add mc++ subcards

bias.md

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+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 

explanability.md

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+Field                                                                                                  |  Response
+:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------
+Intended Application & Domain:                                                                         |  Response Customization in Large Language Model Development
+Model Type:                                                                                            |  Text-to-Float Transformer
+Intended User:                                                                                         |  Developers  customizing model response across different applications and domains.
+Output:                                                                                                |  Float
+Describe how the model works:                                                                          |  Generates score based on the quality of the last assistant response in conversation 
+Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of:  |  Not Applicable
+Verified to have met prescribed quality standards:  |  Yes
+Technical Limitations: | This model may not work well beyond input sequence length of 4, 096 tokens.
+Performance Metrics:                                                                                   |  Throughput and Latency
+Potential Known Risks:                                                                                 |  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.
+Licensing:                                                                                             |  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.

privacy.md

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+
+Field                                                                                                                              |  Response
+:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------
+Generatable or reverse engineerable personal data?                                                     |  No
+Personal data used to create this model?                                                                                       |  None Known.  For data included in the base Llama 3.3 model, [reference the Llama 3.3 model card.](https://github.com/meta-llama/llama-models/blob/main/models/llama3_3/MODEL_CARD.md)
+How often is the dataset reviewed (if applicable)?                                                                                                     |  Before Release
+Is there provenance for all datasets used in training?                                                                                                     |  Yes
+Does data labeling (annotation, metadata) comply with privacy laws? | Yes
+

safety.md

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+Field                                               |  Response
+:---------------------------------------------------|:----------------------------------
+Model Application(s):                               |  Conversation, Question Answering, Summarization
+Describe the life-critical impact (if present).   |  N/A
+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 3.3 Community License Agreement](https://www.llama.com/llama3_3/license/). Built with Llama. 
+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.