Add model card and Bias/Explainability/Privacy/Safety subcards (MR !102 f73d94f)

README.md

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+---
+library_name: transformers
+license: other
+license_name: openmdw-1.1
+license_link: >-
+  https://openmdw.ai/license/1-1/
+pipeline_tag: text-generation
+language:
+- en
+base_model:
+- nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
+base_model_relation: finetune
+datasets:
+- nvidia/Nemotron-Math-Proofs-v3-RL
+tags:
+- nvidia
+- pytorch
+- nemotron-3
+- latent-moe
+- mtp
+track_downloads: true
+---
+# Nemotron-3-Labs-Ultra-Math-RL
+
+*Referred to as Nemotron-3-Ultra-RL in the technical report.*
+
+## Description
+
+Nemotron-3-Labs-Ultra-Math-RL is a decoder-only transformer language model specialized for mathematical reasoning, trained to solve difficult mathematical problems and identify mistakes in proofs, and deployed as part of an ensemble system that achieved a gold-medal level score at the International Mathematical Olympiad 2026. Full details can be found at [Accompanying Tech Report](https://github.com/NVIDIA-NeMo/Skills/blob/main/recipes/nemotron-imo-tts/paper.pdf).
+Nemotron-3-Labs-Ultra-Math-RL was developed by NVIDIA as a part of Nemotron.
+
+This model is ready for commercial and non-commercial use.
+
+### License/Terms of Use
+
+Governing Download Terms: Use of this model is governed by the [OpenMDW License Agreement, version 1.1](https://raw.githubusercontent.com/OpenMDW/OpenMDW/refs/heads/main/1.1/LICENSE.OpenMDW-1.1) (OpenMDW-1.1).
+
+### Deployment Geography
+
+Global
+
+### Use Case
+
+Researchers and developers focused on AI-driven mathematical reasoning and proof verification, aiming to advance open models for solving complex math problems and improving reasoning capabilities.
+
+### Release Date
+
+**HuggingFace:** September 3, 2026 via https://huggingface.co/collections/nvidia/nemotron-labs-imo-2026
+
+## Reference(s)
+
+* [Accompanying Tech Report](https://github.com/NVIDIA-NeMo/Skills/blob/main/recipes/nemotron-imo-tts/paper.pdf)
+* [nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16)
+* [nvidia/Nemotron-Math-Proofs-v3-RL](https://huggingface.co/datasets/nvidia/Nemotron-Math-Proofs-v3-RL)
+* [nvidia/Nemotron-IMO-Bench](https://huggingface.co/datasets/nvidia/Nemotron-IMO-Bench)
+* Inference pipeline and submitted proofs: https://github.com/NVIDIA-NeMo/Skills/tree/main/recipes/nemotron-imo-tts
+* RL training recipe: https://github.com/NVIDIA-NeMo/RL/blob/imo-26-ultra-v3/docs/guides/nemotron-3-ultra-imo.md
+
+## Model Architecture
+
+**Architecture Type:** Mamba2-Transformer Hybrid Latent Mixture of Experts (LatentMoE) with Multi-Token Prediction (MTP)
+**Network Architecture:** Nemotron Hybrid LatentMoE
+**This model was developed based on [nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16).**
+**Number of model parameters:** 550B Total / 55B Active
+
+## Input
+
+**Input Type(s):** Text
+**Input Format(s):** String
+**Input Parameters:** One-Dimensional (1D)
+**Other Properties Related to Input:** Maximum context length up to 1M tokens
+
+## Output
+
+**Output Type(s):** Text
+**Output Format:** String
+**Output Parameters:** One-Dimensional (1D)
+**Other Properties Related to Output:** Maximum context length up to 1M tokens
+
+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.
+
+## Software Integration
+
+**Runtime Engine(s):** vLLM
+**Supported Hardware Microarchitecture Compatibility:**
+* NVIDIA Blackwell
+* NVIDIA Hopper
+
+**Supported Operating System(s):** Linux
+
+The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
+
+## Model Version(s)
+
+Nemotron-3-Labs-Ultra-Math-RL v1
+
+## Deployment
+
+The Ultra BF16 checkpoint is a frontier-scale model. The minimum recommended hardware is:
+
+* **Single-node:** 8× B200 (≈1.5 TB aggregate HBM — fits BF16 weights plus KV cache with headroom)
+* **Multi-node:** ≥8 GPUs across H100 / H200 / GB200 / GB300, orchestrated with Ray v2
+
+For more detailed information, please see the [nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16) model card and [this cookbook](https://github.com/NVIDIA-NeMo/Nemotron/blob/main/usage-cookbook/Nemotron-3-Ultra/vllm_cookbook.ipynb).
+
+Recommended container: `vllm/vllm-openai:v0.22.0`.
+
+```shell
+export MODEL_CKPT=PATH/TO/MODEL/CHECKPOINT
+```
+
+**8× B200 single-node deployment:**
+
+```shell
+docker run -d --name nemotron-ultra-vllm \
+  --gpus all \
+  --ipc=host \
+  --network=host \
+  --shm-size=16g \
+  --ulimit memlock=-1 \
+  --ulimit stack=67108864 \
+  -v $MODEL_CKPT:/model:ro \
+  -e VLLM_WORKER_MULTIPROC_METHOD=spawn \
+  -e SAFETENSORS_FAST_GPU=1 \
+  -e NVIDIA_TF32_OVERRIDE=1 \
+  -e VLLM_LOGGING_LEVEL=INFO \
+  vllm/vllm-openai:v0.22.0 \
+  /model \
+  --host 0.0.0.0 \
+  --port 8000 \
+  --served-model-name nvidia/Nemotron-3-Labs-Ultra-Math-RL \
+  --trust-remote-code \
+  --tensor-parallel-size 8 \
+  --enable-expert-parallel \
