Large language models are becoming popular for enhancing business operations and customer experiences. For instance, say your company has an application that processes customer inquiries. You could integrate a large language model (LLM) to streamline and enhance this process. Instead of the tedious traditional method of looking up information, which can lead to inaccurate and unsatisfactory results, the multimodal LLM can generate human-like responses based on its training and your business's data. This can significantly improve both functionality and user experience. In this article, we’ll explore how to train your LLM so you can enhance your application to meet your specific goals. We’ll discuss the steps involved in training a model so you can integrate a customized LLM into your application, improving functionality and user experience while avoiding common challenges and optimizing performance.
To take some of the complexity out of this process, Lamatic’s generative AI tech stack offers a valuable solution to help you achieve your goals. Our approach can help you successfully train and integrate a customized large language model (LLM) into your application, enhancing functionality and user experience while avoiding common challenges and optimizing performance.
Why Should You Train Your Large Language Models(LLMs)?
Customization: Tailoring LLMs to Your Needs
Pretrained LLMs are versatile but designed to cater to various applications. Training your own LLM empowers you to tailor the model to your needs. Whether you’re in healthcare, finance, legal, or any other industry, a customized LLM can be fine-tuned to:
- Understand domain-specific terminologies
- Context
- Nuances
This level of personalization can drastically enhance the accuracy and relevance of the model’s outputs.Imagine you’re a medical institution looking to develop an AI-powered diagnosis system. By training a custom LLM on medical literature, you can create a model that understands:
- Complex medical jargon
- Interprets patient symptoms
- Generates accurate diagnostic insights
This level of customization enhances the model’s accuracy and relevance in the medical domain.
Control: Becoming an AI Architect
When you train your LLM, you take the reins of AI architecture and development. Relying solely on third-party providers for LLMs can leave you at the mercy of their updates, limitations, and data privacy concerns. Training your LLM gives you full control over model updates, improvements, and data privacy, ensuring your AI strategy aligns with your organization’s goals and values.Let's take a use case of finance. Financial organization requires a nuanced understanding of market trends, regulations, and economic indicators. You gain control over the model’s learning process by training your LLM. You can fine-tune it to:
- Predict market movements
- Analyze regulatory changes
- Generate insightful reports
This empowers you to adapt the model to dynamic financial landscapes while ensuring data security.
Cost Efficiency: Optimizing Resources
Large Language Models (LLMs) can be computationally expensive, mainly when used extensively. Training your LLM allows you to optimize its size and complexity to match your requirements. You save on computational resources and costs by crafting leaner models that deliver targeted results. This is particularly valuable for startups and organizations looking to maximize efficiency without compromising AI capabilities.
A Cost-Effective Solution
Startup Virtual Assistant Startups often operate with limited resources. By training a custom LLM for a virtual assistant, you can optimize its size and complexity. The assistant can be tailored to handle tasks relevant to your business, reducing computational overhead. This approach allows you to provide personalized user experiences without stretching your budget.
Domain Expertise: Addressing Specific Challenges
Every industry comes with unique challenges and opportunities. Training your own LLM equips you to address these challenges head-on. For instance, in medical research, an LLM trained in medical literature can provide insights that generalized LLMs might miss.
A custom LLM can offer specialized code generation tailored to your preferred programming languages and practices in software development. Let’s take an example of a law firm.
Automating Legal Document Creation with AI
Legal Document Generation Law firms deal with intricate legal documents that require precise language and understanding of legal terminology. You can create a model specializing in generating legal documents by training your LLM. This custom-trained LLM ensures accurate and contextually appropriate content, enhancing efficiency in document preparation.
Ethical AI: Mitigating Bias and Privacy Concerns
Developing your LLM allows you to ensure ethical AI practices from the ground up. You can curate datasets that are unbiased and representative of your application domain. Additionally, you can implement privacy measures to protect sensitive information, assuring users that their data is handled responsibly. Example Use case. An LLM for a hiring platform involves addressing bias concerns. You can ensure fairness in candidate evaluations by curating a diverse dataset and training your model. The LLM can:
- Anonymize personal data
- Analyze skills
- Provide insights without perpetuating bias
This fosters ethical hiring practices.
