fblgit commited on
Commit
3965ead
1 Parent(s): 01adb9e

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +38 -14
README.md CHANGED
@@ -1,33 +1,30 @@
1
  ---
2
- library_name: peft
 
 
 
 
 
3
  tags:
4
  - generated_from_trainer
5
  base_model: Qwen/Qwen2.5-1.5B-Instruct
6
  model-index:
7
  - name: miniclaus-qw1.5B-UNAMGS
8
  results: []
 
 
9
  ---
10
 
11
  # miniclaus-qw1.5B-UNAMGS
 
 
 
12
 
13
  [<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)
14
 
15
- This model was trained from scratch on the None dataset.
16
  It achieves the following results on the evaluation set:
17
  - Loss: 0.7193
18
 
19
- ## Model description
20
-
21
- More information needed
22
-
23
- ## Intended uses & limitations
24
-
25
- More information needed
26
-
27
- ## Training and evaluation data
28
-
29
- More information needed
30
-
31
  ## Training procedure
32
 
33
  ### Training hyperparameters
@@ -75,3 +72,30 @@ The following hyperparameters were used during training:
75
  - Pytorch 2.3.0+cu121
76
  - Datasets 3.0.1
77
  - Tokenizers 0.20.1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  ---
2
+ language:
3
+ - en
4
+ license: other
5
+ license_name: qwen
6
+ license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
7
+ library_name: transformers
8
  tags:
9
  - generated_from_trainer
10
  base_model: Qwen/Qwen2.5-1.5B-Instruct
11
  model-index:
12
  - name: miniclaus-qw1.5B-UNAMGS
13
  results: []
14
+ datasets:
15
+ - Magpie-Align/Magpie-Pro-MT-300K-v0.1
16
  ---
17
 
18
  # miniclaus-qw1.5B-UNAMGS
19
+ Trained with `Magpie-Align/Magpie-Pro-MT-300K-v0.1`
20
+
21
+ Using MGS & UNA (MLP) on this tiny but powerful model.
22
 
23
  [<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)
24
 
 
25
  It achieves the following results on the evaluation set:
26
  - Loss: 0.7193
27
 
 
 
 
 
 
 
 
 
 
 
 
 
28
  ## Training procedure
29
 
30
  ### Training hyperparameters
 
72
  - Pytorch 2.3.0+cu121
73
  - Datasets 3.0.1
74
  - Tokenizers 0.20.1
75
+
76
+
77
+ ## Citations
78
+ ```
79
+ @misc{miniclaus-qw15,
80
+ title={MiniClaus: 1.5B UNAMGS},
81
+ author={Xavier Murias},
82
+ year={2024},
83
+ publisher = {HuggingFace},
84
+ journal = {HuggingFace repository},
85
+ howpublished = {\url{https://huggingface.co/fblgit/miniclaus-qw1.5B-UNAMGS}},
86
+ }
87
+
88
+ @misc{qwen2.5,
89
+ title = {Qwen2.5: A Party of Foundation Models},
90
+ url = {https://qwenlm.github.io/blog/qwen2.5/},
91
+ author = {Qwen Team},
92
+ month = {September},
93
+ year = {2024}
94
+ }
95
+ @article{qwen2,
96
+ title={Qwen2 Technical Report},
97
+ 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},
98
+ journal={arXiv preprint arXiv:2407.10671},
99
+ year={2024}
100
+ }
101
+ ```