According to Forbes, fine-tuning a large language model is often three times less expensive than creating one from scratch. Even so, you’ll need a decent budget and access to a large dataset.
You’ll also need extra computational resources and a fair amount of time. For a small startup or individual developer, this is challenging because they have limited access to large GPU clusters.
It’s more difficult, but not impossible. You just need to get a little creative. This article aims to give you a start. We’ll look at cost-effective strategies that allow you to fine-tune an LLM without breaking the bank.
Leveraging Transfer Learning and Pre-Trained Models
Transfer learning is one of the most cost-effective approaches to tweaking LLMS. In this case, you’ll use a pre-trained model that has already learned patterns from a lot of text. When you start tweaking it, you’ll have a model that has already learned general language representations.
You don’t need as much computational power as you would for training from scratch. In addition, you can use a more focused, task-specific dataset to reduce your expenditure in terms of time and hardware.
Parameter-Efficient Fine-Tuning Techniques
LLMs can contain millions of parameters. In an ideal world, you’d refine all of these. However, in reality, this approach would be resource-intensive. You can use one of the following LLM fine-tuning techniques to reduce the memory footprint and computational cost.
In each case, you only tweak a subset of the model’s parameters rather than the whole thing.
Adapters
These are small, task-specific layers that you insert into the LLM. You train them during fine-tuning. The pre-trained weights of the LLM stay fixed, but you update the adapter layers. This cuts back on the parameters you need to train.
Using adapters ranks as one of the top methods to save resources in this area.
Low-Rank Adaptation (LoRA)
With this method, you only update some of the low-rank matrices. You’ll choose the ones that are most important for your final use case.
Prefix-Tuning and Prompt-Tuning
With these approaches, you change your inputs rather than the internal weights. You need to carefully consider the task-specific prompts that guide the pre-trained model in performing the desired task.
Data-Efficient LLM Fine-Tuning
If you have limited resources, it can be challenging to find a large, high-quality dataset. You’d have to collect, curate, and label data, and this can be expensive. What you can do is look into the following data-efficient fine-tuning techniques that focus on performance.
Few-Shot Learning
This technique uses a small number of labeled examples. You focus on using highly relevant data to train your model. It’s useful when it’s hard to get labeled data.
Active Learning
With this method, the model picks out the most informative samples in a dataset. It then prioritizes these for labeling. This approach saves you money because the model doesn’t label the entire dataset.
Data Augmentation
Data augmentation artificially increases the size of your training dataset by applying transformations to the data you have. It does this by:
- Paraphrasing
- Back-translation
- Replacing synonyms
Efficient Infrastructure and Cloud Resources
You need to spend what money you have on the right infrastructure. Using on-premise hardware can be costly. It’s also hard to scale if you have to use a large model. What you can do instead is to use a cloud-based alternative.
Spot Instances and Preemptible VMs
Many cloud providers, such as AWS, Google Cloud, and Azure, offer spot instances or preemptible VMs. The advantage is that they’re a lot cheaper than regular on-demand services.
Spot instances let you use excess cloud computing capacity at a much lower rate. The downside is that you may not have bandwidth during high-peak periods. However, since fine-tuning isn’t typically time-sensitive, this isn’t an issue.
Optimized Compute Resources
You’ll need to consider whether the model you choose benefits from a GPU or TPU-based environment. Check with your cloud provider about what they recommend for your project. You should always consider the cost-performance trade-off. In some cases, you can work with a slightly slower option if it means saving a lot of money.
Multi-Node Parallelism
When you have to use a large model, this lets you split the workload across several machines or nodes. You can, therefore, use a network of computers in parallel rather than investing in a higher-performing, expensive machine.
Quantization and Pruning for Model Optimization
The last technique we’ll look at focuses on removing bloat.
Quantization
Quantization reduces the precision of the model’s weights. You’ll usually drop them from 32-bit floating point numbers to 8-bit integers.
Does that sound a little like a foreign language? It’s a bit of a technical way of saying that you’ll use less memory and computational power. You do lose a bit when it comes to precision, but it’s not a huge drop.
Pruning
Do you know how you trim the dead wood off trees every year? Doing this allows the tree to concentrate its energy on new growth instead of wasting it on useless limbs. Pruning in terms of LLMs is when you fine-tune and deploy a leaner model. It’s a great way to maintain high performance and lower resource requirements.
Say, for example, you want to create modern tools for business forecasting. You can remove references relating to personal finances.
Conclusion
Fine-tuning an LLM when you have to keep an eye on the budget is challenging but not impossible. You need to start with the right infrastructure to support efficient training. You can then reduce the resources you need by combining the fine-tuning strategies we mentioned above.
You can either change the parameters, the source data, or what you input. You can also make the most of the data you have with some of the clever techniques we spoke about.
There’s no reason to let a limited budget put you off fine-tuning your own LLM. With a little careful planning, you can make it work.
