How to Build a Private LLM: A Complete Guide

The use of LLM has significantly increased in various industries, powering applications from virtual assistants to customer support and content creation. The global LLM market was $1.59 billion in 2023 and is estimated to grow $259.8 billion by 2030. By 2025, approximately 750 million apps are projected to incorporate LLM technology.

While LLMs are revolutionizing workflows within various industries, there is also a growing need for privacy and security. The public LLMs are trained on an extensive dataset, which introduces potential risks when using them for sensitive or proprietary tasks. More organizations are moving towards private LLMs to develop custom solutions and address data security concerns. A personal LLM offers greater control over data and involves in-house model training, which maintains privacy.

This guide will walk you through the process of building a private LLM, providing a complete roadmap for your organization to leverage the transformative power of LLMs.

What are LLMs?

Large Language Models (LLMs) are advanced artificial intelligence models. They can analyze, understand, interpret, and generate text content relevant to your input query. These models are trained on large datasets, often containing billions of words, enabling them to learn the intricacies of the language, content, and grammar.

A crucial part of LLM functionality is AI tokenization, the process of breaking down data into smaller units called tokens. These tokens can represent whole worlds, parts of words, or even characters. Tokenization helps the models process language more efficiently.

LLMs can perform various complex text-based tasks such as answering questions, summarizing information, generating content, translating languages, and even conducting meaningful conversations. They leverage deep learning algorithms, specifically neural networks, to recognize patterns and associations within the data.

Some of the popular LLMs include GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers). You can interact with these LLMs using natural language prompts, and they will provide results accordingly.

Types of LLMs

There are many types of LLMs available in the market, but most of them are classified based on three factors: architecture, availability, and domain-specific. Let’s look at the types of LLMs based on these classifications:

Type of LLMs Based on Architecture

• Autoregressive LLMs: Autoregression is a method to predict the next word in a sequence by looking at the previous words. This approach is often applied in tasks such as generating language content, storytelling, and summarization. A popular example of an Autoregressive model is GPT.
• ‍Autoencoding LLMs: Autoregressive models are designed to understand and analyze the text by reconstructing the input data from compressed representations. These models capture complex relationships between the words, and provide deep understanding of sentence structure and meaning. A well-known example of this model is BERT.
• ‍Encoder-Decoder LLMs: The Encoder-Decoder LLM model consists of two parts. The first part is the encoder, which processes the input data, and the second part is the decoder, which generates the output. These models are useful for tasks where input and output are in sequences, such as language translation and summarization. An example of this model is T5.

Types of LLMs Based on Availability

• Open-Source LLMs:  Open-source LLMs are language models whose source code is publicly available, enabling you to access, use, and modify this code freely. The availability of the source code allows you to adapt and build applications based on the existing LLM framework. Examples of open-source LLMs include GPT-2 and Bloom.‍
• Proprietary LLMs: Proprietary or private LLMs are language models maintained by specific companies. They are not publicly accessible and are offered as a paid service or API. The companies regularly update and optimize these models to sustain high performance. Proprietary models are usually more secure and come with customer support, but they lack the customization found in open-source models. Some examples of these models include GPT-4 by OpenAI and Google’s Brad.

Domain Specific Models

Domain-specific LLMs are designed to perform tasks related to specific fields like healthcare, legal, financial, or scientific. These models are trained to understand the unique vocabulary and context of a particular field and generate relevant outputs.

For example, an LLM model trained on medical literature, clinical data, and healthcare-related documents can understand medical terminology and assist in clinical decision-making.

Understanding the Need for a Private LLM

There are several reasons why you should consider building a private LLM.

• Data Security and Privacy: The main objective behind creating a private LLM is to secure the critical data of your organization and protecting it from getting leaked or misused. You can employ techniques like federated learning and implement access controls to minimize exposure during training and usage.‍
• Control Over Data: Unlike public models, a private LLM provides you with more control over your data. You train and build the private model using your own datasets and code. This control offers flexibility to adjust and fine-tune the model, aligning it more precisely with your organization's goals.
• ‍Compliance with Industry Regulations: Many industries are governed by strict compliance standards, such as GDPR or HIPAA. A private LLM enables your organization to maintain compliance more efficiently by implementing necessary data-handling protocols directly into the model. This facilitates complete visibility and control over how data is managed, processed, and stored.
• ‍Improved Performance for Specific Tasks: With private LLM, you can fine-tune the models for specific tasks or domains. This customization enhances the model’s understanding of terminology related to a particular domain, such as medicine, literature, or language. The targeted training of private LLMs enhances the output accuracy compared to general-purpose LLMs.

