5 tips for fine-tuning LLMs

Why fine-tuning matters?

LLMs are equipped with general-purpose capabilities handling a wide range of tasks including text generation, translation, summarization, and question answering. Despite being so powerful in global performance, they still fail in specific task-oriented problems or in specific domains like medicine, law, etc. LLM fine-tuning is the process of taking pre-trained LLM and training it further on smaller, specific datasets to enhance its performance on domain-specific tasks such as understanding medical jargon in healthcare. Whether you’re building an LLM from scratch or augmenting an LLM with additional finetuning data, following these tips will deliver a more robust model.

1. Prioritize data quality

When fine-tuning LLMs, think of the model as a dish and the data as its ingredients. Just as a delicious dish relies on high-quality ingredients, a well-performing model depends on high-quality data.

The principle of “garbage in, garbage out” states: that if the data you feed into the model is flawed, no amount of hyperparameter tuning or optimization will salvage its performance.

Here are practical tips for curating datasets so you can acquire good-quality data:

1. Understand your objectives: Before gathering data, clarify your application’s goals and the type of output you expect, then ensure that you only collect relevant data.
2. Prioritize data quality over quantity: A smaller, high-quality dataset is often more effective than a large, noisy one.
3. Remove noise: Clean your dataset by removing irrelevant or erroneous entries. Address missing values with imputation techniques or remove incomplete records to maintain data integrity. Data augmentation techniques can enhance the size and diversity of the dataset while also preserving its quality.

2. Choose the right model architecture

Selecting the right model architecture is crucial for optimizing the performance of LLMs as different architectures that are designed to handle various types of tasks. There are two highly notable LLMs BERT and GPT.

Decoder-only models like GPT excel in tasks involving text generation making them ideal for conversational agents and creative writing, while encoder-only models like BERT are more suitable for tasks involving context understanding like text classification or named entity recognition.

Fine-tuning considerations

Consider setting these parameters properly for efficient finetuning:

• Learning rate: It is the most important parameter that dictates how quickly a model updates its weights. Although it is specified by trial and error method, you can initially start with the rate that they have termed to be optimal in the research paper of the base model. However, keep in mind that this optimal rate may not work as well if your dataset is smaller than the one used for benchmarking. For fine-tuning LLMs, a learning rate of 1e-5 to 5e-5 is often recommended.
• Batch size: Batch size specifies the number of data samples a model processes in one iteration. Bigger batch sizes can boost training but demand more memory. Similarly, smaller batch sizes allow a model to thoroughly process every single record. The preference for batch size must align with the hardware capabilities as well as the dataset for optimal results.
• Warmup steps: This is used to gradually increase the learning rate from a small initial value to a peak value. This approach can stabilize initial training and help the model find a better path toward convergence.
• Epochs: LLMs often require only 1-3 epochs for fine-tuning as they can learn from a dataset with minimal exposure. Training for more epochs may result in overfitting. Implement early stopping to prevent overfitting.

Techniques like GridSearch or Random Search can be used to experiment with different hyperparameters for tuning them.

3. Balance computational resources

LLMs are incredibly powerful but also notoriously resource-intensive due to their vast size and complex architecture. Fine-tuning these models requires a significant amount of computational power. This leads to a need for high-end GPUs, specialized hardware accelerators, and extensive distributed training frameworks.

Leveraging scalable computational resources such as AWS and Google Cloud can provide the necessary power to handle these demands, but they come with a cost especially when running multiple finetuning iterations. If you are taking the time to finetune your own LLM, investing in dedicated hardware can not only save on training and finetuning costs but also the price to keep it running can add up quickly.

A. Understand your fine-tuning objectives

Model parameters are the weights that are optimized during the training steps. Fine-tuning a model involves adjusting the model parameters to optimize its performance for a specific task or domain.

Based on how many parameters we adjust during the fine-tuning process, we have different types of finetuning:

1. Full-fine tuning: In this method, we adjust all the weights of the pre-trained model, recalibrating the entire parameters for this new task/domain. This approach allows the model to develop a deep understanding of the new domain, potentially leading to superior performance. However, this method is resource-intensive, requiring appropriate computational power and memory.
2. Parameter-efficient finetuning: In contrast to full fine-tuning, Parameter-Efficient Fine-Tuning (PEFT) updates a small subset of a model’s parameters while keeping the rest frozen. This results in having a much smaller number of trainable parameters than in the original model (in some cases, just 15-20% of the original weights). Techniques like LoRA can reduce the number of trainable parameters by 10,000 times, making memory requirements much more manageable, perfect for saving time, and able to run on more constrained hardware resources.

