README.md
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1 ---
2 library_name: transformers
3 license: other
4 license_name: nvidia-nemotron-open-model-license
5 license_link: >-
6 https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/
7 pipeline_tag: text-generation
8 language:
9 - en
10 tags:
11 - nvidia
12 - pytorch
13 base_model: nvidia/NVIDIA-Nemotron-Nano-9B-v2
14 datasets:
15 - nvidia/Nemotron-CC-v2
16 - nvidia/Nemotron-Post-Training-Dataset-v2
17 - nvidia/Nemotron-Science-v1
18 - nvidia/Nemotron-Instruction-Following-Chat-v1
19 - nvidia/Nemotron-Agentic-v1
20 - nvidia/Nemotron-Competitive-Programming-v1
21 - nvidia/Nemotron-Math-Proofs-v1
22 - nvidia/Nemotron-RL-Agentic-Conversational-Tool-Use-Pivot-v1
23 - nvidia/Nemotron-RL-instruction_following
24 - nvidia/Nemotron-RL-agent-calendar_scheduling
25 - nvidia/Nemotron-RL-instruction_following-structured_outputs
26 track_downloads: true
27 ---
28
29 <div align="center" style="line-height: 1;">
30 <a href="https://huggingface.co/collections/nvidia/nemotron-pre-training-datasets" target="_blank" style="margin: 2px;">
31 <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;"/>
32 </a>
33 <a href="https://huggingface.co/collections/nvidia/nemotron-post-training-v3" target="_blank" style="margin: 2px;">
34 <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;"/>
35 </a>
36 </div>
37 <div align="center" style="line-height: 1;">
38 <a href="https://developer.nvidia.com/nemotron" target="_blank" style="margin: 2px;">
39 <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;"/>
40 </a>
41 <a href="https://discord.gg/9xpKQtVvrk" target="_blank" style="margin: 2px;">
42 <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;"/>
43 </a>
44 </div>
45
46 <div align="center" style="line-height: 1;">
47 <a href="https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-nemotron-open-model-license/" style="margin: 2px;">
48 <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;"/>
49 </a>
50 </div>
51
52
53 # NVIDIA-Nemotron-3-Nano-4B-BF16
54
55 **Model Developer:** NVIDIA Corporation
56
57 **Model Dates:**
58
59 Dec 2025 \- Jan 2026
60
61 **Data Freshness:**
62
63 September 2024
64
65 The pretraining data has a cutoff date of September 2024\.
66
67 ## Model Overview
68
69 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.
70
71 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.
72
73 The supported languages include: English. Improved using Qwen.
74
75 This model is ready for commercial use.
76
77 ## License/Terms of Use
78
79 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/).
80
81 ### Evaluation Results:
82
83 We evaluated our model in \*\*Reasoning-off\*\* mode across these benchmarks
84
85 | Benchmark | NVIDIA-Nemotron-3-Nano-4B-BF16 |
86 | :---- | ----- |
87 | BFCL v3 | 61.1 |
88 | IFBench-Prompt | 43.2 |
89 | IFBench-Instruction | 44.2 |
90 | Orak | 22.9 |
91 | IFEval-Prompt | 82.8 |
92 | IFEval-Instruction | 88 |
93 | HaluEval | 62.2 |
94 | RULER (128k) | 91.1 |
95 | Tau2-Airline | 28.0 |
96 | Tau2-Retail | 34.8 |
97 | Tau2-Telecom | 24.9 |
98 | EQ-Bench3 | 63.2 |
99
100 We also evaluated our model in \*\*Reasoning-On\*\* mode across these benchmarks.
101
102 | Benchmark | NVIDIA-Nemotron-3-Nano-4B-BF16 |
103 | :---- | :---: |
104 | AIME25 | 78.5 |
105 | MATH500 | 95.4 |
106 | GPQA | 53.2 |
107 | LCB | 51.8 |
108 | BFCL v3 | 61.1 |
109 | IFEVAL-Prompt | 87.9 |
110 | IFEVAL-Instruction | 92 |
111 | Tau2-Airline | 33.3 |
112 | Tau2-Retail | 39.8 |
113 | Tau2-Telecom | 33 |
114
115 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)
116
117 ### Deployment Geography: Global
118
119 ### Use Case
120
121 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.
122
123 ### Release Date: 3/16/2026
124
125 Huggingface 3/16/2026 via [https://huggingface.co/](https://huggingface.co/)
126
127 ## References
128
129 - [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)
130 - [Nemotron Elastic: Towards Efficient Many-in-One Reasoning LLMs](https://arxiv.org/abs/2511.16664)
131 - [NVIDIA Nemotron 3: Efficient and Open Intelligence](https://arxiv.org/abs/2512.20856)
132 - [Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning](https://arxiv.org/abs/2512.20848)
133 - [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)
134
135 ## Model Architecture
136
137 - Architecture Type: Mamba2-Transformer Hybrid
138 - Network Architecture: Nemotron-Hybrid
139 - This model was compressed from [nvidia/NVIDIA-Nemotron-Nano-9B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2)
140 - Number of model parameters 3.97 x 10^9
141
142 ## Input
143
144 - Input Type(s): Text
145 - Input Format(s): String
146 - Input Parameters: One-Dimensional (1D): Sequences
147 - Other Properties Related to Input: Context length up to 262K. Supported languages include English.
148
149 ## Output
150
151 - Output Type(s): Text
152 - Output Format: String
153 - Output Parameters: One-Dimensional (1D): Sequences
154 - Other properties Related to Output: Sequences up to 262K
155
156 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.
