Llama 3 3 Nemotron Super 49B V1 GGUF

🚀 Llama-3.3-Nemotron-Super-49B-v1

Llama-3.3-Nemotron-Super-49B-v1 is a large language model offering a great balance between accuracy and efficiency, suitable for various AI applications.

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🚀 Quick Start

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
5. The model will include <think></think> if no reasoning was necessary in Reasoning ON model, this is expected behaviour

You can try this model out through the preview API, using this link: Llama-3_3-Nemotron-Super-49B-v1.

✨ Features

• High Efficiency: Using a novel Neural Architecture Search (NAS) approach, it greatly reduces the model’s memory footprint, enabling larger workloads and fitting on a single GPU at high workloads (H200).
• Enhanced Reasoning: Underwent a multi - phase post - training process to enhance both reasoning and non - reasoning capabilities.
• Multi - language Support: Supports English, coding languages, and other non - English languages such as German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

📦 Installation

Use It with Transformers

We recommend using the transformers package with version 4.48.3.

Use It with vLLM

pip install vllm==0.8.3

💻 Usage Examples

Basic Usage

Use It with Transformers

import torch
import transformers

model_id = "nvidia/Llama-3_3-Nemotron-Super-49B-v1"
model_kwargs = {"torch_dtype": torch.bfloat16, "trust_remote_code": True, "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 = "on"

print(pipeline([{"role": "system", "content": f"detailed thinking {thinking}"},{"role": "user", "content": "Solve x*(sin(x)+2)=0"}]))

Advanced Usage

Use It with vLLM

An example on how to serve with vLLM:

python3 -m vllm.entrypoints.openai.api_server \
  --model "nvidia/Llama-3_3-Nemotron-Super-49B-v1" \
  --trust-remote-code \
  --seed=1 \
  --host="0.0.0.0" \
  --port=5000 \
  --served-model-name "nvidia/Llama-3_3-Nemotron-Super-49B-v1" \
  --tensor-parallel-size=8 \
  --max-model-len=32768 \
  --gpu-memory-utilization 0.95 \
  --enforce-eager

📚 Documentation

Model Overview

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 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.

This model is part of the Llama Nemotron Collection. You can find the other model(s) in this family here:

• Llama-3.1-Nemotron-Nano-8B-v1
• Llama-3.1-Nemotron-Ultra-253B-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.
Additional Information: Llama 3.3 Community License Agreement. Built with Llama.

Model Developer: NVIDIA

Model Dates: Trained between November 2024 and February 2025

Data Freshness: The pretraining data has a cutoff of 2023 per Meta Llama 3.3 70B

Use Case:

Developers designing AI Agent systems, chatbots, RAG systems, and other AI - powered applications. Also suitable for typical instruction - following tasks.

Release Date:

3/18/2025

References

• [2505.00949] Llama-Nemotron: Efficient Reasoning Models
• [2411.19146] Puzzle: Distillation-Based NAS for Inference-Optimized LLMs
• [2502.00203] Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment

Model Architecture

Architecture Type: Dense decoder - only Transformer model
Network Architecture: Llama 3.3 70B Instruct, customized through Neural Architecture Search (NAS)

The model is a derivative of Meta’s Llama-3.3-70B-Instruct, using Neural Architecture Search (NAS). The NAS algorithm results in non - standard and non - repetitive blocks. This includes the following:

• Skip attention: In some blocks, the attention is skipped entirely, or replaced with a single linear layer.
• Variable FFN: The expansion/compression ratio in the FFN layer is different between blocks.

We utilize a block - wise distillation of the reference model, where for each block we create multiple variants providing different tradeoffs of quality vs. computational complexity, discussed in more depth below. We then search over the blocks to create a model which meets the required throughput and memory (optimized for a single H100 - 80GB GPU) while minimizing the quality degradation. The model then undergoes knowledge distillation (KD), with a focus on English single and multi - turn chat use - cases. The KD step included 40 billion tokens consisting of a mixture of 3 datasets - FineWeb, Buzz - V1.2 and Dolma.

Intended use

Llama-3.3-Nemotron-Super-49B-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

Output

Model Version

1.0 (3/18/2025)

Software Integration

Inference

Training Datasets

A large variety of training data was used for the knowledge distillation phase before post - training pipeline, 3 of which included: FineWeb, Buzz - V1.2, and Dolma.

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.

In conjunction with this model release, NVIDIA has released 30M samples of post - training data, as public and permissive. Please see [Llama - Nemotron - Postraining - Dataset - v1](https://huggingface.co/datasets/nvidia/Llama - Nemotron - Post - Training - Dataset - v1).

Distribution of the domains is as follows:

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: Hybrid: Automated, Human, Synthetic

Evaluation Datasets

We used the datasets listed below to evaluate Llama-3.3-Nemotron-Super-49B-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.

⚠️ Important 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.

Arena - Hard

MATH500

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

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

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"

🔧 Technical Details

The model uses Neural Architecture Search (NAS) to customize the Llama 3.3 70B Instruct architecture. The NAS algorithm results in non - standard and non - repetitive blocks, such as skip attention and variable FFN. We also utilize block - wise distillation of the reference model and knowledge distillation (KD) with a focus on English single and multi - turn chat use - cases.

📄 License

Your use of this model is governed by the NVIDIA Open Model License. Additional Information: Llama 3.3 Community License Agreement. Built with Llama.