Llama-3.1-Nemotron-70B-Instruct

llmarena.ru Created at 21.03.2025 16:53

0.525

The overall result

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Ratings for leaderboard tasks

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Task name	Result	Metric
LCS	0.168	Accuracy
RCB	0.58 / 0.528	Accuracy F1 macro
USE	0.314	Grade norm
RWSD	0.523	Accuracy
PARus	0.93	Accuracy
ruTiE	0.818	Accuracy
MultiQ	0.615 / 0.444	F1 Exact match
CheGeKa	0.351 / 0.255	F1 Exact match
ruModAr	0.447	Exact match
MaMuRAMu	0.813	Accuracy
ruMultiAr	0.314	Exact match
ruCodeEval	0 / 0 / 0	Pass@k
MathLogicQA	0.478	Accuracy
ruWorldTree	0.975 / 0.781	Accuracy F1 macro
ruOpenBookQA	0.89 / 0.724	Accuracy F1 macro

Evaluation on open tasks:

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Task name

Result

Metric

BPS

0.925

Accuracy

ruMMLU

0.756

Accuracy

SimpleAr

0.98

Exact match

ruHumanEval

0 / 0 / 0

Pass@k

ruHHH

0.82

ruHateSpeech

0.785

ruDetox

0.181

ruEthics

	Correct	God	Ethical
Virtue	0.227	0.258	0.256
Law	0.218	0.239	0.235
Moral	0.248	0.269	0.267
Justice	0.209	0.218	0.225
Utilitarianism	0.199	0.231	0.222

Information about the submission:

Mera version

v.1.2.0

Torch Version

2.5.1

The version of the codebase

30667dc

CUDA version

12.4

Precision of the model weights

auto

Seed

1234

Batch

Transformers version

4.49.0

The number of GPUs and their type

Architecture

local-chat-completions

Team:

llmarena.ru

Name of the ML model:

Llama-3.1-Nemotron-70B-Instruct

Link to the ML model:

https://build.nvidia.com/nvidia/llama-3_1-nemotron-70b-instruct

Model size

70.0B

Model type:

API

Opened

SFT

Additional links:

https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct

Description of the training:

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.

License:

Llama 3.1 Community License Agreement

Inference parameters

Generation Parameters:
rucodeeval - do_sample=true;temperature=0.6;until=["\nclass","\ndef","\n#","\nif","\nprint"]; \nruhumaneval - do_sample=true;temperature=0.6;until=["\nclass","\ndef","\n#","\nif","\nprint"];

System prompt:
Реши задачу по инструкции ниже. Не давай никаких объяснений и пояснений к своему ответу. Не пиши ничего лишнего. Пиши только то, что указано в инструкции. Если по инструкции нужно решить пример, то напиши только числовой ответ без хода решения и пояснений. Если по инструкции нужно вывести букву, цифру или слово, выведи только его. Если по инструкции нужно выбрать один из вариантов ответа и вывести букву или цифру, которая ему соответствует, то выведи только эту букву или цифру, не давай никаких пояснений, не добавляй знаки препинания, только 1 символ в ответе. Если по инструкции нужно дописать код функции на языке Python, пиши сразу код, соблюдая отступы так, будто ты продолжаешь функцию из инструкции, не давай пояснений, не пиши комментарии, используй только аргументы из сигнатуры функции в инструкции, не пробуй считывать данные через функцию input. Не извиняйся, не строй диалог. Выдавай только ответ и ничего больше.

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Ratings by subcategory

Metric: Grade Norm

Model, team	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	8_0	8_1	8_2	8_3	8_4
Llama-3.1-Nemotron-70B-Instruct llmarena.ru	0.433	0.333	0.767	0.3	0.1	0.533	0.1	-	0.067	0.033	0.133	0.033	0.233	0.133	0.033	0.483	0	0.067	0.1	0.1	0.1	0.633	0.233	0.267	0.233	0.625	0.2	0.5	0.467	0.467	0.6

Model, team	Honest	Helpful	Harmless
Llama-3.1-Nemotron-70B-Instruct llmarena.ru	0.787	0.78	0.897

