--- title: NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 canonical_url: "https://www.modelscope.cn/models/nv-community/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16" md_url: "https://www.modelscope.cn/models/nv-community/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16.md" repository: nv-community/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 last_updated: 2026-10-15 license: other pipeline_tag: image-text-to-text tasks: - image-text-to-text model_type: - nemotron_h_omni architectures: - NemotronH_Omni_Reasoning_V3 parameters: 124.5B tensor_type: - BF16 - F32 - I64 library_name: - safetensors language: - en - es - fr - de - it - ja downloads: 36 stars: 3 tags: - nvidia - pytorch - nemotron-3.5 --- # NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 > NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 - nv-community 在 ModelScope 开源的模型。NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 nv-community/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 是 ModelScope 魔搭社区上的 124.5B 参数image-text-to-text模型，采用 other 许可。 - **Repository**: nv-community/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 - **License**: other - **Tasks**: image-text-to-text - **Parameters**: 124.5B - **Tags**: nvidia, pytorch, nemotron-3.5 - **Downloads**: 36 - **Stars**: 3 - **Last updated**: 2026-10-15 Source: https://www.modelscope.cn/models/nv-community/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 --- # NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16
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![image](accuracy_plot.png) ## Description **NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16** is the full-precision release of NVIDIA Nemotron 3.5 Super VL, a vision-language model for reasoning, coding, tool use, document understanding, and video understanding. The BF16 weights support customization, domain adaptation, and full-precision evaluation. For optimized deployment, see the [NVFP4 release](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4). The model combines interleaved Mamba-2, Mixture-of-Experts (MoE), and attention layers with a C-RADIO vision encoder and a vision-language connector. It has **120B total parameters** and **12B active parameters** and supports context lengths up to **1M tokens**. Multi-Token Prediction (MTP) enables native speculative decoding. This model is ready for commercial or non-commercial use. ## Model Summary | | | |:---|:---| | **Total Parameters** | 120B (12B active) | | **Architecture** | Mamba-2–Transformer hybrid Mixture of Experts with a C-RADIO vision encoder | | **Precision** | BF16 (full-precision reference weights) | | **Context Length** | Up to 1M tokens; deployment settings are listed below | | **Inputs / Outputs** | Text, images, and video / text | | **Deployment** | 4× H100 or 2× GB200 | | **Speculative Decoding** | Native Multi-Token Prediction (MTP) | | **Reasoning Mode** | Configurable through the chat template (`enable_thinking=True/False`) | | **Recommended Sampling** | Text: temperature 1.0, top_p 0.95; vision: temperature 0.6, top_p 0.95 | | **Best For** | Customization, domain adaptation, and full-precision evaluation | | **License** | [OpenMDW-1.1](https://openmdw.ai/license/1-1/) | ## Model Overview **Model Developer:** NVIDIA Corporation ### What is Nemotron? NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents. ## Quick Start For efficient inference on DGX Spark or data center GPUs, see the [NVFP4 card](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4). The example below serves BF16 on **4× H100**. Use Docker with NVIDIA GPU support and the `vllm/vllm-openai:nightly-3f1bcfd82bb2e93fcd364fc7a5491718453a77e2` or (`vllm/vllm-openai:v0.32.0`) image. If the model requires authentication, set `HF_TOKEN` in your shell before launching the container. ```bash export MODEL=nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 ``` ```bash vllm serve $MODEL \ --served-model-name nemotron-3.5-super-vl \ --tensor-parallel-size 4 \ --trust-remote-code \ --gpu-memory-utilization=0.85 \ --allowed-local-media-path=/ \ --max-num-seqs=32 \ --limit-mm-per-prompt '{"video": 1, "image": 1}' \ --video-pruning-rate 0.5 \ --media-io-kwargs '{"video": {"fps": 2, "num_frames": 256}}' \ --enable-auto-tool-choice \ --tool-call-parser qwen3_coder \ --reasoning-parser nemotron_v3 \ --speculative_config.method mtp \ --speculative_config.num_speculative_tokens 3 ``` Wait for the server to finish loading the model and compiling kernels, then check the endpoint: ```bash curl --fail http://localhost:8000/v1/models ``` All vLLM and SGLang examples use port `8000` and the API model name `nemotron-3.5-super-vl`. See the [API Client](#api-client) for text, image, video, and tool-calling examples. ## License/Terms of Use **Governing Download Terms:** Use of this model is governed by the [OpenMDW License Agreement, version 1.1](https://openmdw.ai/license/1-1/). ## Benchmarks We evaluated our model on the following benchmarks: | Benchmark | Nemotron 3.5 SuperGA BF16 | Qwen3.5-122B-A10B | Qwen3.8-Flash-Next | DeepSeek-V4-Flash0731 | Nemotron 3 Ultra | Inkling-Small | GLM-5.3-Flash | | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | | GPQA-Diamond | 89.52 | 86.7 | 92.7 | 89.6 | 87.0 | 88.6 | 91.2 | | HLE, text-only | 31.79 | 24.2 | 39.6 | 35.8 | 29.2 | 33.3 | 36.47 | | MMLU-ProX | 76.91 | 83.2 | 83.0 | 85.5 | 83.1 | 84.0 | 83.0 | | AA-Omniscience accuracy | 26.20 | 26.2 | 27.9 | 45.0 | 25.3 | 35.4 | 34.2 | | AA-Omniscience hallucination rate | 30.0 | 84.5 | 41.3 | 95.9 | 22.1 | 59.6 | 30.3 | | AA-Omniscience index | 4.03 | -36.1 | -1.9 | -7.8 | 8.8 | -3.1 | 14.3 | | CritPt | 10.86 | 1.1 | — | 12.6 | 3.1 | 9.1 | — | | APEX shortlist | 88.36 | 54.7 | 91.8 | 88.7 | 75.0 | 71.5 | 77.1 | | LiveCodeBench v6 | 88.22 | 76.7 | 94.1 | 94.6 | 89.2 | 88.3 | 80.6 | | SciCode, subtask | 47.13 | 39.8 | 46.6 | 49.3 | 42.0 | 48.3 | 50.05 | | AA-LCR v1.1 | 68.62 | 75.1 | 78.4 | 79.7 | 75.2 | 77.3 | 79.5 | | GraphWalks | 89.71 | 86.1 | 76.1 | 65.4 | 63.3 | 55.7 | 88.4 | | IFBench | 70.86 | 67.6 | 75.6 | 76.2 | 82.9 | 79.3 | 57.7 | | LMArena v3 proxy | 52.67 | 47.0 | 35.2 | 49.8 | 34.3 | 53.6 | 61.0 | | WMT24++ | 84.80 | 86.3 | 86.2 | 85.5 | 82.7 | 84.6 | 86.1 | | tau2-bench retail | 69.24 | 55.4 | 71.6 | 73.8 | 73.7 | 71.8 | 78.2 | | tau2-bench airline | 76.50 | 63.5 | 80.8 | 83.5 | 79.8 | 62.7 | 81.8 | | tau2-bench telecom | 73.19 | 87.2 | 90.4 | 98.2 | 83.0 | 90.1 | 98.5 | | tau3-bench banking | 18.88 | 14.2 | 44.5 | 38.6 | 18.4 | — | 34.8 | | GDPval-AA v2 | 1211 | 861 | 1561 | 1382 | 1148 | 1313 | 1563 | | BrowseComp | 78 | 58.1 | 89.0 | 84.2 | 67.85 | 74.1 | 86.6 | | SWE-bench Verified | 71.10 | 71.9 | 78.7 | 75.4 | 73.9 | 75.27 | 79.10 | | SWE-bench Multilingual | 67.72 | 62.0 | 77.10 | 72.0 | 68.8 | 71.4 | 78.30 | | SWE-bench Pro | 51.78 | 44.9 | 60.37 | 56.91 | 49.66 | 56.54 | 59.70 | | Terminal-Bench 2.1 | 66.70 | 48.70 | 85.80 | 76.80 | 54.80 | 53.40 | 82.70 | | BabyVision | 29.60 | 35.7 | 46.8 | N/A | N/A | 24.7 | 39.1 | | MMLongBench-Doc | 62.82 | 63.7 | 67.9 | N/A | N/A | 49.06 | 65.8 | | V* | 85.60 | 91.1 | 93.0 | N/A | N/A | 70.5 | 88.1 | | OCR-Reasoning | 65.08 | 64.3 | 67.1 | N/A | N/A | 54.7 | 64.6 | | MathVision | 78.16 | 83.3 | 87.5 | N/A | N/A | 78.8 | 82.5 | | HLE, full set | 29.20 | 24.4 | 38.8 | N/A | N/A | 31.6 | 35.1 | | OCRBench v2, EN | 68.12 | 68.3 | 67.9 | N/A | N/A | 54.2 | 61.8 | | OCRBench v2, ZH | 59.15 | 67.3 | 68.3 | N/A | N/A | 52.4 | 56.8 | | CharXiv reasoning | 80.82 | 83.5 | 85.8 | N/A | N/A | 77.8 | 81.9 | | Video-MME v2 | 32.85 | 32.0 | 45.1 | N/A | N/A | — | 14.8 | | OSWorld-Verified | 51.14 | 51.5 | 55.8 | N/A | N/A | — | 14.78 | | BrowseComp [offline] | 78 | 58 | 89 | 83 | 44 (reported) | 74 | 86.6 | | WebVoyager [offline] | 77.72 | 54.89 | 90.39 | not applicable | not applicable | 5.25 | 72.64 | | AA-Briefcase-Lite [offline] | 1137 (-) \| 1252 (-) \| 40 | 551 \| 591 \| 22 | 1636 \| 1324 \| 76 | 1469 \| 1277 \| 80 | 665 (836) \| 866 (874) \| 47 | 765 \| 684 \| 17 | 1785 (1658) \| 1428 (1408) \| 76 | | AutomationBench-AA [offline] | 32.44 | 27.25 | 72.06 | 63.86 | 23.28 | 32.83 | 70.17 | | GDP.pdf [offline] | 6.6 (AA 6.6) | 8.8 (AA 3.4) | 17.4 (AA 15.6) | 10.8 (AA 11.0) | 8.4 (AA 5.0) | 8.6 (AA 11.2) | 14.8 (AA 15.4) | | Terminal Bench 4 [offline] | 1 | 0 | 20.71 | 3.54 | 0 | 0.5 | 15.15 | | DeepSWE [offline] | 24.65 | 0.4 | 55.57 | 42.3 | 3 | 18 | 57.77 / 63.4 (reported) | We evaluated Nemotron 3.5 Super on a comprehensive suite of benchmarks spanning agentic capabilities, reasoning and knowledge, multimodal, conversational ability and instruction following, long-context understanding, and multilingual performance. All evaluation results for Nemotron 3.5 Super and baselines were collected via NeMo Gym. For reproducibility purposes, more details on the evaluation settings can be found in the [NeMo Gym recipes folder](https://github.com/NVIDIA-NeMo/Gym) and the [corresponding reproducibility tutorial](https://github.com/NVIDIA-NeMo/Gym/blob/main/nemotron_recipes/super-3.5/reproducibility.md). The published recipes specify the benchmark-specific containers, prompts, inference parameters, parser configurations, and scoring settings used to produce the results. There were a few benchmarks that were not yet onboarded in our open source tools, and for these we used either their official open source implementation or an internal scaffolding that we plan to open source in the future: BabyVision, CharXiv-RQ, MathVision, MMLongBench-Doc, OCR-Reasoning, OCRBench-v2, V*, Video-MME-v2. ### Agentic Coding Benchmarks Additional harness-level coding-agent results for SWE-Bench Pro and Terminal-Bench 2.1 are shown below. ![image](harness_comparisons.png) ### Deployment Geography: Global ### Use Case This model is intended for developers who want to customize a post-trained vision-language model, adapt it to domain-specific data, develop quantized variants, or run full-precision inference and evaluation. ### Release Date Hugging Face - 10/15/2026 via [Hugging Face](https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16) ## Reference(s) * [NVIDIA Nemotron 3.5 Super VL and Lightning Technical Report](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3.5-Super-and-Lightning-Technical-Report.pdf) ## Model Architecture - **Architecture Type:** Mamba-2–Transformer hybrid Mixture of Experts (MoE) with Multi-Token Prediction (MTP) - **Network Architecture:** Nemotron Hybrid MoE with 512 routed experts per MoE layer, a C-RADIO vision encoder, and a vision-language connector - **Number of Model Parameters:** 120B total / 12B active ## Model Design The model combines sparse expert computation, attention, and Mamba-2 layers to support long-context processing. The vision encoder and connector turn visual inputs into representations processed by the language backbone. A shared MTP head predicts future tokens recursively for speculative decoding. The model supports up to 1M tokens of context. The prompt, visual inputs, conversation history, tool results, and generated response share this context window. Individual deployment recipes may use a smaller context window to accommodate the available GPU memory and concurrency. ## Training Methodology **Stage 1: Foundation and Multi-Token Prediction** The language backbone is initialized from Nemotron 3 Super Base. A shared MTP head is trained for 100B tokens while the backbone remains frozen, followed by 1T tokens of distillation from Nemotron 3 Ultra Base. **Stage 2: Continued Pre-Training and Mid-Training** The model undergoes 100B tokens of Indic continued pre-training, a 35B-token extension to 1M context, 67B tokens of software-engineering mid-training at 256K context, and a final 9B-token long-context recovery stage. Software-engineering data includes repositories, issue–pull-request pairs, commit diffs, build and CI logs, and agent trajectories. **Stage 3: Vision-Language Training** A C-RADIO vision encoder and connector extend the language backbone to visual inputs, including images, document pages, and video frames. **Stage 4: Supervised Fine-Tuning** A two-stage supervised fine-tuning pipeline jointly trains the language and vision components on vision, reasoning, code, tools, chat, safety, and long-context data. Training uses packed sequences of up to 524,288 tokens. **Stage 5: Reinforcement Learning and Distillation** Reinforcement learning with verifiable rewards, reinforcement learning from human feedback, and specialized teacher training improve reasoning, agentic, chat, and vision capabilities. Multi-teacher on-policy distillation consolidates these capabilities into a single model. **Stage 6: MTP Boosting** The MTP head is further trained against the frozen post-trained backbone to improve speculative-decoding acceptance. ## Input - **Input Type(s):** Text, images, and video - **Input Format(s):** Text strings, images, and sampled video frames - Video: mp4, up to 2 minutes. - Image: Red, Green, Blue (RGB) (jpeg, png) - **Input Parameters:** - Video: Three-Dimensional (3D) - Image: Two-Dimensional (2D) - Text: One-Dimensional (1D) - **Other Properties Related to Input:** Maximum context length up to 1M tokens. Documents can be supplied as page images with accompanying text. ## Output - **Output Type(s):** Text - **Output Format:** String - **Output Parameters:** One-dimensional sequences - **Other Properties Related to Output:** Maximum context length up to 1M tokens. Our AI models are designed and optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. ## Software Integration - **Runtime Engine(s):** vLLM; SGLang - **Hardware:** - NVIDIA Blackwell - NVIDIA Hopper - **Operating System(s):** Linux The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment. ## Model Version(s) * v1.0 - GA ## Quick Start Guide ### Choose your deployment | Hardware | Runtime | Configuration | |:---|:---|:---| | 4× H100 | vLLM | Text, image, and video; TP4; MTP with 3 draft tokens | | 2× GB200 | vLLM | Text, image, and video; TP2; MTP with 5 draft tokens | ### Speculative Decoding The serving examples enable the model's native MTP head. No separate drafter download is required for these vLLM and SGLang recipes. In vLLM, `num_speculative_tokens` controls draft length; in SGLang, the MTP recipe uses `EAGLE` with `speculative-eagle-topk 1`. Performance depends on hardware, request length, and concurrency. To compare with standard decoding, remove the speculative configuration while keeping the model, workload, sampling parameters, and other server settings the same. ### vLLM Use `vllm/vllm-openai:nightly-3f1bcfd82bb2e93fcd364fc7a5491718453a77e2` for these commands (or `vllm/vllm-openai:v0.32.0`). #### 4× H100 (up to 1M Context Length) Use this command if you need to serve the model with up to 1M context length. ```bash vllm serve $MODEL \ --served-model-name nemotron-3.5-super-vl \ --tensor-parallel-size 4 \ --trust-remote-code \ --async-scheduling \ --allowed-local-media-path=/ \ --max-num-seqs 32 \ --enable-mamba-shared-prefix-checkpoint \ --limit-mm-per-prompt '{"video": 1, "image": 1}' \ --video-pruning-rate 0.5 \ --media-io-kwargs '{"video": {"fps": 2, "num_frames": 256}}' \ --enable-auto-tool-choice \ --tool-call-parser qwen3_coder \ --reasoning-parser nemotron_v3 \ --speculative_config.method mtp \ --speculative_config.num_speculative_tokens 5 ``` #### 2× GB200 (up to 1M Context Length) Use this command if you need to serve the model with up to 1M context length. ```bash vllm serve $MODEL \ --served-model-name nemotron-3.5-super-vl \ --tensor-parallel-size 2 \ --trust-remote-code \ --async-scheduling \ --allowed-local-media-path=/ \ --max-num-seqs 32 \ --enable-mamba-shared-prefix-checkpoint \ --limit-mm-per-prompt '{"video": 1, "image": 1}' \ --video-pruning-rate 0.5 \ --media-io-kwargs '{"video": {"fps": 2, "num_frames": 256}}' \ --enable-auto-tool-choice \ --tool-call-parser qwen3_coder \ --reasoning-parser nemotron_v3 \ --speculative_config.method mtp \ --speculative_config.num_speculative_tokens 5 ``` For the multimodal examples, each request can contain one image and one video. The video configuration samples up to 256 frames at 2 FPS with a pruning rate of 0.5. For 1080p videos, use 1 FPS and up to 128 frames; 2 FPS and 256 frames can be used for lower-resolution video such as 720p. Start with short MP4 clips of one to two minutes. ### SGLang Use `lmsysorg/sglang:v0.5.20`. The example below runs on **two GB200 GPUs**. ```bash docker run --rm --gpus all --ipc=host \ -p 8000:8000 \ -e HF_TOKEN \ -v "$HOME/.cache/huggingface:/root/.cache/huggingface" \ --entrypoint sglang lmsysorg/sglang:v0.5.20 \ serve --model-path "$MODEL_CKPT" \ --served-model-name nemotron-3.5-super-vl \ --host 0.0.0.0 \ --port 8000 \ --trust-remote-code \ --tp-size 2 \ --reasoning-parser nemotron_3 \ --tool-call-parser qwen3_coder \ --mamba-ssm-dtype float16 \ --enable-mamba-cache-stochastic-rounding \ --mamba-cache-philox-rounds 5 \ --speculative-algorithm EAGLE \ --speculative-num-steps 5 \ --speculative-eagle-topk 1 ``` ### API Client Install the OpenAI-compatible client: ```bash pip install openai ``` #### Text and reasoning ```python from openai import OpenAI client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY") model = "nemotron-3.5-super-vl" response = client.chat.completions.create( model=model, messages=[{"role": "user", "content": "Explain why binary search takes logarithmic time."