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 reaches Arena Hard of 85.0, AlpacaEval 2 LC of 57.6 and GPT-4-Turbo MT-Bench of 8.98, which are known to be predictive of LMSys Chatbot Arena Elo

As of 1 Oct 2024, this model is #1 on all three automatic alignment benchmarks (verified tab for AlpacaEval 2 LC), edging out strong frontier models such as GPT-4o and Claude 3.5 Sonnet.

As of Oct 24th, 2024 the model has Elo Score of 1267(+-7), rank 9 and style controlled rank of 26 on ChatBot Arena leaderboard.

This model was trained using RLHF (specifically, REINFORCE), Llama-3.1-Nemotron-70B-Reward and HelpSteer2-Preference prompts on a Llama-3.1-70B-Instruct model as the initial policy.

If you prefer to use the model in the HuggingFace Transformers codebase, we have done a model conversion format into Llama-3.1-Nemotron-70B-Instruct-HF .

Try hosted inference for free at build.nvidia.com - it comes with an OpenAI-compatible API interface.

See details on our paper at https://arxiv.org/abs/2410.01257 - as a preview, this model can correctly the question How many r in strawberry? without specialized prompting or additional reasoning tokens:

A sweet question!
Let’s count the “R”s in “strawberry”:
1. S
2. T
3. R
4. A
5. W
6. B
7. E
8. R
9. R
10. Y
There are **3 “R”s** in the word “strawberry”.

Note: This model is a demonstration of our techniques for improving helpfulness in general-domain instruction following. It has not been tuned for performance in specialized domains such as math.

License

Your use of this model is governed by the NVIDIA Open Model License. Additional Information: Llama 3.1 Community License Agreement. Built with Llama.

Evaluation Metrics

As of 1 Oct 2024, Llama-3.1-Nemotron-70B-Instruct performs best on Arena Hard, AlpacaEval 2 LC (verified tab) and MT Bench (GPT-4-Turbo)

Usage:

We demonstrate inference using NVIDIA NeMo Framework, which allows hassle-free model deployment based on NVIDIA TRT-LLM, a highly optimized inference solution focussing on high throughput and low latency.

Pre-requisite: You would need at least a machine with 4 40GB or 2 80GB NVIDIA GPUs, and 150GB of free disk space.

1. Please sign up to get free and immediate access to NVIDIA NeMo Framework container. If you don’t have an NVIDIA NGC account, you will be prompted to sign up for an account before proceeding.

2. If you don’t have an NVIDIA NGC API key, sign into NVIDIA NGC, selecting organization/team: ea-bignlp/ga-participants and click Generate API key. Save this key for the next step. Else, skip this step.

3. On your machine, docker login to nvcr.io using

   docker login nvcr.io
   Username: $oauthtoken
   Password: <Your Saved NGC API Key>
   

4. Download the required container

   docker pull nvcr.io/nvidia/nemo:24.05.llama3.1
   

5. Download the checkpoint

   git lfs install
   git clone https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct
   

6. Run Docker container (In addition, to use Llama3.1 tokenizer, you need to export HF_HOME=<YOUR_HF_HOME_CONTAINING_TOKEN_WITH_LLAMA3.1_70B_ACCESS>)

   docker run --gpus all -it --rm --shm-size=150g -p 8000:8000 -v ${PWD}/Llama-3.1-Nemotron-70B-Instruct:/opt/checkpoints/Llama-3.1-Nemotron-70B-Instruct,${HF_HOME}:/hf_home -w /opt/NeMo nvcr.io/nvidia/nemo:24.05.llama3.1
   

7. Within the container, start the server in the background. This step does both conversion of the nemo checkpoint to TRT-LLM and then deployment using TRT-LLM. For an explanation of each argument and advanced usage, please refer to NeMo FW Deployment Guide

   HF_HOME=/hf_home python scripts/deploy/nlp/deploy_inframework_triton.py --nemo_checkpoint /opt/checkpoints/Llama-3.1-Nemotron-70B-Instruct --model_type="llama" --triton_model_name nemotron --triton_http_address 0.0.0.0 --triton_port 8000 --num_gpus 2 --max_input_len 3072 --max_output_len 1024 --max_batch_size 1 &
   

8. Once the server is ready (i.e. when you see this messages below), you are ready to launch your client code

   Started HTTPService at 0.0.0.0:8000
   Started GRPCInferenceService at 0.0.0.0:8001
   Started Metrics Service at 0.0.0.0:8002
   

   python scripts/deploy/nlp/query_inframework.py -mn nemotron -p "How many r in strawberry?" -mol 1024
   

References(s):

• HelpSteer2-Preference
• SteerLM method
• HelpSteer
• HelpSteer2
• Introducing Llama 3.1: Our most capable models to date
• Meta's Llama 3.1 Webpage
• Meta's Llama 3.1 Model Card

Model Architecture:

Architecture Type: Transformer <br> Network Architecture: Llama 3.1 <br>

Input:

Input Type(s): Text <br> Input Format: String <br> Input Parameters: One Dimensional (1D) <br> Other Properties Related to Input: Max of 128k tokens<br>

Output:

Output Type(s): Text <br> Output Format: String <br> Output Parameters: One Dimensional (1D) <br> Other Properties Related to Output: Max of 4k tokens <br>

Software Integration:

Supported Hardware Microarchitecture Compatibility: <br>

• NVIDIA Ampere <br>
• NVIDIA Hopper <br>
• NVIDIA Turing <br> Supported Operating System(s): Linux <br>

Model Version:

v1.0

Training & Evaluation:

• REINFORCE implemented in NeMo Aligner

Datasets:

Data Collection Method by dataset <br>

• [Hybrid: Human, Synthetic] <br>

Labeling Method by dataset <br>

• [Human] <br>

Link:

• HelpSteer2

Properties (Quantity, Dataset Descriptions, Sensor(s)): <br>

• 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 <br> Test Hardware: H100, A100 80GB, A100 40GB <br>

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.

Please report security vulnerabilities or NVIDIA AI Concerns here.

Citation

If you find this model useful, please cite the following works

@misc{wang2024helpsteer2preferencecomplementingratingspreferences,
      title={HelpSteer2-Preference: Complementing Ratings with Preferences}, 
      author={Zhilin Wang and Alexander Bukharin and Olivier Delalleau and Daniel Egert and Gerald Shen and Jiaqi Zeng and Oleksii Kuchaiev and Yi Dong},
      year={2024},
      eprint={2410.01257},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2410.01257}, 
}