Update README.md

README.md

@@ -1,4 +1,4 @@
-# NVIDIA-Nemotron-Nano-12B-v2-Base-BF16
+# NVIDIA-Nemotron-Nano-12B-v2-Base
 
 **Model Developer:** NVIDIA Corporation
 
@@ -16,7 +16,7 @@ The pretraining data has a cutoff date of May 1, 2025\.
 
 ## Description
 
-NVIDIA-Nemotron-Nano-12B-v2-Base-BF16 is a large language model (LLM) developed by NVIDIA that is designed as a completion model for a given piece of text. It uses a hybrid model architecture that consists primarily of Mamba-2 and MLP layers with just six Attention layers. The model features a context length of 128K. The supported languages include: English, Spanish, French, German, Japanese, Italian, Portuguese, Chinese, Arabic, Danish, Korean, Dutch, Polish, Russian, Swedish, and Thai.
+NVIDIA-Nemotron-Nano-12B-v2-Base is a large language model (LLM) developed by NVIDIA that is designed as a completion model for a given piece of text. It uses a hybrid model architecture that consists primarily of Mamba-2 and MLP layers with just six Attention layers. The model features a context length of 128K. The supported languages include: English, Spanish, French, German, Japanese, Italian, Portuguese, Chinese, Arabic, Danish, Korean, Dutch, Polish, Russian, Swedish, and Thai.
 
 This model is ready for commercial/non-commercial use. \<br\>
 
@@ -104,7 +104,7 @@ The integration of foundation and fine-tuned models into AI systems requires add
 
 # Training, Testing, and Evaluation Datasets:
 
-NVIDIA-Nemotron-Nano-12B-v2-Base-BF16 is pre-trained on a large corpus of high-quality curated and synthetically-generated data. It is trained in the English language, as well as 15 multilingual languages and 43 programming languages. Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including legal, math, science, finance, and more. We also include a small portion of question-answering, and alignment style data to improve model accuracy. The model was trained for approximately twenty trillion tokens.
+NVIDIA-Nemotron-Nano-12B-v2-Base is pre-trained on a large corpus of high-quality curated and synthetically-generated data. It is trained in the English language, as well as 15 multilingual languages and 43 programming languages. Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including legal, math, science, finance, and more. We also include a small portion of question-answering, and alignment style data to improve model accuracy. The model was trained for approximately twenty trillion tokens.
 
 **Data Modality:**  Text **The total size:**  10,648,823,153,919 Tokens **Total number of datasets:** 141 **Dataset partition:** *Training \[100%\], testing \[0%\], validation \[0%\]*  
 **Time period for training data collection:** 2013 to May 1, 2025  
@@ -359,13 +359,11 @@ We evaluated our model on the following benchmarks:
 
 *Table 2: Accuracy of Nemotron-Nano-V2-Base models versus existing SoTA models on multilingual benchmarks. N-Nano-V2 is short for Nemotron-Nano-V2. The distilled N-Nano-V2-9B-Base is compared against Qwen3-8B-Base and Gemma3-12B-Base, and the best score is highlighted in each row.*
 
-’
-
 ## Inference
 
 - ## Engines: HF, vLLM, TRT-LLM
 
-- ## Test Hardware NVIDIA A10G 24GB, H100 80GB
+- ## Test Hardware NVIDIA A100 80GB, H100 80GB
 
 ## Ethical Considerations
 
@@ -407,7 +405,7 @@ Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.
 | Verified to have met prescribed NVIDIA quality standards: | Yes |
 | Performance Metrics: | Accuracy, Throughput, and User-side throughput |
 | Potential Known Risks: | The model was optimized explicitly for instruction following and as such is more susceptible to prompt injection and jailbreaking in various forms as a result of its instruction tuning. This means that the model should be paired with additional rails or system filtering to limit exposure to instructions from malicious sources  \-- either directly or indirectly by retrieval (e.g. via visiting a website)  \-- as they may yield outputs that can lead to harmful, system-level outcomes up to and including remote code execution in agentic systems when effective security controls including guardrails are not in place. The model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. |
-| Licensing: | GA: [NVIDIA Open Model License Agreement](https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nvidia.com%2Fen-us%2Fagreements%2Fenterprise-software%2Fnvidia-open-model-license%2F&data=05%7C02%7Cysuhara%40nvidia.com%7C72ec0b4887a44a71c85808ddda01c000%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C638906423286956339%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=45LwrIpNjVPgKSqFQ3p6e4B%2BoRQoGFoWQenWUhimPok%3D&reserved=0) |
+| Licensing: | GA: GOVERNING TERMS: Use of this model is governed by the [NVIDIA Open Model License Agreement](https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nvidia.com%2Fen-us%2Fagreements%2Fenterprise-software%2Fnvidia-open-model-license%2F&data=05%7C02%7Cysuhara%40nvidia.com%7C72ec0b4887a44a71c85808ddda01c000%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C638906423286956339%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=45LwrIpNjVPgKSqFQ3p6e4B%2BoRQoGFoWQenWUhimPok%3D&reserved=0). |
 
 ## Privacy
 
@@ -431,9 +429,8 @@ Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.
 | :---- | :---- |
 | Model Application Field(s): | Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning, Customer Service |
 | Describe the life critical impact (if present). | Not Applicable |
-| Description of methods implemented in data acquisition or processing, if any, to address other types of potentially harmful data in the training, testing, and validation data: | We used a guard model for content safety to exclude potentially harmful data from  training.  |
+| Description of methods implemented in data acquisition or processing, if any, to address other types of potentially harmful data in the training, testing, and validation data: | We used a guard model for content safety to exclude potentially harmful data from training.  |
 | Description of any methods implemented in data acquisition or processing, if any, to address illegal or harmful content in the training data, including, but not limited to, child sexual abuse material (CSAM) and non-consensual intimate imagery (NCII) | We used a Gemma-3 4B-based guard model trained on [Nemotron Content Safety Dataset v2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) for content safety to exclude potentially illegal or harmful content from the training. |
-| Use Case Restrictions: | GA: Use of this model is governed by the [NVIDIA Open Model License Agreement](https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nvidia.com%2Fen-us%2Fagreements%2Fenterprise-software%2Fnvidia-open-model-license%2F&data=05%7C02%7Cysuhara%40nvidia.com%7C72ec0b4887a44a71c85808ddda01c000%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C638906423286956339%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=45LwrIpNjVPgKSqFQ3p6e4B%2BoRQoGFoWQenWUhimPok%3D&reserved=0). |
+| Use Case Restrictions: | GA: Abide by the [NVIDIA Open Model License Agreement](https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nvidia.com%2Fen-us%2Fagreements%2Fenterprise-software%2Fnvidia-open-model-license%2F&data=05%7C02%7Cysuhara%40nvidia.com%7C72ec0b4887a44a71c85808ddda01c000%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C638906423286956339%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=45LwrIpNjVPgKSqFQ3p6e4B%2BoRQoGFoWQenWUhimPok%3D&reserved=0). |
 | Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development.  Restrictions enforce dataset access during training, and dataset license constraints adhered to. |
-| This AI model was developed based on our policies to ensure responsible data handling and risk mitigation. The datasets used for training have been scanned for harmful content and illegal content, consistent with our policies including scanning for Child Sexual Abuse Material (CSAM). Ongoing review and monitoring mechanisms are in place based on our policies and to maintain data integrity. | True. We use [Nemotron Content Safety Dataset V2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) and an internal safety dataset specialized for minority sexuality for content safety evaluation to ensure the safety of this model. |
-
+| This AI model was developed based on our policies to ensure responsible data handling and risk mitigation. The datasets used for training have been scanned for harmful content and illegal content, consistent with our policies including scanning for Child Sexual Abuse Material (CSAM). Ongoing review and monitoring mechanisms are in place based on our policies and to maintain data integrity. | True. We use [Nemotron Content Safety Dataset V2](https://huggingface.co/datasets/nvidia/Aegis-AI-Content-Safety-Dataset-2.0) and an internal safety dataset specialized for minority sexuality for content safety evaluation to ensure the safety of this model. |