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---
language:
- en
license: other
library_name: transformers
tags:
- generated_from_trainer
base_model:
- Qwen/Qwen2.5-7B-Instruct
datasets:
- Magpie-Align/Magpie-Qwen2.5-Pro-1M-v0.1
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
model-index:
- name: cybertron-v4-qw7B-UNAMGS
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: 60.84
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/cybertron-v4-qw7B-UNAMGS
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: 37.71
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/cybertron-v4-qw7B-UNAMGS
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: 29.91
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/cybertron-v4-qw7B-UNAMGS
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: 10.85
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/cybertron-v4-qw7B-UNAMGS
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: 12.69
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/cybertron-v4-qw7B-UNAMGS
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: 38.89
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/cybertron-v4-qw7B-UNAMGS
name: Open LLM Leaderboard
---
# cybertron-v4-qw7B-UNAMGS
**UNA IS BACK** Cybertron v4 UNA-MGS, Based on the amazing Qwen2.5 7B
**SCORING #1 7-8B LLM WITH NO CONTAMINATION 21.11.2024 with avg. 31.82**
This special edition went thru UNA at MLP layers just like [miniclaus-1.5B](https://huggingface.co/fblgit/miniclaus-qw1.5B-UNAMGS)
Here we use our novel approach called `MGS`. Its up to you to figure out what it means. On top of that we used `UNA: Uniform Neural Alignment`
Cybertron V4 went thru SFT with `MGS & UNA` over `Magpie-Align/Magpie-Qwen2.5-Pro-1M-v0.1` dataset.
## Contamination Benchmark
https://gair-nlp.github.io/benbench/
- MATH:
```
5gram-Qwen2.5-7B-Instruct-orgn-MATH-test.jsonl: 37.52666666666667
5gram-Qwen2.5-7B-Instruct-orgn-MATH-train.jsonl: 46.36666666666667
```
vs
```
5gram-UNA-cybertron-v4-qw7B-MGS-orgn-MATH-test.jsonl: 37.42666666666667
5gram-UNA-cybertron-v4-qw7B-MGS-orgn-MATH-train.jsonl: 46.053333333333335
```
vs
```
5gram-Homer-v0.5-orgn-MATH-test.jsonl: 38.77333333333333
5gram-Homer-v0.5-orgn-MATH-train.jsonl: 47.16666666666667
```
## Quantz
Available at bartowski repo
https://huggingface.co/bartowski/cybertron-v4-qw7B-UNAMGS-GGUF
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__cybertron-v4-qw7B-UNAMGS)
| Metric |Value|
|-------------------|----:|
|Avg. |31.82|
|IFEval (0-Shot) |60.84|
|BBH (3-Shot) |37.71|
|MATH Lvl 5 (4-Shot)|29.91|
|GPQA (0-shot) |10.85|
|MuSR (0-shot) |12.69|
|MMLU-PRO (5-shot) |38.89|
## MGS & UNA & Details
* MGS, `1+1 = 2 and not 3`
* UNA, `1+1 = 2 obviously`
We also followed https://arxiv.org/pdf/2410.21228 insights.
## Training procedure
1 Epoch as usual.
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
```
datasets:
- path: Magpie-Align/Magpie-Qwen2.5-Pro-1M-v0.1
split: train
type: chat_template
field_messages: conversations
message_field_role: from
message_field_content: value
roles:
user: ["human", "user"]
assistant: ["gpt", "assistant", "ai"]
system: ["system"]
```
### Training hyperparameters
The following hyperparameters were used during training:
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.7824 | 0.0003 | 1 | 0.5555 |
| 0.5489 | 0.0503 | 144 | 0.4848 |
| 0.5348 | 0.1006 | 288 | 0.4732 |
| 0.5256 | 0.1509 | 432 | 0.4670 |
| 0.5172 | 0.2012 | 576 | 0.4621 |
| 0.4882 | 0.2515 | 720 | 0.4578 |
| 0.4848 | 0.3018 | 864 | 0.4550 |
| 0.4678 | 0.3520 | 1008 | 0.4522 |
| 0.4686 | 0.4023 | 1152 | 0.4502 |
| 0.4775 | 0.4526 | 1296 | 0.4474 |
| 0.4464 | 0.5029 | 1440 | 0.4454 |
| 0.4772 | 0.5532 | 1584 | 0.4438 |
| 0.4546 | 0.6035 | 1728 | 0.4425 |
| 0.4661 | 0.6538 | 1872 | 0.4411 |
| 0.4569 | 0.7041 | 2016 | 0.4399 |
| 0.4529 | 0.7544 | 2160 | 0.4390 |
| 0.4409 | 0.8047 | 2304 | 0.4380 |
| 0.4405 | 0.8550 | 2448 | 0.4370 |
| 0.4642 | 0.9053 | 2592 | 0.4363 |
| 0.4566 | 0.9556 | 2736 | 0.4359 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.45.2 (UNA & MGS patch)
- 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}
}
@article{xu2024benchmarking,
title={Benchmarking Benchmark Leakage in Large Language Models},
author={Xu, Ruijie and Wang, Zengzhi and Fan, Run-Ze and Liu, Pengfei},
year={2024},
journal={arXiv preprint arXiv:2404.18824},
url={https://arxiv.org/abs/2404.18824}
}
``` |