MTS AI Chat 7B

MTS AI Created at 11.02.2024 22:10

0.479

The overall result

The submission does not contain all the required tasks

Ratings for leaderboard tasks

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Task name	Result	Metric
LCS	0.094	Accuracy
RCB	0.532 / 0.53	Accuracy F1 macro
USE	0.128	Grade norm
RWSD	0.615	Accuracy
PARus	0.834	Accuracy
ruTiE	0.574	Accuracy
MultiQ	0.361 / 0.278	F1 Exact match
CheGeKa	0.083 / 0.046	F1 Exact match
ruModAr	0.717	Exact match
ruMultiAr	0.233	Exact match
MathLogicQA	0.407	Accuracy
ruWorldTree	0.846 / 0.845	Accuracy F1 macro
ruOpenBookQA	0.763 / 0.762	Accuracy F1 macro

Evaluation on open tasks:

Go to the ratings by subcategory

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Task name

Result

Metric

BPS

0.276

Accuracy

ruMMLU

0.689

Accuracy

SimpleAr

0.955

Exact match

ruHumanEval

0.018 / 0.088 / 0.177

Pass@k

ruHHH

0.719

ruHateSpeech

0.758

ruDetox

0.229

ruEthics

	Correct	God	Ethical
Virtue	-0.276	-0.313	-0.419
Law	-0.28	-0.283	-0.381
Moral	-0.279	-0.319	-0.417
Justice	-0.247	-0.295	-0.378
Utilitarianism	-0.223	-0.267	-0.338

Information about the submission:

Mera version

Torch Version

The version of the codebase

CUDA version

Precision of the model weights

Seed

Butch

Transformers version

The number of GPUs and their type

Architecture

Team:

MTS AI

Name of the ML model:

MTS AI Chat 7B

Link to the ML model:

https://huggingface.co/mistralai/Mistral-7B-v0.1

Architecture description:

Mistral 7B model architecture

Description of the training:

Mistral trained on proprietary DPO and SFT datasets

Pretrain data:

Training Details:

License:

Proprietary model developed by MTS AI

Strategy, generation and parameters:

Code version v.1.1.0 All the parameters were not changed. Inference details: torch 2.1.0 + Cuda 11.8. max length 6012 tokens

Comments about inference:

we run the model using MERA github repo without any changes using hf inference script

Expand information

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
MTS AI Chat 7B MTS AI	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-

Model, team	Honest	Helpful	Harmless
MTS AI Chat 7B MTS AI	0.672	0.661	0.828

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
MTS AI Chat 7B MTS AI	0.8	0.625	0.6	0.743	0.762	0.8	0.667	0.647	0.5	0.9	0.727	0.6	0.9	0.577	0.545	0.6	0.8	0.667	0.4	0.9	0.2	0.788	0.8	0.706	0.6	0.636	0.875	0.714	0.9	0.636	0.3	0.667	0.4	0.8	0.818	0.9	0.7	0.714	0.6	0.5	0.772	0.7	0.8	0.5	0.875	0.9	0.9	0.4	0.5	0.9	0.591	0.75	0.765	0.867	0.542	0.394	0.704

Model, team	SIM	FL	STA
MTS AI Chat 7B MTS AI	0.724	0.584	0.517

Coorect

Good

Ethical

Model, team	Virtue	Law	Moral	Justice	Utilitarianism
MTS AI Chat 7B MTS AI	-0.276	-0.28	-0.279	-0.247	-0.223

Model, team	Virtue	Law	Moral	Justice	Utilitarianism
MTS AI Chat 7B MTS AI	-0.313	-0.283	-0.319	-0.295	-0.267

Model, team	Virtue	Law	Moral	Justice	Utilitarianism
MTS AI Chat 7B MTS AI	-0.419	-0.381	-0.417	-0.378	-0.338

Model, team	Women	Men	LGBT	Nationalities	Migrants	Other
MTS AI Chat 7B MTS AI	0.75	0.771	0.765	0.757	0.571	0.787

MTS AI Chat 7B

Ratings for leaderboard tasks

Evaluation on open tasks:

Information about the submission:

Team:

Name of the ML model:

Link to the ML model:

Architecture description:

Description of the training:

Pretrain data:

Training Details:

License:

Strategy, generation and parameters:

Comments about inference:

Ratings by subcategory

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