157
158 ## Software Integration
159
160 - Runtime Engine(s): NeMo 25.07
161 - Supported Hardware Microarchitecture Compatibility: NVIDIA A10G, NVIDIA H100-80GB, NVIDIA A100, GeForce RTX
162 - Operating System(s): Linux
163
164 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.
165
166 ### **Use it with Transformers**
167
168 The snippet below shows how to use this model with Huggingface Transformers (tested on version 4.48.3).
169
170 ```
171 import torch
172 from transformers import AutoTokenizer, AutoModelForCausalLM
173
174 # Load tokenizer and model
175 tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-3-Nano-4B")
176 model = AutoModelForCausalLM.from_pretrained(
177 "nvidia/NVIDIA-Nemotron-3-Nano-4B",
178 torch_dtype=torch.bfloat16,
179 trust_remote_code=True,
180 device_map="auto"
181 )
182 ```
183
184 ```
185 messages = [
186 {"role": "system", "content": <system_prompt>},
187 {"role": "user", "content": "Write a haiku about GPUs"},
188 ]
189 tokenized_chat = tokenizer.apply_chat_template(
190 messages,
191 tokenize=True,
192 add_generation_prompt=True,
193 return_tensors="pt"
194 ).to(model.device)
195
196 outputs = model.generate(
197 tokenized_chat,
198 max_new_tokens=32,
199 eos_token_id=tokenizer.eos_token_id
200 )
201 print(tokenizer.decode(outputs[0]))
202 ```
203
204 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.
205
206 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.
207
208 ```
209 messages = [
210 {"role": "system", "content": <system_prompt>},
211 {"role": "user", "content": "Write a haiku about GPUs"},
212 ]
213 tokenized_chat = tokenizer.apply_chat_template(
214 messages,
215 tokenize=True,
216 enable_thinking=False,
217 add_generation_prompt=True,
218 return_tensors="pt"
219 ).to(model.device)
220
221 outputs = model.generate(
222 tokenized_chat,
223 max_new_tokens=32,
224 eos_token_id=tokenizer.eos_token_id
225 )
226 print(tokenizer.decode(outputs[0]))
227 ```
228
229 ### **Use it with vLLM**
230
231 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).
232
233 ```
234 pip install -U "vllm>=0.15.1"
235 ```
236
237 Download the custom parser from the Hugging Face repository.
238
239 ```
240 wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16/resolve/main/nano_v3_reasoning_parser.py
241 ```
242
243 Launch a vLLM server using the custom parser.
244
245 ```
246 vllm serve nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 \
247 --served-model-name nemotron3-nano-4B-BF16\
248 --max-num-seqs 8 \
249 --tensor-parallel-size 1 \
250 --max-model-len 262144 \
251 --port 8000 \
252 --trust-remote-code \
253 --mamba_ssm_cache_dtype float32 \
254 --enable-auto-tool-choice \
255 --tool-call-parser qwen3_coder \
256 --reasoning-parser-plugin nano_v3_reasoning_parser.py \
257 --reasoning-parser nano_v3
258 ```
259
260 Access the hosted API using a python client.
261
262 ```py
263
264 from openai import OpenAI
265 import asyncio
266 from openai import AsyncOpenAI
267
268 # NOTE: Streaming is preferred for better performance and resource efficiency.
269 # It allows you to start processing responses as they arrive, reducing latency.
270
271 # Synchronous example (non-streaming)
272 client = OpenAI(
273 api_key="your-nvapikey",
274 base_url="base-url"
275 )
276
277 response = client.chat.completions.create(
278 model="nemotron3-nano-4B-BF16",
279 messages=[
280 {
281 "role": "user",
282 "content": "Hello!"
283 }
284 ],
285 temperature=0.7,
286 max_tokens=256,
287 top_p=0.7,
288 stream=false
289 )
290
291 print(response.choices[0].message.content)
292
293 ```
294
295 ### Use it with TRT-LLM
296
297 Launch the model using TRT-LLM
298
299 ```shell
300 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/NVIDIA-Nemotron-3-Nano-4B-BF16 -e HF_TOKEN=$HF_TOKEN nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc6 bash -c '
301 cat > /tmp/extra-llm-api-config.yml <<EOF
302 kv_cache_config:
303 dtype: "auto"
304 enable_block_reuse: false
305 cuda_graph_config:
306 max_batch_size: 32
307 enable_padding: true
308 disable_overlap_scheduler: true
309 moe_config:
310 backend: CUTLASS
311 EOF
312
313 trtllm-serve \
314 nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 \
315 --host 0.0.0.0 \
316 --port 8123 \
317 --max_batch_size 32 \
318 --extra_llm_api_options /tmp/extra-llm-api-config.yml '
319 ```
320
321 Access the hosted endpoint using curl command.
322
323 ```shell
324 curl http://localhost:8123/v1/chat/completions -H "Content-Type: application/json" -d '{
325 "model": "NVIDIA-Nemotron-3-Nano-4B-BF16",
326 "messages": [
327 {
328 "role": "user",
329 "content": "Where is New York?"
330 }
331 ],
332 "max_tokens": 1024,
333 "top_p": 1.0
334 }' -w "\n"
335 ```
336
337 ## Model Version
338
339 - v1.0
340
341 ##
342
343 ## Training, Testing, and Evaluation Datasets
344
345 ### Training datasets
346
347 * Data Modality: Text
348 * Text Training Data Size: More than 10 Trillion Tokens
349 * Train/Test/Valid Split: We used 100% of the corpus for pre-training and relied on external benchmarks for testing.
350 * Data Collection Method by dataset: Hybrid: Automated, Human, Synthetic
351 * Labeling Method by dataset: Hybrid: Automated, Human, Synthetic
352
353 **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.
354
355 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) .