Step-by-Step Guide on Building Your Own Private LLM

Developing an LLM model requires extensive computational resources. There isn’t a single ideal approach to building these models, as each design depends on the specific goals and requirements of your application. For instance, a domain-specific training dataset might be more inclusive than a content generation model. Below are the typical steps that you can follow to build a private LLM for your organization:

1: Set Your Objectives

Begin by defining the purpose of your LLM. What do you plan to use this model for? Are you building an internal communication model or commercializing it for content generation, or is it for specific research? Outlining the goals and objectives will help you clearly carry out the necessary steps.

2: Choose an Appropriate Architecture

Selecting the architecture for your private LLM is the technical decision you must make. Consider a distributed, modular architecture if scalability and collaboration are the priority. You can employ a transformer-based architecture like BERT or GPT, which is helpful for various NLP tasks. If your organization wants to build an LLM to handle sequential tasks, then you can opt for encoder-decoder architecture.

3: Data Collection and Preprocessing

Building a private LLM requires a well-curated dataset that aligns with your objective. Collect domain-specific data from sources like internal databases and reports. After relevant data is collected, you can process it by cleaning, formatting, and performing LLM Tokenization.  Tokenization involves breaking the text into smaller units and representing it in a format that the model can understand. You can use techniques like Byte-Pair encoding or SentencePiece to convert text into tokens, ensuring compatibility and correct representation of data during training.

4: Training Your Model

Training can be done by either fine-tuning the existing model or feeding it with new training data. Fine-tuning involves adjusting a pre-trained model as per your specific needs by feeding it with the relevant domain data. This method needs fewer computational resources, which makes it cost-efficient.

Another method is to train your LLM model from scratch. You need to curate a data set that aligns with your objectives. To improve the training efficiency, you can implement techniques like curriculum learning (gradually increasing the difficulty of training data) or weight decay to prevent data overfitting.

5: Secure the Model

To secure the LLM model, you can start by implementing access controls to limit who can access, modify, or use it. Regularly audit the model’s access logs to identify and address potential vulnerabilities. You should also secure the data pipeline that supplies information to your LLM.

6: Monitoring and Governance

To keep your LLM effective and compliant, implement monitoring tools that track its performance and accuracy. You can set up alerts or bias detections and conduct routine audits to access the output against governance guidelines.

7: User Education and Ethical Usage

To educate the user on usage and ethical considerations, you can develop comprehensive documentation that outlines the guidelines and best practices for using LLM. In addition to this, conduct regular training sessions for your data teams to cover ethical considerations and the risk associated with the misuse in real-world scenarios. Legal awareness is another key aspect, so educate your team about relevant guidelines like GDPR or CCPA for responsible data handling.

Building a Private LLM Using Airbyte

To develop a private LLM, you first need to gather and consolidate relevant data into a centralized location for efficient access and management. This data must then be transformed into a format suitable for LLM training.

To streamline this process and make your data LLM-ready, you can leverage AI-powered data integration platforms like Airbyte. It provides over 400+ pre-built connectors that enable you to extract data from various sources and load it into your desired destination system. With Airbyte, you can handle diverse data types, including structured, semi-structured, and unstructured data, essential for preparing your datasets for LLM training.

Here are some of the key features of Airbyte:

• Custom Connectors: If you can't find the connector you need, you can utilize Airbyte's Connector Development Kit (CDK) to create custom connectors in under 30 minutes. Furthermore, the Connector Builder's AI-assist functionality scans through the API documentation you provided and pre-fills the fields, drastically reducing setup time.
• ‍Streamlined GenAI Workflows: Airbyte supports popular vector databases, such as Chroma, Pinecone, Qdrant, Milvus, and Weaviate. You can leverage Airbyte's built-in RAG transformations, like chunking, embedding, and indexing, to convert raw data into vector embeddings. These embeddings can then be stored in vector stores to enhance the efficiency of LLM responses.
• ‍PyAirbyte: You can leverage PyAirbyte, a Python-based open-source library, to extract data from dispersed sources by utilizing Airbyte connectors directly within your developer environment. PyAirbyte cached data is compatible with several Python libraries, such as Pandas and SQL-based tools, as well as leading AI frameworks like LlamaIndex and LangChain, to facilitate the development of LLM-powered applications.
• ‍Change Data Capture (CDC): With Airbyte's CDC feature, you can capture incremental changes made at the source data system and reflect them in the destination. By leveraging this functionality, you can ensure that the responses generated by your LLM are based on the updated data.

Now, let's see how Agent Cloud, an open-source GUI platform, leveraged Airbyte to streamline the process of developing LLMs.

‍Agent Cloud is an open-source platform used for building and deploying LLM-powered chat applications. It internally uses Airbyte to manage data pipelines that support splitting, chunking, and embedding data from diverse sources, including NoSQL databases like MongoDB. As discussed above, Airbyte simplifies the ingestion of data into the vector store, both during the initial setup and subsequent scheduled updates, ensuring that the information within the vector store remains updated.

