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Model: unsloth/NVIDIA-Nemotron-3-Nano-4B-GGUF Source: Original Platform
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README.md
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README.md
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---
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license: other
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license_name: nvidia-nemotron-open-model-license
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license_link: >-
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https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/
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pipeline_tag: text-generation
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language:
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- en
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tags:
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- nvidia
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- pytorch
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base_model: nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
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track_downloads: true
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---
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# GGUFs still converting, please wait until live.
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# Read our How to [Run Nemotron 3 Nano Guide!](https://docs.unsloth.ai/models/nemotron-3)
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<div>
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<p style="margin-top: 0;margin-bottom: 0;">
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<em>See <a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0 GGUFs</a> for our quantization benchmarks.</em>
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</p>
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<div style="display: flex; gap: 5px; align-items: center; ">
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<a href="https://github.com/unslothai/unsloth/">
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<img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
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</a>
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<a href="https://discord.gg/unsloth">
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<img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
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</a>
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<a href="https://docs.unsloth.ai/models/nemotron-3">
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<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
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</a>
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</div>
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</div>
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- Note `<think>` and `</think>` are separate tokens, so use `--special` if needed.
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- You can also fine-tune the model with Unsloth.
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---
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<div align="center" style="line-height: 1;">
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<a href="https://huggingface.co/collections/nvidia/nemotron-pre-training-datasets" target="_blank" style="margin: 2px;">
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<img alt="Pre-Training Datasets" src="https://img.shields.io/badge/🗄️_Pre--Training_Datasets-Available_Here-76B900?logoColor=white" style="display: inline-block; vertical-align: middle;"/>
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</a>
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<a href="https://huggingface.co/collections/nvidia/nemotron-post-training-v3" target="_blank" style="margin: 2px;">
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<img alt="Post-Training Datasets" src="https://img.shields.io/badge/🗄️_Post--Training_Datasets-Available_Here-76B900?logoColor=white" style="display: inline-block; vertical-align: middle;"/>
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</a>
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</div>
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<div align="center" style="line-height: 1;">
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<a href="https://developer.nvidia.com/nemotron" target="_blank" style="margin: 2px;">
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<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;"/>
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</a>
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<a href="https://discord.gg/9xpKQtVvrk" target="_blank" style="margin: 2px;">
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<img alt="Discord" src="https://img.shields.io/badge/Discord-NVIDIA%20AI%20Developer-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
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</a>
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</div>
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<div align="center" style="line-height: 1;">
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<a href="https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/" style="margin: 2px;">
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<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;"/>
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</a>
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</div>
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# NVIDIA-Nemotron-3-Nano-4B-BF16
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**Model Developer:** NVIDIA Corporation
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**Model Dates:**
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Dec 2025 \- Jan 2026
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**Data Freshness:**
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September 2024
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The pretraining data has a cutoff date of September 2024\.
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## Model Overview
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NVIDIA-Nemotron-3-Nano-4B-BF16 is a small language model (SLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. It responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be controlled via a system prompt. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks.
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The model has been compressed from NVIDIA-Nemotron-Nano-9B-v2 using the Nemotron [Elastic](https://arxiv.org/pdf/2511.16664) framework. The details of the parent model NVIDIA-Nemotron-Nano-9B-v2 can be found in ([Nemotron-H tech report](https://arxiv.org/abs/2504.03624)). The model uses a hybrid architecture consisting primarily of Mamba-2 and MLP layers combined with just four Attention layers.
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The supported languages include: English. Improved using Qwen.
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This model is ready for commercial use.
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## License/Terms of Use
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Governing Terms: Use of this model is governed by the [NVIDIA Nemotron Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/).
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### Deployment Geography: Global
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### Use Case
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NVIDIA-Nemotron-3-Nano-4B is an edge-ready small language model intended for Agentic AI in edge platforms (Jetson Thor, GeForce RTX, DGX Spark). It targets key-uses including AI gaming NPCs (teammates / companions), local voice assistants (for devices, apps, and games), and IoT automation. It is to be used in English and coding languages.