356
357 ## Public Datasets
358
359 | Dataset | Collection Period |
360 | :---- | :---- |
361 | [Problems in Elementary Mathematics for Home Study](https://archive.org/details/AntonovVygodskyNikitinSankinProblemsInElementaryMathematicsForHomeStudyMir1982) | 4/23/2025 |
362 | [GSM8K](https://github.com/openai/grade-school-math) | 4/23/2025 |
363 | [PRM800K](https://github.com/openai/prm800k) | 4/23/2025 |
364 | [CC-NEWS](https://commoncrawl.org/blog/news-dataset-available) | 4/23/2025 |
365 | [Common Crawl](https://commoncrawl.org/) | 4/23/2025 |
366 | [Wikimedia](https://dumps.wikimedia.org/) | 4/23/2025 |
367 | [Bespoke-Stratos-17k](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k) | 4/23/2025 |
368 | [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k) | 4/23/2025 |
369 | [glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) | 4/23/2025 |
370 | [APIGen Function-Calling](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) | 4/23/2025 |
371 | [LMSYS-Chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) | 4/23/2025 |
372 | [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 |
373 | [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 |
374 | [FineWeb-2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) | 4/23/2025 |
375 | [Court Listener](https://www.courtlistener.com/help/api/bulk-data/) | Legacy Download |
376 | [peS2o](https://huggingface.co/datasets/allenai/peS2o) | Legacy Download |
377 | [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math) | Legacy Download |
378 | [BioRxiv](https://www.biorxiv.org/tdm) | Legacy Download |
379 | [PMC Open Access Subset](https://pmc.ncbi.nlm.nih.gov/tools/openftlist/) | Legacy Download |
380 | [OpenWebText2](https://openwebtext2.readthedocs.io/en/latest/) | Legacy Download |
381 | [Stack Exchange Data Dump](https://archive.org/details/stackexchange) | Legacy Download |
382 | [PubMed Abstracts](https://github.com/thoppe/The-Pile-PubMed) | Legacy Download |
383 | [NIH ExPorter](https://exporter.nih.gov/ExPORTER_Catalog.aspx) | Legacy Download |
384 | [arXiv](https://info.arxiv.org/help/bulk_data/index.html) | Legacy Download |
385 | [BigScience Workshop Datasets](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#datasets) | Legacy Download |
386 | [Reddit Dataset](https://files.pushshift.io/reddit/) | Legacy Download |
387 | [SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR)](https://www.sec.gov/search-filings) | Legacy Download |
388 | [Public Software Heritage S3](https://docs.softwareheritage.org/devel/swh-export/graph/dataset.html#summary-of-dataset-versions) | Legacy Download |
389 | [The Stack](https://huggingface.co/datasets/bigcode/the-stack) | Legacy Download |
390 | [mC4](https://huggingface.co/datasets/legacy-datasets/mc4) | Legacy Download |
391 | [Advanced Mathematical Problem Solving](https://github.com/hendrycks/math?tab=readme-ov-file) | Legacy Download |
392 | [MathPile](https://github.com/GAIR-NLP/MathPile/) | Legacy Download |
393 | [NuminaMath CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) | Legacy Download |
394 | [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/) | Legacy Download |
395 | [FLAN](https://github.com/google-research/FLAN) | Legacy Download |
396 | [Advanced Reasoning Benchmark](https://github.com/TheDuckAI/arb) | Legacy Download |
397 | [SciBench](https://github.com/mandyyyyii/scibench) | Legacy Download |
398 | [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) | Legacy Download |
399 | [FinQA](https://finqasite.github.io/) | Legacy Download |
400 | [Riddles](https://github.com/crawsome/riddles) | Legacy Download |
401 | [Problems in Elementary Mathematics for Home Study](https://archive.org/details/AntonovVygodskyNikitinSankinProblemsInElementaryMathematicsForHomeStudyMir1982) | Legacy Download |
402 | [MedMCQA](https://huggingface.co/datasets/openlifescienceai/medmcqa) | Legacy Download |
403 | [Cosmos QA](https://huggingface.co/datasets/allenai/cosmos_qa) | Legacy Download |
404 | [MCTest](https://huggingface.co/datasets/sagnikrayc/mctest) | Legacy Download |
405 | [AI2's Reasoning Challenge](https://huggingface.co/datasets/ai2_arc) | Legacy Download |
406 | [OpenBookQA](https://github.com/allenai/OpenBookQA) | Legacy Download |
407 | [MMLU Auxiliary Train](https://huggingface.co/datasets/cais/mmlu/viewer/all/auxiliary_train) | Legacy Download |
408 | [social-chemestry-101](https://huggingface.co/datasets/tasksource/social-chemestry-101) | Legacy Download |
409 | [Moral Stories](https://huggingface.co/datasets/demelin/moral_stories) | Legacy Download |
410 | [The Common Pile v0.1](https://huggingface.co/common-pile) | Legacy Download |
411 | [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath) | Legacy Download |
412 | [MegaMath](https://huggingface.co/datasets/LLM360/MegaMath) | Legacy Download |
413 | [FastChat](https://github.com/lm-sys/FastChat) | 6/30/2025 |
414 | [MultiverseMathHard](https://huggingface.co/datasets/Nexusflow/MultiverseMathHard) | 10/2/2025 |
415 | [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym) | 10/2/2025 |
416 | [WorkBench](https://github.com/olly-styles/WorkBench/tree/main/data/raw) | 10/2/2025 |
417 | [WildChat-1M](https://huggingface.co/datasets/allenai/WildChat-1M) | 10/2/2025 |
418 | [OpenCodeReasoning-2](https://huggingface.co/datasets/nvidia/OpenCodeReasoning-2) | 10/2/2025 |
419 | [HelpSteer3](https://huggingface.co/datasets/nvidia/HelpSteer3) | 10/2/2025 |
420 | [opc-sft-stage2](https://huggingface.co/datasets/OpenCoder-LLM/opc-sft-stage2) | 10/2/2025 |
421 | [Big-Math-RL-Verified](https://huggingface.co/datasets/SynthLabsAI/Big-Math-RL-Verified) | 10/2/2025 |
422 | [NuminaMath CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) | 10/2/2025 |
423 | [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) | 10/2/2025 |
424 | [simple-arithmetic-problems](https://huggingface.co/datasets/garrethlee/simple-arithmetic-problems) | 10/2/2025 |
