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URL: https://huggingface.co/Magpie-Align/Llama-3.1-8B-Magpie-Align-SFT-v0.1

⇱ Magpie-Align/Llama-3.1-8B-Magpie-Align-SFT-v0.1 · Hugging Face


👁 Magpie

🐦 Llama-3.1-8B-Magpie-Align-SFT-v0.1

Project Web: https://magpie-align.github.io/

Arxiv Technical Report: https://arxiv.org/abs/2406.08464

Codes: https://github.com/magpie-align/magpie

Abstract

About This Model

This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B on

It achieves performance comparable with the official Llama-3.1-8B-Instruct Model with SFT only!

  • Alpaca Eval 2 (GPT-4-Turbo-1106): 24.79 (LC), 25.05 (WR)
  • Arena Hard: 21.0

Other Information

License: Please follow Meta Llama 3 Community License (Data) and Meta Llama 3.1 Community License (Model).

Conversation Template: Please use Llama 3 official chat template for the best performance.

Questions? Please contact Zhangchen by email.

Citation

If you find the model, data, or code useful, please cite our paper:

@article{xu2024magpie,
 title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing}, 
 author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
 year={2024},
 eprint={2406.08464},
 archivePrefix={arXiv},
 primaryClass={cs.CL}
}

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-05
  • train_batch_size: 1
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • gradient_accumulation_steps: 16
  • total_train_batch_size: 128
  • total_eval_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 79
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss
0.7863 0.0024 1 0.7710
0.5422 0.2007 85 0.4937
0.476 0.4014 170 0.4382
0.4594 0.6021 255 0.4174
0.4383 0.8028 340 0.4057
0.4397 1.0035 425 0.3978
0.3927 1.1845 510 0.3956
0.3895 1.3852 595 0.3934
0.3832 1.5859 680 0.3925
0.3957 1.7866 765 0.3924

Framework versions

  • Transformers 4.43.1
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1

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