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### Release Date: 3/16/2026
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Huggingface 3/16/2026 via [https://huggingface.co/](https://huggingface.co/)
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## References
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- [NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model](https://research.nvidia.com/labs/adlr/files/NVIDIA-Nemotron-Nano-2-Technical-Report.pdf)
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- [Nemotron Elastic: Towards Efficient Many-in-One Reasoning LLMs](https://arxiv.org/abs/2511.16664)
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- [NVIDIA Nemotron 3: Efficient and Open Intelligence](https://arxiv.org/abs/2512.20856)
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- [Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning](https://arxiv.org/abs/2512.20848)
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- [Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Super-Technical-Report.pdf)
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## Model Architecture
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- Architecture Type: Mamba2-Transformer Hybrid
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- Network Architecture: Nemotron-Hybrid
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- This model was compressed from [nvidia/NVIDIA-Nemotron-Nano-9B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2)
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- Number of model parameters 3.97 x 10^9
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## Input
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- Input Type(s): Text
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- Input Format(s): String
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- Input Parameters: One-Dimensional (1D): Sequences
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- Other Properties Related to Input: Context length up to 262K. Supported languages include English.
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## Output
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- Output Type(s): Text
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- Output Format: String
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- Output Parameters: One-Dimensional (1D): Sequences
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- Other properties Related to Output: Sequences up to 262K
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Our 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.
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## Software Integration
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- Runtime Engine(s): NeMo 25.07
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- Supported Hardware Microarchitecture Compatibility: NVIDIA A10G, NVIDIA H100-80GB, NVIDIA A100, GeForce RTX
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- Operating System(s): Linux
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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.
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### **Use it with Transformers**
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The snippet below shows how to use this model with Huggingface Transformers (tested on version 4.48.3).
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```
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# Load tokenizer and model
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tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-3-Nano-4B")
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model = AutoModelForCausalLM.from_pretrained(
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"nvidia/NVIDIA-Nemotron-3-Nano-4B",
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torch_dtype=torch.bfloat16,
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trust_remote_code=True,
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device_map="auto"
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)
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```
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```
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messages = [
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{"role": "system", "content": <system_prompt>},
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{"role": "user", "content": "Write a haiku about GPUs"},
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]
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tokenized_chat = tokenizer.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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outputs = model.generate(
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tokenized_chat,
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max_new_tokens=32,
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eos_token_id=tokenizer.eos_token_id
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)
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print(tokenizer.decode(outputs[0]))
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```
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temperature=1.0 and top\_p=0.95 are recommended for reasoning tasks, while temperature=0.6 and top\_p=0.95 are recommended for tool calling.
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If you’d like to use reasoning off, add enable\_thinking=False to apply\_chat\_template(). By default, enable\_thinking is set to be True.
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```
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messages = [
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{"role": "system", "content": <system_prompt>},
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{"role": "user", "content": "Write a haiku about GPUs"},
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]
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tokenized_chat = tokenizer.apply_chat_template(
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messages,
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tokenize=True,
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enable_thinking=False,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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outputs = model.generate(
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tokenized_chat,
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max_new_tokens=32,
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eos_token_id=tokenizer.eos_token_id
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)
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print(tokenizer.decode(outputs[0]))
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```
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### **Use it with vLLM**
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We need vllm\>=0.15.1 for this model. If you are on Jetson Thor or DGX Spark, please use [this vllm container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/vllm?version=26.02-py3).
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```
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pip install -U "vllm>=0.15.1"
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```
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Download the custom parser from the Hugging Face repository.
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```
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wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16/resolve/main/nano_v3_reasoning_parser.py
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```
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Launch a vLLM server using the custom parser.
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```
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vllm serve nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 \
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--served-model-name nemotron3-nano-4B-BF16\
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--max-num-seqs 8 \
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--tensor-parallel-size 1 \
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--max-model-len 262144 \
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--port 8000 \
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--trust-remote-code \
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--mamba_ssm_cache_dtype float32 \
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--enable-auto-tool-choice \
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--tool-call-parser qwen3_coder \
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--reasoning-parser-plugin nano_v3_reasoning_parser.py \
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--reasoning-parser nano_v3
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```
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Access the hosted API using a python client.
