Update Model Card

README.md

@@ -18,7 +18,7 @@ tags:
 
 ## Model Overview 
 
-![Accuracy](accuracy_plot.png)
+![Accuracy Plot](./accuracy_plot.png)
 
 Llama-3.1-Nemotron-Ultra-253B-v1 is a large language model (LLM) which is a derivative of [Meta Llama-3.1-405B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct) (AKA the *reference model*). It is a reasoning model that is post trained for reasoning, human chat preferences, and tasks, such as RAG and tool calling. The model supports a context length of 128K tokens. This model fits on a single 8xH100 node for inference.
 
@@ -30,7 +30,7 @@ This model is ready for commercial use.
 
 For more details on how the model was trained, please see [this blog](https://developer.nvidia.com/blog/build-enterprise-ai-agents-with-advanced-open-nvidia-llama-nemotron-reasoning-models/).
 
-![Training Process](training_flowchart.png)
+![Training Flow](./training_flowchart.png)
 
 This model is part of the Llama Nemotron Collection. You can find the other model(s) in this family here: 
 
@@ -63,8 +63,8 @@ Developers designing AI Agent systems, chatbots, RAG systems, and other AI-power
 **Architecture Type:** Dense decoder-only Transformer model  
 **Network Architecture:** Llama-3.1-405B-Instruct, customized through Neural Architecture Search (NAS)
 
-**This model was developed based on Llama-3.1-405B-Instruct 
-** This model has 253B model parameters. 
+**This model was developed based on Llama-3.1-405B-Instruct <br> 
+** This model has 253B model parameters. <br> 
 
 The model is a derivative of Llama 3.1-405B-Instruct, using Neural Architecture Search (NAS). The NAS algorithm results in non-standard and non-repetitive blocks. This includes the following: 
 
@@ -93,9 +93,9 @@ Llama-3.1-Nemotron-Ultra-253B-v1 is a general purpose reasoning and chat model i
 ## Software Integration  
 - **Runtime Engine:** Transformers  
 - **Recommended Hardware Microarchitecture Compatibility:**   
-- NVIDIA Hopper  
-- NVIDIA Ampere  
--**\[Preferred/Supported\] Operating System(s):** Linux \<br\>
+  - NVIDIA Hopper  
+  - NVIDIA Ampere  
+- **Preferred Operating System(s):** Linux 
 
 ## Model Version  
 1.0 (3/18/2025)
@@ -172,11 +172,11 @@ print(pipeline([{"role": "system", "content": f"detailed thinking {thinking}"},{
 - Transformers
 
 **Test Hardware:**  
-      - BF16:   
-- 8x NVIDIA H100-80GB   
-- 4x NVIDIA B100  
-     - FP 8  
-- 4x NVIDIA H100-80GB  
+- BF16:   
+  - 8x NVIDIA H100-80GB   
+  - 4x NVIDIA B100  
+- FP 8  
+  - 4x NVIDIA H100-80GB  
    
 ## Training and Evaluation Datasets
 
@@ -322,86 +322,6 @@ User Prompt Template:
 
 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. 
 
