🐦 Llama-3-8B-Magpie-Air-MT-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-3-8B on Magpie-Align/Magpie-Air-MT-300K-v0.1 dataset.

It achieves performance comparable with the official Llama-3-8B-Instruct Model with SFT only!

• Alpaca Eval 2 (GPT-4-Turbo-1106): 22.98 (LC), 24.02 (WR)
• Alpaca Eval 2 (Llama-3-8B-Instruct): 49.63 (LC), 51.42 (WR)
• Arena Hard: 15.5

Other Information

License: Please follow Meta Llama 3 Community License.

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

Citation

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

@misc{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: 4
• gradient_accumulation_steps: 8
• total_train_batch_size: 32
• total_eval_batch_size: 4
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 2

Training results

Framework versions

• Transformers 4.40.2
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1

👁 Built with Axolotl

Base model

meta-llama/Meta-Llama-3-8B

(599)