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---
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.**

![cybertron-v4-MGS](https://huggingface.co./fblgit/cybertron-v4-qw7B-MGS/resolve/main/cybertron_v4MGS.png)

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](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-MGS)

|      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.
[<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)

### 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}
}
```