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```py
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from openai import OpenAI
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import asyncio
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from openai import AsyncOpenAI
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# NOTE: Streaming is preferred for better performance and resource efficiency.
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# It allows you to start processing responses as they arrive, reducing latency.
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# Synchronous example (non-streaming)
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client = OpenAI(
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api_key="your-nvapikey",
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base_url="base-url"
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)
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response = client.chat.completions.create(
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model="nemotron3-nano-4B-BF16",
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messages=[
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{
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"role": "user",
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"content": "Hello!"
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}
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],
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temperature=0.7,
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max_tokens=256,
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top_p=0.7,
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stream=false
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)
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print(response.choices[0].message.content)
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```
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### Use it with TRT-LLM
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Launch the model using TRT-LLM
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```shell
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docker run -v /home/root/.cache/huggingface/:/root/.cache/huggingface/ --rm --ulimit memlock=-1 --ulimit stack=67108864 --gpus=all --ipc=host --network host -d -e MODEL=NVIDIA-Nemotron-3-Nano-4B-BF16 -e HF_TOKEN=$HF_TOKEN nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc6 bash -c '
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cat > /tmp/extra-llm-api-config.yml <<EOF
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kv_cache_config:
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dtype: "auto"
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enable_block_reuse: false
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cuda_graph_config:
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max_batch_size: 32
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enable_padding: true
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disable_overlap_scheduler: true
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moe_config:
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backend: CUTLASS
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EOF
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trtllm-serve \
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NVIDIA-Nemotron-3-Nano-4B-BF16 \
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--host 0.0.0.0 \
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--port 8123 \
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--max_batch_size 32 \
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--extra_llm_api_options /tmp/extra-llm-api-config.yml '
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```
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Access the hosted endpoint using curl command.
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```shell
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curl http://localhost:8123/v1/chat/completions -H "Content-Type: application/json" -d '{
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"model": "NVIDIA-Nemotron-3-Nano-4B-BF16",
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"messages": [
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{
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"role": "user",
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"content": "Where is New York?"
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}
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],
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"max_tokens": 1024,
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"top_p": 1.0
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}' -w "\n"
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```
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## Model Version
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- v1.0
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##
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## Training, Testing, and Evaluation Datasets
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### Training datasets
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* Data Modality: Text
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* Text Training Data Size: More than 10 Trillion Tokens
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* Train/Test/Valid Split: We used 100% of the corpus for pre-training and relied on external benchmarks for testing.
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* Data Collection Method by dataset: Hybrid: Automated, Human, Synthetic
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* Labeling Method by dataset: Hybrid: Automated, Human, Synthetic
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**Properties:** The post-training corpus for NVIDIA-Nemotron-3-Nano-4B consists of English and multilingual text (German, Spanish, French, Italian, Korean, Portuguese, Russian, Japanese, Chinese and English). Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including code, legal, math, science, finance, and more. We also include a small portion of question-answering, and alignment style data to improve model accuracies. For several of the domains listed above we used synthetic data, specifically reasoning traces, from DeepSeek R1/R1-0528, Qwen3-235B-A22B, Nemotron 4 340B, Qwen2.5-32B-Instruct-AWQ, Qwen2.5-14B-Instruct, Qwen 2.5 72B.
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More details on the datasets and synthetic data generation methods can be found in the technical report [NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model](https://research.nvidia.com/labs/adlr/files/NVIDIA-Nemotron-Nano-2-Technical-Report.pdf) .
