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How to align a model to preferences with DPO with TRL

Train a model on chosen versus rejected answers with DPOTrainer or the TRL dpo command.

Short answer: Install TRL with pip install trl; then run trl dpo --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct with --dataset_name argilla/Capybara-Preferences and --output_dir Qwen2.5-0.5B-DPO; Or in Python load trl-lib/ultrafeedback_binarized and pass it to DPOTrainer, then call trainer.train(). All steps.

cost. Preference training on a GPU can run for hours.

Who it is forAn engineer who has preference data and wants responses that match it.

The problemSupervised fine-tuning alone does not teach a model which of two answers is better.

The outcomeA DPO-trained model saved to disk.

What you need first

  • Python with pip
  • A GPU is strongly recommended; the README covers NVIDIA, AMD and AWS Trainium setups
  • Access to Hugging Face Hub models and datasets

Steps

  1. Install TRL with pip install trl
  2. Run trl dpo --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct with --dataset_name argilla/Capybara-Preferences and --output_dir Qwen2.5-0.5B-DPO
  3. Or in Python load trl-lib/ultrafeedback_binarized and pass it to DPOTrainer, then call trainer.train()
  4. Compare answers from the base and trained models

Prompt for your agent

With huggingface/trl, train a DPO model using `trl dpo --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct` and `--dataset_name argilla/Capybara-Preferences`, then write a small script that compares base and trained answers on 10 prompts from <my list>.

Caveat. This card is written from the huggingface/trl README, not from a test run by Deduped. Before you start, make sure you have Python with pip. Check the repo for newer instructions before you rely on these steps.

Use this in your AI agent

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Using the Deduped MCP, get the setup steps for huggingface/trl and "Align a model to preferences with DPO"

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