Specifically, we obtain this model by extrapolating (alpha = 0.3) from the weights of the SFT and DPO/RLHF checkpoints, achieving superior alignment with human preference.

Note: This is an experimental model, as I have not comprehensively evaluated its Chinese ability. Unexpected issues may occur when we apply extrapolation to the DPO/RLHF alignment training for new languages (e.g., Chinese).

Evaluation Results

Evaluation results on the AlpacaEval 2.0 benchmark (you can find the evaluation outputs on the official GitHub repo):

	Win Rate (Ori)	LC Win Rate (Ori)	Win Rate (+ ExPO)	LC Win Rate (+ ExPO)
`HuggingFaceH4/zephyr-7b-alpha`	6.7%	10.0%	10.6%	13.6%
`HuggingFaceH4/zephyr-7b-beta`	10.2%	13.2%	11.1%	14.0%
`berkeley-nest/Starling-LM-7B-alpha`	15.0%	18.3%	18.2%	19.5%
`Nexusflow/Starling-LM-7B-beta`	26.6%	25.8%	29.6%	26.4%
`snorkelai/Snorkel-Mistral-PairRM`	24.7%	24.0%	28.8%	26.4%
`RLHFlow/LLaMA3-iterative-DPO-final`	29.2%	36.0%	32.7%	37.8%
`internlm/internlm2-chat-1.8b`	3.8%	4.0%	5.2%	4.3%
`internlm/internlm2-chat-7b`	20.5%	18.3%	28.1%	22.7%
`internlm/internlm2-chat-20b`	36.1%	24.9%	46.2%	27.2%
`allenai/tulu-2-dpo-7b`	8.5%	10.2%	11.5%	11.7%
`allenai/tulu-2-dpo-13b`	11.2%	15.5%	15.6%	17.6%
`allenai/tulu-2-dpo-70b`	15.4%	21.2%	23.0%	25.7%

Evaluation results on the MT-Bench benchmark (you can find the evaluation outputs on the official GitHub repo):

	Original	+ ExPO
`HuggingFaceH4/zephyr-7b-alpha`	6.85	6.87
`HuggingFaceH4/zephyr-7b-beta`	7.02	7.06
`berkeley-nest/Starling-LM-7B-alpha`	7.82	7.91
`Nexusflow/Starling-LM-7B-beta`	8.10	8.18
`snorkelai/Snorkel-Mistral-PairRM`	7.63	7.69
`RLHFlow/LLaMA3-iterative-DPO-final`	8.08	8.45
`internlm/internlm2-chat-1.8b`	5.17	5.26
`internlm/internlm2-chat-7b`	7.72	7.80
`internlm/internlm2-chat-20b`	8.13	8.26
`allenai/tulu-2-dpo-7b`	6.35	6.38
`allenai/tulu-2-dpo-13b`	7.00	7.26
`allenai/tulu-2-dpo-70b`	7.79	8.03

chujiezheng/Llama3-8B-Chinese-Chat-ExPO

Llama3-8B-Chinese-Chat-ExPO

Evaluation Results

chujiezheng/
Llama3-8B-Chinese-Chat-ExPO