Awesome
ORPO
Updates (24.03.25)
- Sample script for ORPOTrainer in 🤗<a class="link" href="https://github.com/huggingface/trl">TRL</a> is added to
trl/test_orpo_trainer_demo.py - New model, 🤗<a class="link" href="https://huggingface.co/kaist-ai/mistral-orpo-capybara-7k">kaist-ai/mistral-orpo-capybara-7k</a>, is added to 🤗<a class="link" href="https://huggingface.co/collections/kaist-ai/orpo-65efef87544ba100aef30013">ORPO Collection</a>
- Now you can try ORPO in 🤗<a class="link" href="https://github.com/huggingface/trl">TRL</a>, <a class="link" href="https://github.com/OpenAccess-AI-Collective/axolotl">Axolotl</a> and <a class="link" href="https://github.com/hiyouga/LLaMA-Factory">LLaMA-Factory</a>🔥
- 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 <a class="link" href="https://tatsu-lab.github.io/alpaca_eval/">official AlpacaEval Leaderboard</a>🔥
This is the official repository for <a class="link" href="https://arxiv.org/abs/2403.07691">ORPO: Monolithic Preference Optimization without Reference Model</a>. The detailed results in the paper can be found in:
Model Checkpoints
Our models trained with ORPO can be found in:
- Mistral-ORPO-Capybara-7k: 🤗 <a class="link" href="https://huggingface.co/kaist-ai/mistral-orpo-capybara-7k">kaist-ai/mistral-orpo-capybara-7k</a>
- Mistral-ORPO-⍺: 🤗 <a class="link" href="https://huggingface.co/kaist-ai/mistral-orpo-alpha">kaist-ai/mistral-orpo-alpha</a>
- Mistral-ORPO-β: 🤗 <a class="link" href="https://huggingface.co/kaist-ai/mistral-orpo-beta">kaist-ai/mistral-orpo-beta</a>
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-⍺: <a class="link" href="https://wandb.ai/jiwooya1000/PREF/reports/Mistral-ORPO-7B-Training-Log--Vmlldzo3MTE1NzE0?accessToken=rms6o4mg5vo3feu1bvbpk632m4cspe19l0u1p4he3othx5bgean82chn9neiile6">Wandb Report for Mistral-ORPO-⍺</a>
- Mistral-ORPO-β: <a class="link" href="https://wandb.ai/jiwooya1000/PREF/reports/Mistral-ORPO-7B-Training-Log--Vmlldzo3MTE3MzMy?accessToken=dij4qbp6dcrofsanzbgobjsne9el8a2zkly2u5z82rxisd4wiwv1rhp0s2dub11e">Wandb Report for Mistral-ORPO-β</a>
AlpacaEval
<figure>
<img class="png" src="/assets/img/alpaca_blog.png" alt="Description of the image">
<figcaption><b>Figure 1.</b> AlpacaEval 2.0 score for the models trained with different alignment methods.</figcaption>
</figure>
MT-Bench
<figure>
<img class="png" src="/assets/img/mtbench_hf.png" alt="Description of the image">
<figcaption><b>Figure 2.</b> MT-Bench result by category.</figcaption>
</figure>
IFEval
IFEval scores are measured with <a class="link" href="https://github.com/EleutherAI/lm-evaluation-harness">EleutherAI/lm-evaluation-harness</a> 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 <a class="link" href="https://twitter.com/wiskojo/status/1739767758462877823">this tweet</a>.
| 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 |