Examples

Introduction

The examples should work in any of the following settings (with the same script):

single GPU
multi GPUS (using PyTorch distributed mode)
multi GPUS (using DeepSpeed ZeRO-Offload stages 1, 2, & 3)
fp16 (mixed-precision), fp32 (normal precision), or bf16 (bfloat16 precision)

To run it in each of these various modes, first initialize the accelerate configuration with accelerate config

NOTE to train with a 4-bit or 8-bit model, please run

pip install --upgrade trl[quantization]

Accelerate Config

For all the examples, you’ll need to generate a 🤗 Accelerate config file with:

accelerate config # will prompt you to define the training configuration

Then, it is encouraged to launch jobs with accelerate launch!

Maintained Examples

File	Description
`examples/scripts/alignprop.py`	This script shows how to use the `AlignPropTrainer` to fine-tune a diffusion model.
`examples/scripts/bco.py`	This script shows how to use the KTOTrainer with the BCO loss to fine-tune a model to increase instruction-following, truthfulness, honesty and helpfulness using the openbmb/UltraFeedback dataset.
`examples/scripts/chat.py`	This script allows you to load and use a model as a chatbot.
`examples/scripts/cpo.py`	This script shows how to use the CPOTrainer to fine-tune a model to increase helpfulness and harmlessness using the Anthropic/hh-rlhf dataset.
`examples/scripts/ddpo.py`	This script shows how to use the DDPOTrainer to fine-tune a stable diffusion model using reinforcement learning.
`examples/scripts/dpo_visual.py`	This script shows how to use the DPOTrainer to fine-tune a Vision Language Model to reduce hallucinations using the openbmb/RLAIF-V-Dataset dataset.
`examples/scripts/dpo.py`	This script shows how to use the DPOTrainer to fine-tune a stable to increase helpfulness and harmlessness using the Anthropic/hh-rlhf dataset.
`examples/scripts/kto.py`	This script shows how to use the KTOTrainer to fine-tune a model.
`examples/scripts/orpo.py`	This script shows how to use the ORPOTrainer to fine-tune a model to increase helpfulness and harmlessness using the Anthropic/hh-rlhf dataset.
`examples/scripts/ppo_multi_adapter.py`	This script shows how to use the PPOTrainer to train a single base model with multiple adapters. Requires you to run the example script with the reward model training beforehand.
`examples/scripts/ppo.py`	This script shows how to use the PPOTrainer to fine-tune a sentiment analysis model using IMDB dataset.
`examples/scripts/reward_modeling.py`	This script shows how to use the RewardTrainer to train a reward model on your own dataset.
`examples/scripts/sft.py`	This script shows how to use the SFTTrainer to fine-tune a model or adapters into a target dataset.
`examples/scripts/vsft_llava.py`	This script shows how to use the SFTTrainer to fine-tune a Vision Language Model in a chat setting. The script has only been tested on a LLaVA 1.5 model so users may see unexpected behaviour in other model architectures.

Here are also some easier-to-run colab notebooks that you can use to get started with TRL:

File	Description
`examples/notebooks/best_of_n.ipynb`	This notebook demonstrates how to use the “Best of N” sampling strategy using TRL when fine-tuning your model with PPO.
`examples/notebooks/gpt2-sentiment.ipynb`	This notebook demonstrates how to reproduce the GPT2 imdb sentiment tuning example on a jupyter notebook.
`examples/notebooks/gpt2-control.ipynb`	This notebook demonstrates how to reproduce the GPT2 sentiment control example on a jupyter notebook.

We also have some other examples that are less maintained but can be used as a reference:

research_projects: Check out this folder to find the scripts used for some research projects that used TRL (LM de-toxification, Stack-Llama, etc.)

Distributed training

All of the scripts can be run on multiple GPUs by providing the path of an 🤗 Accelerate config file when calling accelerate launch. To launch one of them on one or multiple GPUs, run the following command (swapping {NUM_GPUS} with the number of GPUs in your machine and --all_arguments_of_the_script with your arguments.)

accelerate launch --config_file=examples/accelerate_configs/multi_gpu.yaml --num_processes {NUM_GPUS} path_to_script.py --all_arguments_of_the_script

You can also adjust the parameters of the 🤗 Accelerate config file to suit your needs (e.g. training in mixed precision).

Distributed training with DeepSpeed

Most of the scripts can be run on multiple GPUs together with DeepSpeed ZeRO-{1,2,3} for efficient sharding of the optimizer states, gradients, and model weights. To do so, run following command (swapping {NUM_GPUS} with the number of GPUs in your machine, --all_arguments_of_the_script with your arguments, and --deepspeed_config with the path to the DeepSpeed config file such as examples/deepspeed_configs/deepspeed_zero1.yaml):

accelerate launch --config_file=examples/accelerate_configs/deepspeed_zero{1,2,3}.yaml --num_processes {NUM_GPUS} path_to_script.py --all_arguments_of_the_script

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