Innovation and Differentiation: Setting New Standards
Training your own LLM sets you apart as an innovator and a visionary. As AI technology becomes increasingly accessible, having a proprietary LLM showcases your commitment to pushing the boundaries of innovation. Custom LLMs can lead to novel applications, improved user experiences, and groundbreaking solutions that set new industry standards.
AI-Powered Content Creation: A Creative Edge
Let’s take an example of a media company that aims to differentiate itself with unique content. By training a custom LLM for content creation, you empower it to generate articles, stories, and scripts tailored to your brand’s voice and style. This innovation sets you apart by delivering content that aligns precisely with your creative vision.
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Step-by-Step Guide on How to Train Your Own LLM
1. Define Your Objective — Clarifying Your AI’s Purpose
Before starting to train a large language model, it’s crucial to determine the model’s purpose. Think of this as setting the destination on your GPS before starting a road trip. Are you aiming to create a conversational chatbot, a content generator, or a specialized AI for a particular industry?
Being crystal clear about your objective will steer your subsequent decisions and shape your LLM’s development path. Consider the specific use cases you want your LLM to excel in. Are you targeting customer support, content creation, or data analysis? Each objective will require:
- Distinct data sources
- Model architectures
- Evaluation criteria
Consider the unique challenges and requirements of your chosen domain. For instance, if you’re developing an AI for healthcare, you’ll need to navigate privacy regulations and adhere to strict ethical standards.
In summary, the first step is all about vision and purpose. It’s about understanding what you want your LLM to achieve, who its end users will be, and the problems it will solve. With a well-defined objective, you’re ready to embark on the journey of training your LLM.
2. Gathering and Preparing Your Data
Data is the heart and soul of any LLM. It’s the raw material that your AI will use to learn and generate human-like text. To gather the right data, you need to be strategic and meticulous. First, identify your company’s internal data sources, such as:
- Emails
- Planning/projection documents
- Project/product/technical documentation
- Policy/HR documentation
- Reference documentation
- Budget tracking
Diversity is key. Ensure your dataset represents the following:
- Various topics
- Writing styles
- Contexts
This diversity will help your LLM become more adaptable and capable of handling various tasks. Then, create a pipeline to curate all the data in a data store or warehouse. This process would include transforming, cleaning, and standardizing all data. Documents and emails could be labeled under their relevant project name, or they can be categorized based on project phase, such as:
- Planning
- Design
- Implementation
- Others
Data Cleaning, Preparation, and Tokenization
Outdated documents and emails would be discarded. You can also use data versioning tools to manage your datasets effectively. Note that when collecting data, be mindful of copyright and licensing issues. Ensure you have the necessary permissions to use the texts in your dataset. After multiple iterations of data cleaning and preparation, it is ready for tokenization.
3. Tokenization
During tokenization, the preprocessed dataset is converted into a vocabulary of tokens. The tokens can be:
- Characters
- Words
- Parts of words
- Punctuations
- Phrases
- Regular expressions
- Special characters
Once the tokens are obtained, you can remove stop words and apply two linguistic techniques: stemming (remove common prefixes or suffixes from tokens) and lemmatization (find the base word).
Breaking Down Text into Meaningful Units
They can simplify the tokens and build a more accurate token dictionary. Python-based natural language processing libraries like NLTK and spaCy provide multiple variations of tokenization methods that can tokenize almost any type of raw textual data. OpenAI provides a tokenizer called Tiktoken that works well with GPT models. Choose a method based on your requirements, or write your custom tokenization method.
4. Building Your Model Architecture
The model architecture is the brain of your LLM application. We can use the transformer model as the foundation of our architecture. Since we are building our language model, we need to decide what kind of transformer model we want. For instance, we use an encoder-only transformer (such as the BERT family), a decoder-only transformer (such as the GPT family), or an encoder-decoder transformer as the base model.
Each configuration is good for specific use cases. We need to decide which configuration to use and how many layers of encoder/decoder blocks would be required to process our training data. This process requires multiple iterations of experimentation before reaching an optimal model architecture.
