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.
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.
Llama-3.1-Nemotron-70B-Instruct-HF has been converted from Llama-3.1-Nemotron-70B-Instruct to support it in the HuggingFace Transformers codebase. Please note that evaluation results might be slightly different from the Llama-3.1-Nemotron-70B-Instruct as evaluated in NeMo-Aligner, which the evaluation results below are based on.
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.
By accessing this model, you are agreeing to the LLama 3.1 terms and conditions of the license, acceptable use policy and Meta’s privacy policy
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)
| Model | Arena Hard | AlpacaEval | MT-Bench | Mean Response Length |
|---|---|---|---|---|
| Details | (95% CI) | 2 LC (SE) | (GPT-4-Turbo) | (# of Characters for MT-Bench) |
| Llama-3.1-Nemotron-70B-Instruct | 85.0 (-1.5, 1.5) | 57.6 (1.65) | 8.98 | 2199.8 |
| Llama-3.1-70B-Instruct | 55.7 (-2.9, 2.7) | 38.1 (0.90) | 8.22 | 1728.6 |
| Llama-3.1-405B-Instruct | 69.3 (-2.4, 2.2) | 39.3 (1.43) | 8.49 | 1664.7 |
| Claude-3-5-Sonnet-20240620 | 79.2 (-1.9, 1.7) | 52.4 (1.47) | 8.81 | 1619.9 |
| GPT-4o-2024-05-13 | 79.3 (-2.1, 2.0) | 57.5 (1.47) | 8.74 | 1752.2 |
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 v4.44.0, torch v2.4.0 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-3.1-Nemotron-70B-Instruct-HF"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "How many r in strawberry?"
messages = [{"role": "user", "content": prompt}]
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=4096, pad_token_id = tokenizer.eos_token_id)
generated_tokens =response_token_ids[:, len(tokenized_message['input_ids'][0]):]
generated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
print(generated_text)
# See response at top of model card
E-Mail: Zhilin Wang
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},
}
@misc{wang2024helpsteer2,
title={HelpSteer2: Open-source dataset for training top-performing reward models},
author={Zhilin Wang and Yi Dong and Olivier Delalleau and Jiaqi Zeng and Gerald Shen and Daniel Egert and Jimmy J. Zhang and Makesh Narsimhan Sreedhar and Oleksii Kuchaiev},
year={2024},
eprint={2406.08673},
archivePrefix={arXiv},
primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
}
Architecture Type: Transformer
Network Architecture: Llama 3.1
Input Type(s): Text
Input Format: String
Input Parameters: One Dimensional (1D)
Other Properties Related to Input: Max of 128k tokens
Output Type(s): Text
Output Format: String
Output Parameters: One Dimensional (1D)
Other Properties Related to Output: Max of 4k tokens
Supported Hardware Microarchitecture Compatibility:
v1.0
Data Collection Method by dataset
Labeling Method by dataset
Link:
Properties (Quantity, Dataset Descriptions, Sensor(s)):
Engine: Triton
Test Hardware: H100, A100 80GB, A100 40GB
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Base model
meta-llama/Llama-3.1-70B