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.
This extrapolated model achieves the
40.6%
win rate and
45.8%
LC win rate on
AlpacaEval 2.0
, outperforming the original
Llama-3-Instruct-8B-SimPO
's 40.5% and 44.7%, respectively.
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
Runs of QuantFactory Llama-3-Instruct-8B-SimPO-ExPO-GGUF on huggingface.co
888
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
0
30-day runs
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