+  --dtype bfloat16 \
+  --max-model-len 262144 \
+  --gpu-memory-utilization 0.90 \
+  --max-num-seqs 16 \
+  --max-num-batched-tokens 32768 \
+  --enable-chunked-prefill \
+  --enable-prefix-caching \
+  --reasoning-parser nemotron_v3 \
+  --enable-auto-tool-choice \
+  --tool-call-parser qwen3_coder \
+  --mamba-ssm-cache-dtype float16 \
+  --mamba-backend flashinfer \
+  --enable-mamba-cache-stochastic-rounding \
+  --mamba-cache-philox-rounds 5 \
+  --speculative-config '{"method": "nemotron_h_mtp", "num_speculative_tokens": 5}' \
+  --model-loader-extra-config '{"enable_multithread_load": true, "num_threads": 96}'
+```
+
+Context length defaults to 256k above. To use up to 1M, set `VLLM_ALLOW_LONG_MAX_MODEL_LEN=1` and `--max-model-len 1048576`.
+
+## Training, Testing, and Evaluation Datasets
+
+### Training Dataset
+
+**Data Modality:** Text
+
+**Text Training Data Size:** 9,597 proof-generation prompts (50,430,007 bytes)
+**Data Collection Method by dataset:** Hybrid: Automated, manually-collected, Synthetic
+**Labeling Method by dataset:** Hybrid: Automated, manually-labeled, Synthetic
+**Properties (Quantity, Dataset Descriptions, Sensor(s)):** Nemotron-Math-Proofs-v3-RL is a long-form mathematical reasoning dataset for reinforcement learning. The release contains 9,597 proof-generation prompts. The dataset uses [NeMo Gym](https://github.com/NVIDIA-NeMo/gym)-compatible, single-turn user prompts derived from hard proof problems in the [AoPS](https://artofproblemsolving.com/) subset of [nvidia/Nemotron-Math-Proofs-v1](https://huggingface.co/datasets/nvidia/Nemotron-Math-Proofs-v1). The `train` split asks the policy to produce a rigorous solution and self-evaluation. Policy responses and realized rewards are generated during training and are not stored in the file. See [nvidia/Nemotron-Math-Proofs-v3-RL](https://huggingface.co/datasets/nvidia/Nemotron-Math-Proofs-v3-RL).
+
+### Testing Dataset
+
+**Data Collection Method by dataset:** Hybrid: Automated, manually-collected, Synthetic<br>
+**Labeling Method by dataset:** Hybrid: Automated, manually-labeled, Synthetic<br>
+**Properties (Quantity, Dataset Descriptions, Sensor(s)):** This corpus comprises only benchmarks for assessing the quality of mathematical proof generation and verification.
+
+### Evaluation Dataset
+
+**Benchmark Score:** As part of an ensemble system, this model achieved a gold-medal-level score at the International Mathematical Olympiad 2026. Additional evaluation results are available in the [Accompanying Tech Report](https://github.com/NVIDIA-NeMo/Skills/blob/main/recipes/nemotron-imo-tts/paper.pdf).
+
+**Data Collection Method by dataset:** Hybrid: Automated, manually-collected, Synthetic<br>
+**Labeling Method by dataset:** Hybrid: Automated, manually-labeled, Synthetic<br>
+**Properties (Quantity, Dataset Descriptions, Sensor(s)):** This corpus comprises only benchmarks for assessing the quality of mathematical proof generation and verification.
+
+## Inference
+
+**Acceleration Engine:** vLLM, PyTorch
+**Hardware Requirements (GPU Architecture, Model):**
+* NVIDIA Ampere - A100 80 GB
+* NVIDIA Hopper - H100
+
+## 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. 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](./explainability.md), [Bias](./bias.md), [Safety & Security](./safety.md), and [Privacy](./privacy.md) Subcards.
+Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).

bias.md

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+Field                                                                                               |  Response
+:---------------------------------------------------------------------------------------------------|:---------------
+Participation considerations from adversely impacted groups [protected classes](https://calcivilrights.ca.gov/disputeresolution/protected-characteristics/) in model design and testing:  |  [None]
+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.
+Bias Metric (If Measured):                                                   |  BBQ Accuracy Scores in Ambiguous Contexts . https://github.com/nyu-mll/BBQ/

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
+List the groups (or group characteristics) for which this was tested to produce comparable outcomes:  |  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 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).

privacy.md

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+Privacy Information
+Nemotron-3-Labs-Ultra-Math-SFT/RL model 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 for more information. https://www.nvidia.com/en-us/about-nvidia/privacy-policy/

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 
+Use Case Restrictions:                              |  Use of this model is governed by the OpenMDW License Agreement, version 1.1 (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.