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Students, here are 5 key things to know when learning how to train large language models
Large language models (LLMs) are currently all the rage. These artificial intelligence (AI) models, trained on vast datasets, can generate text, translate languages, write code, and perform many other valuable tasks.
OpenAI’s ChatGPT kickstarted the LLM trend, drawing hundreds of millions of users within a short period. Many more models have emerged with valuable features, such as Anthropic’s Claude and Jasper AI. Corporations have begun training their custom large language models (LLMs), and so have colleges, students, and everyday hobbyists.
Training LLMs has become easily accessible, thanks to an increasing abundance of computing power. However, there are key things to know before learning how to train your LLM. This guide dives into these key considerations to know when training LLMs.
1. Data preparation is vital
Large language models (LLMs) are only as good as the datasets they’re trained on. Hence, you need to spend sufficient time to transform raw data into formats that LLMs can easily learn from.
Training data is often sourced from public resources like websites, journals, social media, and code repositories. The type of data to gather depends on the LLM’s purpose. For example, your focus could be building an LLM to help with your engineering coursework. In that case, your course lectures, notes, and related books will be the data to train the LLM on.
After gathering your data in its raw format, you have to refine it to make it more useful for your LLM. Let’s dive into these steps below.
- Data cleaning
Before using any dataset to train an LLM, it should be thoroughly cleaned to enhance its usefulness. Cleaning includes
Removing duplicates. Examine the dataset for redundant entries and remove them. This way, your LLM won’t perform redundant calculations and increase computing costs unnecessarily.
Missing values. Datasets often have missing values in some areas. For example, in a survey, respondents may purposely withhold some data, resulting in missing variables for your dataset. In that case, you should determine how to handle the missing data, such as replacing it with estimated values or deleting the missing parts altogether.
Addressing structural errors. Your data may have formatting errors, such as incorrect SI units attached to variables, wrong date formats, or improperly formatted code from a repository. It’s crucial to find and correct such errors before uploading your dataset for LLM training.
Text cleaning. Review the text data to remove irrelevant and frequently repeated words. You can remove words or symbols without much meaning and correct capitalization errors. This step seems trivial, but it is essential for your LLM’s accuracy.
You can clean datasets manually, but it’s a time-consuming and often inefficient process. Instead, you should pair manual checks with automated cleaning tools like Ringlead. These automated tools let you spend less effort cleaning vast datasets, freeing up valuable time as a student on a tight schedule.
Fortunately, student LLMs typically don’t involve massive datasets that require multiple people and the most advanced tools to refine. They have less-bulky datasets that you can usually handle yourself and with automated tools.
2. Choose your model architecture carefully
Large language models have different architectures. You should choose the right one that strikes a balance between your model size and its complexity.
The main LLM architectures include:
- Encoder-Decoder
This architecture comprises two main components. The Encoder accepts inputs and converts them into abstract continuous representations of those inputs. The Decoder then translates the abstract representations into intelligible outputs.
The Encoder-Decoder architecture enables LLMs to handle the most complex language tasks. The drawback is that this architecture requires substantial computing power that students usually don’t have access to.
- Encoder-only
The Encoder-only architecture converts inputs into contextualized representations without directly generating new sequences. In simpler terms, it excels at processing text, but it doesn't generate new text.
This architecture is suitable for LLMs that classify and translate text, as they involve processing existing text rather than generating new text from scratch. An example is the Bidirectional Encoder Representations from Transformers (BERT) model developed by Google.
- Decoder-only
Decoder-only models are designed to generate the next part of an input sequence based on the previous input. They can't "understand" the entire input. Instead, they predict the next probable word in the sequence of a given input.
Decoder-only models are suitable for generative tasks, where your goal is to produce coherent and relevant text. For example, if you want an LLM that answers questions about your school coursework, a decoder-only architecture is the ideal model. Popular models like OpenAI’s GPT-4 and Google’s Bard use this architecture.
Factors to consider when choosing your LLM architecture include:
Complexity. LLMs rely on finite computing resources. Consider the computing power at your disposal and use an architecture that fits within it.
Security. The architecture should be secure, especially when you intend to release the LLM to the public.
Purpose. The purpose of your LLM affects which architecture to choose. For example, decoder-only models excel at text generation, while encoder-only models are well-suited for translation.
3. Training methods matter a lot
Having a refined dataset for training your LLM is one thing, and using the most efficient training techniques is another. You need to optimize the training process to minimize the computing costs required to create a reliable large language model (LLM).
Key strategies to optimize your LLM training include
- Model compression
You can compress your model size without noticeable performance losses. This process can be achieved by pruning and knowledge distillation.
Pruning removes parts of the model that are less important, such as its weights. Knowledge distillation transfers knowledge from a larger, complex model (called the teacher) to a smaller, more efficient model (called the student).
There’s also quantization, which reduces model size by representing its parameters with fewer bits than the standard 32-bit floating-point format. These techniques help your model consume less computing power while maintaining optimal performance.
- Hardware selection
You can use specialized artificial intelligence (AI) hardware to speed up your LLM’s inference, instead of using everyday graphical processing units (GPUs).
