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_vlm.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/ppo.py | This script shows how to use the PPOTrainer to fine-tune a model to improve its ability to continue text with positive sentiment or physically descriptive language |
examples/scripts/ppo/ppo_tldr.py | This script shows how to use the PPOTrainer to fine-tune a model to improve its ability to generate TL;DR summaries. |
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/sft_vlm.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 with LLaVA 1.5, LLaVA 1.6, and Llama-3.2-11B-Vision-Instruct models 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_scriptYou 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