How to fine-tune large language models (LLMs)

Introduction

As the field of artificial intelligence (AI) continues to advance, large language models like GPT-4 have emerged as incredibly powerful tools. These models are pre-trained on massive datasets, enabling them to generate text that is coherent and contextually relevant. Fine-tuning plays a vital role in adapting these models to specific tasks or domains, allowing for optimal performance. In these tutorials, I will delve into the steps and best practices for fine-tuning large language models, ensuring their effectiveness in various applications.

Define your task and dataset

• Clearly define the specific task the language model should address, such as text classification or sentiment analysis.
• Collect a representative dataset that aligns with the task's domain, ensuring diversity, balance, and absence of biases. The dataset should be large enough to enable the model to learn the task's nuances effectively.
• Validate the dataset for accuracy and consistency, ensuring it contains accurate annotations and labels. It's important to review the dataset to minimize errors and noise that could hinder the fine-tuning process.

Choose the Right Pre-trained Model

• The choice of pre-trained model lays the foundation for the subsequent fine-tuning process, making it a critical decision.
• Opting for a pre-trained model that matches your target domain increases the chances of achieving better performance and accuracy in your specific task.
• When dealing with language-related tasks, starting with a pre-trained model trained on a multilingual dataset can provide a broader linguistic context and potentially enhance the model's language understanding capabilities.

Prepare Your Data

Once you have collected your dataset, it is crucial to preprocess the data to ensure optimal training. This process typically involves performing necessary transformations and adjustments to the data.

• Tokenization: Convert text into a sequence of tokens that the model can process.
• Padding and truncating: Standardize sequence lengths by adding padding or truncating longer sequences.
• Data splitting: Divide the dataset into training, validation, and test sets.

Set Hyperparameters

Hyperparameters are adjustable parameters that govern the training process, and fine-tuning them plays a crucial role. Among the key hyperparameters to focus on are:

• Learning rate: Controls the step size during optimization.
• Batch size: Determines the number of examples used in each update of the model weights.
• Number of epochs: Specifies the number of times the entire dataset is passed through the model during training.
• Weight decay: Helps prevent overfitting by adding a penalty to the loss function based on the model's weights.

Monitor Training and Validate Performance

• Monitoring loss and accuracy metrics on both the training and validation sets during model training helps assess the model's performance and generalization ability.
• Keeping an eye on these metrics enables the detection of overfitting, where the model memorizes the training data too well but fails to perform well on new, unseen data.
• Similarly, underfitting can be identified by analyzing the metrics, indicating that the model is not capturing the underlying patterns and complexity of the data effectively.

Evaluate and Iterate

• Evaluating the model's performance on the held-out test set provides an objective measure of its effectiveness and allows for comparison with other models or baselines.
• Analyzing the results helps identify areas where the model may be lacking or performing poorly, guiding further improvements and iterations in the fine-tuning process.
• Performing a qualitative analysis by manually reviewing generated text samples allows for a subjective assessment of the model's coherence, fluency, and understanding of the specific domain, helping to uncover any limitations or areas for refinement.

Address Biases and Ethical Concerns

• Carefully evaluating the model for biases is crucial, as large language models can unintentionally amplify or propagate biases present in their training data, potentially leading to unfair or discriminatory outputs
• Corrective actions should be taken to address biases, which may involve adjusting the dataset by removing or reweighting biased samples, retraining the model with improved data representation, or applying techniques like rule-based filtering to prevent biased outputs
• Employing methods such as adversarial training can also help mitigate biases by explicitly training the model to resist or counteract biased patterns, promoting fairness and inclusivity in its outputs

Conclusion

• Fine-tuning large language models is essential for tailoring them to specific tasks and domains, enabling better performance and task-specific understanding.
• Key steps in the fine-tuning process include data selection and preparation, pre-trained model selection, hyperparameter tuning, performance monitoring, and evaluation, all aimed at optimizing the model for the target task.
• It is important to incorporate ethical considerations and address potential biases throughout the fine-tuning process to ensure the resulting model is not only accurate but also responsible and unbiased.

Codebase Explanation in Gist

01. Data Collator For Completion Only LM

DataCollatorForCompletionOnlyLM(tokenizer=GPT2TokenizerFast(name_or_path='EleutherAI/gpt-neo-125M',
                                vocab_size=50257,
                                model_max_length=2048,
                                is_fast=True,
                                padding_side='right',
                                truncation_side='right',
                                special_tokens={'bos_token': AddedToken("<|endoftext|>",
                                                              rstrip=False,
                                                              lstrip=False,
                                                              single_word=False,
                                                              normalized=True),
                                                'eos_token': AddedToken("<|endoftext|>",
                                                              rstrip=False,
                                                              lstrip=False,
                                                              single_word=False,
                                                              normalized=True),
                                                'unk_token': AddedToken("<|endoftext|>",
                                                              rstrip=False,
                                                              lstrip=False,
                                                              single_word=False,
                                                              normalized=True),
                                                'pad_token': '<|endoftext|>'
                                                },
                                clean_up_tokenization_spaces=True),
                                mlm=False,
                                mlm_probability=0.15,
                                pad_to_multiple_of=8,
                                tf_experimental_compile=False,
                                return_tensors='pt')

• Tokenizer:
  • The GPT2TokenizerFast is used to tokenize text data for input into the language model.
  • name_or_path='EleutherAI/gpt-neo-125M': This specifies the pre-trained model to be used. In this case, it's "gpt-neo-125M" by EleutherAI.
  • vocab_size=50257: The size of the vocabulary used by tokenizer.
  • model_max_length=2048: The maximum length of the input text that the tokenizer can handle.
  • is_fast=True: This indicates whether to use the fast version of the tokenizer, which is optimized for performance.
• Special Tokens
  • Special tokens are added to the tokenizer to handle specific tasks like beginning of sequence, end of sequence, unknown tokens, etc.
  • bos_token, eos_token, unk_token: These are special tokens for beginning of sequence, end of sequence, and unknown tokens, respectively.
  • 'pad_token': The padding token is set to an empty string to avoid adding padding tokens during tokenization.
• Clean Up Tokenization Spaces:
  • clean_up_tokenization_spaces=True: This setting indicates whether to clean up the spaces in the tokenization process.
• MLM (Masked Language Modeling):
  • mlm=False: This specifies whether the language model will be trained using Masked Language Modeling. In this case, it's set to False, so the model is not trained with MLM.
• MLM Probability
  • mlm_probability=0.15`: If MLM were set to True, this parameter would determine the probability of masking tokens during training.
• Pad to Multiple of 8:
  • pad_to_multiple_of=8: This parameter ensures that the input length is a multiple of 8. It's helpful for optimizing memory usage during training.
• TensorFlow Experimental Compile:
  • tf_experimental_compile=False: This setting indicates whether to use experimental compilation in TensorFlow. In this case, it's set to False.
• Return Tensors:
  • return_tensors='pt': The data collator will return PyTorch tensors as output.