}], temperature=1.0, top_p=0.95, max_tokens=16384, extra_body={"chat_template_kwargs": {"enable_thinking": True}}, ) choice = response.choices[0] if choice.finish_reason == "length": raise RuntimeError("The response reached max_tokens; increase the output budget.") print(choice.message.content) ``` Set `enable_thinking` to `False` to disable reasoning. For medium reasoning effort, use `{"enable_thinking": True, "medium_effort": True}`. Reasoning tokens and the final answer share the `max_tokens` budget. #### Images Use a multimodal server configuration. The example below sends a local image as a base64 data URL. ```python import base64 from pathlib import Path from openai import OpenAI client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY") image_bytes = Path("image.png").read_bytes() image_url = "data:image/png;base64," + base64.b64encode(image_bytes).decode() response = client.chat.completions.create( model="nemotron-3.5-super-vl", messages=[{"role": "user", "content": [ {"type": "text", "text": "Describe the image and any visible text."}, {"type": "image_url", "image_url": {"url": image_url}}, ]}], temperature=1.0, top_p=0.95, max_tokens=16384, ) if response.choices[0].finish_reason == "length": raise RuntimeError("The response reached max_tokens; increase the output budget.") print(response.choices[0].message.content) ``` #### Video with vLLM Use a multimodal vLLM configuration with the video sampling settings above. ```python import base64 from pathlib import Path from openai import OpenAI client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY") video_bytes = Path("video.mp4").read_bytes() video_url = "data:video/mp4;base64," + base64.b64encode(video_bytes).decode() response = client.chat.completions.create( model="nemotron-3.5-super-vl", messages=[{"role": "user", "content": [ {"type": "text", "text": "Summarize the events in this video in order."}, {"type": "video_url", "video_url": {"url": video_url}}, ]}], temperature=1.0, top_p=0.95, max_tokens=16384, ) if response.choices[0].finish_reason == "length": raise RuntimeError("The response reached max_tokens; increase the output budget.") print(response.choices[0].message.content) ``` #### Tool calling The model requests a tool call; the application executes it and returns the result. This complete example uses a local inventory lookup. ```python import json from openai import OpenAI client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY") model = "nemotron-3.5-super-vl" inventory = {"keyboard": 12, "monitor": 5} tools = [{"type": "function", "function": { "name": "get_inventory", "description": "Return the inventory count for a product.", "parameters": { "type": "object", "properties": {"product": {"type": "string", "enum": ["keyboard", "monitor"]}}, "required": ["product"], "additionalProperties": False, }, }}] messages = [{"role": "user", "content": "How many keyboards are in stock?"}] def complete(history, tool_choice="auto"): response = client.chat.completions.create( model=model, messages=history, tools=tools, tool_choice=tool_choice, temperature=1.0, top_p=0.95, max_tokens=16384, extra_body={"chat_template_kwargs": {"enable_thinking": True}}, ) choice = response.choices[0] if choice.finish_reason == "length": raise RuntimeError("The response was truncated; increase max_tokens.") return choice.message message = complete(messages) messages.append(message.model_dump(exclude_none=True)) if not message.tool_calls: print(message.content) else: for call in message.tool_calls: arguments = json.loads(call.function.arguments) if (call.function.name != "get_inventory" or not isinstance(arguments, dict) or set(arguments) != {"product"} or not isinstance(arguments["product"], str) or arguments["product"] not in inventory): raise ValueError("Invalid inventory tool call") product = arguments["product"] messages.append({ "role": "tool", "tool_call_id": call.id, "content": json.dumps({"product": product, "count": inventory[product]}), }) print(complete(messages, tool_choice="none").content) ``` ## Training, Testing, and Evaluation Datasets ### Training **Data Modality:** Text, images, and video **Foundation Training Data Size:** More than 20 trillion tokens **Image Training Data Size:** 1 Million to 1 Billion Images **Text Training Data Size:** More than 10 Trillion Tokens **Video Training Data Size:** 10,000 to 1 Million Hours **Foundation Data Collection Period:** 2013 to December 2025 **Data Collection Method:** Hybrid: automated, manually-collected, and synthetic **Labeling Method:** Hybrid: automated, manually-labeled, and synthetic The foundation corpus contains curated and synthetic text covering English, 19 other spoken languages, and 43 programming languages. Sources include webpages, dialogue, articles, code, and other written materials across math, science, legal, finance, and general knowledge domains. The post-training corpus includes reasoning, coding, tool use, instruction following, structured outputs, chat, safety, and long-context tasks, together with image and video understanding. Synthetic trajectories are filtered for structural correctness, repetition, and task validity. For more information about the datasets used to train this model, please see the [Public Summary of Training Content](https://assets.ngc.nvidia.com/products/api-catalog/nemotron-3.5-super-vl/Nemotron_3.5_Super_VL_EU_Training_Content_Summary.pdf)
Detailed dataset information #### **Base Pre-Training Corpus (Nemotron 3 Foundation)** The foundation of the model is trained on the Nemotron 3 corpus, comprising the following datasets from the [Nemotron Pretraining Datasets collection](https://huggingface.co/collections/nvidia/nemotron-pre-training-datasets): | Dataset Collection | Token Counts | Description | | :--- | :--- | :--- | | **Nemotron-CC-v2** & **v2.1** | 9.1T | A massive collection of English web data filtered from Common Crawl, including 2.5T+ tokens of new organic, translated, and synthetically rephrased content. | | **Nemotron-CC-Code-v1** | 427.9B | High-quality code tokens extracted from Common Crawl using the Lynx + LLM pipeline to preserve structure and equations. | | **Nemotron-Pretraining-Code-v1** & **v2** & **v3** | 1.7T | Curated GitHub code references with multi-stage filtering, deduplication, and large-scale synthetic code data. | | **Nemotron-CC-Math-v1** | 133.3B | High-quality math pre-training dataset preserving LaTeX formatting and mathematical structures. | | **Nemotron-Pretraining-Specialized-v1** & **v1.1** & **v1.2** & **Nemotron-Pretraining-SFT-v1** | 660.0B | Synthetic datasets targeting specialized domains such as STEM reasoning and scientific coding. | | **Nemotron-Pretraining-Legal-v1** | 4.3B | Synthetic datasets targeting the legal domain. | ### Public Datasets | Dataset | Collection Period | | :---- | :---- | | [GSM8K](https://github.com/openai/grade-school-math) | 4/23/2025 | | [CC-NEWS](https://commoncrawl.org/blog/news-dataset-available) | 4/23/2025 | | [Common Crawl](https://commoncrawl.org/) | 4/23/2025 | | [Wikimedia](https://dumps.wikimedia.org/) | 4/23/2025 | | [Bespoke-Stratos-17k](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k) | 4/23/2025 | | [tigerbot-kaggle-leetcodesolutions-en-2k](https://huggingface.co/datasets/TigerResearch/tigerbot-kaggle-leetcodesolutions-en-2k) | 4/23/2025 | | [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 | | [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 | | [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 | | [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 | | [SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR)](https://www.sec.gov/search-filings) | 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 | | [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 | | [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 | | [FastChat](https://github.com/lm-sys/FastChat/blob/main/playground/data/dummy.json) | 10/2/2025 | | [News Commentary](https://opus.nlpl.eu/News-Commentary.php) | 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 | | NVClip (NV-CC-Img-Text-Dataset) | 2022-2026 | | [ScaleCUA, Processed Training Subset](https://huggingface.co/datasets/OpenGVLab/ScaleCUA-Data) | 2022-2026 | | [RoboInter-VQA](https://huggingface.co/datasets/InternRobotics/RoboInter-VQA) | 2022-2026 | | [Open Images V6, Object-Detection Training Records](https://storage.googleapis.com/openimages/web/index.html) | 2022-2026 | | YT1B PLM Video QA, Custom No-Thinking and Safety-Filtered | 2022-2026 | | YT1B PLM Dense Captions, No-Thinking and Safety-Filtered | 2022-2026 | | [RoboVQA](https://robovqa.github.io/) | 2022-2026 | | YT1B PLM Thinking Traces, Safety-Filtered | 2022-2026 | | Boyi Short-Video QA | 2022-2026 | | Boyi Short-Video QA, Custom No-Thinking and Safety-Filtered | 2022-2026 | | K710 Video Classification, Full Training Snapshot | 2022-2026 | | Molmo2-VideoCapQA | 2022-2026 | | Molmo2-VideoCapQA, Custom No-Thinking and Safety-Filtered | 2022-2026 | | Molmo2-VideoPoint, Custom No-Thinking and Safety-Filtered | 2022-2026 | | Molmo2-VideoPoint | 2022-2026 | ### **Crawled and Scraped from Online Sources by NVIDIA** The English Common Crawl data was downloaded from the Common Crawl Foundation (see their FAQ for details on their crawling) and includes the snapshots CC-MAIN-2013-20 through CC-MAIN-2025-13. The data was subsequently deduplicated and filtered in various ways used in the Nemotron-CC data pipeline. Additionally, we extracted data for fifteen languages from the following three Common Crawl snapshots: CC-MAIN-2024-51, CC-MAIN-2025-08, CC-MAIN-2025-18. The fifteen languages included were Arabic, Chinese, Danish, Dutch, French, German, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Swedish, and Thai. As we did not have reliable multilingual model-based quality classifiers available, we applied just heuristic filtering instead—similar to what we did for lower quality English data in the Nemotron-CC pipeline, but selectively removing some filters for some languages that did not work well. Deduplication was done in the same way as for Nemotron-CC. The GitHub Crawl was collected using the GitHub REST API and the Amazon S3 API. Each crawl was operated in accordance with the rate limits set by its respective source, either GitHub or S3. We collect raw source code and subsequently remove any having a license which does not exist in our permissive-license set. | Dataset | Modality | Dataset Size | Collection Period | Collecting Organisation | | :---- | :---- | :---- | :---- | :---- | | English Common Crawl | Text | 3.36T | 4/8/2025 | NVIDIA Advanced Deep Learning Research | | English Common Crawl 1.1 | Text | Not disclosed | 10/2/2025 | NVIDIA Advanced Deep Learning Research | | Multilingual Common Crawl | Text | 812.7B | 5/1/2025 | NVIDIA Advanced Deep Learning Research | | GitHub Crawl | Text | 747.4B | 4/29/2025 | NVIDIA Advanced Deep Learning Research | | GitHub Crawl 1.1 | Text | 172.7B | 9/30/2025 | NVIDIA Advanced Deep Learning Research | | [Sangraha](https://huggingface.co/datasets/ai4bharat/sangraha) | Text | 109,263,115 indexed source sequences: 77,231,480 verified and 32,031,635 unverified | Undisclosed | NVIDIA Advanced Deep Learning Research | | LITA Chunked Video Labels, Custom No-Thinking Dense Captions | Text (video labels) | 151,349 records | Undisclosed | NVIDIA Advanced Deep Learning Research| | LITA Chunked Video Labels, Cleaned v2 | Text (video labels) | 150,434 records | Undisclosed | NVIDIA Advanced Deep Learning Research | | Single-Page Document Labels, Qwen3.5 v2 | Text and images | 50,930 records | Undisclosed | NVIDIA | | Processed YouTube Videos, Cosmos Dense Captions | Text and video | 274,472 records | Undisclosed | NVIDIA | ## Private Non-publicly Accessible Datasets of Third Parties | Dataset | Model(s) used | |---------|---------------| | Global Regulation | Unknown | | TAUS Translation Memory | Unknown | | Scale HLE | Unknown | | HackerRank Coding | Unknown | | RL data for Search | Gemini 3; GPT-5 | | Mercor SWE-AgentsV1 | Undisclosed | | KeenSight Batch 1, Relabeled Subset | Qwen3.5-122B-A10B | | KeenSight Batch 1, Remaining Subset | Qwen3.5-122B-A10B | | Camio Multilingual OCR and Document Layout | Undisclosed | | Chinese Visual Question Answering, Relabeled Subset | Qwen3.5-122B-A10B | | Camio Chinese OCR, Cleaned Variant | Qwen3.5-122B-A10B | | Camio English OCR, Cleaned Variant | Qwen3.5-122B-A10B | | KeenSight Batch 2, Remaining Subset | Qwen3.5-122B-A10B | | KeenSight Batch 1, Safety-Filtered Trace Variant | Qwen3.5-122B-A10B | | Chinese Visual Question Answering, Safety-Filtered Trace Variant | Qwen3.5-122B-A10B | | Camio Chinese Question Answering, Verified-Correct Subset | Qwen3.5 | | KeenSight Chinese OCR, Filtered Subset | DeepSeek-OCR | | Camio Synthetic Trading-Graph Question Answering | Undisclosed | | Camio Synthetic Menu and Receipt Question Answering | Qwen3.5-122B-A10B | | Camio Chinese OCR, Formatted Variant | Qwen3.5-122B-A10B | | Camio Synthetic Time-Series Question Answering | Undisclosed | | Camio English OCR, Formatted Variant | Qwen3.5-122B-A10B | | Lean Formal Proofs — RL | Undisclosed | | Document-Grounded Finance Reasoning, Patch Tasks — RL | Undisclosed | | Math CoT — SFT | Kimi K3 | | Chat — SFT | Undisclosed | | Chat, Reasoning Off — SFT | Undisclosed | | Conversational Tool-Use Simulation — RL | Undisclosed | | Math CoT, Low Variant — SFT | Kimi K3 | | Document-Grounded Finance Reasoning — SFT | Undisclosed | | Safety Preference v0.3 — RL | Undisclosed | | Response-Length Instruction Following, Reasoning Off — SFT | Undisclosed | | Math Python TIR, Low Variant — SFT | Kimi K3 | | Math Python TIR — SFT | Kimi K3 | | General-Purpose Tool Calling — SFT | Qwen3.5-397B-A17B; Gemma-4-31B-it | | Knowledge Agent — SFT | Undisclosed | | DataFunnel arXiv Science4 v1.0 — RL | DeepSeek-V4-Flash (problem extraction); GLM-5.2 (refinement); Kimi K3 and K3 V5 (verification and filtering); Super 3.5 r14 (difficulty profiling); Qwen3-235B-A22B-Instruct-2507-FP8 (reward judging) | | Structured Outputs v3 — RL | gpt-oss-120b | | Response-Length Instruction Following — SFT | Undisclosed | | Filtered CUDA Coding — SFT | Undisclosed | | Reasoning Gym — RL | Undisclosed | | Competitive Coding Challenges v2 — SFT | GLM-5.2 | | Multi-Turn Instruction Following, Reasoning Off — SFT | Undisclosed | | Multi-Turn Instruction Following — SFT | Undisclosed | | Instruction Following, Filtered RLHF Prompts — SFT | Undisclosed | | Citation Formatting — RL | gpt-oss-120b | | Long-Context General Data — SFT | Undisclosed | | Free-Form Formatting — RL | gpt-oss-120b | | Structured Outputs v4 — RL | Undisclosed | | Tool-Call Schema — RL | No model used for RL conversion; source-generation models undisclosed | | Competitive Coding, OCR Python — SFT | DeepSeek-V4-Pro | | Lean Formal Proofs — SFT | gpt-oss-120b; Goedel-Prover-V2-32B; Qwen2.5-32B-Instruct | | Citation Instruction Following — RL | Undisclosed | | Search Graph Walks — SFT | MiniMax-M2 | | General-Purpose Tool Calling, Reasoning Off — SFT | Undisclosed | | Knowledge Agent Tau3, Reasoning Off — SFT | Undisclosed | | ARC-AGI Puzzle Reasoning — SFT | Kimi-K2.5; Qwen3-235B-A22B-Thinking-2507; DeepSeek-V3.2; Qwen3-Next-80B-A3B-Thinking; GLM-4.5-Air-FP8; gpt-oss-120b; GLM-4.7-FP8; Ring-1T; Ministral-3-14B-Reasoning-2512 | | Calendar — RL | Undisclosed | | ACEProof — SFT | Undisclosed | | Long Translation — SFT | Undisclosed | | Agentless Software Engineering — SFT | Undisclosed | | Long-Context PDF Translation v1.0 — SFT | DeepSeek-V4-Flash (translation generation); GPT-5.6 Sol (filtering and judging) | | KeenSight Batch 2, Relabeled and Filtered Subset | Qwen3.5-122B-A10B; Gemini 3 Flash Preview (filtering) | | Camio Chinese Letter QA, Cleaned `entrace_cleaned` Variant | Qwen3-VL-30B-A3B-Thinking-FP8; gpt-oss-120b; Qwen3.5-122B-A10B; GPT-4o mini (filtering and quality checks) | | Camio Chinese Letter QA, Cleaned Variant | Qwen3-VL-30B-A3B-Thinking-FP8; gpt-oss-120b; Qwen3.5-122B-A10B; GPT-4o mini (filtering and quality checks) | | Camio Chinese Text QA, Cleaned `entrace_cleaned` Variant | Qwen3-VL-30B-A3B-Thinking-FP8; gpt-oss-120b; Qwen3.5-122B-A10B; GPT-4o mini (filtering and quality checks) | ## Private Non-publicly Accessible Datasets