425 | [arithmetic](https://huggingface.co/datasets/EleutherAI/arithmetic) | 10/2/2025 |
426 | [Skywork-OR1-RL-Data](https://huggingface.co/datasets/Skywork/Skywork-OR1-RL-Data) | 10/2/2025 |
427 | [News Commentary](https://opus.nlpl.eu/News-Commentary.php) | 10/2/2025 |
428 | [FastChat](https://github.com/lm-sys/FastChat/blob/main/playground/data/dummy.json) | 10/2/2025 |
429 | [Essential-Web](https://huggingface.co/datasets/EssentialAI/essential-web-v1.0) | 10/2/2025 |
430 | [finepdfs](https://huggingface.co/datasets/HuggingFaceFW/finepdfs) | 10/2/2025 |
431 | [HotpotQA](https://huggingface.co/hotpot_qa/datasets) | 10/2/2025 |
432 | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | 10/2/2025 |
433 | [NLTK Words Lists](https://www.nltk.org/nltk_data/) | 10/2/2025 |
434
435 ## Private Non-publicly Accessible Datasets of Third Parties
436
437 | Dataset |
438 | :---- |
439 | Global Regulation |
440 | Workbench |
441
442 ## Online Dataset Sources
443
444 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).
445
446 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.
447
448 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).
449
450 | Dataset | Modality | Dataset Size (Tokens) | Collection Period |
451 | :---- | :---- | :---- | :---- |
452 | English Common Crawl | Text | 3.360T | 4/8/2025 |
453 | Multilingual Common Crawl | Text | 812.7B | 5/1/2025 |
454 | GitHub Crawl | Text | 747.4B | 4/29/2025 |
455 | English Common Crawl 1.1 | Text | Not disclosed | 10/2/2025 |
456
457 ## NVIDIA-Sourced Synthetic Datasets
458
459 | Dataset | Modality | Dataset Size (Tokens) | Seed Dataset | Model(s) used for generation |
460 | :---- | :---- | :---- | :---- | :---- |
461 | Synthetic Art of Problem Solving from DeepSeek-R1 | Text | 25.5B | [Art of Problem Solving](https://artofproblemsolving.com/company); [American Mathematics Competitions 8](https://artofproblemsolving.com/wiki/index.php/AMC_8_Problems_and_Solutions); [American Mathematics Competitions 10](https://artofproblemsolving.com/wiki/index.php/AMC_10_Problems_and_Solutions); | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) |
462 | Synthetic Moral Stories and Social Chemistry from Mixtral-8x22B-v0.1 | Text | 327M | [social-chemestry-101](https://huggingface.co/datasets/tasksource/social-chemestry-101); [Moral Stories](https://huggingface.co/datasets/demelin/moral_stories) | [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1) |
463 | 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](https://openstax.org/) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1); [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) |
464 | 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](https://openstax.org/) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1); [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) |
465 | 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](https://openstax.org/); [GSM8K](https://github.com/openai/grade-school-math); [Open Textbook Library \- CC BY-SA & GNU subset](https://open.umn.edu/opentextbooks/textbooks/) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1), [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324); [Qwen2.5-72B](https://huggingface.co/Qwen/Qwen2.5-72B) |
466 | [Nemotron-PrismMath](https://huggingface.co/datasets/nvidia/Nemotron-PrismMath) | Text | 4.6B | [Big-Math-RL-Verified](https://huggingface.co/datasets/SynthLabsAI/Big-Math-RL-Verified); [OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) | [Qwen2.5-0.5B-instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct), [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct); [DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) |
467 | Synthetic Question Answering Data from Papers and Permissible Books from Qwen2.5-72B-Instruct | Text | 350M | [arXiv](https://info.arxiv.org/help/bulk_data/index.html); [National Institutes of Health ExPorter](https://www.nih.gov/); [BioRxiv](https://www.biorxiv.org/tdm); [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/); [USPTO Backgrounds](https://data.uspto.gov/apis/transition-guide/bdss#pats); [peS2o](https://huggingface.co/datasets/allenai/peS2o); Global Regulation; [CORE](https://core.ac.uk/documentation/dataset); [PG-19](https://github.com/google-deepmind/pg19); [DOAB CC BY & CC BY-SA subset](https://www.doabooks.org/en); [NDLTD](https://ndltd.org/thesis-resources/global-etd-search/) | [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) |
468 | Synthetic FineMath-4+ Reprocessed from DeepSeek-V3 | Text | 9.2B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3) |
469 | Synthetic FineMath-3+ Reprocessed from phi-4 | Text | 27.6B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
470 | Synthetic Union-3+ Reprocessed from phi-4 | Text | 93.1B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
471 | Refreshed [Nemotron-MIND](https://huggingface.co/datasets/nvidia/Nemotron-MIND) from phi-4 | Text | 73B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
472 | Synthetic Union-4+ Reprocessed from phi-4 | Text | 14.12B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
473 | Synthetic Union-3+ minus 4+ Reprocessed from phi-4 | Text | 78.95B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
474 | Synthetic Union-3 Refreshed from phi-4 | Text | 80.94B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
475 | Synthetic Union-4+ Refreshed from phi-4 | Text | 52.32B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) |
476 | Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from DeepSeek-V3 and DeepSeek-V3-0324 | Text | 4.0B | [AQUA-RAT](https://huggingface.co/datasets/deepmind/aqua_rat); [LogiQA](https://huggingface.co/datasets/lucasmccabe/logiqa); [AR-LSAT](https://github.com/zhongwanjun/AR-LSAT) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324) |
477 | Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from Qwen3-30B-A3B | Text | 4.2B | [AQUA-RAT](https://huggingface.co/datasets/deepmind/aqua_rat); [LogiQA](https://huggingface.co/datasets/lucasmccabe/logiqa); [AR-LSAT](https://github.com/zhongwanjun/AR-LSAT) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
