Llama 3.3 Nemotron 70B Feedback

🚀 Llama-3.3-Nemotron-70B-Feedback

Llama-3.3-Nemotron-70B-Feedback is a large language model based on Meta-Llama-3.3-70B-Instruct. It offers feedback on the helpfulness of LLM-generated responses to user queries and is available for commercial use.

🚀 Quick Start

You can use the model using HuggingFace Transformers library with 2 or more 80GB GPUs (NVIDIA Ampere or newer) with at least 150GB of free disk space to accomodate the download.

This code has been tested on Transformers v4.45.0, torch v2.3.0a0+40ec155e58.nv24.3 and 2 A100 80GB GPUs, but any setup that supports meta-llama/Llama-3.1-70B-Instruct should support this model as well. If you run into problems, you can consider doing pip install -U transformers.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "nvidia/Llama-3.3-Nemotron-70B-Feedback"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)


def generate_feedback(messages, model, tokenizer, temperature=0.7):
    tokenized_message = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True)
    response_token_ids = model.generate(tokenized_message['input_ids'].cuda(),attention_mask=tokenized_message['attention_mask'].cuda(),  max_new_tokens=128, pad_token_id = tokenizer.eos_token_id, num_return_sequences=1, temperature=temperature)
    generated_tokens =response_token_ids[:, len(tokenized_message['input_ids'][0]):]
    generated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
    return generated_text

prompt = "What is the distance between the Earth and the Sun?"
response = "The distance from Earth to the Sun is 93 million miles"

messages = [
        {"role": "user", "content": prompt},
        {"role": "assistant", "content": response},
        {"role": "user", "content": "Evaluate the response to the previous prompt in terms of how helpful it is overall. Start the evaluation with the statement - The response is {not / slightly / partially / mostly / perfectly} helpful. Then provide a brief explanation of the evaluation in 2 to 10 sentences."}
    ]

feedback = generate_feedback(messages, model, tokenizer, temperature=0.7)
print(feedback)

## Illustrative example - feedback generated might not be identical since temperature sampling is used

# The response is partially helpful. It provides a concise answer to the prompt. However, the lack of additional information or context limits its usefulness. It could have been more informative by including the average distance in astronomical units (AU) and explaining the variation in distance due to the elliptical orbit.

✨ Features

• Feedback Provision: Llama-3.3-Nemotron-70B-Feedback provides feedback regarding the helpfulness of LLM generated responses to user queries.
• Commercial Use: This model is ready for commercial use.

📚 Documentation

Model Overview

Llama-3.3-Nemotron-70B-Feedback is a large language model that leverages Meta-Llama-3.3-70B-Instruct as the foundation and is fine-tuned using Supervised Finetuning to provide feedback regarding the helpfulness of LLM generated responses to user queries.

Arena Hard LeaderBoard

As of 18 Mar 2025, augmenting models with the Feedback-Edit Inference Time Scaling (ITS) approach leads to the highest performance on Arena Hard.

The Feedback-Edit Inference Time Scaling system comprise of the following models:

1. Llama-3.3-Nemotron-70B-Feedback
2. Llama-3.3-Nemotron-70B-Edit
3. Llama-3.3-Nemotron-70B-Select

Use Case

Llama-3.3-Nemotron-70B-Feedback provides feedback regarding the helpfulness of LLM generated responses to user queries, for users who are interested in improving performance through Inference-Time-Scaling for general-domain, open-ended tasks.

Release Date

03/18/2025

References

• Dedicated Feedback and Edit Models Empower Inference-Time Scaling for Open-Ended General-Domain Tasks
• HelpSteer2-Preference
• SteerLM method
• HelpSteer
• HelpSteer2
• The future of AI: Built with Llama
• Meta's Llama 3.3 Webpage
• Meta's Llama 3.3 Model Card

Model Architecture

We developed this model using Llama-3.3-70B-Instruct as its foundation. This model contains 70 billion parameters.

Input

Output

Software Integration

Training and Testing Datasets

Training Datasets

Testing Datasets

Inference

Limitations

The model was trained on data that contains toxic language, unsafe content, 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.

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.

📄 License

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

🔧 Technical Details

Model Version

v1.0

Citation

If you find this model useful, please cite the following work:

@misc{wang2025dedicatedfeedbackeditmodels,
      title={Dedicated Feedback and Edit Models Empower Inference-Time Scaling for Open-Ended General-Domain Tasks}, 
      author={Zhilin Wang and Jiaqi Zeng and Olivier Delalleau and Daniel Egert and Ellie Evans and Hoo-Chang Shin and Felipe Soares and Yi Dong and Oleksii Kuchaiev},
      year={2025},
      eprint={2503.04378},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.04378},
}