fine-tune gpt-4o-mini

1. Prepare Your Environment

Install Dependencies: Make sure you have all the necessary libraries installed for fine-tuning. You'll need:

Python 3.7+
PyTorch (or another compatible deep learning library)
Hugging Face's transformers and datasets libraries
Datasets that you want to fine-tune on

Install dependencies using:

pip install torch transformers datasets

Set Up a GPU Environment: Fine-tuning large models benefits from GPU acceleration, so ensure you're using a machine with a GPU. You can use cloud services like Google Colab, AWS, or GCP for this if you don’t have local GPU support.

2. Get the Pre-trained Model

Load the Base Model: Hugging Face provides pre-trained models that can be easily fine-tuned. To load the GPT-4-mini, use the appropriate model identifier, like gpt-4-mini or any related available variant.

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "gpt-4-mini"  # Adjust the name if needed
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

3. Prepare Your Dataset

Choose or Prepare the Dataset: You’ll need a dataset that's suited for your task. Hugging Face's datasets library is great for easily accessing a wide range of preformatted datasets, or you can load your own.

from datasets import load_dataset
dataset = load_dataset("your_dataset_name")  # Choose an appropriate dataset

Preprocess the Data: Tokenize the dataset so it's in a format that the model can understand. For text, you'll want to tokenize each input and output sequence appropriately.

def tokenize_function(examples):
	return tokenizer(examples["text"], truncation=True, padding=True)
	tokenized_datasets = dataset.map(tokenize_function, batched=True)

4. Set Up Fine-Tuning Parameters

Define Training Arguments: The training arguments control how the fine-tuning will happen. You can set parameters like batch size, learning rate, and the number of epochs.

from transformers import TrainingArguments
	training_args = TrainingArguments(
	output_dir="./results",  # Output directory for the model
	per_device_train_batch_size=8,
	per_device_eval_batch_size=8,
	num_train_epochs=3,
	logging_dir="./logs",  # Logging directory
	evaluation_strategy="epoch",  # Evaluation after each epoch
	save_steps=500,  # Save checkpoint every 500 steps
	logging_steps=100,  # Log every 100 steps
	)

5. Set Up the Trainer

Create the Trainer Object: Hugging Face provides a Trainer class that simplifies the training process. You need to provide the model, training arguments, and datasets.

from transformers import Trainer, TrainingArguments
	trainer = Trainer(
	model=model,
	args=training_args,
	train_dataset=tokenized_datasets["train"],
	eval_dataset=tokenized_datasets["test"],  # Optional, depending on your setup
	)

6. Start Fine-Tuning

Train the Model: Now, start the fine-tuning process. The model will be trained based on the dataset you've prepared and the configurations you've set.

trainer.train()

7. Monitor Training Progress

Track Training Metrics: You can use TensorBoard or any other visualization tools to track metrics such as loss, accuracy, and other relevant stats.

tensorboard --logdir=./logs

8. Save the Model

Once training is complete, save the fine-tuned model to disk for later use:

      model.save_pretrained("./fine_tuned_gpt4o_mini")
	tokenizer.save_pretrained("./fine_tuned_gpt4o_mini")

9. Evaluate and Test the Model

Evaluate on Test Data: After training, you can evaluate the model using your test dataset to check how well it performs on unseen data.

results = trainer.evaluate()
print(results)

Use the Model for Inference: After evaluation, you can now use the fine-tuned model for generating text or making predictions based on your use case.

inputs = tokenizer("Your prompt here", return_tensors="pt")
	outputs = model.generate(**inputs)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))

10. Optional: Hyperparameter Tuning

If the model's performance isn’t satisfactory, you can try adjusting hyperparameters such as:

Learning rate: Use learning rate schedulers or manually adjust.

Batch size: Larger batch sizes can speed up training but require more memory.

Epochs: More epochs might improve accuracy but could lead to overfitting.

Experimenting with these parameters and adjusting your model’s architecture might help you optimize performance.

Additional Tips:

Data Augmentation: You may want to use data augmentation techniques to expand your dataset, especially if you have a small training set.

Checkpointing: Regularly save checkpoints during training to prevent losing progress if something goes wrong.

Mixed Precision Training: If you're using large models and GPUs, enabling mixed precision training can reduce memory usage and speed up training.

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