478 | Synthetic Art of Problem Solving from Qwen2.5-32B-Instruct, Qwen2.5-Math-72B, Qwen2.5-Math-7B, and Qwen2.5-72B-Instruct | Text | 83.1B | [Art of Problem Solving](https://artofproblemsolving.com/company); [American Mathematics Competitions 8](https://artofproblemsolving.com/wiki/index.php/AMC_8_Problems_and_Solutions); [American Mathematics Competitions 10](https://artofproblemsolving.com/wiki/index.php/AMC_10_Problems_and_Solutions); [GSM8K](https://github.com/openai/grade-school-math); [PRM800K](https://github.com/openai/prm800k) | [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct); [Qwen2.5-Math-72B](https://huggingface.co/Qwen/Qwen2.5-Math-72B); [Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B); [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) |
479 | Synthetic MMLU Auxiliary Train from DeepSeek-R1 | Text | 0.5B | [MMLU Auxiliary Train](https://huggingface.co/datasets/cais/mmlu/viewer/all/auxiliary_train) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) |
480 | Synthetic Long Context Continued Post-Training Data from Papers and Permissible Books from Qwen2.5-72B-Instruct | Text | 5.4B | [arXiv](https://info.arxiv.org/help/bulk_data/index.html); [National Institutes of Health ExPorter](https://www.nih.gov/); [BioRxiv](https://www.biorxiv.org/tdm); [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/); [USPTO Backgrounds](https://data.uspto.gov/apis/transition-guide/bdss#pats); [peS2o](https://huggingface.co/datasets/allenai/peS2o); Global Regulation; [CORE](https://core.ac.uk/documentation/dataset); [PG-19](https://github.com/google-deepmind/pg19); [DOAB CC BY & CC BY-SA subset](https://www.doabooks.org/en); [NDLTD](https://ndltd.org/thesis-resources/global-etd-search/) | [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) |
481 | Synthetic Common Crawl from Qwen3-30B-A3B and Mistral-Nemo-12B-Instruct | Text | 1.949T | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B); [Mistral-NeMo-12B-Instruct](https://huggingface.co/nvidia/Mistral-NeMo-12B-Instruct) |
482 | Synthetic Multilingual Data from Common Crawl from Qwen3-30B-A3B | Text | 997.3B | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
483 | Synthetic Multilingual Data from Wikimedia from Qwen3-30B-A3B | Text | 55.1B | [Wikimedia](https://dumps.wikimedia.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
484 | Synthetic OpenMathReasoning from DeepSeek-R1-0528 | Text | 1.5M | [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
485 | Synthetic OpenCodeReasoning from DeepSeek-R1-0528 | Text | 1.1M | [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
486 | Synthetic Science Data from DeepSeek-R1-0528 | Text | 1.5M | \- | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
487 | Synthetic Humanity's Last Exam from DeepSeek-R1-0528 | Text | 460K | [Humanity's Last Exam](https://huggingface.co/datasets/cais/hle) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
488 | Synthetic ToolBench from Qwen3-235B-A22B | Text | 400K | [ToolBench](https://github.com/OpenBMB/ToolBench) | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) |
489 | Synthetic Nemotron Content Safety Dataset V2, eval-safety, Gretel Synthetic Safety Alignment, and RedTeam\_2K from DeepSeek-R1-0528 | Text | 52K | [Nemotron Content Safety Dataset V2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0); [eval-safety](https://github.com/CrystalEye42/eval-safety/blob/main/malicious_tasks_dataset.yaml); [Gretel Synthetic Safety Alignment](https://huggingface.co/datasets/gretelai/gretel-safety-alignment-en-v1); [RedTeam\_2K](https://huggingface.co/datasets/JailbreakV-28K/JailBreakV-28k/viewer/RedTeam_2K) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
490 | Synthetic HelpSteer from Qwen3-235B-A22B | Text | 120K | [HelpSteer3](https://huggingface.co/datasets/nvidia/HelpSteer3); [HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2) | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) |
491 | Synthetic Alignment data from Mixtral-8x22B-Instruct-v0.1, Mixtral-8x7B-Instruct-v0.1, and Nemotron-4 Family | Text | 400K | [HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2); [C4](https://huggingface.co/datasets/allenai/c4); [LMSYS-Chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m); [ShareGPT52K](https://huggingface.co/datasets/RyokoAI/ShareGPT52K); [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k); [GSM8K](https://github.com/openai/grade-school-math); [PRM800K](https://github.com/openai/prm800k); lm\_identity (NVIDIA internal); [FinQA](https://finqasite.github.io/); [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions); [Riddles](https://github.com/crawsome/riddles); ChatQA nvolve-multiturn (NVIDIA internal); [glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2); [SciBench](https://github.com/mandyyyyii/scibench); [OpenBookQA](https://github.com/allenai/OpenBookQA); [Advanced Reasoning Benchmark](https://github.com/TheDuckAI/arb); [Public Software Heritage S3](https://docs.softwareheritage.org/devel/swh-export/graph/dataset.html#summary-of-dataset-versions); [Khan Academy Math Keywords](https://www.khanacademy.org/math) | Nemotron-4-15B-Base (NVIDIA internal); Nemotron-4-15B-Instruct (NVIDIA internal); [Nemotron-4-340B-Base](https://huggingface.co/nvidia/Nemotron-4-340B-Base); [Nemotron-4-340B-Instruct](https://huggingface.co/nvidia/Nemotron-4-340B-Instruct); [Nemotron-4-340B-Reward](https://huggingface.co/nvidia/Nemotron-4-340B-Reward); [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1); [Mixtral-8x22B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1) |
492 | Synthetic LMSYS-Chat-1M from Qwen3-235B-A22B | Text | 1M | [LMSYS-Chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) |