Agent Cloud utilizes Qdrant as the vector store to manage and store vector embeddings efficiently. When a user inputs a query, the platform's RAG-based application retrieves relevant documents by evaluating the similarity of their vector representations to that of the query vector. This process ensures that responses are accurate and contextually appropriate.

Is RAG (Retrieval Augmented Generation) Different from a Private LLM?

Yes, RAG and Private LLM are two different concepts. RAG combines the strengths of LLMs with external retrieval systems to enhance their ability to generate accurate and contextually relevant responses. RAGs use robust search algorithms to extract relevant data from external sources like web pages and knowledge bases. This information is then preprocessed and integrated with the LLM input, allowing the model to produce content based on up-to-date information beyond its original training data.

Whereas a private LLM is controlled by organizations internally. This model does not rely on external data sources; it generates responses solely based on its pre-trained or fine-tuned parameters.

Significance of a Private LLM

The following points highlight the strategic and operational significance of private models: 

• Customization: By training the model on domain-specific data, you can generate highly relevant responses that directly align with your unique business needs, ensuring higher accuracy and relevance in outputs.‍
• Scalability and Performance: Private LLMs can be optimized for scalability by deploying them on dedicated infrastructure that is tailored to your specific organizational needs. This approach reduces latency and maximizes throughput, ensuring consistent performance even under high-volume workloads.‍
• Reduce Dependency on External Providers: Building LLMs internally significantly reduces reliance on external service providers. This minimizes the risk of data exposure, ensuring that sensitive information remains within your organization’s secure environment.‍
• Protection of Intellectual Property: By using private LLMs, your organization can reduce exposure of intellectual property to third parties or public models. This ensures your organization’s proprietary processes, insights, and innovation are protected and secured.

Challenges and Considerations

• Data Privacy: Sensitive data demands protection. To safeguard your organization's critical data, you can implement strong data encryption, user authentication, and access control measures.
• ‍Managing Resources: To train your LLM models, you need extensive computational resources, including high-performance GPUs and ample storage. Before creating an LLM, evaluate the infrastructure options thoroughly so that you can efficiently manage resources.‍
• Maintaining Data Quality: Poorly curated data can lead to biased model outputs. Establishing clear data governance policies that emphasize data quality, relevance, and ethical sourcing is essential.
• ‍Development and Maintenance Cost: Building and maintaining an LLM model can be costly due to the resources and equipment required. You can plan your budget and opt out of options that can help you save money, such as using shared cloud resources.‍
• Bias Management: Private LLMs can inherit biases from the training data. Incorporate ethical guidelines in the training process and monitor bias through regular assessment using diverse data sets.

Role of Hugging Face in Building Private LLM 

Hugging Face is a collaborative platform for machine learning that you can use to build and deploy AI and LLM models. It offers a centralized repository called Hugging Face Hub that provides over 900k models, 200k datasets, and 300k demo apps (Spaces). These resources are publicly available and can be leveraged to build state-of-the-art ML models.

The Hugging Face Transformers is a widely used open-source library that offers thousands of pre-trained models for various tasks, including text, vision, and audio. Transformer models can also perform tasks using combined modalities, such as table question answering, optical character recognition, visual question answering, and video classification. Transformers provide APIs that facilitate you to quickly download and utilize those pre-trained models on a given text and fine-tune them on your own datasets.

Further, to simplify the creation of LLMs, Hugging Face offers AutoTrain, a no-code solution that enables you to train and fine-tune models effortlessly. Additionally, the platform also supports parameter-efficient fine-tuning techniques like LoRA (Low-Rank Adaptation) to reduce the computational costs of running the models.

Industries Benefiting from Private LLMs

LLMs are revolutionizing many industries by automating and personalizing digital interactions and generating valuable insights. Here are some examples that benefit from the personal LLMs:

• LinkedIn: The platform uses large language models to suggest premium services and products to users. By analyzing users’ professional histories, interests, and activities, LinkedIn delivers tailored recommendations. This targeted approach improves user experience, ensuring members receive relevant suggestions that support their career goals. As a result, it drives subscriptions to LinkedIn's premium offerings.‍
• Amazon: Amazon has integrated generative AI that uses LLMs to assist sellers in creating engaging product descriptions, titles, and listing details. This streamlines the process for sellers to list new products and enrich existing listings, helping customers make purchase decisions more confidently.‍
• Microsoft: Microsoft’s research group is leveraging LLMs to enhance cloud incident management. By automating tasks like root cause analysis and generating mitigation strategies, LLMs can quickly analyze incident tickets containing titles and details about errors and produce actionable recommendations for resolving issues.

Conclusion

LLMs are continuously deriving innovation across various industries. However, there is a growing demand for private LLMs due to privacy, data security, and customization needs. Your organization can adopt private LLMs to gain control over sensitive data and ensure regulatory compliance. You can also tailor these models according to specific tasks while safeguarding proprietary information.