# Llama-3.1-Nemotron-Nano-8B-v1 ## Model Overview Llama-3.1-Nemotron-Nano-8B-v1 is a large language model (LLM) which is a derivative of [Meta Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) (AKA the reference model). It is a reasoning model that is post trained for reasoning, human chat preferences, and tasks, such as RAG and tool calling. Llama-3.1-Nemotron-Nano-8B-v1 is a model which offers a great tradeoff between model accuracy and efficiency. It is created from Llama 3.1 8B Instruct and offers improvements in model accuracy. The model fits on a single RTX GPU and can be used locally. The model supports a context length of 128K. This model underwent a multi-phase post-training process to enhance both its reasoning and non-reasoning capabilities. This includes a supervised fine-tuning stage for Math, Code, Reasoning, and Tool Calling as well as multiple reinforcement learning (RL) stages using REINFORCE (RLOO) and Online Reward-aware Preference Optimization (RPO) algorithms for both chat and instruction-following. The final model checkpoint is obtained after merging the final SFT and Online RPO checkpoints. Improved using Qwen. This model is part of the Llama Nemotron Collection. You can find the other model(s) in this family here: [Llama-3.3-Nemotron-Super-49B-v1](https://huggingface.co/nvidia/Llama-3.3-Nemotron-Super-49B-v1) This model is ready for commercial use. ## License/Terms of Use GOVERNING TERMS: Your use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/). Built with Llama. **Model Developer:** NVIDIA **Model Dates:** Trained between August 2024 and March 2025 **Data Freshness:** The pretraining data has a cutoff of 2023 per Meta Llama 3.1 8B ## Use Case: Developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks. Balance of model accuracy and compute efficiency (the model fits on a single RTX GPU and can be used locally). ## Release Date:
3/18/2025
## References - [\[2502.00203\] Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment](https://arxiv.org/abs/2502.00203) ## Model Architecture **Architecture Type:** Dense decoder-only Transformer model **Network Architecture:** Llama 3.1 8B Instruct ## Intended use Llama-3.1-Nemotron-Nano-8B-v1 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Portuguese, Hindi, Spanish, and Thai) are also supported. # Input: - **Input Type:** Text - **Input Format:** String - **Input Parameters:** One-Dimensional (1D) - **Other Properties Related to Input:** Context length up to 131,072 tokens ## Output: - **Output Type:** Text - **Output Format:** String - **Output Parameters:** One-Dimensional (1D) - **Other Properties Related to Output:** Context length up to 131,072 tokens ## Model Version: 1.0 (3/18/2025) ## Software Integration - **Runtime Engine:** NeMo 24.12
- **Recommended Hardware Microarchitecture Compatibility:** - NVIDIA Hopper - NVIDIA Ampere ## Quick Start and Usage Recommendations: 1. Reasoning mode (ON/OFF) is controlled via the system prompt, which must be set as shown in the example below. All instructions should be contained within the user prompt 2. We recommend setting temperature to `0.6`, and Top P to `0.95` for Reasoning ON mode 3. We recommend using greedy decoding for Reasoning OFF mode 4. We have provided a list of prompts to use for evaluation for each benchmark where a specific template is required You can try this model out through the preview API, using this link: [Llama-3.1-Nemotron-Nano-8B-v1](https://build.nvidia.com/nvidia/llama-3_1-nemotron-nano-8b-v1). See the snippet below for usage with Hugging Face Transformers library. Reasoning mode (ON/OFF) is controlled via system prompt. Please see the example below. Our code requires the transformers package version to be `4.44.2` or higher. ### Example of “Reasoning On:” ```python import torch import transformers model_id = "nvidia/Llama-3.1-Nemotron-Nano-8B-v1" model_kwargs = {"torch_dtype": torch.bfloat16, "device_map": "auto"} tokenizer = transformers.AutoTokenizer.from_pretrained(model_id) tokenizer.pad_token_id = tokenizer.eos_token_id pipeline = transformers.pipeline( "text-generation", model=model_id, tokenizer=tokenizer, max_new_tokens=32768, temperature=0.6, top_p=0.95, **model_kwargs ) # Thinking can be "on" or "off" thinking = "on" print(pipeline([{"role": "system", "content": f"detailed thinking {thinking}"}, {"role": "user", "content": "Solve x*(sin(x)+2)=0"}])) ``` ### Example of “Reasoning Off:” ```python import torch import transformers model_id = "nvidia/Llama-3.1-Nemotron-Nano-8B-v1" model_kwargs = {"torch_dtype": torch.bfloat16, "device_map": "auto"} tokenizer = transformers.AutoTokenizer.from_pretrained(model_id) tokenizer.pad_token_id = tokenizer.eos_token_id pipeline = transformers.pipeline( "text-generation", model=model_id, tokenizer=tokenizer, max_new_tokens=32768, do_sample=False, **model_kwargs ) # Thinking can be "on" or "off" thinking = "off" print(pipeline([{"role": "system", "content": f"detailed thinking {thinking}"}, {"role": "user", "content": "Solve