README.md

---
language:
- en
license: other
license_name: qwen
license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
library_name: transformers
base_model:
- Qwen/Qwen2.5-32B-Instruct
datasets:
- Magpie-Align/Magpie-Pro-300K-Filtered
model-index:
- name: TheBeagle-v2beta-32B-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: 45.03
      name: strict accuracy
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/TheBeagle-v2beta-32B-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: 58.07
      name: normalized accuracy
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/TheBeagle-v2beta-32B-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: 39.43
      name: exact match
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/TheBeagle-v2beta-32B-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: 20.13
      name: acc_norm
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/TheBeagle-v2beta-32B-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: 24.5
      name: acc_norm
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/TheBeagle-v2beta-32B-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: 54.57
      name: accuracy
    source:
      url: >-
        https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/TheBeagle-v2beta-32B-MGS
      name: Open LLM Leaderboard
---

# TheBeagle-v2beta-32B-MGS
This model is an experimental version of our latest innovation: `MGS`. Its up to you to figure out what does it means, but its very explicit.
We didn't applied our known `UNA` algorithm to the forward pass, but they are entirely compatible and operates in different parts of the neural network and in different ways, tho they both can be seen as a regularization technique.

## CHANGELOG
**UPDATE**: 26/Oct
* Updated `tokenizer_config.json` (from the base_model)
* Regenerated Quants (being uploaded)
* Re-submitted Leaderboard Evaluation, MATH & IFeval have relevant updates
* Aligned LICENSE with `Qwen` terms.

## MGS
MGS stands for... Many-Geeks-Searching... and thats it. Hint: `1+1 is 2, and 1+1 is not 3`

We still believe on 1-Epoch should be enough, so we just did 1 Epoch only.

## Dataset
Used here the first decent (corpora & size) dataset on the hub: `Magpie-Align/Magpie-Pro-300K-Filtered`
Kudos to the Magpie team to contribute with some decent stuff that I personally think is very good to ablate.

It achieves the following results on the evaluation set:
- Loss: 0.5378 (1 Epoch), outperforming the baseline model.
## Quants

[All versions available](https://huggingface.co/fblgit/TheBeagle-v2beta-MGS-GGUF/tree/main)

## Licensing terms:

**On top of the Qwen LICENSE, we add an extra term for derivatives to include "Beagle" or "MGS" on the model name, this will help us to track better the study. Thank you**

## Training
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 25
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 9.8642        | 0.0012 | 1    | 0.7195          |
| 2.077         | 0.0507 | 42   | 0.6161          |
| 1.0325        | 0.1014 | 84   | 0.6093          |
| 0.8945        | 0.1520 | 126  | 0.5962          |
| 0.8532        | 0.2027 | 168  | 0.5869          |
| 0.8185        | 0.2534 | 210  | 0.5805          |
| 0.81          | 0.3041 | 252  | 0.5719          |
| 0.7901        | 0.3548 | 294  | 0.5663          |
| 0.7766        | 0.4054 | 336  | 0.5618          |
| 0.7687        | 0.4561 | 378  | 0.5590          |
| 0.7443        | 0.5068 | 420  | 0.5564          |
| 0.7494        | 0.5575 | 462  | 0.5525          |
| 0.7787        | 0.6081 | 504  | 0.5485          |
| 0.7381        | 0.6588 | 546  | 0.5466          |
| 0.7359        | 0.7095 | 588  | 0.5444          |
| 0.7447        | 0.7602 | 630  | 0.5435          |
| 0.7378        | 0.8109 | 672  | 0.5415          |
| 0.7302        | 0.8615 | 714  | 0.5398          |
| 0.7476        | 0.9122 | 756  | 0.5391          |
| 0.715         | 0.9629 | 798  | 0.5378          |


# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) without chat template.
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__TheBeagle-v2beta-32B-MGS)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |40.29|
|IFEval (0-Shot)    |45.03|
|BBH (3-Shot)       |58.07|
|MATH Lvl 5 (4-Shot)|39.43|
|GPQA (0-shot)      |20.13|
|MuSR (0-shot)      |24.50|
|MMLU-PRO (5-shot)  |54.57|

## Thanks
- Qwen Team for their outstanding model
- MagPie Team for contributing plenty of datasets
- Cybertron Cloud Compute

# 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}
}
```