https://github.com/xfactlab/orpo

Official repository for ORPO
https://github.com/xfactlab/orpo

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Official repository for ORPO

• Host: GitHub
• URL: https://github.com/xfactlab/orpo
• Owner: xfactlab
• License: apache-2.0
• Created: 2024-03-11T12:31:38.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-31T06:39:39.000Z (5 months ago)
• Last Synced: 2024-08-01T22:05:40.536Z (3 months ago)
• Language: Python
• Homepage:
• Size: 1.87 MB
• Stars: 390
• Watchers: 6
• Forks: 35
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# **ORPO**

### **`Updates (24.03.25)`**

- [X] Sample script for ORPOTrainer in 🤗TRL is added to `trl/test_orpo_trainer_demo.py`

- [X] New model, 🤗kaist-ai/mistral-orpo-capybara-7k, is added to 🤗ORPO Collection 

- [X] Now you can try ORPO in 🤗TRL, Axolotl and LLaMA-Factory🔥

- [X] We are making general guideline for training LLMs with ORPO, stay tuned🔥

- [X] **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:

- [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kaist-ai%2Fmistral-orpo-beta)

- [AlpacaEval](#alpacaeval)

- [MT-Bench](#mt-bench)

- [IFEval](#ifeval)

### **`Model Checkpoints`**

Our models trained with ORPO can be found in:

- [X] **Mistral-ORPO-Capybara-7k**: 🤗 kaist-ai/mistral-orpo-capybara-7k

- [X] **Mistral-ORPO-⍺**: 🤗 kaist-ai/mistral-orpo-alpha

- [X] **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:

- [X] **Mistral-ORPO-Capybara-7k**: TBU

- [X] **Mistral-ORPO-⍺**: Wandb Report for Mistral-ORPO-⍺

- [X] **Mistral-ORPO-β**: Wandb Report for Mistral-ORPO-β

 

### **`AlpacaEval`**

  

  Figure 1. AlpacaEval 2.0 score for the models trained with different alignment methods.

 

### **`MT-Bench`**

  

  Figure 2. MT-Bench result by category.

 

### **`IFEval`**

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     |