Text Generation
Transformers
Safetensors
PyTorch
nvidia
nemotron-3
latent-moe
mtp
conversational
8-bit precision
danielhanchen's picture
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68e288b verified
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 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: Age, Disability Status, Gender Identity, Nationality, Physical Appearance, Ethnicity, Socioeconomic Status, Sexual Orientation, Religion
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 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 may generate answers that may be inaccurate, omit key information, include irrelevant or redundant text, or produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.
Licensing: Use of this model is governed by the OpenMDW License Agreement, version 1.1 (OpenMDW-1.1).