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## Public Datasets
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| Dataset | Collection Period |
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| :---- | :---- |
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| [Problems in Elementary Mathematics for Home Study](https://archive.org/details/AntonovVygodskyNikitinSankinProblemsInElementaryMathematicsForHomeStudyMir1982) | 4/23/2025 |
|
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| [GSM8K](https://github.com/openai/grade-school-math) | 4/23/2025 |
|
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| [PRM800K](https://github.com/openai/prm800k) | 4/23/2025 |
|
||||
| [CC-NEWS](https://commoncrawl.org/blog/news-dataset-available) | 4/23/2025 |
|
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| [Common Crawl](https://commoncrawl.org/) | 4/23/2025 |
|
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| [Wikimedia](https://dumps.wikimedia.org/) | 4/23/2025 |
|
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| [Bespoke-Stratos-17k](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k) | 4/23/2025 |
|
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| [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k) | 4/23/2025 |
|
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| [glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) | 4/23/2025 |
|
||||
| [APIGen Function-Calling](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) | 4/23/2025 |
|
||||
| [LMSYS-Chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) | 4/23/2025 |
|
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| [Open Textbook Library \- CC BY-SA & GNU subset](https://open.umn.edu/opentextbooks/textbooks/) and [OpenStax \- CC BY-SA subset](https://openstax.org/) | 4/23/2025 |
|
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| [Advanced Reasoning Benchmark](https://github.com/TheDuckAI/arb), [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k), [PRM800K](https://github.com/openai/prm800k), and [SciBench](https://github.com/mandyyyyii/scibench) | 4/23/2025 |
|
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| [FineWeb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) | 4/23/2025 |
|
||||
| [Court Listener](https://www.courtlistener.com/help/api/bulk-data/) | Legacy Download |
|
||||
| [peS2o](https://huggingface.co/datasets/allenai/peS2o) | Legacy Download |
|
||||
| [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math) | Legacy Download |
|
||||
| [BioRxiv](https://www.biorxiv.org/tdm) | Legacy Download |
|
||||
| [PMC Open Access Subset](https://pmc.ncbi.nlm.nih.gov/tools/openftlist/) | Legacy Download |
|
||||
| [OpenWebText2](https://openwebtext2.readthedocs.io/en/latest/) | Legacy Download |
|
||||
| [Stack Exchange Data Dump](https://archive.org/details/stackexchange) | Legacy Download |
|
||||
| [PubMed Abstracts](https://github.com/thoppe/The-Pile-PubMed) | Legacy Download |
|
||||
| [NIH ExPorter](https://exporter.nih.gov/ExPORTER_Catalog.aspx) | Legacy Download |
|
||||
| [arXiv](https://info.arxiv.org/help/bulk_data/index.html) | Legacy Download |
|
||||
| [BigScience Workshop Datasets](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#datasets) | Legacy Download |
|
||||
| [Reddit Dataset](https://files.pushshift.io/reddit/) | Legacy Download |
|
||||
| [SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR)](https://www.sec.gov/search-filings) | Legacy Download |
|
||||
| [Public Software Heritage S3](https://docs.softwareheritage.org/devel/swh-export/graph/dataset.html#summary-of-dataset-versions) | Legacy Download |
|
||||
| [The Stack](https://huggingface.co/datasets/bigcode/the-stack) | Legacy Download |
|
||||
| [mC4](https://huggingface.co/datasets/legacy-datasets/mc4) | Legacy Download |
|
||||
| [Advanced Mathematical Problem Solving](https://github.com/hendrycks/math?tab=readme-ov-file) | Legacy Download |
|
||||
| [MathPile](https://github.com/GAIR-NLP/MathPile/) | Legacy Download |
|
||||
| [NuminaMath CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) | Legacy Download |
|
||||
| [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/) | Legacy Download |
