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README.md

Finetuning Gemma-2B using DPO

In this demo, we train the Gemma-2B model to both understand instructions and produce outputs that align with human preferences. We start with supervised fine-tuning (SFT) it with an instruction dataset so that the model gets better at answering user queries. Next, we use Direct Preference Optimization (DPO) and a dataset of human preferences to make the model output align better with human preferences.

To get started, first install Determined on your local machine:

pip install determined

To perform SFT step:

det e create sft.yaml .

To perform DPO step:

det e create dpo.yaml .

You can view the actual training code in sft_finetune.py and dpo_finetune.py.

Configuration

Change configuration options in sft.yaml or dpo.yaml. Some important options are:

  • slots_per_trial: the number of GPUs to use.
  • per_device_train_batch_size: the batch size per GPU.
  • training_args: training parameters such as learning rate or the number of training epochs.

For SFT step, you can also set:

  • dataset_subsets: update ratios of datasets.
  • data_collator: turns on/off training on completions only.
  • chat_tokens: adds special chat tokens to the vocabulary.
  • max_seq_length: sets maximum sequence length.

For DPO step, you have the following options:

  • dpo_beta: sets beta value for DPO training.
  • dpo_loss: one of the available dpo losses to use: "sigmoid", "hinge", "cdpo", "ipo", "kto_pair". You can learn more about it here.
  • max_length, max_prompt_length, and max_target_length: sets maximum sequence length for combined prompt and target, prompt, target, respectively.

Testing

Test your model's generation capabilities, you can use the inference script that generates outputs for a number of samples from Intel/orca_dpo_pairs dataset:

python inference.py [--exp_id <exp_id>] [--trial_id <trial_id>] [--output_file <filename>] [--number_of_samples n]

Where

  • <exp_id> is the id of your finetuning experiment in the Determined UI. Use either --exp_id or --trial_id.
  • <trial_id> is the id of your finetuning trial in the Determined UI. Use either --exp_id or --trial_id.
  • <filename> is the csv file with model generations.
  • <n> is the number of samples from the dataset you want to use for generation. To use CPU instead of GPU, add --device cpu.

To test the pretrained model (not finetuned), leave out --exp_id and --trial_id. For example:

python inference.py

Contributors