by NVIDIA | Dataset | Model(s) used | |---------|---------------| | Simple Minesweeper | Undisclosed | | Simple Sudoku | Undisclosed | | Multitool Typewriter Hard | Undisclosed | | Machine Translation of News Commentary and TAUS Translation Memory | Undisclosed | | Machine Translation of STEM - | [Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) | | Competitive Coding RL data from Nemotron Cascade | Undisclosed | | Long context RL | Undisclosed | | Single-step SWE RL for patch generation | Undisclosed | | OpenHands SWE | Undisclosed | ## NVIDIA-Sourced Synthetic Datasets (Pre-Training) | Dataset | Modality | Dataset Size | Seed Dataset | Model(s) used for generation | | :---- | :---- | :---- | :---- | :---- | | Nemotron-Pretraining-Fact-Seeking | Text | 35.0B | [FineWiki](https://huggingface.co/datasets/HuggingFaceFW/finewiki) | [Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507) | | Nemotron-Pretraining-Legal | Text | 4.3B | CommonPile (caselaw_access_project_filtered); California Code of Regulations; Judicial Ethics Opinions; [GLOBALCIT](https://globalcit.eu/); [CUAD](https://www.atticusprojectai.org/cuad); Nemotron Personas; [ToSDR Terms of Service Corpus](https://www.kaggle.com/datasets/sonu1607/tosdr-terms-of-service-corpus); CodeHima/TOS_Dataset; [ContractNLI](https://stanfordnlp.github.io/contract-nli/); CaseHOLD; Code of Federal Regulations; [Canadian Case Law](https://huggingface.co/datasets/a2aj/canadian-case-law) (subsets that allow commercial use) | [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) | | Nemotron-Pretraining-Formal-Logic | Text | 128M | [Nemotron Personas](https://huggingface.co/datasets/nvidia/Nemotron-Personas-USA) | [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) | | Nemotron-Pretraining-Economics | Text | 73.4M | - | [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507) | | Nemotron-Pretraining-Multiple-Choice | Text | 1.6B | [MMLU Auxiliary Train](https://huggingface.co/datasets/cais/mmlu/viewer/all/auxiliary_train) | [DeepSeek-V3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) | | Nemotron-Pretraining-Code-Concepts | Text | 7.3B | - | [gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b); [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) | | Nemotron-Pretraining-Unconditional-Algorithmic | Text | 196.5M | - | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) | | More Synthetic Tasks from DeepSeek-V3 and Qwen3-235B-A22B | Text | 1.1B | train splits of acp_bench; ai2_arc; babi; gsm8k; hendrycks_math; IFEval; MedText; mediqa_qa; mlqa; MMLU-Pro; mmlu-pro-plus; MMLU-ProX; nq_open; tinyGSM8k; truthful_qa; truthfulqa-multi; MATH-lighteval; mmlu; awesome-chatgpt-prompts; super_glue | [DeepSeek v3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) | | Synthetic Tasks from DeepSeek-V3 and Qwen3-235B-A22B | Text | 6.7B | train splits of Into the Unknown; AI2 ARC (AI2 Reasoning Challenge); BLiMP (Benchmark of Linguistic Minimal Pairs); CommonSenseQA; GLUE; HeadQA; Hendrycks Ethics; Memo Trap; modus-tollens; NeQA; pattern-matching-suppression; mastermind_24_mcq_random; mastermind_24_mcq_close; quote-repetition; redefine-math; Repetitive Algebra; sig-figs; MMLU-Pro; MC-TACO; MedConceptsQA; MMLU_dataset; OpenbooksQA; PIQA (Physical Interaction Question Answering); SocialIQA; SuperGLUE; tinyAI2_arc; tinyMMLU; tinyWinogrande; TruthfulQA; WebQuestions; Winogrande; GPQA; MBPP | [DeepSeek v3](https://huggingface.co/deepseek-ai/DeepSeek-V3); [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) | | Synthetic Art of Problem Solving from DeepSeek-R1 | Text | 40B | [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) | | Synthetic Moral Stories and Social Chemistry from Qwen3-235B-A22B-Thinking-2507 and Mixtral-8x22B-v0.1 | Text | 15.2M | [social-chemestry-101](https://huggingface.co/datasets/tasksource/social-chemestry-101); [Moral Stories](https://huggingface.co/datasets/demelin/moral_stories) | [Qwen3-235B-A22B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507); [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1) | | 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) | | 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) | | 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) | | 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) | | [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) | | 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) | | Synthetic Rephrased [Math Data from Common Crawl](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) | | Synthetic Math Data from Common Crawl 4plus | Text | 52.3B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) | | Synthetic Math Data from Common Crawl 3 | Text | 80.9B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) | | 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) | | 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) | | Synthetic Art of Problem Solving from Qwen2.5-32B-Instruct, Qwen2.5-Math-72B, Qwen2.5-Math-7B, and Qwen2.5-72B-Instruct | Text | Undisclosed | [Art of Problem Solving](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) | | 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) | | Synthetic Long Context Continued Post-Training Data from Papers and Permissible Books from Qwen2.5-72B-Instruct | 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/) | [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct) | | Synthetic Common Crawl from Qwen3-30B-A3B and Mistral-Nemo-12B-Instruct | Text | 415.8B | [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) | | Synthetic Multilingual Data from Common Crawl from Qwen3-30B-A3B | Text | Undisclosed | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) | | Synthetic Multilingual Data from Wikimedia from Qwen3-30B-A3B | Text | Undisclosed | [Wikimedia](https://dumps.wikimedia.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) | | Synthetic Math Data from Wikimedia from Nemotron-4-340B-Instruct | Text | Undisclosed | - | [Nemotron-4-340B-Instruct](https://huggingface.co/nvidia/Nemotron-4-340B-Instruct) | | Synthetic Common Crawl Code from phi-4 | Text | 427.9B | [Common Crawl](https://commoncrawl.org/latest-crawl) | [phi-4](https://huggingface.co/microsoft/phi-4) | | Synthetic Scientific Coding from Qwen3-235B-A22B | Text | 1.2B | [Wikimedia](https://dumps.wikimedia.org/) | [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) | | 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) | | 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) | | Translated Synthetic Crawl | Text | 389.9B | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) | | Translated Synthetic Wikipedia | Text | 7.9B | [Wikimedia](https://dumps.wikimedia.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) | | 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) | | Synthetic Search STEM OPENQ from DeepSeek-R1-0528 | Text | Undisclosed | - | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) | | 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) | | Synthetic Offline Search MCQA HLE from DeepSeek-R1-0528 | Text | Undisclosed | - | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) | | 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) | | 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) | | 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) | | Synthetic Code from Qwen3-32B | Text | Undisclosed | English Common Crawl; English Common Crawl 1.1 | [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) | | Synthetic OpenCodeReasoning from DeepSeek-R1 | Text | Undisclosed | [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) | [DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) | | 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) | | Synthetic HackerRank Coding from DeepSeek-R1-0528 | Text | Undisclosed | HackerRank Coding Dataset | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) | | 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) | | 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) | | 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) | | 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) | | 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) | | 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) | | Synthetic Common Crawl from Qwen3-30B-A3B | Text | Undisclosed | [Common Crawl](https://commoncrawl.