Fine-Tuning LLMs: Your Blueprint for Informed AI Choices!

Balancing control and complexity to make informed decisions on when to consider fine-tuning LLMs for your business needs

What’s fine-tuning?

Large Language Models (LLMs) are, well, large. 

This enables them to work on an unimaginable number of problems reasonably well. LLMs are like a pro version of that old wise grandparent, who seems to know just about everything, albeit with a much better memory! These large language models have seen tons of data and almost comprehend what the world used to talk about in both the recent and more distant past; have soaked in opinions of geniuses in their respective areas, all the how-to articles that are out there on the web and God knows what all data! (coz Mira certainly doesn’t ;) )

But does your business really need a model that knows what makes Taylor Swift a marketing genius and how to cure cancer - at the same time? Probably not.

Here comes a concept called fine-tuning, which became ubiquitous in the age of deep learning. It essentially involves taking a pre-trained model (like ChatGPT, Gemini etc. trained on diverse data) and adjusting it for a new task or dataset. You could have also heard about transfer learning, which is a broader concept encompassing fine-tuning.

And why should you care?

Fine-tuning large language models (LLMs) unlocks a range of powerful applications across various industries. Here are some common use cases, that some startups in the industry are already building teams and MVPs for, where fine-tuning can significantly benefit products and businesses:

Customer Service:

• Support Chatbots: Fine-tuned LLMs can power chatbots that understand natural language, answer customer queries more effectively, and even generate personalized responses based on context and past interactions or mimic a certain personality trait. In my experience, fine-tuning a smaller model, in combination with other techniques like RAG (Retrieval Augmented Generation), can help maintain roughly the same quality as larger models, but with much better response time, which can bring in significant value-add for production use cases at scale. This is something that you should really consider experimenting with for your business - now!

Marketing and Content Creation:

•  Targeted Content Generation: Generate personalized marketing copy, product descriptions, or social media content tailored to specific audiences (which hinges on relevant data availability).

•  SEO Optimization: Create SEO-friendly domain specific content that ranks higher in search engine results by understanding search intent and relevant keywords - for your target audience.

Media and Entertainment:

•  Storytelling and Scriptwriting: Generate creative text formats like scripts, poems, or song lyrics, building upon existing ideas and providing a springboard for human creativity - one that reflects a specific style relevant to your company brand.

•  Content Summarization: Create concise summaries of news articles, long documents, or videos, allowing users to quickly grasp key information - which might differ for different use cases and themes within these documents.

E-commerce and Retail:

• Product Recommendation Systems: Develop personalized product recommendations for customers based on your product catalog and customer segments.

• Personalized Search: Fine-tuned LLMs can understand user domain specific search queries with greater nuance, leading to more relevant search results and improved product discovery.

Finance and Legal:

• Fraud Detection: Analyze financial transactions and identify potential fraudulent activities by understanding patterns in language used within emails or communication channels for your product.

• Entity Extraction from Legal Documents: Having worked on entity extraction in at least 4 domains so far, I can attest to the fact that this is rarely as simplistic as it sounds. A generic understanding of the domain becomes essential as even humans find it difficult to categorize some entities. Entity extraction can be highly domain specific when it comes to legal documents and fine-tuning can help a model understand the nuances of the legal domain far more effectively.

These are just a few examples, and the possibilities extend further as LLMs continue to evolve. The specific benefits of fine-tuning will vary depending on the industry and the unique challenges faced by each business.

Reach out on LinkedIn for a discussion on how your business can leverage AI and LLMs.

What do I need to get started?

While each of the factors below greatly depends on the fine-tuning technique you choose, rest assured that there are methods to support a much wider range of use cases across a spectrum of time and cost constraints. (We’ll discuss more about the techniques in my next issue - stay tuned!)

Note: There are fine-tuning techniques to support a much wider range of use cases across a spectrum of time and cost constraints

1. High-quality, task-specific data

This is your model's fuel!  The better your data aligns with your project's goal, the more effective your fine-tuning will be. Some considerations:

Data Quality and Labeling:

• Noise and Errors: Real-world data often contains inconsistencies, typos, grammatical errors, and irrelevant information. Cleaning and correcting this data can be a time-consuming and laborious process, especially for large datasets.

• Limited Labeled Data: Many fine-tuning tasks require labeled data, where each data point has a specific label or category assigned. Obtaining sufficient high-quality labeled data can be expensive and time-consuming, especially for complex tasks.

• Bias and Fairness: Biases present in the training data can be amplified during fine-tuning, leading to models that perpetuate stereotypes or generate discriminatory outputs. Identifying and mitigating bias in your data is crucial for responsible AI development.

