--- 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.** 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` # [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| ![cybertron-v4-MGS](https://huggingface.co/fblgit/cybertron-v4-qw7B-MGS/resolve/main/cybertron_v4MGS.png) ## Training procedure 1 Epoch as usual. [Built with Axolotl](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} } ```