Llama-3.1-Nemotron-Nano-8B-v1 is a large language model (LLM) which is a derivative of Meta 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.
Llama Nemotron Nano VL is a leading document intelligence vision language model (VLMs) that enables the ability to query and summarize images from the physical or virtual world. Llama Nemotron Nano VL is deployable in the data center, cloud and at the edge, including Jetson Orin and laptop by AWQ 4bit quantization through TinyChat framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. This model was trained on commercial images for all three stages of training and supports single image inference. Note: NVIDIA Nemotron Nano v2 12B VL is now available on Huggingface in the BF16, FP8 and NVFP4-QAD formats.
**Llama-3.1-Nemotron-Safety-Guard-8B-v3** is a multilingual content safety model that moderates human-LLM interaction content and classifies user prompts and LLM responses as safe or unsafe. If the content is unsafe, the model additionally returns a response with a list of categories that the content violates. It supports 9 languages: English, Spanish, Mandarin, German, French, Hindi, Japanese, Arabic, and Thai. The base large language model (LLM) is the multilingual Llama-3.1-8B-Instruct model from Meta. NVIDIA’s optimized release is LoRa-tuned on approved datasets and better conforms to NVIDIA’s content safety risk taxonomy and other safety risks in human-LLM interactions. The model is trained using the Nemotron-Safety-Guard-Dataset-v3 dataset which is synthetically curated using the CultureGuard pipeline. The model shows strong zero-shot generalization supporting over 20 languages (en, ar, de, es, fr, hi, ja, th, zh, it, ko, nl, cs, da, fi, iw, pt-BR, pl, ru, sv). The model can be prompted using an instruction and a taxonomy of unsafe risks to be categorized. The instruction format for prompt moderation is shown below under input and output examples. For a detailed description of the dataset and model, please see our paper . Experience the model in the NVIDIA NIM Preview here.
The NVIDIA Llama 3.3 70B Instruct FP8 model is the quantized version of the Meta's Llama 3.3 70B Instruct model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Llama 3.3 70B Instruct FP8 model is quantized with TensorRT Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (Meta-Llama-3.3-70B-Instruct) Model Card. **Input Type(s):** Text **Input Format(s):** String **Input Parameters:** 1D (One Dimensional): Sequences **Other Properties Related to Input:** Context length up to 128K
The NVIDIA Llama 3.3 70B Instruct FP4 model is the quantized version of the Meta's Llama 3.3 70B Instruct model, which is an auto-regressive language model that uses an optimized transformer architecture. For more information, please check here. The NVIDIA Llama 3.3 70B Instruct FP4 model is quantized with TensorRT Model Optimizer. This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (Meta-Llama-3.3-70B-Instruct) Model Card. **Input Type(s):** Text **Input Format(s):** String **Input Parameters:** 1D (One Dimensional): Sequences **Other Properties Related to Input:** Context length up to 128K
Llama-3.3-Nemotron-Super-49B-v1 is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-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. The model supports a context length of 128K tokens. Llama-3.3-Nemotron-Super-49B-v1 is a model which offers a great tradeoff between model accuracy and efficiency. Efficiency (throughput) directly translates to savings. Using a novel Neural Architecture Search (NAS) approach, we greatly reduce the model’s memory footprint, enabling larger workloads, as well as fitting the model on a single GPU at high workloads (H200). This NAS approach enables the selection of a desired point in the accuracy-efficiency tradeoff. For more information on the NAS approach, please refer to this paper. The 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. For more details on how the model was trained, please see our technical report and blog.
Llama-3.3-Nemotron-Super-49B-v1.5 is a significantly upgraded version of Llama-3.3-Nemotron-Super-49B-v1 and is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model). It is a reasoning model that is post trained for reasoning, human chat preferences, and agentic tasks, such as RAG and tool calling. The model supports a context length of 128K tokens. Llama-3.3-Nemotron-Super-49B-v1.5 is a model which offers a great tradeoff between model accuracy and efficiency. Efficiency (throughput) directly translates to savings. Using a novel Neural Architecture Search (NAS) approach, we greatly reduce the model’s memory footprint, enabling larger workloads, as well as fitting the model on a single GPU at high workloads (H200). This NAS approach enables the selection of a desired point in the accuracy-efficiency tradeoff. For more information on the NAS approach, please refer to this paper The 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, Science, and Tool Calling. Additionally, the model went through multiple stages of Reinforcement Learning (RL) including Reward-aware Preference Optimization (RPO) for chat, Reinforcement Learning with Verifiable Rewards (RLVR) for reasoning, and iterative Direct Preference Optimization (DPO) for Tool Calling capability enhancements. The final checkpoint was achieved after merging several RL and DPO checkpoints.