The Bridge Between Text and Numbers
The model architecture also requires an embedding layer to convert the tokens into their numerical representation. This is a critical step because our LLM will perform mathematical calculations on these embedding values to learn language patterns and nuances. Hence, the embedding layer needs to capture and represent important text features accurately.
There are many embedding models available. You can choose the one that represents your task. An excellent place to look for the best-performing embedding models is the Hugging Face MTEB leaderboard. As of today, Nvidia’s NV-Embed-v1 is leading the leaderboard.
We can use it for our internal company LLM use case or compare the results of different embedding models and choose the best one. Once converted, the embedded tokens can be passed onto our LLM model for training.
Guiding the LLM
You have to define a prompt template (such as using LangChain). It is a set of instructions for your LLM to generate a response according to the guidelines set in the template. Here, you can tell the model what kind of input prompts it should expect from the user and what the model’s response to such user prompts should be. As a result, the model’s outcomes can be improved.
5. Using an External Vector Database
A vector database stores vector embeddings – high-dimensional numerical representations of tokens. Remember that these vector embeddings differ from the embedding layer we discussed above. The embedding layer is typically used during LLM training, while a vector database is used during LLM inference (making predictions).
The Powerhouse Behind RAG
Prominent vector databases like pgvector, Pinecone, MongoDB Atlas, and Qdrant are essential for retrieval-augmented generation (RAG) in LLMs. They enable researchers to quantify linguistic relationships and capture detailed contextual information in textual datasets. Acting as external data sources, these databases contain domain-specific factual data, allowing LLMs to access accurate information quickly.
As a result, the user gets a fact-checked and more contextually accurate response from the LLM (most of the time), minimizing the model’s hallucination and bias (discussed below). RAG Architecture Overview of RAG Architecture.
We can create an RAG pipeline containing the entire knowledge base for our internal company LLM. Our custom LLM can query the RAG store to fetch highly accurate answers in response to a user input prompt.
6. Implementing Guardrails
Once the model is trained, we must consider its limitations, particularly bias and hallucination. Suppose our LLM generates an inaccurate response to a user query, i.e., abuse, racial slur, or stereotypical phrase. In that case, we need to stop it in its tracks, i.e., before the response is displayed to the user. Hence, you need a watchdog mechanism to monitor your LLM’s reactions before they can damage your company’s reputation.
Protecting Your LLM
Aporia Guardrails offers a complete set of tools that can mitigate brand-damaging RAG hallucinations and prompt injection attacks. It allows you to set custom AI policies and guidelines for user interactions with your LLM. You can set a list of restricted topics to avoid answering irrelevant questions, or you can present data leakage.
For instance, your company’s documents can contain details about employees’ salary packages or client contracts. Despite your best efforts during data preprocessing, Guardrails would stop the LLM from displaying it to the user if the model learns some of this information during training.
7. Evaluating and Fine-Tuning Your Model
Once the model is trained, it must be evaluated to ensure high-quality performance. You need to determine which evaluation metrics align with your use case. For instance, we are building a question-answering LLM for our internal company documents. Evaluation metrics like ROUGE and MRR are suitable for such tasks. Nevertheless, you should try multiple evaluation schemes to see which ones represent your task more effectively.
Test your trained model thoroughly and evaluate it based on your evaluation scheme. If the desired results are not achieved, you can either retrain the model, which is an uphill task, or fine-tune it using a high-quality domain-specific dataset.
For our company’s internal LLM, we have a good chance of getting optimal performance during model training because we curate the training dataset ourselves, which minimizes the need for a fine-tuning phase.
8. Fine-Tuning (Optional)
Pre-trained language models are trained on a large and diverse corpus of web-scale data, capable of performing a wide range of language tasks. During pre-training, the model learns to recognize generalized language rules, grammar, word usage, and contextual information to predict the next word or sequence of words in a sentence or text passage (this is known as language modeling).
Generally, pre-training is performed using unsupervised learning, which reduces the reliance on expensive annotated data required by supervised learning approaches.
As mentioned in the beginning, Generative Pre-training (GPT) by Open AI was one of the first major pre-trained language models that used the transformer architecture to achieve state-of-the-art results on numerous language modeling tasks at the time.
