Training customization

TRL is designed with modularity in mind so that users to be able to efficiently customize the training loop for their needs. Below are some examples on how you can apply and test different techniques. Note: Although these examples use the DPOTrainer, the customization applies to most (if not all) trainers.

Use different optimizers and schedulers

By default, the DPOTrainer creates a torch.optim.AdamW optimizer. You can create and define a different optimizer and pass it to DPOTrainer as follows:

from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from torch import optim
from trl import DPOConfig, DPOTrainer

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
training_args = DPOConfig(output_dir="Qwen2.5-0.5B-DPO")

optimizer = optim.SGD(model.parameters(), lr=training_args.learning_rate)

trainer = DPOTrainer(
    model=model,
    args=training_args,
    train_dataset=dataset,
    tokenizer=tokenizer,
    optimizers=(optimizer, None),
)
trainer.train()

Add a learning rate scheduler

You can also play with your training by adding learning rate schedulers.

from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from torch import optim
from trl import DPOConfig, DPOTrainer

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
training_args = DPOConfig(output_dir="Qwen2.5-0.5B-DPO")

optimizer = optim.AdamW(model.parameters(), lr=training_args.learning_rate)
lr_scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)

trainer = DPOTrainer(
    model=model,
    args=training_args,
    train_dataset=dataset,
    tokenizer=tokenizer,
    optimizers=(optimizer, lr_scheduler),
)
trainer.train()

Memory efficient fine-tuning by sharing layers

Another tool you can use for more memory efficient fine-tuning is to share layers between the reference model and the model you want to train.

from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import create_reference_model, DPOConfig, DPOTrainer

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
ref_model = create_reference_model(model, num_shared_layers=6)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train[:1%]")
training_args = DPOConfig(output_dir="Qwen2.5-0.5B-DPO")

trainer = DPOTrainer(
    model=model,
    ref_model=ref_model,
    args=training_args,
    train_dataset=dataset,
    tokenizer=tokenizer,
)
trainer.train()

Pass 8-bit reference models

Since trl supports all keyword arguments when loading a model from transformers using from_pretrained, you can also leverage load_in_8bit from transformers for more memory efficient fine-tuning.

Read more about 8-bit model loading in transformers here.

from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from trl import DPOConfig, DPOTrainer

model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
ref_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct", quantization_config= quantization_config)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B-Instruct")
dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
training_args = DPOConfig(output_dir="Qwen2.5-0.5B-DPO")

trainer = DPOTrainer(
    model=model,
    ref_model=ref_model,
    args=training_args,
    train_dataset=dataset,
    tokenizer=tokenizer,
)
trainer.train()

Use the CUDA cache optimizer

When training large models, you should better handle the CUDA cache by iteratively clearing it. To do so, simply pass optimize_cuda_cache=True to DPOConfig:

training_args = DPOConfig(..., optimize_cuda_cache=True)

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