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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 |
+| Bias Metric (If Measured): | [BBQ Accuracy Scores in Ambiguous Contexts](https://github.com/nyu-mll/BBQ/) |
+| Which characteristic (feature) show(s) the greatest difference in performance?: | The model shows high variance in the characteristics when it is used with a high temperature. |
+| Measures taken to mitigate against unwanted bias: | Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) employed to calibrate the model’s reasoning capabilities to maintain logical consistency and appropriate complexity when interacting with or interpreting data from diverse age demographics. |
+| If using internal data, description of methods implemented in data acquisition or processing, if any, to address the prevalence of identifiable biases in the training, testing, and validation data: | The training datasets contain a large amount of synthetic data generated by LLMs. We manually curated prompts. |
+| Tools used to assess statistical imbalances and highlight patterns that may introduce bias into AI models: | [BBQ](https://github.com/nyu-mll/BBQ/) |
+| Tools used to assess statistical imbalances and highlight patterns that may introduce bias into AI models: | These datasets, such as web-scraped finance reasoning data derived from SEC EDGAR filings, science and math problem datasets, OpenResearcher/source-document datasets, Common Crawl, CC-News, Wikimedia, and long-context document datasets, do not collectively or exhaustively represent all demographic groups (and proportionally therein). For instance, these datasets do not contain explicit mentions of demographic classes such as age, gender, or ethnicity in approximately 97% to 99.9% of finance reasoning samples and in over 85% of samples across the broader assessed datasets. In the subset where such terms are present, these datasets contain notable representational skews. For example, ethnicity mentions are often dominated by Middle Eastern contexts (found in finance documents) or "White," "Two or more," and "Black or African American" as the most frequent ethnic identifiers, while references categorized as male-only significantly outnumber those categorized as female-only. Furthermore, gender is explicitly mentioned in approximately 12% of samples across the broader dataset assessment, yet in only 0.9% of finance-specific samples. Dataset-level results vary by source type, with long-context/source-document datasets containing higher explicit demographic mention rates compared to certain web-scraped sources. To mitigate these imbalances, we recommend considering evaluation techniques such as bias audits, fine-tuning with demographically balanced datasets, and mitigation strategies such as counterfactual data augmentation to align with the desired model behavior. This evaluation used a 3,000-sample subset per dataset, identified as the optimal threshold for maximizing embedder accuracy. |
+| Unwanted Bias Testing: | Constrained to English-language inputs. Multi-lingual parity is not currently claimed or guaranteed. |

explainability.md

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+| Field | Response |
+| :---- | :---- |
+| Intended Task/Domain: | Text generation, reasoning, and chat |
+| Model Type: | Text-to-text Mamba2-Transformer Hybrid |
+| Intended Users: | Generative AI creators working with conversational AI models and image content. |
+| Output: | Text |
+| Tools used to evaluate datasets to identify synthetic data and ensure data authenticity. | We used a Gemma-3 4B-based filtering model fine-tuned on [Nemotron Content Safety Dataset v2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) to ensure the quality of synthetic data. |
+| 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. |
+| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable |
+| Technical Limitations & Mitigation: | This model performs particularly well in instruction following regimes, as such may be strongly influenced by untrusted inputs and should be paired with appropriate guardrails and data filtering to better align use-case behaviors when exposed to such data. |
+| Verified to have met prescribed NVIDIA 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 may be influenced by untrusted inputs (prompt injection, indirect prompt injection, jailbreaking, web search, etc.) as a result of its instruction tuning that may degrade safety alignment and other training efforts. This model should be paired with additional guardrails and data filtering to limit exposure to instructions from malicious sources. Bypassing of safety alignment, system guardrails, and filters may allow harmful outcomes up to and including remote code execution in some agentic systems when effective security controls 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 generate and amplify harmful, biased, or otherwise unsafe content reinforcing these biases and return toxic responses especially when prompted with toxic prompts. The model may also 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 may exhibit self-anthropomorphism (e.g., displaying human-like characteristics in dialogue, such as expressing preferences and emotions). In integrated system contexts, the model could potentially be exploited to access or disclose information beyond the model’s intended permissions or scope of operation.|
+| Licensing: | Use of this model is governed by the [OpenMDW License Agreement, version 1.1 (OpenMDW-1.1).](https://raw.githubusercontent.com/OpenMDW/OpenMDW/refs/heads/main/1.1/LICENSE.OpenMDW-1.1) |

privacy.md

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+| Privacy Information |
+| :--- |
+| Nemotron 3.5 Lightning was trained on large-scale publicly available data that may contain images, audio-video, and text relating to people. NVIDIA collected and used this data in compliance with applicable data protection and privacy laws. This model was not designed to derive insights or otherwise learn from any personal data contained in the datasets. |
+| NVIDIA uses a combination of filters, data minimization techniques, and other guardrails to help prevent personal data from being recited by our models. We employ automated tools and data processing techniques during pre-training or training to identify and filter certain categories of personal data. |
+| Please review NVIDIA's [Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/) for more information. |

safety.md

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+| Field | Response |
+| :---- | :---- |
+| Model Application Field(s): | Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning, Customer Service |
+| Describe the life critical impact (if present). | Not Applicable |
+| Description of methods implemented in data acquisition or processing, if any, to address other types of potentially harmful data in the training, testing, and validation data: | We used a guard model for content safety to exclude potentially harmful data from training.  |
+| Description of any methods implemented in data acquisition or processing, if any, to address illegal or harmful content in the training data, including, but not limited to, child sexual abuse material (CSAM) and non-consensual intimate imagery (NCII) | We used a Gemma-3 4B-based guard model trained on [Nemotron Content Safety Dataset v2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) for content safety to exclude potentially illegal or harmful content from the training. |
+| Use Case Restrictions: | Use of this model is governed by the [OpenMDW License Agreement, version 1.1 (OpenMDW-1.1).](https://raw.githubusercontent.com/OpenMDW/OpenMDW/refs/heads/main/1.1/LICENSE.OpenMDW-1.1) |
+| 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. |
+| This AI model was developed based on our policies to ensure responsible data handling and risk mitigation. The datasets used for training have been scanned for harmful content and illegal content, consistent with our policies including scanning for Child Sexual Abuse Material (CSAM). Ongoing review and monitoring mechanisms are in place based on our policies and to maintain data integrity. | True. We use [Nemotron Content Safety Dataset V2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) and an internal safety dataset specialized for minority sexuality for content safety evaluation to ensure the safety of this model. |
+| Safety Testing: | Constrained to English-language inputs. Multi-lingual parity is not currently claimed or guaranteed. |