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) | | Synthetic Wikipedia from Qwen3-30B-A3B | Text | Undisclosed | [Wikimedia](https://dumps.wikimedia.org/) | [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B) | | 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) | | 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) | | 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) | | 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) | | 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) | | 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) | | 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) | | Synthetic Scale HLE from gpt-oss-120b | Text | Undisclosed | Scale HLE | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) | | Synthetic CDQuestions from gpt-oss-120b | Text | Undisclosed | [CDQuestions](https://cdquestions.com/) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) | | 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) | | Synthetic Vedantu from gpt-oss-120b | Text | Undisclosed | [Vedantu](https://www.vedantu.com/) | [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) | | 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) | | 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) | | Synthetic MCQ10 from DeepSeek-R1-0528 | Text | Undisclosed | - | [DeepSeek-R1-0528](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528) | | 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) | | Indic SDG for Nemotron | Text | 711,276,565 indexed source-sequence entries; final training-blend count not confirmed | nvidia/Nemotron-CC-v2 | google/gemma-4-31B-it; google/gemma-4-31B-it-assistant | ## NVIDIA-Sourced Synthetic Datasets (Post-Training) | Dataset | Modality | Dataset Size | Seed Dataset | Model(s) used for generation | | :---- | :---- | :---- | :---- | :---- | | Synthetic Single-Step Tool Use — RL | Text | Approximately 1,433,203 samples | Handwritten prompts; no external seed data | DeepSeek-V3.2; DeepSeek-R1-0528; Qwen3-235B-A22B-Thinking-2507; Qwen3-32B | | Synthetic Sparse Tables with Mixed Prompts | Text and images | 100,000 records | Generated in-house; no external seed data | None; generated without an SDG model | | Synthetic Single-Step Tool Use — RL | Text | Approximately 1,433,203 samples | Handwritten prompts; no external seed data | DeepSeek-V3.2; DeepSeek-R1-0528; Qwen3-235B-A22B-Thinking-2507; Qwen3-32B | | Synthetic Sparse Tables with Mixed Prompts | Text and images | 100,000 records | Generated in-house; no external seed data | None; generated without an SDG model | | Synthetic Competitive MATH Proofs from DeepSeek-V4-Pro | Text | Undisclosed | [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions] | [deepseek-ai/DeepSeek-V4-Pro] | | Synthetic Hermes Agent Reasoning Traces | Text | Undisclosed | [lambda/hermes-agent-reasoning-traces] | [hermes-agent-generator] | | Synthetic Competitive Coding from DeepSeek-V4-Pro | Text | Undisclosed | [NVCompetitiveCodingV1] | [deepseek-ai/DeepSeek-V4-Pro] | | Synthetic Competitive Science Reasoning from DeepSeek-V4-Pro | Text | Undisclosed | [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [EssentialAI/essential-web-v1.0]; [cdquestions.com]; [Pile-FreeLaw]; [Vedantu]; [askfilo]; [doubtnut]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]; [AAPT]; [ChemData 700K]; [oMeBench]; [Flavor Analysis and Recognition Transformer]; [ChemCoTBench]; [Llama Nemotron Dataset] | [deepseek-ai/DeepSeek-V4-Pro] | | Synthetic Competitive MATH CoT and TIR from Nemotron 5.5 | Text | Undisclosed | [Pile-FreeLaw]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions] | [Nemotron 5.5] | | Vendor Terminal Bench-like Tasks from Mercor | Text | Undisclosed | [Terminal bench like tasks curated by the vendor] | [Undisclosed - purchased dataset] | | Turing Math Data Pack | Text | Undisclosed | [Turing Math Data Pack dataset] | [Undisclosed - purchased dataset] | | Synthetic Holdout, Skywork, DAPO, and Turing Math from GPT-5.5 | Text | Undisclosed | [DocQA-RL-1.6K]; [DAPO-Math-17k] | [GPT-5.5] | | Synthetic Long Context RL from QwenLong L1 and DocQA-RL-1.6K | Text | Undisclosed | [DocQA-RL-1.6K] | Undisclosed | | Synthetic Competitive Coding Gym Tasks | Text | Undisclosed | [NVCompetitiveCodingV1.1] | Undisclosed | | Synthetic Finance SEC Search Agent from GPT-OSS-120B and Qwen3 | Text | Undisclosed | [SEC filings from sec.gov] | [GPT-OSS-120B]; [Qwen3-235B-A22B-Instruct]; [Qwen3-4B-Instruct] | | Synthetic Structured Outputs from Qwen3-30B-A3B-Instruct-2507, Qwen3-30B-A3B-Thinking-2507, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | [Nemotron-RL-agent-structured-outputs-v1] | [Qwen3-30B-A3B-Instruct-2507]; [Qwen3-235B-A22B-Instruct-2507] | | Synthetic Long Context Equivalence Rule from Qwen3-235B-A22B-Thinking-2507 and DeepSeek-R1 | Text | Undisclosed | [Long-context SFT data] | [Qwen/Qwen3-235B-A22B-Thinking-2507]; [Deepseek-ai/DeepSeek-R1] | | Synthetic Science RL Data Blend from Qwen2.5-32B | Text | Undisclosed | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [Qwen2.5-32B] | | Synthetic Abstention Data from Nemotron Super v3 | Text | Undisclosed | [Go abstention Dataset] | [nvidia/nvidia/nemotron-3-super-v3] | | Synthetic Chemistry Data from Nemotron Super v3 | Text | Undisclosed | [ChemData 700K] | [nvidia/nvidia/nemotron-3-super-v3] | | Synthetic Tool Call Schema for RL | Text | 469,983 | [UltraTool]; [ToolEyes]; [AutoTools]; [API-Bank]; [Nemotron-Personas-USA]; [Salesforce xLAM function-calling]; [Glaive function-calling-v2]; [Agent-Ark/Toucan-1.5M] | [DeepSeek-V3.2]; [GLM-4.6]; [gpt-oss-120b]; [Kimi-K2-Instruct] | | Synthetic Freeform Text Formatting from GPT-OSS-120B | Text | Undisclosed | [In-house data] | [GPT OSS 120B - Apache 2.0] | | Synthetic Citation Formatting from GPT-OSS-120B | Text | Undisclosed | [In-house data] | [GPT OSS 120B - Apache 2.0] | | Droid Harness Pivot Vendor Data | Text | Undisclosed | [Droid Harness Pivot vendor data] | Undisclosed | | Synthetic HotpotQA Training Data from Qwen3-235B | Text | Undisclosed | [HotpotQA] | [Qwen3-235B] | | Synthetic Natural Language Math Proofs from Nemotron 5.5 | Text | Undisclosed | [AMC8, AMC10, and AIME problem sets hosted on Art of Problem Solving]; [Pile-StackExchange] | [Nemotron 5.5] | | Synthetic Stack Overflow OpenQ | Text | Undisclosed | [Pile-FreeLaw] | Undisclosed | | Chemistry Ether0 Vendor Data | Text | Undisclosed | [Chemistry ether0 vendor data] | Undisclosed | | Synthetic Litmus-Bench Chemistry from ChEMBL | Text | Undisclosed | [ChEMBL]; [Nemo Gym RL dataset generated from ChEMBL with RDKit] | Undisclosed | | Synthetic ZINC Chemistry from Nemotron Super v3 | Text | Undisclosed | [ZINC] | [Nemotron Super v3] | | ARC-AGI Gym Environment | Text | Undisclosed | [ARC-AGI-2] | [ARC-AGI-2] | | Synthetic Agentic Search Tool-Use from DeepSeek-V3.2 | Text | Undisclosed | [Mercor Data] | [DeepSeek-V3.2] | | Synthetic Text-To-SQL | Text | 96,564 | [In-house Text-to-SQL data] | [gpt-oss-120b] | | Dialog Memory Vendor Data | Text | Undisclosed | [Patronus external vendor agreement] | Undisclosed | | Synthetic Indirect Prompt Injection from Nemotron Super v3 and Qwen3-Next-80B-A3B-Instruct | Text | Undisclosed | [In-house indirect prompt injection data] | [nvidia/nemotron-3-super-v3, qwen/qwen3-next-80b-a3b-instruct] | | Synthetic Malicious Code and Agentic Security | Text | Undisclosed | [In-house malicious-code / agentic-security data] | Undisclosed | | Synthetic Single-Step SWE Patch Selection | Text | Undisclosed | [SWE-Gym Dataset]; [SWE Bench Verified Benchmark] | [ground truth and task checks] | | Synthetic Natural Language Math Final Answers from Nemotron 5.5 | Text | Undisclosed | [AMC8, AMC10, and AIME problem sets hosted on Art of Problem Solving]; [Pile-StackExchange] | [nemotron 5.5] | | Synthetic Simple Math Prompts for Token Efficiency | Text | Undisclosed | [In-house simple math prompts] | Undisclosed | | Synthetic Abstention Data from Nemotron Super v3 (CRAG) | Text | Undisclosed | [CRAG] | [nvidia/nvidia/nemotron-3-super-v3] | | Synthetic Agentless SWE | Text | 242,536 | [SWE-Rebench-V2]; [SWEbench Training Set]; [R2E-Gym/R2E-Gym-Subset]; [SWE-Gym/SWE-Gym]; [SWE-Rebench] | [openai/gpt-oss-120b] | | Synthetic Agentless SWE from DeepSeek-R1-0528 | Text | 209,976 | [SWE-Bench-Train]; [SWE-Fixer-Train]; [SWE-reBench]; [SWE-Smith] | [deepseek-ai/DeepSeek-R1-0528] | | Synthetic Agentic CUDA Traces from GLM-4.7 | Text | 2,276 | [Internal CUDA task data] | [GLM-4.7] | | Synthetic