mistral-orpo-capybara-7k/README.md

language, license, base_model, datasets, pipeline_tag, model-index

language	license	base_model	datasets	pipeline_tag	model-index
en	mit	mistralai/Mistral-7B-v0.1	argilla/distilabel-capybara-dpo-7k-binarized	text-generation	name results Mistral-ORPO-Capybara-7k task dataset metrics source type text-generation name type AlpacaEval 2 (LC) AlpacaEval type value name AlpacaEval 2.0 15.88% Win Rate url name https://tatsu-lab.github.io/alpaca_eval/ self-reported task dataset metrics source type text-generation name type MT-Bench MT-Bench type value name MT-Bench 7.444 Score url name https://github.com/lm-sys/FastChat/blob/main/fastchat/llm_judge/ self-reported

Mistral-ORPO-Capybara-7k (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-ORPO-Capybara-7k is fine-tuned for 2.5 hours on four A100s exclusively on the 7k instances of the distilled Capybara paired multi-turn conversation dataset, argilla/distilabel-capybara-dpo-7k-binarized, by Argilla.

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

👍 Model Performance

1) AlpacaEval & MT-Bench

Model Name	Size	Align	MT-Bench	AlpacaEval 2.0 (LC)
Mistral-ORPO-Capybara-7k	7B	ORPO	7.44	15.9
Mistral-ORPO-β	7B	ORPO	7.32	14.7
Zephyr β	7B	DPO	7.34	13.2
TULU-2-DPO	13B	DPO	7.00	11.6
Llama-2-Chat	7B	RLHF	6.27	5.4
Llama-2-Chat	13B	RLHF	6.65	8.4

2) IFEval

Model Type	Prompt-Strict	Prompt-Loose	Inst-Strict	Inst-Loose
Mistral-ORPO-Capybara-7k	0.5083	0.5083	0.5827	0.6127
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

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("kaist-ai/mistral-orpo-capybara-7k")
tokenizer = AutoTokenizer.from_pretrained("kaist-ai/mistral-orpo-capybara-7k")
# 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}
}