en license: mit base_model:
mistralai/Mistral-7B-v0.1 datasets:
argilla/ultrafeedback-binarized-preferences-cleaned pipeline_tag: text-generation model-index:
name: Mistral-ORPO-β results:

AI2 Reasoning Challenge (25-Shot)
- task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics:
  - type: acc_norm name: normalized accuracy value: 61.18 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
HellaSwag (10-shot)
- task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics:
  - type: acc_norm name: normalized accuracy value: 84.03 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
TruthfulQA (0-shot)
- task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics:
  - type: mc2 value: 47.69 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
GSM8k (5-shot)
- task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics:
  - type: acc name: accuracy value: 39.8 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
MMLU (5-Shot)
- task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics:
  - type: acc name: accuracy value: 63.26 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
Winogrande (5-shot)
- task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics:
  - type: acc name: accuracy value: 79.24 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta
- task: type: text-generation dataset: name: AlpacaEval 1 type: AlpacaEval metrics:
  - type: AlpacaEval 1.0 value: 91.16% name: Win Rate source: url: https://tatsu-lab.github.io/alpaca_eval/ name: Leaderboard
- task: type: text-generation dataset: name: AlpacaEval 2 type: AlpacaEval metrics:
  - type: AlpacaEval 2.0 value: 12.57% name: Win Rate source: url: https://tatsu-lab.github.io/alpaca_eval/ name: Leaderboard
- task: type: text-generation dataset: name: MT-Bench type: MT-Bench metrics:
  - type: MT-Bench value: 7.322 name: Score source: url: https://github.com/lm-sys/FastChat/blob/main/fastchat/llm_judge/ name: self-reported

Mistral-ORPO-β (7B)

Mistral-ORPO is a fine-tuned version of mistralai/Mistral-7B-v0.1 using the odds ratio preference optimization (ORPO). With ORPO, the model directly learns the preference without the supervised fine-tuning warmup phase. Mistral-ORPO-β is fine-tuned exclusively on the 61k instances of the cleaned version of UltraFeedback, argilla/ultrafeedback-binarized-preferences-cleaned, by Argilla.

Github Repository: https://github.com/xfactlab/orpo

👍 Model Performance

1) AlpacaEval & MT-Bench

Model Name	Size	Align	MT-Bench	AlpacaEval 1.0	AlpacaEval 2.0
Mistral-`ORPO`-⍺	7B	`ORPO`	7.23	87.92	11.33
Mistral-`ORPO`-β	7B	`ORPO`	7.32	91.41	12.20
Zephyr β	7B	DPO	7.34	90.60	10.99
TULU-2-DPO	13B	DPO	7.00	89.5	10.12
Llama-2-Chat	7B	RLHF	6.27	71.37	4.96
Llama-2-Chat	13B	RLHF	6.65	81.09	7.70

2) IFEval

Model Type	Prompt-Strict	Prompt-Loose	Inst-Strict	Inst-Loose
Mistral-ORPO-⍺	0.5009	0.5083	0.5995	0.6163
Mistral-ORPO-β	0.5287	0.5564	0.6355	0.6619

🗺️ MT-Bench by Category

🖥️ Inference

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("kaist-ai/mistral-orpo-beta")
tokenizer = AutoTokenizer.from_pretrained("kaist-ai/mistral-orpo-beta")

# Apply chat template
query = [{'role': 'user', 'content': 'Hi! How are you doing?'}]
prompt = tokenizer.apply_chat_template(query, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors='pt')

# Generation with specific configurations
output = model.generate(
  **inputs,
  max_new_tokens=128,
  do_sample=True,
  temperature=0.7
)
response = tokenizer.batch_decode(output)

#<|user|>
#Hi! How are you doing?</s>
#<|assistant|>
#I'm doing well, thank you! How are you?</s>

📎 Citation

@misc{hong2024orpo,
      title={ORPO: Monolithic Preference Optimization without Reference Model}, 
      author={Jiwoo Hong and Noah Lee and James Thorne},
      year={2024},
      eprint={2403.07691},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

RichardErkhov/kaist-ai_-_mistral-orpo-beta-awq

Original model description:

AI2 Reasoning Challenge (25-Shot)

HellaSwag (10-shot)

TruthfulQA (0-shot)

GSM8k (5-shot)

MMLU (5-Shot)

Winogrande (5-shot)