metadata
library_name: transformers
license: apache-2.0
datasets:
- argilla/dpo-mix-7k
language:
- en
Phi2-PRO
phi2-pro is a fine-tuned version of microsoft/phi-2 on argilla/dpo-mix-7k preference dataset using Odds Ratio Preference Optimization (ORPO). The model has been trained for 1 epoch.
💥 LazyORPO
This model has been trained using LazyORPO. A colab notebook that makes the training process much easier. Based on ORPO paper
🎭 What is ORPO?
Odds Ratio Preference Optimization (ORPO) proposes a new method to train LLMs by combining SFT and Alignment into a new objective (loss function), achieving state of the art results. Some highlights of this techniques are:
- 🧠 Reference model-free → memory friendly
- 🔄 Replaces SFT+DPO/PPO with 1 single method (ORPO)
- 🏆 ORPO Outperforms SFT, SFT+DPO on PHI-2, Llama 2, and Mistral
- 📊 Mistral ORPO achieves 12.20% on AlpacaEval2.0, 66.19% on IFEval, and 7.32 on MT-Bench out Hugging Face Zephyr Beta
💻 Usage
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
torch.set_default_device("cuda")
model = AutoModelForCausalLM.from_pretrained("abideen/phi2-pro", torch_dtype="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("abideen/phi2-pro", trust_remote_code=True)
inputs = tokenizer('''
"""
Write a detailed analogy between mathematics and a lighthouse.
"""''', return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)