SuperQAI2050/STEM_Code

NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16

<div align="center" style="line-height: 1;"> <a href="https://build.nvidia.com/nvidia/nemotron-3-nano-30b-a3b" target="blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/🤖Chat-Nemotron3Nano-536af5?color=76B900&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://arxiv.org/abs/2512.20848" target="blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/📝Paper-Read Now!-536af5?color=76B900&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/collections/nvidia/nemotron-pre-training-datasets" target="blank" style="margin: 2px;"> <img alt="Pre-Training Datasets" src="https://img.shields.io/badge/🗄️Pre--TrainingDatasets-AvailableHere-76B900?logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> </div> <div align="center" style="line-height: 1;"> <a href="https://developer.nvidia.com/nemotron" target="blank" style="margin: 2px;"> <img alt="Homepage" src="https://img.shields.io/badge/🏠Nemotron Developer Page-Learn More Here!-536af5?color=76B900&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://discord.gg/9xpKQtVvrk" target="blank" style="margin: 2px;"> <img alt="Homepage" src="https://img.shields.io/badge/Discord-NVIDIA%20AI%20Developer-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> </a> </div>

<div align="center" style="line-height: 1;"> <a href="https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/" style="margin: 2px;"> <img alt="License" src="https://img.shields.io/badge/License-NVIDIA Nemotron Open Model License-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/> </a> </div>

[image]

Model Overview

Model Developer: NVIDIA Corporation

Model Dates:

September 2025 \- December 2025

Data Freshness:

The pre-training data has a cutoff date of June 25, 2025.

Description

NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16 is a base large language model (LLM) trained from scratch by NVIDIA, with the next token prediction loss. It provides a good starting point for instruction fine-tuning.

This model is ready for commercial use.

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.

License/Terms of Use

GOVERNING TERMS: Use of this model is governed by the NVIDIA Nemotron Open Model License Agreement.

Base Benchmark Evaluations

We evaluated our model on the following benchmarks:

All evaluation results were collected via Nemo Evaluator SDK and LM Evaluation Harness. The open source container on LM Evaluation Harness packaged via NVIDIA's Nemo Evaluator SDK used for evaluations can be found here. A reproducibility tutorial along with all configs can be found in Nemo Evaluator SDK examples.

Deployment Geography: Global

Use Case

This model is intended for developers and researchers building instruction-following LLMs.

Supported languages include: English, Spanish, French, German, Japanese, Italian, Chinese, Arabic, Hebrew, Hindi, Korean, Czech, Danish, Dutch, Finnish, Polish, Portuguese, Thai, Swedish, and Russian.

Release Date:

December 15, 2025 via Hugging Face

Reference(s)

• —NVIDIA Nemotron 3 model family on Hugging Face
• —NVIDIA Nemotron 2 model family on Hugging Face
• —NVIDIA Nemotron 3 White Paper

Model Architecture

• —Architecture Type: Mamba2-Transformer Hybrid Mixture of Experts (MoE)
• —Network Architecture: Nemotron Hybrid MoE

• —Number of model parameters: 30B

Training Methodology

Stage 1: Pre-Training

• —NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16 model was pre-trained using crawled and synthetic code, math, science, and general knowledge data. All datasets are disclosed in the Training, Testing, and Evaluation Datasets section of this document. Major portions of the pre-training corpus are released in the Nemotron-Pre-Training-Datasets collection.
• —Software used for pre-training: Megatron-LM

The end-to-end training recipe is available in the NVIDIA Nemotron Developer Repository. Evaluation results can be replicated using the NeMo Evaluator SDK. More details on the datasets and synthetic data generation methods can be found in the technical report NVIDIA Nemotron 3 Nano.

Input

• —Input Type(s): Text

• —Input Format(s): String

• —Input Parameters: One-Dimensional (1D): Sequences

• —Maximum input size: 128K tokens

• —Other Properties Related to Input: Supported languages include: English, Spanish, French, German, Japanese, Italian, Chinese, Arabic, Hebrew, Hindi, Korean, Czech, Danish, Dutch, Finnish, Polish, Portuguese, Thai, Swedish, and Russian.

Output

• —Output Type(s): Text

• —Output Format: String

• —Output Parameters: One-Dimensional (1D): Sequences

• —Maximum output size: 128K tokens

Our AI models are designed and 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): NeMo 25.11.01
• —Supported Hardware Microarchitecture Compatibility: NVIDIA H100-80GB, NVIDIA A100
• —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.

Use it with Transformers

The snippet below shows how to use this model with Huggingface Transformers (tested on version 4.57.3). We recommend using NeMo Framework 25.11.01 to ensure all required libraries are available.

Please note that the model supports up to a 1M context size, although the default context size in the Hugging Face configuration is 256k due to higher VRAM requirements.

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

prompt = "The capital of France is"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=32,
    do_sample=False,
    eos_token_id=tokenizer.eos_token_id
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Model Version(s)

• —v1.0

Training, Testing, and Evaluation Datasets

Data Modality: Text The total size: 10,648,823,153,919 Tokens Total number of datasets: 141 Dataset partition: Training \[100%\], testing \[0%\], validation \[0%\] Time period for training data collection: 2013 to May 1, 2025 Time period for testing data collection: 2013 to May 1, 2025 Time period for validation data collection: 2013 to May 1, 2025 Data Collection Method by dataset: Hybrid: Automated, Human, Synthetic

NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16 is pre-trained on a large corpus of high-quality curated and synthetically-generated data. It is trained in the English language, as well as 19 other languages and 43 programming languages. 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. The model was trained for approximately 25 trillion tokens.

Alongside the model, we release our final pre-training data, as outlined in this section. For ease of analysis, there is a sample set that is ungated. For all remaining code, math and multilingual data, gating and approval is required, and the dataset is permissively licensed for model training purposes.

More details on the datasets and synthetic data generation methods can be found in the technical report NVIDIA Nemotron 3 Nano.

Public dataset

Private Non-publicly Accessible Datasets of Third Parties

Private Non-publicly Accessible Datasets by NVIDIA

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 described in the Nemotron-CC paper. 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 (for additional details, refer to the technical report).

NVIDIA-Sourced Synthetic Datasets

Training Dataset

We use a considerable amount of synthetic data. Out of 10.6 trillion tokens, 3,534,013,958,278 tokens are synthetically generated.

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. Additionally, we used data from Wikipedia and FineWeb-2 (Penedo et al., 2025\) for these fifteen languages.

We collect a total of 922,476,782,017 tokens of code in 43 different languages.

Evaluation Dataset

• —Data Collection Method by dataset: Hybrid: Human, Synthetic
• —Labeling Method by dataset: Hybrid: Automated, Human, Synthetic

Inference

• —Engines: HF, vLLM, TRT-LLM

• —Test Hardware: NVIDIA A100 80GB, H100 80GB

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. When downloaded or used in accordance with our Trustworthy AI terms of service, 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.

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 & Security.

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

Please report security vulnerabilities or NVIDIA AI Concerns here.

Citation

@misc{nvidia_nemotron_nano_v3_2025,
  title  = {{Nemotron 3 Nano}: Open, Efficient Mixture-of-Experts Hybrid {Mamba}-{Transformer} Model for {Agentic} Reasoning},
  author = {{NVIDIA}},
  year   = {2025},
  url    = {https://arxiv.org/abs/2512.20848},
  note   = {Technical report}
}