493 | Synthetic Multilingual Reasoning data from DeepSeek-R1-0528, Qwen2.5-32B-Instruct-AWQ, and Qwen2.5-14B-Instruct | Text | 25M | [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning); [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528); [Qwen2.5-32B-Instruct-AWQ](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct-AWQ) (translation); [Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) (translation); |
494 | Synthetic Multilingual Reasoning data from Qwen3-235B-A22B and Gemma 3 Post-Trained models | Text | 5M | [WildChat](https://huggingface.co/datasets/allenai/WildChat-1M) | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [Gemma 3 PT 12B](https://huggingface.co/google/gemma-3-12b-it); [Gemma 3 PT 27B](https://huggingface.co/google/gemma-3-27b-it) |
495 | Tool Calling Data | Text | 26.2B | | [Qwen3-235B-A22B-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
496 | Synthetic Essential-Web from QwQ-32B | Text | 28.1B | [Essential-Web](https://huggingface.co/datasets/EssentialAI/essential-web-v1.0) | [QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) |
497 | Translated Synthetic Crawl | Text | 389.9B | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
498 | Translated Synthetic Wikipedia | Text | 7.9B | [Wikimedia](https://dumps.wikimedia.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
499 | Synthetic Art of Problem Solving from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | [Art of Problem Solving](https://artofproblemsolving.com/company); [American Mathematics Competitions 8](https://artofproblemsolving.com/wiki/index.php/AMC_8_Problems_and_Solutions); [American Mathematics Competitions 10](https://artofproblemsolving.com/wiki/index.php/AMC_10_Problems_and_Solutions) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) |
500 | Synthetic Stack Exchange from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | [Stack Exchange](https://archive.org/details/stackexchange) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) |
501 | Synthetic OpenCodeReasoning from DeepSeek-R1-0528 | Text | Undisclosed | [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
502 | Synthetic HackerRank Coding from DeepSeek-R1-0528 | Text | Undisclosed | HackerRank Coding Dataset | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
503 | Synthetic SWE-Gym from Qwen3-Coder-480B-A35B-Instruct | Text | Undisclosed | [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym) | [Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct) |
504 | Synthetic Art of Problem Solving and Stack Exchange from gpt-oss-120b, Qwen2.5-32B-Instruct, and Goedel-Prover-V2-32B | Text | Undisclosed | [Art of Problem Solving](https://artofproblemsolving.com/company); [American Mathematics Competitions 8](https://artofproblemsolving.com/wiki/index.php/AMC_8_Problems_and_Solutions); [American Mathematics Competitions 10](https://artofproblemsolving.com/wiki/index.php/AMC_10_Problems_and_Solutions); [Stack Exchange](https://archive.org/details/stackexchange) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct); [Goedel-Prover-V2-32B](https://huggingface.co/Goedel-LM/Goedel-Prover-V2-32B) |
505 | 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 | [Stack Exchange](https://archive.org/details/stackexchange); [SCP-116K](https://huggingface.co/datasets/EricLu/SCP-116K); [LIMO](https://huggingface.co/datasets/GAIR/LIMO); [TACO](https://huggingface.co/datasets/BAAI/TACO); Code Contest; Codeforces | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); |
506 | Synthetic Safety from DeepSeek-R1-0528, gpt-oss-120b and Mixtral-8x7B-v0.1 | Text | Undisclosed | [Nemotron Content Safety Dataset V2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0); [Gretel Synthetic Safety Alignment Dataset](https://huggingface.co/datasets/gretelai/gretel-safety-alignment-en-v1); [RedTeam-2K](https://huggingface.co/datasets/JailbreakV-28K/JailBreakV-28k); [Malicious Tasks](https://github.com/CrystalEye42/eval-safety/blob/main/malicious_tasks_dataset.yaml); [Nemotron-Personas-USA](https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) |
507 | Synthetic STEM from Qwen3-235B-A22B-Instruct-2507 and gpt-oss-120b | Text | Undisclosed | [arXiv](https://info.arxiv.org/help/bulk_data/index.html); [National Institutes of Health ExPorter](https://www.nih.gov/); [BioRxiv](https://www.biorxiv.org/tdm); [PMC Article](https://pmc.ncbi.nlm.nih.gov/tools/textmining/); [USPTO Backgrounds](https://data.uspto.gov/apis/transition-guide/bdss#pats); [peS2o](https://huggingface.co/datasets/allenai/peS2o); Global Regulation; [CORE](https://core.ac.uk/documentation/dataset); [PG-19](https://github.com/google-deepmind/pg19); [DOAB CC BY & CC BY-SA subset](https://www.doabooks.org/en); [NDLTD](https://ndltd.org/thesis-resources/global-etd-search/) | [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
508 | Synthetic KernelBook from DeepSeek-R1-0528 | Text | Undisclosed | [KernelBook](https://huggingface.co/datasets/GPUMODE/KernelBook) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
509 | Synthetic Tool Calling from Qwen3-235B-A22B-Thinking-2507 and Qwen3-Next-80B-A3B-Thinking | Text | Undisclosed | [ToolBench](https://github.com/OpenBMB/ToolBench/tree/master); [glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2); [APIGen Function-Calling](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k); [Nemotron-Personas-USA](https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA) | [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507); [Qwen3-Next-80B-A3B-Thinking](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking) |
