File size: 8,468 Bytes
a572096 f72f227 a572096 f72f227 a572096 f72f227 a572096 f72f227 a572096 f72f227 a572096 f72f227 a572096 f72f227 a572096 3c431dc a572096 f72f227 a572096 f72f227 a572096 f72f227 a572096 f72f227 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 |
---
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}
}
``` |