|
||||
| [FLAN](https://github.com/google-research/FLAN) | Legacy Download |
|
||||
| [Advanced Reasoning Benchmark](https://github.com/TheDuckAI/arb) | Legacy Download |
|
||||
| [SciBench](https://github.com/mandyyyyii/scibench) | Legacy Download |
|
||||
| [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) | Legacy Download |
|
||||
| [FinQA](https://finqasite.github.io/) | Legacy Download |
|
||||
| [Riddles](https://github.com/crawsome/riddles) | Legacy Download |
|
||||
| [Problems in Elementary Mathematics for Home Study](https://archive.org/details/AntonovVygodskyNikitinSankinProblemsInElementaryMathematicsForHomeStudyMir1982) | Legacy Download |
|
||||
| [MedMCQA](https://huggingface.co/datasets/openlifescienceai/medmcqa) | Legacy Download |
|
||||
| [Cosmos QA](https://huggingface.co/datasets/allenai/cosmos_qa) | Legacy Download |
|
||||
| [MCTest](https://huggingface.co/datasets/sagnikrayc/mctest) | Legacy Download |
|
||||
| [AI2's Reasoning Challenge](https://huggingface.co/datasets/ai2_arc) | Legacy Download |
|
||||
| [OpenBookQA](https://github.com/allenai/OpenBookQA) | Legacy Download |
|
||||
| [MMLU Auxiliary Train](https://huggingface.co/datasets/cais/mmlu/viewer/all/auxiliary_train) | Legacy Download |
|
||||
| [social-chemestry-101](https://huggingface.co/datasets/tasksource/social-chemestry-101) | Legacy Download |
|
||||
| [Moral Stories](https://huggingface.co/datasets/demelin/moral_stories) | Legacy Download |
|
||||
| [The Common Pile v0.1](https://huggingface.co/common-pile) | Legacy Download |
|
||||
| [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath) | Legacy Download |
|
||||
| [MegaMath](https://huggingface.co/datasets/LLM360/MegaMath) | Legacy Download |
|
||||
| [FastChat](https://github.com/lm-sys/FastChat) | 6/30/2025 |
|
||||
| [MultiverseMathHard](https://huggingface.co/datasets/Nexusflow/MultiverseMathHard) | 10/2/2025 |
|
||||
| [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym) | 10/2/2025 |
|
||||
| [WorkBench](https://github.com/olly-styles/WorkBench/tree/main/data/raw) | 10/2/2025 |
|
||||
| [WildChat-1M](https://huggingface.co/datasets/allenai/WildChat-1M) | 10/2/2025 |
|
||||
| [OpenCodeReasoning-2](https://huggingface.co/datasets/nvidia/OpenCodeReasoning-2) | 10/2/2025 |
|
||||
| [HelpSteer3](https://huggingface.co/datasets/nvidia/HelpSteer3) | 10/2/2025 |
|
||||
| [opc-sft-stage2](https://huggingface.co/datasets/OpenCoder-LLM/opc-sft-stage2) | 10/2/2025 |
|
||||
| [Big-Math-RL-Verified](https://huggingface.co/datasets/SynthLabsAI/Big-Math-RL-Verified) | 10/2/2025 |
|
||||
| [NuminaMath CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) | 10/2/2025 |
|
||||
| [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) | 10/2/2025 |
|
||||
| [simple-arithmetic-problems](https://huggingface.co/datasets/garrethlee/simple-arithmetic-problems) | 10/2/2025 |
|
||||
| [arithmetic](https://huggingface.co/datasets/EleutherAI/arithmetic) | 10/2/2025 |
|
||||
| [Skywork-OR1-RL-Data](https://huggingface.co/datasets/Skywork/Skywork-OR1-RL-Data) | 10/2/2025 |
|
||||
| [News Commentary](https://opus.nlpl.eu/News-Commentary.php) | 10/2/2025 |
|
||||
| [FastChat](https://github.com/lm-sys/FastChat/blob/main/playground/data/dummy.json) | 10/2/2025 |
|
||||
| [Essential-Web](https://huggingface.co/datasets/EssentialAI/essential-web-v1.0) | 10/2/2025 |
|
||||
| [finepdfs](https://huggingface.co/datasets/HuggingFaceFW/finepdfs) | 10/2/2025 |
|
||||
| [HotpotQA](https://huggingface.co/hotpot_qa/datasets) | 10/2/2025 |
|
||||
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | 10/2/2025 |
|
||||
| [NLTK Words Lists](https://www.nltk.org/nltk_data/) | 10/2/2025 |
|
||||
|
||||
## Private Non-publicly Accessible Datasets of Third Parties
|
||||
|
||||
| Dataset |
|
||||
| :---- |
|
||||
| Global Regulation |
|
||||
| Workbench |
|
||||
|
||||
## Online Dataset Sources
|
||||
|
||||
The English Common Crawl data was downloaded from the Common Crawl Foundation (see their [FAQ](https://commoncrawl.org/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](https://arxiv.org/abs/2412.02595).
|
||||
|
||||
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).