Math Proofs from DeepSeek-V3.2-Speciale | Text | 820,772 | [Nemotron-Math-Proofs-v1] | [SDG: DeepSeek-V3.2-Speciale]; [Filter: proof validation] | | Synthetic Multilingual SFT from DeepSeek-V3 | Text | 1,245,284 | [Nano v3 SFT data] | [DeepSeek-V3] | | Synthetic Agentic Code from gpt-oss-120b | Text | 109,086 | [NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1] | [openai/gpt-oss-120b] | | Synthetic Agentic CLI and Web Skills from gpt-oss-120b | Text | 27,418 | [NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1] | [openai/gpt-oss-120b] | | Synthetic Agentic Coding from gpt-oss-120b | Text | 160,531 | [NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1] | [openai/gpt-oss-120b] | | Synthetic OpenCode Agentic Tasks from gpt-oss-120b | Text | 614,773 | [NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1] | [openai/gpt-oss-120b] | | Synthetic SWE Unverified | Text | Undisclosed | [NVAgenticCLIPrompts-v1]; [NVAgenticSkills-v1]; [NVAgenticCLIMultiTurnPrompts-v1]; [NVAgenticCLIPrompts-Web-v1] | [gpt-oss-120b]; [Qwen/Qwen3-Coder-480B-A35B-Instruct]; [GLM-4.7-Flash] | | Synthetic ARC-AGI Ultra Data | Text | 192,016 | [ARC-AGI-2]; [arc dataset collection] | [ARC-AGI-2] | | Synthetic LiveCodeBench TIR from DeepSeek-R1-0528 | Text | 1,283,398 | [Nemotron-X training datasets] | [DeepSeek-R1-0528] | | Synthetic Verilog and SystemVerilog Code from DeepSeek-R1-0528 and GPT-OSS-120B | Text | 1,233,247 | [Verilog/SystemVerilog seed code] | [SDR: DeepSeek R1 0528 and GPT-OSS-120B]; [Filtering: Claude 4 Sonnet] | | Synthetic Aider Python Tasks from DeepSeek-R1-0528 | Text | 236,099 | [Exercism (GitHub Python)] | [Deepseek R1 0528] | | Synthetic Chat Reasoning-Off Data from GLM-5 | Text | 646,738 | [lmarena-ai/repochat-arena-preference-4k user prompts] | [Multi-turn conversations generated by GLM-5 with best-of-4 selection via Qwen3-Nemotron-235B-A22B-GenRM] | | Synthetic Chat Reasoning-On Data from GLM-5 | Text | 644,286 | [lmarena-ai/repochat-arena-preference-4k user prompts]; [lmarena-ai/arena-expert-5k user prompts]; [lmarena-ai/arena-human-preference-55k user prompts]; [lmarena-ai/arena-human-preference-100k user prompts]; [lmarena-ai/arena-human-preference-140k user prompts] | [Multi-turn conversations generated by GLM-5 with best-of-4 selection via Qwen3-Nemotron-235B-A22B-GenRM] | | Synthetic Multilingual Safety from Riva-Translate-4B-Instruct-v1.1 | Text | 132,067 | [Safety SFT Data: Ultra] | [nvidia/Riva-Translate-4B-Instruct-v1.1] | | Synthetic Science Reasoning Effort Medium | Text | 502,722 | [science-reasoning-effort-medium-v0] | Undisclosed | | Synthetic Telecom Tool-Use Trajectories from gpt-oss-120b | Text | 12,455 | [Existing Tau2 telecom trajectories originally generated with DeepSeek V3.2] | [gpt-oss-120b] | | Synthetic Terminal Bench Data from OpenReasoningv2 | Text | Undisclosed | [OpenCodeReasoningv2]; [OpenMathReasoning]; [nemo-swe-bench-repos]; [SWE-Rebench]; [SWE-Fixer-110K] | [OpenReasoningv2] | | Synthetic Tulu Instruction Following from DeepSeek-R1-0528 | Text | 105,361 | [Nemotron-X training datasets] | [DeepSeek-R1-0528] | | Synthetic Instruction Following from gpt-oss-120b | Text | 151,988 | [IFEval]; [IFEvalG] | [gpt-oss-120b] | | Synthetic Instruction Following for RL | Text | Undisclosed | [WildChat-1M]; [LMSYS-340B-Eval Dataset]; [LMSYS-Chat-1M Prompts]; [IFEval]; [IFEvalG] | [Qwen/Qwen3-235B-A22B-Thinking-2507]; [gpt-oss-120b]; [Qwen3-235B-A22B-Instruct-2507] | | Synthetic Identity Data from Qwen3-Next-80B-A3B-Instruct and Qwen3-235B-A22B-Instruct-2507 | Text | 25,992 | [Hand-written prompts] | [Qwen3-Next-80B-A3B-Instruct]; [Qwen3-235B-A22B-Instruct-2507] | | Synthetic Terminus Ultra Agentic Reasoning Blend | Text | 96,881 | [ARC-AGI-2]; [OpenCodeReasoningv2]; [OpenMathReasoning]; [SWE-Fixer-110K]; [SWE-Rebench]; [SWE-Smith] | [DeepSeek-V3.2]; [Qwen3-235B-A22B-Thinking-2507]; [Ring-1T]; [Kimi-K2.5]; [GLM-4.7-FP8]; [Qwen3-Next-80B-A3B-Thinking]; [gpt-oss-120b]; [Ministral-3-14B-Reasoning-2512]; [LM-4.5-Air-FP8] | | Synthetic STEM from Qwen3-235B-A22B-Thinking-2507 | Text | 1,174,694 | [IChO-IPhO-RL-v2]; [Physics-Big Dataset]; Scale HLE; [OpenMathReasoning]; [OpenCodeReasoning] | [Qwen3-235B-A22B-Thinking-2507] | | Synthetic STEM from Qwen3-235B-A22B-Instruct-2507 and gpt-oss-120b | Text | Undisclosed | [arXiv]; [National Institutes of Health ExPorter]; [BioRxiv]; [PMC Article]; [USPTO Backgrounds]; [peS2o]; Global Regulation; [CORE]; [PG-19]; [DOAB CC BY & CC BY-SA subset]; [NDLTD] | [Qwen3-235B-A22B-Instruct-2507]; [gpt-oss-120b] | | Translation Data from TAUS | Text | 1,618,055 | [TAUS proprietary dataset] | Undisclosed | | Synthetic Art of Problem Solving and Stack Exchange from gpt-oss-120b, Qwen2.5-32B-Instruct, and Goedel-Prover-V2-32B | Text | 860,469 | [Nemotron-Math-Proofs-v1] | [Goedel-Prover-V2-32B] | | Synthetic Art of Problem Solving and Stack Exchange from gpt-oss-120b | Text | 1,201,815 | [Upstream released math dataset]; [AoPS]; [StackOverflow / StackExchange] | [gpt-oss-120b] | | Synthetic Multilingual Science and Code data from DeepSeek-R1, DeepSeek-R1-0528, Qwen2.5-32B-Instruct, and Qwen3-235B-A22B, translated with Qwen2.5-32B-Instruct and Qwen2.5-14B-Instruct | Text | Undisclosed | [Nano-V3 SFT Data (without tool call)] | [Qwen/Qwen2.5-14B-Instruct]; [Qwen/Qwen3-4B-Thinking-2507] | | Synthetic Multilingual Science and Code data from DeepSeek-R1, DeepSeek-R1-0528, Qwen2.5-32B-Instruct, and Qwen3-235B-A22B, translated with Qwen2.5-32B-Instruct and Qwen2.5-14B-Instruct (Stack Exchange lineage) | Text | Undisclosed | [Stack Exchange]; [SCP-116K]; [LIMO]; [TACO]; Code Contest; Codeforces | [DeepSeek-R1]; [DeepSeek-R1-0528]; [Qwen2.5-32B-Instruct]; [Qwen3-235B-A22B] | | Synthetic Search Graph Walk | Text | 6,977 | [Wikidata / Wikipedia KnowledgeBase] | [MiniMaxAI/MiniMax-M2] | | Synthetic Agentic Diverse Domains | Text | 281,537 | [Handwritten prompts (synthetic; no external seed data used)] | [SDG model: deepseek-ai/DeepSeek-V3.2, deepseek-ai/DeepSeek-R1-0528, Qwen/Qwen3-235B-A22B-Thinking-2507, Qwen/Qwen3-32B]; [Filtering model: openai/gpt-oss-120b, Qwen/Qwen3-32B, Qwen/Qwen3-235B-A22B-Instruct-2507] | | Synthetic Long Context from Qwen3-235B-A22B-Instruct-2507 | Text | Undisclosed | [Long-context SFT seed blend (pre-training blend + nano-v1 post-training data)]; [Long-context SFT data: lc_nothink 256k, MRCR 200k, RULER 256k]; [AALCR seed blend: SEC Filings, CC, Wikipedia, FinePDFs, ArXiv, Pile-NIH ExPorter, BioRxiv, PMC Article, USPTO Backgrounds, peS2o, Global Regulations, CORE, Gutenberg (PG-19), DOAB CC-BY, NDLTD, Amps, StackExchange, MathPile, Numinas] | [Qwen/Qwen3-235B-A22B-Thinking-2507]; [deepseek-ai/DeepSeek-R1]; [Qwen3-30B-A3B] | | Synthetic Nemotron Math SFT from DeepSeek-V3.2-Speciale | Text | 1,900,553 | [Nemotron-Math-v2 (AOPS and StackExchange-math problems)] | [DeepSeek-V3.2-Speciale] | | Synthetic Nemotron Math TIR from DeepSeek-V3.2 | Text | 1,789,258 | [Nemotron-Math-v2 (AOPS and StackExchange-math problems)] | [DeepSeek-V3.2] | | Synthetic NemoCascade OCR Distillation from gpt-oss-120b | Text | 682,864 | [Nemotron-X training datasets] | [gpt-oss-120b] | | Synthetic CUDA 100k | Text | 93,086 | [KernelBook]; [HuggingFace Transformers]; [FlashInfer] | [gpt-oss-120b]; [DeepSeek-R1-0528] | | Synthetic Science MCQ and QA Diversity from GPT-OSS and Kimi-K2 | Text | 30,358 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] | | Synthetic Science HLE with Python from GPT-OSS and Kimi-K2 | Text | 85,184 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] | | Synthetic Science Search and Python from GPT-OSS and Kimi-K2 | Text | 6,179 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] | | Synthetic Science Search from GPT-OSS and Kimi-K2 | Text | 32,554 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] | | Synthetic Finance Reasoning from GPT-OSS-120B and Qwen3-235B-A22B-Instruct-2507 | Text | 326,700 | [SEC filings] | [GPT-OSS-120B, Qwen3-235B-A22B-Instruct-2507] | | Synthetic Science Diversity MCQ from GPT-OSS and Kimi-K2 | Text | 532,942 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] | | Synthetic Science Diversity OpenQ from GPT-OSS and Kimi-K2 | Text | 131,045 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] | | Synthetic Science Reasoning No-Tool from GPT-OSS and Kimi-K2 | Text | 2,085,600 | [doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)] | [GPT-OSS]; [Kimi-K2] | | Synthetic Text-To-SQL from gpt-oss-120b | Text | Undisclosed | [In-house Text-to-SQL data] | [gpt-oss-120b] | | Synthetic Tool Call Schema for RL (extended) | Text | 707,967 | [UltraTool]; [ToolEyes]; [AutoTools]; [API-Bank]; [Nemotron-Personas-USA]; [Salesforce xLAM function-calling]; [Glaive function-calling-v2]; [Agent-Ark/Toucan-1.5M] | [DeepSeek-V3.2]; [GLM-4.6]; [gpt-oss-120b]; [Kimi-K2-Instruct] | | Synthetic Safety from gemma-3-4b-it, Nemotron-Nano-9B-v2, and gpt-oss-120b | Text | 44,091 | [Safety SFT Data] | [google/gemma-3-4b-it]; [Nemotron-Nano-9B-v2]; [gpt-oss-120b] | | Synthetic Safety from DeepSeek-R1-0528, gpt-oss-120b, DeepSeek-R1-Distill-Qwen-7B, and Mixtral-8x7B-v0.1 | Text | Undisclosed | [Nemotron Content Safety Dataset V2]; [Gretel Synthetic Safety Alignment Dataset]; [RedTeam-2K]; [Malicious Tasks]; [Nemotron-Personas-USA] | [DeepSeek-R1-0528]; [gpt-oss-120b]; [DeepSeek-R1-Distill-Qwen-7B]; [Qwen3-30B-A3B-Thinking-2507]; [Qwen3-235B-A22B-Instruct-2507]; [Mixtral-8x7B-v0.1] | | Synthetic Tool Calling from Qwen3-235B-A22B-Thinking-2507 and Qwen3-Next-80B-A3B-Thinking | Text | Undisclosed | [ToolBench]; [glaive-function-calling-v2]; [APIGen Function-Calling]; [Nemotron-Personas-USA] | [Qwen3-235B-A22B-Thinking-2507]; [Qwen3-Next-80B-A3B-Thinking] | | Synthetic Chat from gpt-oss-120b, Mixtral-8x22B-Instruct-v0.1, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | [C4]; [LMSYS-Chat-1M]; [ShareGPT]; [GSM8K]; [PRM800K]; [FinQA]; [WikiTableQuestions]; [Riddles]; [glaive-function-calling-v2]; [SciBench]; [tigerbot-kaggle-leetcodesolutions-en-2k]; [OpenBookQA]; [Advanced Reasoning Benchmark]; Software Heritage; [Khan Academy Math Keywords]; [WildChat-1M]; [Nemotron-Personas-USA] | [gpt-oss-120b]; [Mixtral-8x22B-Instruct-v0.1]; [Qwen3-235B-A22B-Instruct-2507]; [Qwen3-235B-A22B-Thinking-2507] | | Synthetic Tool Use Interactive Agent from gpt-oss-120b, DeepSeek-R1-0528, Qwen3-32B, and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | NVIDIA Internal | [gpt-oss-120b]; [DeepSeek-R1-0528]; [Qwen3-32B]; [Qwen3-235B-A22B-Thinking-2507] | | Synthetic DocFinQA and SWE-smith from Qwen3-Coder-480B-A35B-Instruct and Kimi-K2-Thinking | Text | Undisclosed | [DocFinQA]; [SWE-smith] | [Qwen3-Coder-480B-A35B-Instruct]; [Kimi-K2-Thinking] | | Synthetic SWE-Gym from Qwen3-Coder-480B-A35B-Instruct | Text | Undisclosed | [SWE-Gym] | [Qwen3-Coder-480B-A35B-Instruct] | | Synthetic SWE-Gym and R2E-Gym-Subset from Qwen3-Coder-480B-A35B-Instruct | Text | Undisclosed | [SWE-Gym]; [R2E-Gym-Subset] | [Qwen3-Coder-480B-A35B-Instruct] | | Synthetic SWE-Gym and R2E-Gym-Subset from DeepSeek-R1-0528 | Text | Undisclosed | [SWE-Gym]; [R2E-Gym-Subset] | [DeepSeek-R1-0528] | | Synthetic HelpSteer, LMSYS-Chat-1M, and Nemotron-Personas-USA from gpt-oss-120b, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507 | Text | Undisclosed | [HelpSteer2]; [HelpSteer3]; [LMSYS-Chat-1M]; [Nemotron-Personas-USA] | [gpt-oss-120b]; [Qwen3-235B-A22B-Instruct-2507]; [Qwen3-235B-A22B-Thinking-2507] | | Synthetic Nemotron-Personas-USA from gpt-oss-120b and Qwen3-8B | Text | Undisclosed | [Nemotron-Personas-USA] | [gpt-oss-120b]; [Qwen3-8B] | | Vendor Terminal Bench-like Tasks (Droid) | Text | Undisclosed | [Droid Harness Pivot vendor data] | Undisclosed | | Synthetic BabyVisionQA Reasoning — Combined SFT | Text and images | 135,207 records | Procedurally generated BabyVision-style visual-reasoning images | Qwen3.5-397B-A17B-FP8 | | Synthetic BabyVision Tool-Calling SFT, Relative-Path-Corrected Variant | Text and images | 81,494 records | NVIDIA internal BabyVision tool-calling prompts and media | Qwen3.5-397B-A17B-FP8; Kimi-K2.6 | | Synthetic Chinese OCR v5, NMH5R | Text and images | 29,108 records | Webster's Unabridged Dictionary; rendered Chinese text mixed with English words | Undisclosed; synthetic text rendering is described | | Synthetic Chinese OCR v5, Augmented and Cleaned | Text and images | 28,576 records | Webster's Unabridged Dictionary; augmented synthetic Chinese OCR images | Undisclosed; synthetic text rendering is described | | Synthetic Multimodal Safety, Energon Format | Text and images | 22,560 records | NVIDIA Safety-team text prompts and safe responses | Edify (image generation) | | Synthetic Multimodal Safety, NMH5R and Safety-Filtered | Text and images | 22,541 records | Multimodal safety records with Edify-generated images | Edify (image generation) | | Synthetic ASCII OCR v5, NMH5R | Text and images | 14,533 records | Webster's Unabridged Dictionary; synthetic ASCII renderings | Undisclosed; synthetic text rendering is described | | Synthetic English OCR v5, NMH5R | Text and images | 14,525 records | Webster's Unabridged Dictionary; synthetic English renderings | Undisclosed; synthetic text rendering is described | ## Language Distribution in Post-Training For our post-training recipe, we focused on the following languages in addition to English: French, German, Italian, Japanese, Spanish, and Chinese. Those languages were represented in the form of multilingual reasoning and translation tasks.
### Additional Training Data | Data | Composition and Purpose | |:---|:---| | Indic continued pre-training | 100B tokens: 10B selected Indic tokens and 90B tokens from the preceding blend. | | Software-engineering mid-training | 67B tokens: whole repositories (20%), issue–pull-request pairs (41%), commit diffs (3%), build and CI logs (1%), agent traces (10%), pre-training replay (20%), and long-context data (5%). Sources are decontaminated against SWE-bench and Terminal-Bench repositories. | | Long-context pre-training | A 35B-token extension to 1M context, followed by a 9B-token long-context recovery stage. | | Vision-language data | Image and video data from the Nemotron 3 Nano Omni training corpus, including visual question answering, charts, tables, OCR, multi-page documents, visual grounding, and video understanding. | | Post-Training Data | Composition and Purpose | |:---|:---| | Additional synthetic vision data | Chart understanding, grounded perception, multimodal reasoning, and visual tool-use trajectories, including data generated with Qwen3.5 and Kimi models. | | Reasoning trajectories | Mathematics and STEM responses generated with Kimi K3; mathematical proofs generated with DeepSeek V4 Pro; competitive-programming trajectories generated with GLM-5.2. | | Chat and safety | Refreshed chat responses from Qwen, Kimi, and Gemma models, with additional safety data. | | Long-context supervised data | 512K-context data, synthetic graph reasoning and transduction tasks, long-horizon search and coding trajectories, and documents containing up to hundreds of pages. | | Long-document translation | Chunked translations of PG19 and FinePDFs-Edu documents generated with DeepSeek V4 Flash. | ## Testing Datasets **Data Collection Method:** Hybrid: automated, manually-collected, and synthetic **Labeling Method:** Hybrid: automated, manually-labeled, and synthetic **Properties:** Standard benchmarks and task-specific test suites covering reasoning, coding, tool use, instruction following, long context, and visual understanding. ## Evaluation Datasets **Data Collection Method:** Hybrid: automated, manually-collected, and synthetic **Labeling Method:** Hybrid: automated, manually-labeled, and synthetic **Properties:** Standard benchmarks and task-specific test suites covering reasoning, coding, tool use, instruction following, long context, and visual understanding. ## Inference * **Acceleration Engine:** PyTorch * **Test Hardware:** * NVIDIA Hopper * H100 * H200 * NVIDIA Grace Blackwell * GB200 * GB300 * NVIDIA Blackwell * B200 * B300 The deployment examples above provide configurations for NVIDIA Hopper and Blackwell systems. Choose the precision, context length, and concurrency appropriate for the application and available memory. ## 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 terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. We advise against circumvention of any provided safety guardrails contained in the Model without a substantially similar guardrail appropriate for your use case. For more details: [Safety](./safety.md) and [Explainability](./explainability.md) subcards. For more detailed information on ethical considerations for this model, please see the Model Card++ [Bias](./bias.md), and [Privacy](./privacy.md) subcards. Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).