metadata
license: other
license_name: qwen
license_link: https://huggingface.co./Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
datasets:
- Magpie-Align/Magpie-Qwen2.5-Pro-1M-v0.1
base_model:
- Qwen/Qwen2.5-7B-Instruct
library_name: transformers
tags:
- generated_from_trainer
language:
- en
model-index:
- name: cybertron-v4-qw7B-MGS
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 62.64
name: strict accuracy
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/cybertron-v4-qw7B-MGS
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 37.04
name: normalized accuracy
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/cybertron-v4-qw7B-MGS
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 27.72
name: exact match
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/cybertron-v4-qw7B-MGS
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 8.05
name: acc_norm
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/cybertron-v4-qw7B-MGS
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 13.2
name: acc_norm
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/cybertron-v4-qw7B-MGS
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 38.59
name: accuracy
source:
url: >-
https://huggingface.co./spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/cybertron-v4-qw7B-MGS
name: Open LLM Leaderboard
cybertron-v4-qw7B-MGS
WE ARE BACK Cybertron v4, #1 LLM in its class. Based on the amazing Qwen2.5 7B
Scoring #1 LLM of 7B and 8B at 30.10.2024.
Here we use our novel approach called MGS. Its up to you to figure out what it means.
Cybertron V4 went thru SFT over Magpie-Align/Magpie-Qwen2.5-Pro-1M-v0.1
Quantz
Avaialble at https://huggingface.co./bartowski/cybertron-v4-qw7B-MGS-GGUF
MGS
Being fair:
https://arxiv.org/pdf/2410.21228
MGS, among other things.. a strategy of tackling corpora forgetful.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 31.21 |
| IFEval (0-Shot) | 62.64 |
| BBH (3-Shot) | 37.04 |
| MATH Lvl 5 (4-Shot) | 27.72 |
| GPQA (0-shot) | 8.05 |
| MuSR (0-shot) | 13.20 |
| MMLU-PRO (5-shot) | 38.59 |
Try Cybertron v4!
Thanks to @rombodawg for contributing with a free to use Inference space hosted at:
https://huggingface.co./spaces/rombodawg/Try_fblgit_cybertron-v4-qw7B-MGS
Training procedure
1 Epoch as usual.
Training hyperparameters
The following hyperparameters were used during training:
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7405 | 0.0007 | 1 | 0.5760 |
| 0.6146 | 0.0502 | 71 | 0.5045 |
| 0.5908 | 0.1003 | 142 | 0.4930 |
| 0.5669 | 0.1505 | 213 | 0.4854 |
| 0.5575 | 0.2007 | 284 | 0.4811 |
| 0.535 | 0.2508 | 355 | 0.4765 |
| 0.5161 | 0.3010 | 426 | 0.4736 |
| 0.5268 | 0.3511 | 497 | 0.4726 |
| 0.5119 | 0.4013 | 568 | 0.4701 |
| 0.5329 | 0.4515 | 639 | 0.4687 |
| 0.5167 | 0.5016 | 710 | 0.4673 |
| 0.5105 | 0.5518 | 781 | 0.4660 |
| 0.5203 | 0.6020 | 852 | 0.4653 |
| 0.5035 | 0.6521 | 923 | 0.4646 |
| 0.4903 | 0.7023 | 994 | 0.4641 |
| 0.5031 | 0.7525 | 1065 | 0.4628 |
| 0.5147 | 0.8026 | 1136 | 0.4629 |
| 0.5037 | 0.8528 | 1207 | 0.4620 |
| 0.5029 | 0.9029 | 1278 | 0.4620 |
| 0.492 | 0.9531 | 1349 | 0.4621 |
Framework versions
- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.3.0+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
Citations
@misc{thebeagle-v2,
title={TheBeagle v2: MGS},
author={Xavier Murias},
year={2024},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co./fblgit/TheBeagle-v2beta-32B-MGS}},
}
@misc{qwen2.5,
title = {Qwen2.5: A Party of Foundation Models},
url = {https://qwenlm.github.io/blog/qwen2.5/},
author = {Qwen Team},
month = {September},
year = {2024}
}
@article{qwen2,
title={Qwen2 Technical Report},
author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
journal={arXiv preprint arXiv:2407.10671},
year={2024}
}