Llama 2 7B
Created at 12.01.2024 11:15Assessment of the main tasks: 0.327
The table will scroll to the left
| Task name | Result | Metric |
|---|---|---|
| LCS | 0.106 | Accuracy |
| RCB | 0.349 / 0.272 | Avg. F1 / Accuracy |
| USE | 0.014 | Grade Norm |
| RWSD | 0.504 | Accuracy |
| PARus | 0.532 | Accuracy |
| ruTiE | 0.5 | Accuracy |
| MultiQ | 0.081 / 0.011 | F1-score/EM |
| CheGeKa | 0.021 / 0 | F1 / EM |
| ruModAr | 0.367 | EM |
| ruMultiAr | 0.124 | EM |
| MathLogicQA | 0.277 | Accuracy |
| ruWorldTree | 0.545 / 0.543 | Avg. F1 / Accuracy |
| ruOpenBookQA | 0.475 / 0.471 | Avg. F1 / Accuracy |
Evaluation on open tasks:
It is not taken into account in the overall ratingThe table will scroll to the left
| Task name | Result | Metric | ||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BPS | 0.426 | Accuracy | ||||||||||||||||||||||||
| ruMMLU | 0.452 | Accuracy | ||||||||||||||||||||||||
| SimpleAr | 0.839 | EM | ||||||||||||||||||||||||
| ruHumanEval | 0.007 / 0.034 / 0.067 | pass@k | ||||||||||||||||||||||||
| ruHHH |
0.5
|
Accuracy | ||||||||||||||||||||||||
| ruHateSpeech |
0.536
|
Accuracy | ||||||||||||||||||||||||
| ruDetox |
|
Overall average score (J) Assessment of the preservation of meaning (SIM) Assessment of naturalness (FL) Style Transfer Accuracy (STA) |
||||||||||||||||||||||||
| ruEthics |
Table results:
[[-0.115, -0.124
, -0.11, -0.129
, -0.097], |
5 MCC |
Information about the submission:
Team:
MERA
Name of the ML model:
Llama 2 7B
Link to the ML model:
https://huggingface.co/meta-llama/Llama-2-7b-hfAdditional links:
https://arxiv.org/abs/2307.09288Architecture description:
Llama 2 is an auto-regressive language model that uses an optimized transformer architecture.
Description of the training:
Authors used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
Pretrain data:
Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources.
Training Details:
Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens.
License:
A custom commercial license is available at: https://ai.meta.com/resources/models-and-libraries/llama-downloads/
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