B. Model compression methods

Techniques such as pruning, quantization, and knowledge distillation can also make the finetuning process more manageable and efficient.

• Pruning removes less important or redundant model parameters, which can reduce complexity without sacrificing too much accuracy.
• Quantization converts model parameters to lower-precision formats, which can significantly decrease the model’s size and computational requirements. Depending on the model, the reduced floating point precision can have little to no effect on accuracy.
• Knowledge distillation transfers the knowledge from a large, complex model to a smaller, more efficient one, making it easier for deployment.

C. Optimization strategies

Employing optimization algorithms like Stochastic Gradient Descent (SGD), Adam, and RMSprop enables precise parameter adjustments making the fine-tuning process efficient.

4. Continuous evaluation and iteration

Once the LLM has been fine-tuned, it involves continuous monitoring and periodic updates to maintain its performance over time. Key factors to consider include data drift, which involves shifts in the statistical properties of input data, and model drift, which refers to changes in the relationship between inputs and outputs over time.

Thus, iterative finetuning must be applied which adjusts the model parameters in response to these drifts, ensuring the model continues to deliver accurate results over time.

To evaluate the model’s performance, both quantitative and qualitative methods are essential. Qualitative evaluation techniques like accuracy, F1 score, BLEU score, perplexity, etc. can be used to measure how well the model is performing.

On the other hand, Qualitative evaluation techniques can be used to assess the model’s performance in real-world scenarios. Manual testing by domain experts needs to be conducted to evaluate the output from the model and the feedback must be applied to the model iteratively following the technique of Reinforcement learning from human feedback (RLHF).

Incremental learning allows the model to continuously learn from new data without requiring a complete retrain, making it adaptable to data and model drifts.

5. Address bias and fairness

During the finetuning, we must ensure that our model does not produce any output that discriminates based on gender, or race, and ensure that models prioritize fairness.

Biases can be caused by two main reasons:

• Biased data: If the data used during the training is not representative of the real-world condition, then data biases are likely. It may be due to sampling techniques where more data is fed to a certain group while the other group is underrepresented in the data. It may also be caused due to historical biases where there is underrepresentation in the historical data, such as the historically prejudiced tendency to consider women for roles like homemakers or designers while men are favored for superior positions.
• Algorithmic bias: It occurs due to the inherent assumptions and design choices within the algorithms themselves. For example, if a certain feature is given more weight during training, it can lead to biased predictions. For instance, a loan approval system that prioritizes applicants from certain locations or races over others.

Bias mitigation techniques

• Fairness-aware algorithms: Develop algorithms to ensure the fine-tuned model makes fair decisions across different demographic groups. They incorporate fairness constraints like equal opportunity – where the model has equal true positives across all demographic groups, or equalized odds – where the model has equal false positive and false negative rates across all groups. This ensures equitable outcomes by balancing predictions to avoid disadvantaging any particular group.
• Bias detection: Regularly analyze training data and model predictions to identify biases based on demographic attributes such as race, gender, or age; and address potential sources of bias early on.
• Data augmentation: Enhance the training data to improve diversity and representativeness, especially for underrepresented groups, ensuring the model generalizes well across a broader range of scenarios.
• Debiasing techniques: It involves methods like reweighing, in-processing, and post-processing. Reweighing balances the model’s focus and reduces bias by giving more weight to underrepresented examples. In-processing applies debiasing strategies during training to reduce bias. Post-processing modifies model predictions after training to align with fairness criteria.

Conclusion

Fine-tuning LLMs for specific domains and other purposes has been a trend among companies looking to harness their benefits for businesses and domain-specific datasets. Fine-tuning not only enhances the performance in custom tasks, it also acts as a cost-effective solution.

By selecting the right model architecture, ensuring high-quality data, applying appropriate methodologies, and committing to continuous evaluation and iterations, you can substantially improve the performance and reliability of the fine-tuned models. These strategies ensure that your model not only performs efficiently but also aligns with ethical standards and real-world requirements. Read about fine-tuning with this related post on RAG vs. Fine-tuning here.

When running any AI model, the right hardware can make a whole world of difference, especially in critical applications like healthcare and law. These tasks rely on precise work and high-speed delivery hence the need for dedicated high-performance computing. These offices can’t utilize cloud-based LLMs due to the security risk posed to client and patient data.