Nvidia’s H100 chipset is the standard adopted by students and researchers to train LLMs. It's the gold standard due to its exceptional computing performance and scalability – you can easily add more computing power to train your LLM on larger datasets if needed.
Other chipsets, like AMD’s MI300X, are also gaining popularity among students and academic researchers. These chipsets are too expensive for most students to buy upfront – the H100 costs at least $25,000, and the MI300X costs upwards of $10,000. Instead, many cloud computing platforms let you rent them hourly for LLM training.
Costs vary depending on your cloud computing platform, but the H100 runs anywhere from $2 to $6 hourly, and the MI300 has a similar price range. These hourly costs can add up to substantial amounts, necessitating the need to refine datasets to save training costs.
- Regular updates
LLM training is never a one-and-done affair. You should keep your model updated with new data to ensure optimal performance. Suppose you build an AI chatbot that answers questions for yourself and other students. You should keep training it with the latest educational materials, ensuring it can give accurate answers to new questions.
Keep tabs on the LLM’s hardware and software infrastructure and ensure everything is set up optimally. For example, beware of hitting the memory limits on your allocated GPUs. If you’re near this limit, you can delete some training data to avoid surpassing it, or you can rent more GPU power to absorb the growing dataset.
4. Security is paramount
LLMs are imperfect and can be abused for malicious purposes. For example, LLMs can generate false information that causes problems for students. Improperly configured LLMs can leak sensitive data, such as user credentials. These risks underscore the importance of considering safety when training your LLM.
When training LLMs, you should take several steps to make them secure, including:
- Data anonymization
Transform any personal data in your training dataset into a format that can’t be traced back to individuals. You can achieve this through various techniques like generalization, suppression, and perturbation.
Generalization entails replacing specific data points with broader ranges of values. An example is replacing physical addresses with ZIP codes or replacing a person’s age of 35 with the range of “30-39.” Generalization reduces the specificity of the data but retains enough information to train your LLM.
Perturbation means adding random noise to data to hide its original values. Remember that in generalization, we replaced a specific age with a broader range. In perturbation, we can add 2 to the age, so 30 will become “32” in the dataset– this same formula can be applied to all ages in the dataset.
Perturbation lets you maintain the overall structure of the data while retaining valuable information to train your LLM on.
Suppression involves deleting personal data from the training set altogether. For instance, you can remove the entire field of physical addresses and ages instead of generalizing it.
Suppression eliminates the chance of linking data back to an individual, but it can make you lose valuable training information for your LLM. It should be applied carefully, mainly to data that you see no other way to anonymize.
- Encryption
You can encrypt data during transmission between different parts of your training pipeline. This encryption can be achieved with standard protocols like Transport Layer Security (TLS).
AI computing platforms like CoreWeave encrypt data at rest using protocols like the Advanced Encryption Standard (AES). Hence, any data you upload to these platforms to train your models remains secure. Security is a core consideration when choosing a platform to rent computing power and train your models on.
- Two-factor authentication
When you sign up for a platform to train your models, enable two-factor authentication to protect your account and prevent unauthorized access.
Two-factor authentication involves requiring two modes of identification before granting access to your account. First, you’ll input your correct username or email address and password. Then, a one-time PIN will be generated and sent to your mobile number, email, or linked authenticator app. This PIN, which expires after a short time, grants access to your account.
With two-factor authentication enabled, malicious actors can’t access your cloud computing account even if they somehow get your credentials.
- Access control
Students often train large language models (LLMs) as part of a group. In that case, your group should implement robust access control on the platform used to train your models. The group administrator can set user permissions, deciding who can upload or modify model training data and who can only view the data.
- Penetration testing
Students can be crafty, not always in a bad way. Your group can purposely try to break the LLM system to discover vulnerabilities. Try feeding prompts that instruct the LLM to reveal sensitive data and monitor how it responds.
Picture yourself as an attacker and try different strategies to penetrate your LLM’s security. These simulated cyberattacks help you identify key security issues before releasing your LLM for public use.
5. Frequent monitoring is necessary
We mentioned earlier that LLM training requires continuously updating datasets to ensure optimal performance. It’s even more than that. LLMs can be unpredictable, so you should frequently monitor their performance and ensure conformance with academic and industry standards.
You can set evaluation benchmarks for your LLM, including accuracy, hallucination rate, and energy consumption. Then, you’ll constantly test the LLM to ensure it meets these benchmarks. If it lags from these benchmarks, it’s a signal to tweak some characteristics to help it meet up.
For example, if you observe your LLM’s accuracy reducing over time, it’s a hint to remove duplication and irrelevant parts of your training dataset. You can also verify if your LLM uses the right tokenizer, which converts raw text data into numerical representations (tokens) that your model understands.
Whether for personal, group, or public use, frequent evaluation ensures your LLM’s reliability and helps identify issues before they cause problems.
Final words
Nothing makes you familiar with LLMs like training one yourself. It’s an excellent activity to try as a student. LLM training prepares you for a future career as an AI engineer or a general programmer in a tech industry where AI has become inseparable.
We’ve explained what you should know when training LLMs. These factors help you avoid amateur mistakes and train efficient LLMs that achieve the most with minimal computing power.