Data Size and Efficiency:

• Large Data Requirements: As discussed earlier, large models often require massive amounts of data for effective fine-tuning. This can be a significant hurdle, especially when dealing with limited resources or limited access to large datasets.

• Data Processing Overhead: Preprocessing large text datasets for LLM training can be computationally expensive and time-consuming.

Data Relevance and Alignment:

• Domain Specificity: LLMs are often pre-trained on massive datasets of general text. Fine-tuning for a specific domain might require specialized data that reflects the terminology and nuances of that domain. Finding or creating domain-specific data can be challenging.

• Task-Data Mismatch: The data used for fine-tuning might not perfectly align with the specific task you're aiming for. This mismatch can lead to sub-optimal performance or the model learning irrelevant patterns from the data. Or it might not contain some corner cases that your task entails.

Data Privacy and Security:

• Data Privacy: If your data contains sensitive or personal information, you'll need to ensure proper anonymization or privacy-preserving techniques are applied during data preparation.

• Data Security: Safeguarding your training data from unauthorized access or manipulation is crucial, especially when dealing with sensitive information.

2. Hardware muscle

As you might already be aware, GPUs (graphics processing unit) are ideal for accelerating the training process, especially for larger, complex models. Choosing the right hardware for both inference and training depends on:

• Model Size: Larger models (1 Trillion parameters) require more powerful hardware than smaller models (7 Billion parameters), just to fit into your GPU for inference (when models make a prediction). Rough estimation is around 1GB-4GB per 1B parameters, depending upon precision of your weights (int8 to float32).

• Fine-tuning method: Full fine-tuning needs more GPU memory than PEFT (Parameter Efficient Fine-Tuning) techniques.

• Dataset Size: Processing larger datasets can strain hardware resources. Consider the size and complexity of your data.

• Training Time: How long are you willing to wait for fine-tuning to complete? Faster hardware translates to quicker training.

• Budget: Hardware costs can vary significantly. Determine your budget constraints before exploring options.

3. Technical expertise

Deep experience with libraries like TensorFlow or PyTorch will help you navigate the fine-tuning process. Depending on the complexity of your fine-tuning needs, you might need the following expertise in your team:

• Understanding of LLMs: This includes a grasp of the core concepts behind LLMs, such as transformers, attention mechanisms, and pre-training.

• Experience in Deep Learning Libraries like Tensorflow and PyTorch

• Ability to Use Pre-built Fine-tuning Scripts: Several open-source libraries like Hugging Face Transformers offer pre-built scripts for fine-tuning various LLMs. Being able to use these scripts effectively can get you started with basic fine-tuning tasks.

• Data Preprocessing Skills: Team should be able to clean, preprocess, and format your data for LLM training.

• Hyperparameter Tuning: Fine-tuning often involves adjusting various hyperparameters like learning rate, batch size, optimizer settings etc. Understanding how these parameters influence training and metrics as well as being able to experiment with them effectively is crucial.

• Troubleshooting GPU-Related Issues: Being familiar with common GPU-related issues during deep learning training, such as insufficient VRAM or driver compatibility problems, can help you identify and address them efficiently.

• Basic GPU Monitoring Tools: Knowing how to use tools like NVIDIA System Management Interface (nvidia-smi) or cloud platform monitoring dashboards to view GPU metrics like memory usage, temperature, and utilization can be helpful.

• Model Architecture and Customization: Expertise in understanding LLM architectures and potentially modifying them for specific tasks can be advantageous for advanced fine-tuning.

• Research and Development Skills: Staying updated on the latest research in LLM fine-tuning techniques like adapter modules, or knowledge distillation is helpful for pushing the boundaries of performance.

• Experience with Distributed Training: For very large models or massive datasets, you might need expertise in distributed training techniques that leverage multiple GPUs or machines to parallelize the training process.

4. Patience for experimentation

Fine-tuning is an iterative journey. Be prepared for your team to take time to try different approaches and tweak parameters to find the optimal fit.

• Trial and Error: Finding the optimal fine-tuning approach often involves testing different techniques, hyperparameters (learning rate, batch size, etc.), and even potentially modifying the model architecture. This iterative process requires patience from your team to experiment and analyze results.

• Data Exploration: Fine-tuning success hinges on the quality and relevance of your data. Be prepared to explore different data sources, iteratively refining your cleaning and pre-processing techniques to find the best fit for your specific task.

• Unexpected Challenges: Technical hurdles or unforeseen issues might arise during fine-tuning. Patience is crucial for troubleshooting and finding solutions.

5. Time and cost investment

Training powerful models can take time and they cost a lot - specially if you need multiple experiments. Factor this in when planning your project.

