Update README.md

README.md

@@ -30,10 +30,38 @@ As of 15 May 2025, this model achieves the highest [JudgeBench](https://huggingf
 
 See details on how this model was trained at [https://arxiv.org/abs/2505.11475](https://arxiv.org/abs/2505.11475)
 
+This model is ready for commercial/non-commercial use.
+
 ## License/Terms of Use:
 
 GOVERNING TERMS: 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/) . Additional Information: [Llama 3.3 Community License Agreement](https://www.llama.com/llama3_3/license/). Built with Llama.
 
+
+### Deployment Geography
+
+Global
+
+## Use Case:
+
+Llama-3.3-Nemotron-70B-Reward labels an LLM-generated response to a user query with a reward score.
+
+## Release Date:
+
+HuggingFace 06/27/2025 via https://huggingface.co/nvidia/Llama-3.3-Nemotron-70B-Reward
+
+## References:
+
+* [HelpSteer3-Preference](https://arxiv.org/abs/2505.11475)
+* [HelpSteer2-Preference](https://arxiv.org/abs/2410.01257)
+* [SteerLM method](https://arxiv.org/abs/2310.05344)
+* [HelpSteer](https://arxiv.org/abs/2311.09528)
+* [HelpSteer2](https://arxiv.org/abs/2406.08673)
+* [The future of AI: Built with Llama](https://ai.meta.com/blog/future-of-ai-built-with-llama/) 
+* [Meta's Llama 3.3 Webpage](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_3) 
+* [Meta's Llama 3.3 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_3/MODEL_CARD.md)
+
+
+
 ## RM-Bench LeaderBoard
 
 As of 15 May 2025, our reward models trained with HelpSteer3-Preference are the top performing Bradley-Terry reward models on [RM-Bench](https://arxiv.org/abs/2410.16184), an improved variant of RewardBench for evaluating Reward Models in Chat, Math, Code and Safety.
@@ -63,32 +91,11 @@ As of 15 May 2025, our reward models trained with HelpSteer3-Preference are the
 *Note that Skywork-Reward-Gemma-2-27B was the best performing reward model reported on JudgeBench and we evaluated all other models.* 
 
 
-## Use Case:
-
-Llama-3.3-Nemotron-70B-Reward labels an LLM-generated response to a user query with a reward score.
-
-## Release Date:
-
-05/30/2025
-
-
-## References:
-
-* [HelpSteer3-Preference](https://arxiv.org/abs/2505.11475)
-* [HelpSteer2-Preference](https://arxiv.org/abs/2410.01257)
-* [SteerLM method](https://arxiv.org/abs/2310.05344)
-* [HelpSteer](https://arxiv.org/abs/2311.09528)
-* [HelpSteer2](https://arxiv.org/abs/2406.08673)
-* [The future of AI: Built with Llama](https://ai.meta.com/blog/future-of-ai-built-with-llama/) 
-* [Meta's Llama 3.3 Webpage](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_3) 
-* [Meta's Llama 3.3 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_3/MODEL_CARD.md)
-
-
 ## Model Architecture: 
 **Architecture Type:** Transformer <br>
 **Network Architecture:** Llama 3.3 <br>
 
-We developed this model using Llama-3.3-70B-Instruct as its foundation. This model contains 70 billion parameters.
+We developed this model using [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) as its foundation. This model contains 70 billion parameters.
 
 ## Input:
 **Input Type(s):** Text <br>
@@ -102,6 +109,8 @@ We developed this model using Llama-3.3-70B-Instruct as its foundation. This mod
 **Output Parameters:** One-Dimensional (1D) <br>
 **Other Properties Related to Output:**  The float value represents the quality of the response, with a higher value representing higher quality. <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:
 **Runtime Engine(s):** <br>
 * [NeMo - 24.05.llama.3.1] <br>
@@ -109,7 +118,7 @@ We developed this model using Llama-3.3-70B-Instruct as its foundation. This mod
 **Supported Hardware Microarchitecture Compatibility:** <br>
 * NVIDIA Ampere <br>
 * NVIDIA Hopper <br>
-* NVIDIA Turing
+* NVIDIA Turing <br>
 
 **Supported Operating System(s):** Linux <br>
 
@@ -117,7 +126,7 @@ We developed this model using Llama-3.3-70B-Instruct as its foundation. This mod
 
 You can use the model using HuggingFace Transformers library with 2 or more 80GB GPUs (NVIDIA Ampere or newer) with at least 150GB of free disk space to accomodate the download.
 
-This code has been tested on Transformers v4.45.0, torch v2.3.0a0+40ec155e58.nv24.3 and 2 A100 80GB GPUs, but any setup that supports meta-llama/Llama-3.1-70B-Instruct should support this model as well. If you run into problems, you can consider doing pip install -U transformers.
+This code has been tested on Transformers v4.45.0, torch v2.3.0a0+40ec155e58.nv24.3 and 2 H100 80GB GPUs, but any setup that supports meta-llama/Llama-3.1-70B-Instruct should support this model as well. If you run into problems, you can consider doing pip install -U transformers.
 
 ```python
 import torch
@@ -148,7 +157,7 @@ for response in [good_response, bad_response]:
 ## Model Version: 
 v1.0
 
-# Training and Testing Datasets: 
+# Training, Testing and Evaluation Datasets: 
 
 ## Training Datasets:
 
@@ -178,14 +187,44 @@ v1.0
 **Properties:** <br>
 * 1,614 prompts, each with a pair of responses as well as human preferences between the pair of responses.
 
+## Evaluation Datasets
+
+**Dataset Name:** RM-Bench <br>
+**Dataset Link:** https://huggingface.co/datasets/THU-KEG/RM-Bench
+
+**Data Collection Method by dataset** <br>
+* [Hybrid: Human, Synthetic] <br>
+
+**Labeling Method by dataset** <br>
+* [Hybrid: Human, Synthetic] <br>
+
+**Properties:** <br>
+* 1,327 prompts, each with three pairs of responses as well as preferences between the pair of responses.
+
+
+**Dataset Name:** JudgeBench <br>
+**Dataset Link:** https://huggingface.co/datasets/ScalerLab/JudgeBench
+
+**Data Collection Method by dataset** <br>
+* [Hybrid: Human, Synthetic] <br>
+
+**Labeling Method by dataset** <br>
+* [Hybrid: Human, Synthetic] <br>
+
+**Properties:** <br>
+* 350 prompts, each with a pair of responses as well as preferences between the pair of responses.
+
+
 
 # Inference:
-**Engine:** [Triton](https://developer.nvidia.com/triton-inference-server) <br>
+**Engine:** PyTorch <br>
 **Test Hardware:** H100, A100 80GB, A100 40GB <br>
 
 
 ## 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 supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. 
+For more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](explainability.md), [Bias](bias.md), [Safety & Security](safety.md), and [Privacy](privacy.md) Subcards.  
+
 Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
 
 ## Citation