- Sample script for ORPOTrainer in 🤗TRL is added to
trl/test_orpo_trainer_demo.py - New model, 🤗kaist-ai/mistral-orpo-capybara-7k, is added to 🤗ORPO Collection
- Now you can try ORPO in 🤗TRL, Axolotl and LLaMA-Factory🔥
- We are making general guideline for training LLMs with ORPO, stay tuned🔥
- Mistral-ORPO-β achieved a 14.7% in the length-controlled (LC) win rate on official AlpacaEval Leaderboard🔥
This is the official repository for ORPO: Monolithic Preference Optimization without Reference Model. The detailed results in the paper can be found in:
Our models trained with ORPO can be found in:
- Mistral-ORPO-Capybara-7k: 🤗 kaist-ai/mistral-orpo-capybara-7k
- Mistral-ORPO-⍺: 🤗 kaist-ai/mistral-orpo-alpha
- Mistral-ORPO-β: 🤗 kaist-ai/mistral-orpo-beta
And the corresponding logs for the average log probabilities of chosen/rejected responses during training are reported in:
- Mistral-ORPO-Capybara-7k: TBU
- Mistral-ORPO-⍺: Wandb Report for Mistral-ORPO-⍺
- Mistral-ORPO-β: Wandb Report for Mistral-ORPO-β
IFEval scores are measured with EleutherAI/lm-evaluation-harness by applying the chat template. The scores for Llama-2-Chat (70B), Zephyr-β (7B), and Mixtral-8X7B-Instruct-v0.1 are originally reported in this tweet.
| Model Type | Prompt-Strict | Prompt-Loose | Inst-Strict | Inst-Loose |
|---|---|---|---|---|
| Llama-2-Chat (70B) | 0.4436 | 0.5342 | 0.5468 | 0.6319 |
| Zephyr-β (7B) | 0.4233 | 0.4547 | 0.5492 | 0.5767 |
| Mixtral-8X7B-Instruct-v0.1 | 0.5213 | 0.5712 | 0.6343 | 0.6823 |
| Mistral-ORPO-⍺ (7B) | 0.5009 | 0.5083 | 0.5995 | 0.6163 |
| Mistral-ORPO-β (7B) | 0.5287 | 0.5564 | 0.6355 | 0.6619 |
