ORPO Trainer

Overview

Odds Ratio Preference Optimization (ORPO) was introduced in ORPO: Monolithic Preference Optimization without Reference Model by Jiwoo Hong, Noah Lee, and James Thorne.

The abstract from the paper is the following:

While recent preference alignment algorithms for language models have demonstrated promising results, supervised fine-tuning (SFT) remains imperative for achieving successful convergence. In this paper, we study the crucial role of SFT within the context of preference alignment, emphasizing that a minor penalty for the disfavored generation style is sufficient for preference-aligned SFT. Building on this foundation, we introduce a straightforward and innovative reference model-free monolithic odds ratio preference optimization algorithm, ORPO, eliminating the necessity for an additional preference alignment phase. We demonstrate, both empirically and theoretically, that the odds ratio is a sensible choice for contrasting favored and disfavored styles during SFT across the diverse sizes from 125M to 7B. Specifically, fine-tuning Phi-2 (2.7B), Llama-2 (7B), and Mistral (7B) with ORPO on the UltraFeedback alone surpasses the performance of state-of-the-art language models with more than 7B and 13B parameters: achieving up to 12.20% on AlpacaEval_{2.0} (Figure 1), 66.19% on IFEval (instruction-level loose, Table 6), and 7.32 in MT-Bench (Figure 12). We release code and model checkpoints for Mistral-ORPO-alpha (7B) and Mistral-ORPO-beta (7B).

It studies the crucial role of SFT within the context of preference alignment. Using preference data the method posits that a minor penalty for the disfavored generation together with a strong adaption signal to the chosen response via a simple log odds ratio term appended to the NLL loss is sufficient for preference-aligned SFT.

Thus ORPO is a reference model-free preference optimization algorithm eliminating the necessity for an additional preference alignment phase thus saving compute and memory.

The official code can be found in xfactlab/orpo.

This post-training method was contributed by Kashif Rasul, Lewis Tunstall and Alvaro Bartolome.

Quick start

This example demonstrates how to train a model using the ORPO method. We use the Qwen 0.5B model as the base model. We use the preference data from the UltraFeedback dataset. You can view the data in the dataset here:

Below is the script to train the model:

# train_orpo.py
from datasets import load_dataset
from trl import ORPOConfig, ORPOTrainer
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
train_dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")

training_args = ORPOConfig(output_dir="Qwen2-0.5B-ORPO", logging_steps=10)
trainer = ORPOTrainer(model=model, args=training_args, processing_class=tokenizer, train_dataset=train_dataset)
trainer.train()

Execute the script using the following command:

accelerate launch train_orpo.py

Distributed across 8 GPUs, the training takes approximately 30 minutes. You can verify the training progress by checking the reward graph. An increasing trend in the reward margin indicates that the model is improving and generating better responses over time.

To see how the trained model performs, you can use the TRL Chat CLI.

$ trl chat --model_name_or_path trl-lib/Qwen2-0.5B-ORPO
<quentin_gallouedec>:
What is the best programming language?

<trl-lib/Qwen2-0.5B-ORPO>:
It's challenging to determine the best programming language as no one language is perfect, as the complexity of a task and the type of project are significant factors. Some popular languages include Java, Python, JavaScript, and
C++. If you have specific needs or requirements for a specific project, it's important to choose the language that best suits those needs.                                                                                          

Here are some other factors to consider when choosing a programming language for a project:

 • Language proficiency: A good programming language is more likely to be easy to understand and use, and will allow developers to collaborate on projects more efficiently.                                     
 • Ease of use: There are tools and libraries available to make programming more accessible, so developers should choose a language that can help them get started easier.
 • Code readability: A clear and concise codebase should be easy to read and understand, especially when working with large projects.
 • Tool and framework support: There are numerous libraries available for Python, Java, and JavaScript, along with tools like IDEs and static code analysis tools.
 • Accessibility: Some languages and tools have features that make them more accessible to developers with disabilities, such as support for screen readers.
 • Version control: As your projects grow and complexity increases, version control tools can be beneficial for tracking changes.

Expected dataset type

ORPO requires a preference dataset. The ORPOTrainer supports both conversational and standard dataset format. When provided with a conversational dataset, the trainer will automatically apply the chat template to the dataset.

Although the ORPOTrainer supports both explicit and implicit prompts, we recommend using explicit prompts. If provided with an implicit prompt dataset, the trainer will automatically extract the prompt from the "chosen" and "rejected" columns. For more information, refer to the preference style section.

Example script

We provide an example script to train a model using the ORPO method. The script is available in examples/scripts/orpo.py

To test the ORPO script with the Qwen2 0.5B model on the UltraFeedback dataset, run the following command:

accelerate launch examples/scripts/orpo.py \
    --model_name_or_path Qwen/Qwen2-0.5B-Instruct \
    --dataset_name trl-lib/ultrafeedback_binarized \
    --num_train_epochs 1 \
    --logging_steps 25 \
    --output_dir Qwen2-0.5B-ORPO

Usage tips

For Mixture of Experts Models: Enabling the auxiliary loss

MOEs are the most efficient if the load is about equally distributed between experts.
To ensure that we train MOEs similarly during preference-tuning, it is beneficial to add the auxiliary loss from the load balancer to the final loss.

This option is enabled by setting output_router_logits=True in the model config (e.g. MixtralConfig).
To scale how much the auxiliary loss contributes to the total loss, use the hyperparameter router_aux_loss_coef=... (default: 0.001) in the model config.

Logged metrics

While training and evaluating we record the following reward metrics:

• rewards/chosen: the mean log probabilities of the policy model for the chosen responses scaled by beta
• rewards/rejected: the mean log probabilities of the policy model for the rejected responses scaled by beta
• rewards/accuracies: mean of how often the chosen rewards are > than the corresponding rejected rewards
• rewards/margins: the mean difference between the chosen and corresponding rejected rewards
• log_odds_chosen: the mean log odds ratio of the chosen responses over the rejected responses
• log_odds_ratio: the mean of the log(sigmoid(log_odds_chosen))
• nll_loss: the mean negative log likelihood loss from the SFT part of the loss over chosen responses

ORPOTrainer

ORPOConfig