The Power of Pre-trained Models
Besides pre-training, an essential step in their pipeline was fine-tuning their pre-trained model on a smaller task-specific supervised dataset. This step improves the quality of outcomes for different downstream tasks. We'll discuss fine-tuning later, but first, let’s discuss the various benefits of pre-trained language models.
Unlocking the Potential of Pre-trained Models
Advantages of Using Pre-trained Models Pre-trained models have transformed the AI ecosystem. They are one of the main reasons for the rapid adoption of AI across domains and industries. In addition, they enable practitioners across fields to utilize the same core models and develop better tools and applications that improve business productivity and efficiency.
Pre-trained models offer numerous advantages, such as Faster training. Typically, pre-trained models don’t require extensive fine-tuning cycles, reducing the downstream model's overall training time.
- Reduced Data Requirements: Pre-training datasets contain trillions of tokens. Conversely, fine-tuning datasets only contain information related to the downstream task, significantly reducing the data volume.
- Better Performance on Downstream Tasks: Since pre-trained models are already trained on internet-scale data, they can adapt efficiently to most downstream tasks, resulting in state-of-the-art model outcomes.
- Knowledge Distillation: Besides fine-tuning, pre-trained models can be used for knowledge distillation – an AI technique used to train smaller models that mimic the performance of larger models, resulting in reduced computational requirements and memory footprint.
- Transfer Learning: The information learned by a pre-trained model is precious. It can be transferred to other AI models that solve different but related tasks. This process is called transfer learning.
- Faster Deployment: Pre-training takes days, at times, months. For instance, GPT-4 took around five to six months of training time on some of the most advanced Nvidia GPUs.
Hence, using a pre-trained model and fine-tuning it for your task can reduce the requirement of computational resources and cut the training time significantly, resulting in quicker time to market for your AI application.
How to Fine-Tune Pre-trained Models
Continuing our example of question-answering LLM for internal company documents, let’s briefly discuss how we can create a fine-tuned LLM using a pre-trained model. First, we need to curate documents and prepare a fine-tuning dataset. Then, we’ll tokenize this data so that our selected pre-trained model can understand it.
After that comes a critical choice: selecting a suitable pre-trained model. Every practitioner must consider multiple factors before choosing a pre-trained model for fine-tuning. This includes determining the similarity between the pre-trained model and the problem you are trying to solve.
You must understand the model’s architecture and complexity to interpret its behavior and performance on your fine-tuning dataset. It may be good to consider if the pre-trained model provides customization options, such as adding layers or features.
Choosing the Right Pre-trained Model for Your LLM
We have numerous proprietary and open-source pre-trained model options for fine-tuning our company LLM. We can choose an open-source model like Llama-3 or similar open models if we want more flexibility. But if you want better performance, we can utilize the GPT-3.5 model (since GPT-4 fine-tuning is currently in the experimental phase) using OpenAI API.
Once the decision is final, fine-tune the model on your curated dataset. Try different hyperparameter configurations to achieve good results. Then, analyze your fine-tuned model performance using evaluation metrics. Once satisfied, your model is ready for deployment.
9. Testing and Deployment
This step involves testing your AI creation with real-world data and deploying it to meet user needs. Test your AI with data that it will encounter in its actual usage. Ensure that it meets your:
- Accuracy
- Response time
- Resource consumption requirements
Testing is essential for identifying any issues or quirks that need to be addressed.
Deployment involves making your AI accessible to users. Depending on your project, this could mean integrating it into a website, app, or system. You might deploy on cloud services or use containerization platforms to manage your AI’s availability. Consider user access and security. Implement user authentication and access controls if needed, especially when handling sensitive data or providing restricted access to your AI.
10. Continuous Improvement
Your AI journey doesn’t end with deployment; it’s an ongoing process of improvement and refinement. Like any other machine learning model, LLMs must be evaluated after training to determine whether training was successful and how the model compares to benchmarks, alternative algorithms, or previous versions.