x*(sin(x)+2)=0"}])) ``` For some prompts, even though thinking is disabled, the model emergently prefers to think before responding. But if desired, the users can prevent it by pre-filling the assistant response. ```python import torch import transformers model_id = "nvidia/Llama-3.1-Nemotron-Nano-8B-v1" model_kwargs = {"torch_dtype": torch.bfloat16, "device_map": "auto"} tokenizer = transformers.AutoTokenizer.from_pretrained(model_id) tokenizer.pad_token_id = tokenizer.eos_token_id # Thinking can be "on" or "off" thinking = "off" pipeline = transformers.pipeline( "text-generation", model=model_id, tokenizer=tokenizer, max_new_tokens=32768, do_sample=False, **model_kwargs ) print(pipeline([{"role": "system", "content": f"detailed thinking {thinking}"}, {"role": "user", "content": "Solve x*(sin(x)+2)=0"}, {"role":"assistant", "content":"\n"}])) ``` ## Inference: **Engine:** Transformers **Test Hardware:** - BF16: - 1x RTX 50 Series GPUs - 1x RTX 40 Series GPUs - 1x RTX 30 Series GPUs - 1x H100-80GB GPU - 1x A100-80GB GPU **Preferred/Supported] Operating System(s):** Linux
## Training Datasets A large variety of training data was used for the post-training pipeline, including manually annotated data and synthetic data. The data for the multi-stage post-training phases for improvements in Code, Math, and Reasoning is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model. Prompts have been sourced from either public and open corpus or synthetically generated. Responses were synthetically generated by a variety of models, with some prompts containing responses for both Reasoning On and Off modes, to train the model to distinguish between two modes. **Data Collection for Training Datasets:**
* Hybrid: Automated, Human, Synthetic
**Data Labeling for Training Datasets:**
* N/A
## Evaluation Datasets We used the datasets listed below to evaluate Llama-3.1-Nemotron-Nano-8B-v1. **Data Collection for Evaluation Datasets:** Hybrid: Human/Synthetic **Data Labeling for Evaluation Datasets:** Hybrid: Human/Synthetic/Automatic ## Evaluation Results These results contain both “Reasoning On”, and “Reasoning Off”. We recommend using temperature=`0.6`, top_p=`0.95` for “Reasoning On” mode, and greedy decoding for “Reasoning Off” mode. All evaluations are done with 32k sequence length. We run the benchmarks up to 16 times and average the scores to be more accurate. > NOTE: Where applicable, a Prompt Template will be provided. While completing benchmarks, please ensure that you are parsing for the correct output format as per the provided prompt in order to reproduce the benchmarks seen below. ### MT-Bench | Reasoning Mode | Score | |--------------|------------| | Reasoning Off | 7.9 | | Reasoning On | 8.1 | ### MATH500 | Reasoning Mode | pass@1 | |--------------|------------| | Reasoning Off | 36.6% | | Reasoning On | 95.4% | User Prompt Template: ``` "Below is a math question. I want you to reason through the steps and then give a final answer. Your final answer should be in \boxed{}.\nQuestion: {question}" ``` ### AIME25 | Reasoning Mode | pass@1 | |--------------|------------| | Reasoning Off | 0% | | Reasoning On | 47.1% | User Prompt Template: ``` "Below is a math question. I want you to reason through the steps and then give a final answer. Your final answer should be in \boxed{}.\nQuestion: {question}" ``` ### GPQA-D | Reasoning Mode | pass@1 | |--------------|------------| | Reasoning Off | 39.4% | | Reasoning On | 54.1% | User Prompt Template: ``` "What is the correct answer to this question: {question}\nChoices:\nA. {option_A}\nB. {option_B}\nC. {option_C}\nD. {option_D}\nLet's think step by step, and put the final answer (should be a single letter A, B, C, or D) into a \boxed{}" ``` ### IFEval Average | Reasoning Mode | Strict:Prompt | Strict:Instruction | |--------------|------------|------------| | Reasoning Off | 74.7% | 82.1% | | Reasoning On | 71.9% | 79.3% | ### BFCL v2 Live | Reasoning Mode | Score | |--------------|------------| | Reasoning Off | 63.9% | | Reasoning On | 63.6% | User Prompt Template: ``` {functions} {user_prompt} ``` ### MBPP 0-shot | Reasoning Mode | pass@1 | |--------------|------------| | Reasoning Off | 66.1% | | Reasoning On | 84.6% | User Prompt Template: ```` You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions. @@ Instruction Here is the given problem and test examples: {prompt} Please use the python programming language to solve this problem. Please make sure that your code includes the functions from the test samples and that the input and output formats of these functions match the test samples. Please return all completed codes in one code block. This code block should be in the following format: ```python # Your codes here ``` ```` ## 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. For more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](explainability.md), [Bias](bias.md), [Safety & Security](safety.md), and [Privacy](privacy.md) Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).