InternVL3-8B-Instruct

MERA Created at 22.01.2026 05:00

Ratings for leaderboard tasks

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Board Result Attempted Score Coverage Place in the rating
Multi 0.045 0.067 0.667 50
Images 0.011 0.011 1 49
Video 0.274 0.274 1 24

Tasks

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Task Modality Result Metric
0.006
EM JudgeScore
0.019
EM JudgeScore
0.019
EM JudgeScore
0
EM JudgeScore
0.032
EM JudgeScore
0.289
EM JudgeScore
0.023
EM JudgeScore
0
EM JudgeScore
0.243
EM JudgeScore
0.013
EM JudgeScore
0.291
EM JudgeScore
0.002
EM JudgeScore
culture 0 / 0.001
business 0 / 0.009
medicine 0 / 0.003
social_sciences 0 / 0.007
fundamental_sciences 0 / 0.005
applied_sciences 0 / 0.01
0.001
EM JudgeScore
biology 0 / 0
chemistry 0 / 0
physics 0 / 0.003
economics 0 / 0
ru 0 / 0.001
all 0 / 0
0.003
EM JudgeScore
biology 0 / 0.018
chemistry 0 / 0
physics 0 / 0.005
science 0 / 0

Information about the submission

Mera version
v1.0.0
Torch Version
2.8.0
The version of the codebase
7e640aa
CUDA version
12.8
Precision of the model weights
bfloat16
Seed
1234
Batch
1
Transformers version
4.57.1
The number of GPUs and their type
1 x NVIDIA A100-SXM4-80GB
Architecture
openai-chat-completions

Team:

MERA

Name of the ML model:

InternVL3-8B-Instruct

Model size

8.0B

Model type:

Opened

SFT

Inference parameters

Generation Parameters:
realvideoqa - until=["\n\n"];do_sample=false;temperature=0; \nruhhh_video - until=["\n\n"];do_sample=false;temperature=0; \ncommonvideoqa - until=["\n\n"];do_sample=false;temperature=0; \nrunaturalsciencevqa_biology - until=["<|endoftext|>"];temperature=0;do_sample=false;max_gen_toks=64; \nrunaturalsciencevqa_chemistry - until=["<|endoftext|>"];temperature=0;do_sample=false;max_gen_toks=64; \nrunaturalsciencevqa_earth_science - until=["<|endoftext|>"];temperature=0;do_sample=false;max_gen_toks=64; \nrunaturalsciencevqa_physics - until=["<|endoftext|>"];temperature=0;do_sample=false;max_gen_toks=64; \nruhhh_image - until=["\n\n"];do_sample=false;temperature=0; \nlabtabvqa - until=["\n\n"];do_sample=false;temperature=0; \nweird - until=["\n\n"];do_sample=false;temperature=0; \nrealvqa - until=["\n\n"];do_sample=false;temperature=0; \nrumathvqa - until=["\n\n"];do_sample=false;temperature=0; \nschoolsciencevqa_biology - until=["<|endoftext|>"];temperature=0;do_sample=false;max_gen_toks=256; \nschoolsciencevqa_chemistry - until=["<|endoftext|>"];temperature=0;do_sample=false;max_gen_toks=256; \nschoolsciencevqa_earth_science - until=["<|endoftext|>"];temperature=0;do_sample=false;max_gen_toks=256; \nschoolsciencevqa_economics - until=["<|endoftext|>"];temperature=0;do_sample=false;max_gen_toks=256; \nschoolsciencevqa_history_all - until=["<|endoftext|>"];temperature=0;do_sample=false;max_gen_toks=256; \nschoolsciencevqa_history_ru - until=["<|endoftext|>"];temperature=0;do_sample=false;max_gen_toks=256; \nschoolsciencevqa_physics - until=["<|endoftext|>"];temperature=0;do_sample=false;max_gen_toks=256; \nruclevr - until=["\n\n"];do_sample=false;temperature=0; \nunisciencevqa_applied_sciences - until=["<|endoftext|>"];temperature=0;do_sample=false;max_gen_toks=256; \nunisciencevqa_business - until=["<|endoftext|>"];temperature=0;do_sample=false;max_gen_toks=256; \nunisciencevqa_cultural_studies - until=["<|endoftext|>"];temperature=0;do_sample=false;max_gen_toks=256; \nunisciencevqa_fundamental_sciences - until=["<|endoftext|>"];temperature=0;do_sample=false;max_gen_toks=256; \nunisciencevqa_health_and_medicine - until=["<|endoftext|>"];temperature=0;do_sample=false;max_gen_toks=256; \nunisciencevqa_social_sciences - until=["<|endoftext|>"];temperature=0;do_sample=false;max_gen_toks=256; \nrucommonvqa - until=["\n\n"];do_sample=false;temperature=0;

The size of the context:
32768