File size: 7,771 Bytes
c2f4cad 54aa151 c2f4cad 54aa151 c2f4cad 3b709ea c2f4cad ce9b1e9 c2f4cad ce9b1e9 c2f4cad 97ab294 c2f4cad ce9b1e9 c2f4cad ce9b1e9 c2f4cad ce9b1e9 c2f4cad 54aa151 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 |
---
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
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.
## Quantz
Soon..
## 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}
}
```
# [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|
|