implementing Fine-Tuning GPT-3.5 for Domain-Specific Language Generation
I'm getting frustrated with I'm testing a new approach and This might be a silly question, but I'm trying to fine-tune OpenAI's GPT-3.5 model using the Hugging Face `transformers` library for a specific domain, but I'm running into some unexpected behavior... After following the examples in the documentation, I keep getting the behavior `RuntimeError: Expected input batch size (64) to match target batch size (32)` during training, and I'm unsure how to resolve it. I've set up my dataset using the `datasets` library and my training configuration is done with the `Trainer` class. Here is the code snippet I'm using: ```python from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments from datasets import load_dataset # Load the tokenizer and model tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2LMHeadModel.from_pretrained('gpt2') # Load and preprocess the dataset train_dataset = load_dataset('your_dataset_name', split='train') train_dataset = train_dataset.map(lambda x: tokenizer(x['text'], truncation=True, padding='max_length', max_length=512), batched=True) # Training arguments training_args = TrainingArguments( output_dir='./results', evaluation_strategy='epoch', learning_rate=5e-5, per_device_train_batch_size=64, num_train_epochs=3, weight_decay=0.01, ) # Trainer initialization trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, ) # Start training trainer.train() ``` I've tried adjusting the batch size in the training arguments, but it doesn't seem to solve the scenario. The dataset itself consists of around 10,000 samples, and each text entry varies in length, which could be a factor. I also checked that `train_dataset` returns the correct shape but still encounter this mismatch. Any advice on how to approach this scenario or best practices for fine-tuning that I might be missing? This is part of a larger service I'm building. Any ideas what could be causing this? I'd really appreciate any guidance on this. Thanks for taking the time to read this!