SOLAR 10.7B Instruct

Russian_NLP Created at 03.02.2024 13:43

0.469

The overall result

The submission does not contain all the required tasks

Ratings for leaderboard tasks

The table will scroll to the left

Task name	Result	Metric
LCS	0.078	Accuracy
RCB	0.523 / 0.503	Accuracy F1 macro
USE	0.04	Grade norm
RWSD	0.654	Accuracy
PARus	0.828	Accuracy
ruTiE	0.7	Accuracy
MultiQ	0.205 / 0.097	F1 Exact match
CheGeKa	0.206 / 0.139	F1 Exact match
ruModAr	0.459	Exact match
ruMultiAr	0.2	Exact match
MathLogicQA	0.396	Accuracy
ruWorldTree	0.884 / 0.884	Accuracy F1 macro
ruOpenBookQA	0.825 / 0.824	Accuracy F1 macro

Evaluation on open tasks:

Go to the ratings by subcategory

The table will scroll to the left

Task name

Result

Metric

BPS

0.359

Accuracy

ruMMLU

0.698

Accuracy

SimpleAr

0.946

Exact match

ruHumanEval

0.013 / 0.067 / 0.134

Pass@k

ruHHH

0.702

ruHateSpeech

0.747

ruDetox

0.041

ruEthics

	Correct	God	Ethical
Virtue	-0.349	-0.391	-0.479
Law	-0.374	-0.327	-0.451
Moral	-0.374	-0.385	-0.484
Justice	-0.343	-0.339	-0.465
Utilitarianism	-0.297	-0.32	-0.384

Information about the submission

Mera version

Torch Version

The version of the codebase

CUDA version

Precision of the model weights

Seed

Batch

Transformers version

The number of GPUs and their type

Architecture

Team:

Russian_NLP

Name of the ML model:

SOLAR 10.7B Instruct

Link to the ML model:

https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0

Additional links:

https://arxiv.org/abs/2312.15166 https://huggingface.co/upstage/SOLAR-10.7B-v1.0

Architecture description:

SOLAR 10.7B Instruct is the instructed version of SOLAR-10.7B, an advanced large language model (LLM) with 10.7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks.

Description of the training:

State-of-the-art instruction fine-tuning methods including supervised fine-tuning (SFT) and direct preference optimization (DPO).

Pretrain data:

SOLAR 10.7B, a large language model (LLM) with 10.7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks.

Training Details:

The following datasets were used: - c-s-ale/alpaca-gpt4-data (SFT) - Open-Orca/OpenOrca (SFT) - in-house generated data utilizing Metamath(SFT, DPO) - Intel/orca_dpo_pairs (DPO) - allenai/ultrafeedback_binarized_cleaned (DPO)

License:

cc-by-nc-4.0

Strategy, generation and parameters:

Code version v.1.1.0 All the parameters were not changed and are used as prepared by the organizers. Details: - 1 x NVIDIA A100 - dtype auto - Pytorch 2.1.2 + CUDA 12.1 - Transformers 4.36.2 - Context length 4096

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
SOLAR 10.7B Instruct Russian_NLP	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-

Model, team	Honest	Helpful	Harmless
SOLAR 10.7B Instruct Russian_NLP	0.623	0.729	0.759

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
SOLAR 10.7B Instruct Russian_NLP	0.7	0.75	0.5	0.629	0.857	0.9	0.8	0.824	0.6	0.9	0.727	0.6	0.6	0.654	0.727	0.4	0.9	0.704	0.7	1	0.2	0.769	0.9	0.608	0.5	0.818	0.563	0.786	0.9	0.727	0.4	0.667	0.5	0.8	0.727	0.8	0.7	0.762	0.3	0.6	0.759	0.8	0.9	0.6	0.813	0.4	1	0.4	0.7	1	0.727	0.875	0.853	0.733	0.375	0.455	0.741

Model, team	SIM	FL	STA
SOLAR 10.7B Instruct Russian_NLP	0.339	0.572	0.183

Coorect

Good

Ethical

Model, team	Virtue	Law	Moral	Justice	Utilitarianism
SOLAR 10.7B Instruct Russian_NLP	-0.349	-0.374	-0.374	-0.343	-0.297

Model, team	Virtue	Law	Moral	Justice	Utilitarianism
SOLAR 10.7B Instruct Russian_NLP	-0.391	-0.327	-0.385	-0.339	-0.32

Model, team	Virtue	Law	Moral	Justice	Utilitarianism
SOLAR 10.7B Instruct Russian_NLP	-0.479	-0.451	-0.484	-0.465	-0.384

Model, team	Women	Men	LGBT	Nationalities	Migrants	Other
SOLAR 10.7B Instruct Russian_NLP	0.769	0.657	0.647	0.784	0.429	0.803

SOLAR 10.7B Instruct

Ratings for leaderboard tasks

Evaluation on open tasks:

Information about the submission

Team:

Name of the ML model:

Link to the ML model:

Additional links:

Architecture description:

Description of the training:

Pretrain data:

Training Details:

License:

Strategy, generation and parameters:

Ratings by subcategory

Confirm the deletion of the sub