The evaluation of LLMs employs both intrinsic and extrinsic tactics. Intrinsic Methods Intrinsic analysis tracks performance based on objective, quantitative metrics that measure the linguistic precision of the model or how successful it is at predicting the next word. These metrics include:
- Language Fluency: Evaluates the naturalness of language produced by the LLM, checking for grammatical correctness and syntactic variety to ensure sentences generated by the model sound as if a human wrote them.
- Coherence: Measures the model's ability to maintain topic consistency across sentences and paragraphs, ensuring that successive sentences support and are logically connected.
- Perplexity: A statistical measure of how well the model predicts a sample. A lower perplexity score indicates the model is better at predicting the next word in a sequence, showing a tighter fit to the observed data.
- BLEU Score (Bilingual Evaluation Understudy): Assesses the correspondence between a machine's output and that of a human, focusing on the precision of translated text or generated responses by counting matching subsequences of words.
- Extrinsic Methods: With recent advancements in LLMs, extrinsic methods are now favored to assess their performance. This involves examining how well the models perform in real-world tasks like problem-solving, reasoning, mathematics, and computer science and competitive exams like:
- GRE
- LSAT
- The US Uniform Bar Exam
Here are a few irrelevant methods commonly used for LLM assessment:
- Questionnaires: Check how the LLM performs on questions intended for humans and compare its score to human performance.
- Common-Sense Inferences: Testing the LLM’s ability to make common-sense, easy inferences for humans.
- Multitasking: Testing a model’s multitasking accuracy across different domains like mathematics, law, and history.
- Factuality: Testing a model’s ability to answer factual questions accurately (and the degree of hallucinations in responses).
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- LLM Function Calling
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- What LLM Does Copilot Use
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- LLM Sentiment Analysis
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4 Key Considerations for Training LLMs
1. Infrastructure Matters: Understanding Computational Requirements to Train Your LLM
Training LLMs require enormous computational resources. LLMs are trained on huge text corpora, typically at least 1000 GB in size. The models employed for training on such datasets have billions of parameters.
Training a model of this size on a single GPU is not feasible, as it would take years to complete. For example, training GPT-3, a previous-generation model with 175 billion parameters, would take 288 years to train on one NVIDIA V100 GPU. Typically, LLMs are trained on thousands of GPUs in parallel. For example, Google trained its PaLM model with 540 billion parameters by distributing training over 6,144 TPU v4 chips.
2. Cost: Understanding the Financial Implications to Train Your LLM
The infrastructure required to train LLMs can be extremely costly, and many organizations need help affording it. Even OpenAI, creator of the GPT series of models and the popular ChatGPT, did not train its models on its infrastructure but instead relied on Microsoft’s Azure cloud platform.
In 2019, Microsoft invested $1 billion in OpenAI, and it is estimated that much of the money was spent on training their LLMs on Azure cloud resources.
3. Model Distribution Strategies: Planning for an Efficient Training Process
Beyond the scale and cost, there are also complex considerations in running LLM training on computing resources. LLMs are first trained on a single GPU to understand their resource requirements. Model parallelism is an essential strategy. This involves distributing models across numerous GPUs, with optimal partitioning designed to enhance memory and I/O bandwidth.
Tensor model parallelism is needed with very large models. This approach distributes individual layers of the model across multiple GPUs. It requires precise coding, configuration, and careful implementation for accurate and efficient execution. LLM training is iterative. Various parallel computing strategies are often used, and researchers experiment with different configurations, adjusting training runs to the model's specific needs and available hardware.
4. Impact of Model Architecture Choices: How Architecture Affects Training Complexity
The chosen LLM architecture has a direct impact on training complexity. Here are a few guidelines for adapting the architecture to the available resources: The model's depth and width (regarding the number of parameters) should be selected to balance available computational resources and complexity. It is preferable to use architectures with residual connections. This makes it easier to optimize resource utilization.
- Determine the need for a Transformer architecture with self-attention because this imposes specific training requirements.
- Identify the model's functional needs, such as generative modeling, bidirectional/masked language modeling, multi-task learning, and multi-modal analysis.
- Perform training runs with familiar models like GPT, BERT, and XLNet to understand their applicability to your use case.
- Determine your tokenization technique: word-based, subword-based, or character-based. This can impact vocabulary size and input length, directly impacting computation requirements.
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