Model, team	Anatomy	Virology	Astronomy	Marketing	Nutrition	Sociology	Management	Philosophy	Prehistory	Human aging	Econometrics	Formal logic	Global facts	Jurisprudence	Miscellaneous	Moral disputes	Business ethics	Biology (college)	Physics (college)	Human Sexuality	Moral scenarios	World religions	Abstract algebra	Medicine (college)	Machine learning	Medical genetics	Professional law	PR	Security studies	Chemistry (школьная)	Computer security	International law	Logical fallacies	Politics	Clinical knowledge	Conceptual_physics	Math (college)	Biology (high school)	Physics (high school)	Chemistry (high school)	Geography (high school)	Professional medicine	Electrical engineering	Elementary mathematics	Psychology (high school)	Statistics (high school)	History (high school)	Math (high school)	Professional accounting	Professional psychology	Computer science (college)	World history (high school)	Macroeconomics	Microeconomics	Computer science (high school)	European history	Government and politics
Llama-3.1-Nemotron-70B-Instruct llmarena.ru	0.704	0.548	0.849	0.88	0.853	0.886	0.806	0.817	0.852	0.758	0.614	0.556	0.54	0.815	0.852	0.76	0.78	0.903	0.567	0.84	0.74	0.848	0.45	0.723	0.571	0.88	0.623	0.685	0.776	0.51	0.78	0.934	0.767	0.929	0.77	0.799	0.51	0.903	0.609	0.734	0.864	0.842	0.724	0.66	0.882	0.699	0.907	0.47	0.525	0.752	0.63	0.886	0.813	0.853	0.87	0.861	0.943

Model, team	SIM	FL	STA
Llama-3.1-Nemotron-70B-Instruct llmarena.ru	0.412	0.606	0.736

Model, team	Anatomy	Virology	Astronomy	Marketing	Nutrition	Sociology	Managment	Philosophy	Pre-History	Gerontology	Econometrics	Formal logic	Global facts	Jurisprudence	Miscellaneous	Moral disputes	Business ethics	Bilology (college)	Physics (college)	Human sexuality	Moral scenarios	World religions	Abstract algebra	Medicine (college)	Machine Learning	Genetics	Professional law	PR	Security	Chemistry (college)	Computer security	International law	Logical fallacies	Politics	Clinical knowledge	Conceptual physics	Math (college)	Biology (high school)	Physics (high school)	Chemistry (high school)	Geography (high school)	Professional medicine	Electrical Engineering	Elementary mathematics	Psychology (high school)	Statistics (high school)	History (high school)	Math (high school)	Professional Accounting	Professional psychology	Computer science (college)	World history (high school)	Macroeconomics	Microeconomics	Computer science (high school)	Europe History	Government and politics
Llama-3.1-Nemotron-70B-Instruct llmarena.ru	0.689	0.891	0.7	0.648	0.882	0.828	0.69	0.737	0.904	0.754	0.833	0.758	0.65	0.783	0.819	0.728	0.682	0.822	0.772	0.86	0.877	0.864	0.867	0.864	0.822	0.864	0.821	0.789	0.895	0.822	0.844	0.859	0.83	0.912	0.727	0.875	0.822	0.822	0.702	0.8	0.91	0.841	0.822	0.867	0.897	0.889	0.897	0.886	0.754	0.93	0.889	0.884	0.835	0.74	0.651	0.76	0.9

Coorect

Good

Ethical

Model, team	Virtue	Law	Moral	Justice	Utilitarianism
Llama-3.1-Nemotron-70B-Instruct llmarena.ru	0.227	0.218	0.248	0.209	0.199

Model, team	Virtue	Law	Moral	Justice	Utilitarianism
Llama-3.1-Nemotron-70B-Instruct llmarena.ru	0.258	0.239	0.269	0.218	0.231

Model, team	Virtue	Law	Moral	Justice	Utilitarianism
Llama-3.1-Nemotron-70B-Instruct llmarena.ru	0.256	0.235	0.267	0.225	0.222

Model, team	Women	Men	LGBT	Nationalities	Migrants	Other
Llama-3.1-Nemotron-70B-Instruct llmarena.ru	0.815	0.657	0.706	0.784	0.714	0.836

Llama-3.1-Nemotron-70B-Instruct

Ratings for leaderboard tasks

Evaluation on open tasks:

Information about the submission:

Team:

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Link to the ML model:

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Additional links:

Description of the training:

License:

Inference parameters

Ratings by subcategory

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