Fine-Tuning an Internal LLM

In today’s financial landscape, leveraging Generative AI (Gen AI) has become increasingly vital. Suppose you want to develop an internal Large Language Model (LLM) to create chatbots and AI assistants. The challenge is that the LLM lacks knowledge about your company's details. While you can use Retrieval Augmented Generation (RAG) to provide the necessary context, you can also fine-tune the LLM to embed that knowledge directly within the model. Let’s explore how to do this.

Understanding Fine-Tuning vs. RAG

Fine-tuning an LLM is a more computationally intensive process compared to using RAG. Given the size of modern LLMs, it’s often impractical to download and run them on local machines. Instead, these models are hosted in the cloud (using services like AWS, Azure, or GCP). Although fine-tuning requires more resources and time, it allows the model to learn and internalize your specific domain knowledge, leading to more accurate and tailored responses. Techniques like Quantization and Low-Rank Adaptation (LoRA) can reduce computational costs, but they may not achieve the same accuracy as full fine-tuning.

Steps for Fine-Tuning an LLM

1. Prepare Your Dataset: Ensure your dataset is a good representation of the information that you want to relay.? Have enough training examples for the model to learn patterns effectively.
2. Choose a Pre-Trained Model: Select a model pre-trained on a relevant corpus (most open-source models can be found on Huggingface). These models have already learned general language patterns.
3. Initialize the Model:? Start with the pre-trained weights from the selected model.? These weights capture general language representations.
4. Fine-Tune on Your Data:? Train the model using your (task-specific) data.? Use labeled data to adjust the model’s parameters, enabling it to learn domain-specific terminology, acronyms, and abbreviations.
5. Evaluate and Deploy:? Assess the model’s performance using validation data.? Once satisfied, deploy the model for use in your applications.

Detailed Fine-Tuning Process

• Tokenization:? Tokenize text data into subword tokens.? Convert tokens into numerical representations for the model to process.
• Model Initialization:? Start with a pre-trained language model (downloaded from Huggingface).? Initialize the model’s weights with pre-trained values.
• Architecture Modifications (Optional):? Modify the model architecture as needed for your task.? Add task-specific layers on top of the base model if necessary.
• Fine-Tuning:? Train the model on your labeled data.? Use gradient-based optimization to update the model’s weights, adapting it to your specific task.
• Hyperparameter Tuning:? Experiment with hyperparameters like learning rate and batch size.? Use grid search or random search to find optimal settings.
• Validation and Early Stopping:? Monitor performance on a validation set during training.? Implement early stopping when validation loss stops improving.
• Evaluation:? Test the fine-tuned model on a separate dataset.? Use appropriate metrics (e.g., accuracy, F1-score) to evaluate performance.

Practical Example

Imagine a company, Asdfg Financial, is a (fabricated) firm offering DeFi and digital assets on the blockchain. Initially, I asked a chatbot, “What is Asdfg Financial and what does it do?” It responded generically, “Asdfg Financial is a financial services company that provides a range of financial products and services to individuals and businesses…,” which was not very helpful.

After fine-tuning the LLM, the response was, “Asdfg Financial is a Digital Currency Securities firm focused on providing investors with access to private market digital asset securities (security tokens) in compliance with regulatory frameworks.” This response is much more accurate and informative.

Conclusion

For one-off, specific information needs, RAG is often more efficient and accurate. However, for embedding broader and more general information across your chatbots and AI agents, fine-tuning is the better approach.

By understanding and applying these techniques, financial professionals can harness the full potential of Gen AI, creating more effective and intelligent AI-driven solutions within their organizations.

Appendix: Useful Python Libraries for Fine-Tuning

• torch
• transformers
• peft (for LoRA)
• from trl import SFTTrainer