• Training Duration: Training large models can take hours, days, or even weeks depending on the model size, dataset size, and hardware capabilities. Be prepared for a time commitment to complete the training process.

• Iteration Time: The iterative nature of fine-tuning means repeated training runs with different configurations. Factor in the time required for each training run and subsequent analysis when estimating project timelines. Number of iterations needed to get to your desired performance varies significantly from case to case.

• Evaluation and Refinement: Evaluating the fine-tuned model's performance after training is essential. This might involve additional time for collecting evaluation data, running tests, and potentially further refinements based on the results.

6. Maintenance

We all would like a world in which we come up with a great model and then forget about our AI pipelines. But this is not feasible in practice.

• Continuous Learning/Incremental Training: Real-world data constantly evolves. Regularly retraining your LLM with new, relevant data can help it adapt to data and concept drift. This involves gathering fresh data, potentially re-preprocessing it, and fine-tuning the model on the new dataset. However, strike a balance between re-training too frequently and neglecting updates altogether.

• Regular Performance Checks: Continuously monitor your LLM's performance on relevant metrics to identify any signs of degradation. This might involve tasks like generating text samples, answering questions, or performing the specific task the model was fine-tuned for.

• Error Analysis: Analyze errors or unexpected outputs from your LLM to understand the root cause of the issue. This can help you identify areas where the model might require additional training data or adjustments to the fine-tuning process.

• Computational Resources: Maintaining an LLM often involves re-training, which again requires significant computational resources. Factor in the cost and availability of GPUs or cloud-based training platforms for ongoing maintenance.

7. Evaluation

Regularly assess your model's performance to identify areas for further refinement.

• Test Data: This dataset should be representative of your current data. To avoid bias, you might need separate datasets or over-sampled minority data.

• Metrics: This helps figure out if fine-tuning your LLM really helps move the needle or not. Evaluation metrics are not easy to come up with. This needs to be something that can lead to tangible outcomes for the business. And also something that is easier for the model to optimize on.

So should I fine-tune LLMs?

Like always, it depends…

What you might get:

• Improved Performance: Fine-tuning an LLM for a specific task can significantly improve its accuracy, reliability, and performance compared to a generic pre-trained model.

• Tailored Solutions: It allows you to create custom models that excel in specific domains or tasks, like product-specific chatbots or legal document analysis tools.

• Complex Reasoning and Subjective Tasks: If your task requires complex reasoning, handling subjective opinions, or understanding lexicon of a specific domain, fine-tuning an LLM can be more effective than simpler prompting techniques or RAG (Retrieval-Augmented Generation).

• Custom Generation Style: If you want your LLM to mimic a personality trait (for eg. for your customer support bots) that you have been unable to achieve using prompting techniques.

• Decreased latency by fine-tuning smaller models to get equivalent performance as compared to larger models.

• Reduced Hallucinations: When neither prompting or RAG based approaches have been useful in reducing hallucinations, it might be the right time to analyze the errors and evaluate a fine-tuning based approach instead.

• More control over Data Privacy and Security rather than relying on third party providers.

• Intellectual Property: Retaining your AI and data intellectual property in-house rather than utilizing the same LLMs that your competitors are using.

However, it is important to note that initiatives to move away from generic commercial or open source LLMs have to be thought of as a more long term investment and might not generate revenues immediately.

All in all, it is worth knowing what fine-tuning an LLM entails and what benefits it might or might not provide and if the time and monetary costs can be justified with the potential benefits.

Hint: foundational generic models might turn out to be a lot cheaper, at least to begin with - thanks to all the investors in the LLM industry!

Use a Generic Pre-trained Model When:

• Your data is limited: Fine-tuning often requires a substantial amount of labeled data specific to your task. If you don't have that, a generic model might still provide reasonable performance.

• The task is general-purpose: If your task is broad and doesn't require deep domain knowledge, a generic model trained on a massive dataset might be sufficient.

• Computational resources are limited: Fine-tuning, especially full fine-tuning, can be computationally expensive. If you don't have access to powerful hardware, a generic model might be more practical.

• Fast prototyping is needed: Generic models are readily available and can be used quickly for initial testing and exploration of your project.

• Tech Team Expertise: If it would require a few months to set up your LLM Infra team, it is best to start with generic models.

• Budget Constraints: Fine-tuning LLMs can be expensive - from data labeling to compute, to tech expertise, to time to iterate and evaluate your experiments there’s a lot that goes into successful AI projects - and all of these cost money and time.

Need assistance in planning requirements and development for your AI project? Reach out on LinkedIn for a discussion.