-For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards \[Insert Link to Model Card++ here\].
-
-Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
-
-## Subcards:
-
-# **Bias**
-
-|Field:|Response:|  
-|:---:|:---:|  
-|Participation considerations from adversely impacted groups (protected classes) in model design and testing:|None|  
-|Measures taken to mitigate against unwanted bias:|None|
-
-| Field: | Response: |
-| :---- | :---- |
-| Participation considerations from adversely impacted groups [(protected classes)](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None |
-| Measures taken to mitigate against unwanted bias: | None |
-
-# **Explainability**
-
-|Field:|Response:|  
-|:---:|:---:|  
-|Intended Application(s) & Domain(s):| Text generation, reasoning, summarization, and question answering. |  
-|Model Type: |Text-to-text transformer |  
-|Intended Users:|This model is intended for developers, researchers, and customers building/utilizing LLMs, while balancing accuracy and efficiency.|  
-|Output:|Text String(s)|  
-|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|  
-|Technical Limitations:| The model was trained on data that contains toxic language, unsafe content, 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.\<br/\>The model demonstrates weakness to alignment-breaking attacks. Users are advised to deploy language model guardrails alongside this model to prevent potentially harmful outputs.\<br/\>The Model may generate answers that are inaccurate, omit key information, or include irrelevant or redundant text.|  
-|Verified to have met prescribed 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 is more susceptible to prompt injection and jailbreaking in various forms as a result of its instruction tuning. This means that the model should be paired with additional rails or system filtering to limit exposure to instructions from malicious sources  -- either directly or indirectly by retrieval (e.g. via visiting a website)  -- as they may yield outputs that can lead to harmful, system-level outcomes up to and including remote code execution in agentic systems when effective security controls including guardrails are not in place.\<br/\>The 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.|  
-|End User License Agreement:| 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/). Additional Information: \[Llama 3.1 Community License Agreement\](https://www.llama.com/llama3\_1/license/). Built with Llama. |
-
-| Field: | Response: |
-| :---- | :---- |
-| Intended Application(s) & Domain(s): | Text generation, reasoning, summarization, and question answering.  |
-| Model Type: | Text-to-text transformer |
-| Intended Users: | This model is intended for developers, researchers, and customers building/utilizing LLMs, while balancing accuracy and efficiency. |
-| Output: | Text String(s) |
-| 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. |
-| Technical Limitations: | The model was trained on data that contains toxic language, unsafe content, 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. The model demonstrates weakness to alignment-breaking attacks. Users are advised to deploy language model guardrails alongside this model to prevent potentially harmful outputs. The Model may generate answers that are inaccurate, omit key information, or include irrelevant or redundant text. |
-| Verified to have met prescribed 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 is more susceptible to prompt injection and jailbreaking in various forms as a result of its instruction tuning. This means that the model should be paired with additional rails or system filtering to limit exposure to instructions from malicious sources  -- either directly or indirectly by retrieval (e.g. via visiting a website)  -- as they may yield outputs that can lead to harmful, system-level outcomes up to and including remote code execution in agentic systems when effective security controls including guardrails 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 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. |
-| End User License Agreement: | 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/). Additional Information: \[Llama 3.1 Community License Agreement\](https://www.llama.com/llama3\_1/license/). Built with Llama. |
-
-# **Privacy**
-
-|Field:|Response:|  
-|:---:|:---:|  
-|Generatable or Reverse engineerable personally-identifiable information?|None|  
-|Was consent obtained for any personal data used?|None Known|  
-|Personal data used to create this model?|None Known|  
-|How often is dataset reviewed?|Before Release|  
-|Is there provenance for all datasets used in training?|Yes|  
-|Does data labeling (annotation, metadata) comply with privacy laws?|Yes|  
-|Applicable NVIDIA Privacy Policy|https://www.nvidia.com/en-us/about-nvidia/privacy-policy/|
-
-| Field: | Response: |
-| :---- | :---- |
-| Generatable or Reverse engineerable personal data? | None |
-| Was consent obtained for any personal data used? | None Known   |
-| Personal data used to create this model? | None Known   |
-| How often is dataset reviewed? | Before Release |
-| Is there provenance for all datasets used in training? | Yes |
-| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |
-| Applicable NVIDIA Privacy Policy | [https://www.nvidia.com/en-us/about-nvidia/privacy-policy/](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) |
-
-# **Safety & Security**
-
-|Field:|Response:|  
-|:---:|:---:|  
-|Model Application(s):|Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning|  
-|Describe life critical application (if present):|None Known (please see referenced Known Risks in the Explainability subcard).|  
-|Use Case Restrictions:|Abide by the \[NVIDIA Open Model License\](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: \[Llama 3.1 Community License Agreement\](https://www.llama.com/llama3\_1/license/). Built with Llama.|  
-|Model and Dataset Restrictions:|The Principle of least privilege (PoLP) is applied limiting access for dataset generation.  Restrictions enforce dataset access during training, and dataset license constraints adhered to. Model checkpoints are made available on Hugging Face and NGC, and may become available on cloud providers' model catalog.|
-
-| Field: | Response: |
-| :---- | :---- |
-| Model Application(s): | Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning |
-| Describe life critical application (if present): | None Known (please see referenced Known Risks in the Explainability subcard). |
-| Use Case Restrictions: | Abide by the  \[NVIDIA Open Model License\](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: \[Llama 3.1 Community License Agreement\](https://www.llama.com/llama3\_1/license/). Built with Llama.  |
-| Model and Dataset Restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation.  Restrictions enforce dataset access during training, and dataset license constraints adhered to. Model checkpoints are made available on Hugging Face and NGC, and may become available on cloud providers' model catalog. |
+For more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](./EXPLAINABILITY.md), [Bias](./BIAS.md), [Safety & Security](./SAFETY&SECURITY.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/).