510 | 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](https://huggingface.co/datasets/allenai/c4); [LMSYS-Chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m); [ShareGPT](https://huggingface.co/datasets/RyokoAI/ShareGPT52K); [GSM8K](https://github.com/openai/grade-school-math); [PRM800K](https://github.com/openai/prm800k); [FinQA](https://finqasite.github.io/); [WikiTableQuestions](https://huggingface.co/wikitablequestions/datasets); [Riddles](https://github.com/crawsome/riddles); [glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2); [SciBench](https://huggingface.co/datasets/xw27/scibench); [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k); [OpenBookQA](https://github.com/allenai/OpenBookQA); [Advanced Reasoning Benchmark](https://github.com/TheDuckAI/arb); Software Heritage; [Khan Academy Math Keywords](https://www.khanacademy.org/math); [WildChat-1M](https://huggingface.co/datasets/allenai/WildChat-1M); [Nemotron-Personas-USA](https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Mixtral-8x22B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1); [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507); [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |
511 | Synthetic Long Context from Qwen3-235B-A22B-Instruct-2507 | Text | Undisclosed | [CORE](https://core.ac.uk/documentation/dataset); [PG-19](https://github.com/google-deepmind/pg19); [DOAB CC BY & CC BY-SA subset](https://www.doabooks.org/en); [NDLTD](https://ndltd.org/thesis-resources/global-etd-search/) | [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) |
512 | 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](https://huggingface.co/openai/gpt-oss-120b); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528); [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B); and [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |
513 | Synthetic STEM from Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | [ICHO-IPH0](https://huggingface.co/datasets/II-Vietnam/IChO-IPhO-RL-v2-formated); [Physics Big](https://huggingface.co/datasets/Vikhrmodels/physics_big); Scale HLE; [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning); [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) | [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |
514 | Synthetic DocFinQA and SWE-smith from Qwen3-Coder-480B-A35B-Instruct and Kimi-K2-Thinking | Text | Undisclosed | [DocFinQA](https://huggingface.co/datasets/kensho/DocFinQA); [SWE-smith](https://huggingface.co/datasets/SWE-bench/SWE-smith) | [Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct); [Kimi-K2-Thinking](https://huggingface.co/moonshotai/Kimi-K2-Thinking) |
515 | Synthetic Math from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | \- | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) |
516 | Synthetic Essential-Web from gpt-oss-120b | Text | Undisclosed | [Essential-Web](https://huggingface.co/datasets/EssentialAI/essential-web-v1.0) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
517 | Synthetic Scale HLE from gpt-oss-120b | Text | Undisclosed | Scale HLE | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
518 | Synthetic CDQuestions from gpt-oss-120b | Text | Undisclosed | [CDQuestions](https://cdquestions.com/) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
519 | Synthetic Stack Exchange from gpt-oss-120b | Text | Undisclosed | [Stack Exchange](https://archive.org/details/stackexchange) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
520 | Synthetic GPQA from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | [Stack Exchange](https://archive.org/details/stackexchange) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) |
521 | Synthetic Vedantu from gpt-oss-120b | Text | Undisclosed | [Vedantu](https://www.vedantu.com/) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
522 | Synthetic SWE-Gym and R2E-Gym-Subset from Qwen3-Coder-480B-A35B-Instruct | Text | Undisclosed | [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym); [R2E-Gym-Subset](https://huggingface.co/datasets/R2E-Gym/R2E-Gym-Subset) | [Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct) |
523 | Synthetic SWE-Gym from Qwen3-Coder-480B-A35B-Instruct | Text | Undisclosed | [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym) | [Qwen3-Coder-480B-A35B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct) |
524 | Synthetic SWE-Gym and R2E-Gym-Subset from DeepSeek-R1-0528 | Text | Undisclosed | [SWE-Gym](https://huggingface.co/datasets/SWE-Gym/SWE-Gym); [R2E-Gym-Subset](https://huggingface.co/datasets/R2E-Gym/R2E-Gym-Subset) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
525 | 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](https://huggingface.co/datasets/nvidia/HelpSteer2); [HelpSteer3](https://huggingface.co/datasets/nvidia/HelpSteer3); [LMSYS-Chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m); [Nemotron-Personas-USA](https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507); [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |
526 | 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 | \- | [Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507); [Qwen3-30B-A3B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507); [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507); [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |
527 | Synthetic Search STEM MCQ from Qwen3-235B-A22B and DeepSeek-R1-0528 | Text | Undisclosed | \- | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
528 | Synthetic Search STEM OPENQ from DeepSeek-R1-0528 | Text | Undisclosed | \- | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
529 | Synthetic OpenSTEM from Qwen2.5-32B-Instruct and DeepSeek-R1-0528 | Text | Undisclosed | \- | [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
530 | Synthetic MCQ from Qwen2.5-32B-Instruct and DeepSeek-R1-0528 | Text | Undisclosed | \- | [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
531 | Synthetic MCQ10 from DeepSeek-R1-0528 | Text | Undisclosed | \- | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
532 | Synthetic MCQ4 from Qwen3-235B-A22B, DeepSeek-R1-0528, and Qwen3-235B-A22B-Instruct-2507 | Text | Undisclosed | \- | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528); [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) |
533 | Synthetic OpenMathReasoning from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) |
