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  1. DZone
  2. Data Engineering
  3. AI/ML
  4. Custom Training of Large Language Models (LLMs): A Detailed Guide With Code Samples

Custom Training of Large Language Models (LLMs): A Detailed Guide With Code Samples

This article provides a comprehensive guide on how to custom-train large language models, such as GPT-4, with code samples and examples.

By 
Suresh Rajasekaran user avatar
Suresh Rajasekaran
·
Apr. 22, 23 · Tutorial
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In recent years, large language models (LLMs) like GPT-4 have gained significant attention due to their incredible capabilities in natural language understanding and generation. However, to tailor an LLM to specific tasks or domains, custom training is necessary. This article offers a detailed, step-by-step guide on custom training LLMs, complete with code samples and examples.

Prerequisites

Before diving in, ensure you have:

  1. Familiarity with Python and PyTorch.
  2. Access to a pre-trained GPT-4 model.
  3. Adequate computational resources (GPUs or TPUs).
  4. A dataset in a specific domain or task for fine-tuning.

Step 1: Prepare Your Dataset

To fine-tune the LLM, you'll need a dataset that aligns with your target domain or task. Data preparation involves:

1.1 Collecting or Creating a Dataset 

Ensure your dataset is large enough to cover the variations in your domain or task. The dataset can be in the form of raw text or structured data, depending on your needs.

1.2 Preprocessing and Tokenization 

Clean the dataset, removing irrelevant information and normalizing the text. Tokenize the text using the GPT-4 tokenizer to convert it into input tokens.

Python
 
from transformers import GPT4Tokenizer 
tokenizer = GPT4Tokenizer.from_pretrained("gpt-4") 
data_tokens = tokenizer(data_text, truncation=True, padding=True, return_tensors="pt")


Step 2: Configure the Training Parameters

Fine-tuning involves adjusting the LLM's weights based on the custom dataset. Set up the training parameters to control the training process:

Python
 
from transformers import GPT4Config, GPT4ForSequenceClassification

config = GPT4Config.from_pretrained("gpt-4", num_labels=<YOUR_NUM_LABELS>)
model = GPT4ForSequenceClassification.from_pretrained("gpt-4", config=config)

training_args = {
    "output_dir": "output",
    "num_train_epochs": 4,
    "per_device_train_batch_size": 8,
    "gradient_accumulation_steps": 1,
    "learning_rate": 5e-5,
    "weight_decay": 0.01,
}


Replace <YOUR_NUM_LABELS> with the number of unique labels in your dataset.

Step 3: Set Up the Training Environment

Initialize the training environment using the TrainingArguments and Trainer classes from the transformers library:

Python
 
from transformers import TrainingArguments, Trainer

training_args = TrainingArguments(**training_args)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=data_tokens
)


Step 4: Fine-Tune the Model

Initiate the training process by calling the train method on the Trainer instance:

Python
 
trainer.train()


This step may take a while depending on the dataset size, model architecture, and available computational resources.

Step 5: Evaluate the Fine-Tuned Model

After training, evaluate the performance of your fine-tuned model using the evaluate method on the Trainer instance:

Python
 
trainer.evaluate()


Step 6: Save and Use the Fine-Tuned Model

Save the fine-tuned model and use it for inference tasks:

Python
 
model.save_pretrained("fine_tuned_gpt4")
tokenizer.save_pretrained("fine_tuned_gpt4")


To use the fine-tuned model, load it along with the tokenizer:

Python
 
model = GPT4ForSequenceClassification.from_pretrained("fine_tuned_gpt4")
tokenizer = GPT4Tokenizer.from_pretrained("fine_tuned_gpt4")


Example input text:

Python
 
input_text = "Sample text to be processed by the fine-tuned model."


Tokenize input text and generate model inputs:

Python
 
inputs = tokenizer(input_text, return_tensors="pt")


Run the fine-tuned model:

Python
 
outputs = model(**inputs)


Extract predictions:

Python
 
predictions = outputs.logits.argmax(dim=-1).item()


Map predictions to corresponding labels:

Python
 
model = GPT4ForSequenceClassification.from_pretrained("fine_tuned_gpt4")
tokenizer = GPT4Tokenizer.from_pretrained("fine_tuned_gpt4")

# Example input text
input_text = "Sample text to be processed by the fine-tuned model."

# Tokenize input text and generate model inputs
inputs = tokenizer(input_text, return_tensors="pt")

# Run the fine-tuned model
outputs = model(**inputs)

# Extract predictions
predictions = outputs.logits.argmax(dim=-1).item()

# Map predictions to corresponding labels
label = label_mapping[predictions]

print(f"Predicted label: {label}")


Replace label_mapping with your specific mapping from prediction indices to their corresponding labels. This code snippet demonstrates how to use the fine-tuned model to make predictions on the new input text.

While this guide provides a solid foundation for custom training LLMs, there are additional aspects you can explore to enhance the process, such as:

  1. Experimenting with different training parameters, like learning rate schedules or optimizers, to improve model performance
  2. Implementing early stopping or model checkpoints during training to prevent overfitting and save the best model at different stages of training
  3. Exploring advanced fine-tuning techniques like layer-wise learning rate schedules, which can help improve performance by adjusting learning rates for specific layers
  4. Performing extensive evaluation using metrics relevant to your task or domain, and using techniques like cross-validation to ensure model generalization 
  5. Investigating the usage of domain-specific pre-trained models or pre-training your model from scratch if the available LLMs do not cover your specific domain well 

By following this guide and considering the additional points mentioned above, you can tailor large language models to perform effectively in your specific domain or task. Please reach out to me for any questions or further guidance.

AI Python (language) Language model

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