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RichardErkhov/
chujiezheng_-_Mistral7B-PairRM-SPPO-ExPO-gguf

Name: chujiezheng_-_Mistral7B-PairRM-SPPO-ExPO-gguf
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Mistral7B-PairRM-SPPO-ExPO - GGUF

Model creator: https://huggingface.co/chujiezheng/
Original model: https://huggingface.co/chujiezheng/Mistral7B-PairRM-SPPO-ExPO/

Name	Quant method	Size
Mistral7B-PairRM-SPPO-ExPO.Q2_K.gguf	Q2_K	2.53GB
Mistral7B-PairRM-SPPO-ExPO.Q3_K_S.gguf	Q3_K_S	2.95GB
Mistral7B-PairRM-SPPO-ExPO.Q3_K.gguf	Q3_K	3.28GB
Mistral7B-PairRM-SPPO-ExPO.Q3_K_M.gguf	Q3_K_M	3.28GB
Mistral7B-PairRM-SPPO-ExPO.Q3_K_L.gguf	Q3_K_L	3.56GB
Mistral7B-PairRM-SPPO-ExPO.IQ4_XS.gguf	IQ4_XS	3.67GB
Mistral7B-PairRM-SPPO-ExPO.Q4_0.gguf	Q4_0	3.83GB
Mistral7B-PairRM-SPPO-ExPO.IQ4_NL.gguf	IQ4_NL	3.87GB
Mistral7B-PairRM-SPPO-ExPO.Q4_K_S.gguf	Q4_K_S	3.86GB
Mistral7B-PairRM-SPPO-ExPO.Q4_K.gguf	Q4_K	4.07GB
Mistral7B-PairRM-SPPO-ExPO.Q4_K_M.gguf	Q4_K_M	4.07GB
Mistral7B-PairRM-SPPO-ExPO.Q4_1.gguf	Q4_1	4.24GB
Mistral7B-PairRM-SPPO-ExPO.Q5_0.gguf	Q5_0	4.65GB
Mistral7B-PairRM-SPPO-ExPO.Q5_K_S.gguf	Q5_K_S	4.65GB
Mistral7B-PairRM-SPPO-ExPO.Q5_K.gguf	Q5_K	4.78GB
Mistral7B-PairRM-SPPO-ExPO.Q5_K_M.gguf	Q5_K_M	4.78GB
Mistral7B-PairRM-SPPO-ExPO.Q5_1.gguf	Q5_1	5.07GB
Mistral7B-PairRM-SPPO-ExPO.Q6_K.gguf	Q6_K	5.53GB
Mistral7B-PairRM-SPPO-ExPO.Q8_0.gguf	Q8_0	7.17GB

Original model description:

language:

en license: apache-2.0 model-index:
name: Mistral7B-PairRM-SPPO-ExPO 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: 36.73 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=chujiezheng/Mistral7B-PairRM-SPPO-ExPO 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: 13.68 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=chujiezheng/Mistral7B-PairRM-SPPO-ExPO 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: 0.91 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=chujiezheng/Mistral7B-PairRM-SPPO-ExPO 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: 3.58 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=chujiezheng/Mistral7B-PairRM-SPPO-ExPO 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: 8.66 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=chujiezheng/Mistral7B-PairRM-SPPO-ExPO 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: 17.24 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=chujiezheng/Mistral7B-PairRM-SPPO-ExPO name: Open LLM Leaderboard

Mistral7B-PairRM-SPPO-ExPO

The extrapolated (ExPO) model based on UCLA-AGI/Mistral7B-PairRM-SPPO and mistralai/Mistral-7B-Instruct-v0.2, as in the "Weak-to-Strong Extrapolation Expedites Alignment" paper.

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 35.4% win rate and 31.8% LC win rate on AlpacaEval 2.0, outperforming the original Mistral7B-PairRM-SPPO's 32.2% and 30.5%, 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

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	13.47
IFEval (0-Shot)	36.73
BBH (3-Shot)	13.68
MATH Lvl 5 (4-Shot)	0.91
GPQA (0-shot)	3.58
MuSR (0-shot)	8.66
MMLU-PRO (5-shot)	17.24

RichardErkhov/chujiezheng_-_Mistral7B-PairRM-SPPO-ExPO-gguf

Original model description:

Mistral7B-PairRM-SPPO-ExPO

Evaluation Results

Open LLM Leaderboard Evaluation Results

RichardErkhov/
chujiezheng_-_Mistral7B-PairRM-SPPO-ExPO-gguf