|
||||
|
||||
| Dataset | Modality | Dataset Size (Tokens) | Collection Period |
|
||||
| :---- | :---- | :---- | :---- |
|
||||
| English Common Crawl | Text | 3.360T | 4/8/2025 |
|
||||
| Multilingual Common Crawl | Text | 812.7B | 5/1/2025 |
|
||||
| GitHub Crawl | Text | 747.4B | 4/29/2025 |
|
||||
| English Common Crawl 1.1 | Text | Not disclosed | 10/2/2025 |
|
||||
|
||||
##
|
||||
|
||||
| Dataset | Collection Period |
|
||||
| :---- | :---- |
|
||||
| [Problems in Elementary Mathematics for Home Study](https://archive.org/details/AntonovVygodskyNikitinSankinProblemsInElementaryMathematicsForHomeStudyMir1982) | 4/23/2025 |
|
||||
| [GSM8K](https://github.com/openai/grade-school-math) | 4/23/2025 |
|
||||
|
||||
##
|
||||
|
||||
### Evaluation Dataset:
|
||||
|
||||
* Data Collection Method by dataset: Hybrid: Human, Synthetic
|
||||
* Labeling Method by dataset: Hybrid: Automated, Human, Synthetic
|
||||
|
||||
#### Evaluation Results:
|
||||
|
||||
#### Benchmark Results (Reasoning On)
|
||||
|
||||
We evaluated our model in \*\*Reasoning-On\*\* mode across these benchmarks.
|
||||
|
||||
| Benchmark | NVIDIA-Nemotron-3-Nano-4B-BF16 |
|
||||
| :---- | :---: |
|
||||
| AIME25 | 78.5 |
|
||||
| MATH500 | 95.4 |
|
||||
| GPQA | 53.2 |
|
||||
| LCB | 51.8 |
|
||||
| BFCL v3 | 61.1 |
|
||||
| IFEVAL-Prompt | 87.9 |
|
||||
| IFEVAL-Instruction | 92 |
|
||||
| Tau2-Airline | 33.3 |
|
||||
| Tau2-Retail | 39.8 |
|
||||
| Tau2-Telecom | 33 |
|
||||
|
||||
We also evaluated our model in \*\*Reasoning-off\*\* mode across these benchmarks
|
||||
|
||||
| Benchmark | NVIDIA-Nemotron-3-Nano-4B-BF16 |
|
||||
| :---- | ----- |
|
||||
| BFCL v3 | 61.1 |
|
||||
| IFBench-Prompt | 43.2 |
|
||||
| IFBench-Instruction | 44.2 |
|
||||
| Orak | 22.9 |
|
||||
| IFEval-Prompt | 82.8 |
|
||||
| IFEval-Instruction | 88 |
|
||||
| HaluEval | 62.2 |
|
||||
| RULER (128k) | 91.1 |
|
||||
| Tau2-Airline | 28.0 |
|
||||
| Tau2-Retail | 34.8 |
|
||||
| Tau2-Telecom | 24.9 |
|
||||
| EQ-Bench3 | 63.2 |
|
||||
|
||||
All evaluations were done using [NeMo-Skills](https://github.com/NVIDIA/NeMo-Skills/tree/main/docs) & [Orak](https://github.com/krafton-ai/Orak). For Orak we evaluated on three games (Super Mario, Darkest Dungeon & StarDew Valley)
|
||||
|
||||
## Inference
|
||||
|
||||
- Engines: HF, vLLM, llama-cpp, TRT-LLM, SGLang
|
||||
- Test Hardware: NVIDIA GeForce RTX, H100 80GB, DGX Spark, Jetson Thor/Orin Nano
|
||||
|
||||
## 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](https://www.nvidia.com/en-us/agreements/trustworthy-ai/terms/), 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](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16/blob/main/safety.md) and [Explainability](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16/blob/main/explainability.md) Subcards.
|
||||
|
||||
For more detailed information on ethical considerations for this model, please see the Model Card++ [Bias](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16/blob/main/bias.md), and [Privacy](https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16/blob/main/privacy.md) Subcards.
|
||||
|
||||
Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
|
||||
Reference in New Issue
Block a user