
+Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.
+Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail).