--- title: "llama-3.1-nemotron-70b-instruct" publisher: "nvidia" type: "endpoint" updated: "2025-05-21T21:00:49.620Z" description: "Llama-3.1-Nemotron-70B-Instruct is a large language model customized by NVIDIA in order to improve the helpfulness of LLM generated responses." canonical: "https://build.nvidia.com/nvidia/llama-3_1-nemotron-70b-instruct" --- # Model Overview ## Description: Llama-3.1-Nemotron-70B-Instruct is a large language model customized by NVIDIA to improve the helpfulness of LLM generated responses to user queries. This model is ready for commercial use. ## Terms of use By accessing this model, you are agreeing to the LLama 3 terms and conditions of the [license](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE), [acceptable use policy](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/USE_POLICY.md) and [Meta’s privacy policy](https://www.facebook.com/privacy/policy/) ## References(s): * [HelpSteer2-Preference](https://arxiv.org/abs/2410.01257) * [SteerLM method](https://arxiv.org/abs/2310.05344) * [HelpSteer](https://arxiv.org/abs/2311.09528) * [HelpSteer2](https://arxiv.org/abs/2406.08673) * [Introducing Llama 3.1: Our most capable models to date](https://ai.meta.com/blog/meta-llama-3-1/) * [Meta's Llama 3.1 Webpage](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1) * [Meta's Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md) ## Model Architecture: **Architecture Type:** Transformer
**Network Architecture:** Llama 3.1
## Input: **Input Type(s):** Text
**Input Format:** String
**Input Parameters:** One Dimensional (1D)
**Other Properties Related to Input:** Max of 128k tokens
## Output: **Output Type(s):** Text
**Output Format:** String
**Output Parameters:** One Dimensional (1D)
**Other Properties Related to Output:** Max of 4k tokens
## Software Integration: **Supported Hardware Microarchitecture Compatibility:**
* NVIDIA Ampere
* NVIDIA Hopper
* NVIDIA Turing
**Supported Operating System(s):** Linux
## Model Version: v1.0 # Training & Evaluation: ## Datasets: **Data Collection Method by dataset**
* [Hybrid: Human, Synthetic]
**Labeling Method by dataset**
* [Human]
**Link:** * [HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2) **Properties (Quantity, Dataset Descriptions, Sensor(s)):**
* 21, 362 prompt-responses built to make more models more aligned with human preference - specifically more helpful, factually-correct, coherent, and customizable based on complexity and verbosity. * 20, 324 prompt-responses used for training and 1, 038 used for validation. # Inference: **Engine:** [Triton](https://developer.nvidia.com/triton-inference-server)
**Test Hardware:** H100, A100 80GB, A100 40GB
## 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 supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). ## Bias Field | Response :---------------------------------------------------------------------------------------------------|:--------------- Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None Measures taken to mitigate against unwanted bias: | None ## Explainability Field | Response :------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------- Intended Application & Domain: | Response Customization in Large Language Model Development Model Type: | Text-to-Text Transformer Intended User: | Developers customizing model response across different applications and domains. Output: | Text Describe how the model works: | Generates a response based on a prior conversation. Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable Verified to have met prescribed quality standards: | Yes Technical Limitations: | This model may not work for non-English languages. Performance Metrics: | Throughput and Latency Potential Known Risks: | The Model may produce output that is biased and toxic based on how it is prompted, producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. The model may also amplify biases and return toxic responses especially when prompted with toxic prompts. Licensing: | [Llama 3.1 Community License Agreement](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE) ## Privacy Field | Response :----------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------- Personal data used to create this model? | None Known. For data included in the base Llama 3.1 model, [reference the Llama 3.1 model card.](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md) Was consent obtained for any personal data used? | Not Applicable for NVIDIA training data; NVIDIA did not introduce personal data through retraining Generatable or reverse engineerable personal data? | Not a known capability. How often is the dataset reviewed (if applicable)? | Before Release Is a mechanism in place to honor data subject right of access or deletion of personal data? | Not Applicable for NVIDIA training data If personal data collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable If personal data collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable If personal data collected for the development of this AI model, was it minimized to only what was required? | Not Applicable for NVIDIA training data Is there provenance for all datasets used in training? | Yes Does data labeling (annotation, metadata) comply with privacy laws? | Not Applicable for NVIDIA training data Is data compliant with data subject requests for data correction or removal, if such a request was made? | Not Applicable for NVIDIA training data ## Safety & Security Field | Response :---------------------------------------------------|:---------------------------------- Model Application(s): | Conversation, Question Answering, Summarization Describe the life-critical impact (if present). | None Known Use Case Restrictions: | See https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE 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. ## Prototype ```python from openai import OpenAI client = OpenAI( base_url = "https://integrate.api.nvidia.com/v1", api_key = "$NVIDIA_API_KEY" ) completion = client.chat.completions.create( model="", messages=[{"role":"user","content":""}], temperature=, top_p=, max_tokens=, stream=NaN ) print(completion.choices[0].message) ``` ```javascript import OpenAI from 'openai'; const openai = new OpenAI({ apiKey: '$NVIDIA_API_KEY', baseURL: 'https://integrate.api.nvidia.com/v1', }) async function main() { const completion = await openai.chat.completions.create({ model: "", messages: [{"role":"user","content":""}], temperature: , top_p: , max_tokens: , stream: , }) process.stdout.write(completion.choices[0]?.message?.content); } main(); ``` ```bash curl https://integrate.api.nvidia.com/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $NVIDIA_API_KEY" \ -d '{ "model": "nvidia/llama-3.1-nemotron-70b-instruct", "messages": [{"role":"user","content":""}], "temperature": , "top_p": , "max_tokens": , "stream": }' ```