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Mistral-ORPO-⍺ (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-⍺ is fine-tuned exclusively on HuggingFaceH4/ultrafeedback_binarized.

👍 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 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

image/png

🖥️ Inference

from transformers import AutoModelForCausalLM, AutoTokenizer

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

# 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}
}
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Evaluation results