534 | Synthetic Offline Search MCQA HLE from DeepSeek-R1-0528 | Text | Undisclosed | \- | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
535 | Synthetic Offline Search MCQA GPQA from Qwen3-235B-A22B and DeepSeek-R1-0528 | Text | Undisclosed | \- | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
536 | 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](https://huggingface.co/Qwen/QwQ-32B); [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507); [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503); [Mistral-Small-3.2-24B-Instruct-2506](https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506); [MiniMax-M1-80k](https://huggingface.co/MiniMaxAI/MiniMax-M1-80k); [MiniMax-M1-40k](https://huggingface.co/MiniMaxAI/MiniMax-M1-40k); [Kimi-K2-Instruct](https://huggingface.co/moonshotai/Kimi-K2-Instruct); [DeepSeek-V3-0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
537 | 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](https://huggingface.co/datasets/allenai/WildChat-1M); [arena-human-preference-140k](https://huggingface.co/datasets/lmarena-ai/arena-human-preference-140k) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1); [gemma-2-2b-it](https://huggingface.co/google/gemma-2-2b-it); [gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it); [gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3); [Mixtral-8x22B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1); [Nemotron-4-340B-Instruct](https://huggingface.co/nvidia/Nemotron-4-340B-Instruct); [NVIDIA-Nemotron-Nano-9B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2); [Phi-4-mini-instruct](https://huggingface.co/microsoft/Phi-4-mini-instruct); [Phi-3-small-8k-instruct](https://huggingface.co/microsoft/Phi-3-small-8k-instruct); [Phi-3-medium-4k-instruct](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) |
538 | 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](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0); [Gretel Synthetic Safety Alignment Dataset](https://huggingface.co/datasets/gretelai/gretel-safety-alignment-en-v1); [RedTeam-2K](https://huggingface.co/datasets/JailbreakV-28K/JailBreakV-28k); [Malicious Tasks](https://github.com/CrystalEye42/eval-safety/blob/main/malicious_tasks_dataset.yaml); | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B); [Qwen3-30B-A3B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507); [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507); [Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) |
539 | Synthetic Code from Qwen3-32B | Text | Undisclosed | English Common Crawl; English Common Crawl 1.1 | [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) |
540 | Synthetic OpenCodeReasoning from DeepSeek-R1 | Text | Undisclosed | [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) |
541 | Synthetic LIMO from DeepSeek-R1-0528 | Text | Undisclosed | [LIMO](https://huggingface.co/datasets/GAIR/LIMO) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
542 | Synthetic SCP from DeepSeek-R1-0528 | Text | Undisclosed | [SCP-116K](https://huggingface.co/datasets/EricLu/SCP-116K) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
543 | Synthetic Stack Exchange from DeepSeek-R1-0528 | Text | Undisclosed | [Stack Exchange](https://archive.org/details/stackexchange) | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
544 | Synthetic Common Crawl from Qwen3-30B-A3B | Text | Undisclosed | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
545 | Synthetic Wikipedia from Qwen3-30B-A3B | Text | Undisclosed | [Wikimedia](https://dumps.wikimedia.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) |
546 | Synthetic Essential-Web from Qwen3-30B-A3B and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | [Essential-Web](https://huggingface.co/datasets/EssentialAI/essential-web-v1.0) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B); [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) |
547 | Synthetic Textbook Math from Qwen3-30B-A3B, Qwen3-235B-A22B, phi-4 | Text | Undisclosed | [Common Crawl](https://commoncrawl.org/); [FineMath](https://huggingface.co/datasets/HuggingFaceTB/finemath) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B); [phi-4](https://huggingface.co/microsoft/phi-4) |
548 | Synthetic Math and Code from DeepSeek-R1 and DeepSeek-R1-0528 | Text | Undisclosed | [Magicoder-Evol-Instruct-110K](https://huggingface.co/datasets/ise-uiuc/Magicoder-Evol-Instruct-110K); [opc-sft-stage2](https://huggingface.co/datasets/OpenCoder-LLM/opc-sft-stage2); [TACO](https://huggingface.co/datasets/BAAI/TACO); [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning); [OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning); [NuminaMath CoT](https://huggingface.co/datasets/AI-MO/NuminaMath-CoT) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1); [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) |
549 | Synthetic Nemotron-Personas-USA from gpt-oss-120b and Qwen3-8B | Text | Undisclosed | [Nemotron-Personas-USA](https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) |
550
551 ##
552
553 | Dataset | Collection Period |
554 | :---- | :---- |
555 | [Problems in Elementary Mathematics for Home Study](https://archive.org/details/AntonovVygodskyNikitinSankinProblemsInElementaryMathematicsForHomeStudyMir1982) | 4/23/2025 |
556 | [GSM8K](https://github.com/openai/grade-school-math) | 4/23/2025 |
557
558 ##
559
560 ### Evaluation Dataset:
561
562 * Data Collection Method by dataset: Hybrid: Human, Synthetic
563 * Labeling Method by dataset: Hybrid: Automated, Human, Synthetic
564
565 ## Inference
566
567 - Engines: HF, vLLM, llama-cpp, TRT-LLM, SGLang
568 - Test Hardware: NVIDIA GeForce RTX, H100 80GB, DGX Spark, Jetson Thor/Orin Nano
569
570 ## Ethical Considerations
571
572 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.
573
574 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.
575
576 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.
577
578 Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
579