Training Your Own LLM Without Coding

guest_blog 23 Oct, 2023 • 9 min read

Introduction

Generative AI, a captivating field that promises to revolutionize how we interact with technology and generate content, has taken the world by storm. In this article, we’ll explore the fascinating realm of Large Language Models (LLMs), their building blocks, the challenges posed by closed-source LLMs, and the emergence of open-source models. We’ll also delve into H2O’s LLM ecosystem, including tools and frameworks like h2oGPT and LLM DataStudio that empower individuals to train LLMs without extensive coding skills.

Training Your Own LLM Without Coding | DataHour by Favio Vazquez

Learning Objectives:

  • Understand the concept and applications of Generative AI with Large Language Models (LLMs).
  • Recognize the challenges of closed-source LLMs and the advantages of open-source models.
  • Explore H2O’s LLM ecosystem for AI training without extensive coding skills.

Building Blocks of LLMs: Foundation Models and Fine Tuning

Before we dive into the nuts and bolts of LLMs, let’s step back and grasp the concept of generative AI. While predictive AI has been the norm, generative AI flips the script, focusing on forecasting based on historical data patterns. It equips machines with the ability to create new information from existing datasets.

Imagine a machine learning model capable of predicting and generating text, summarizing content, classifying information, and more—all from a single model. This is where Large Language Models (LLMs) come into play.

Building blocks of LLMs: Foundation model, Fine-tuning, RLHF

LLMs follow a multi-step process, starting with a foundation model. This model requires an extensive dataset to train on, often on the order of terabytes or petabytes of data. These foundation models learn by predicting the next word in a sequence to understand the patterns within the data.

Foundation model for training LLMs

Once the foundation model is established, the next step is fine-tuning. During this phase, supervised fine-tuning on curated datasets is employed to mold the model into the desired behavior. This can involve training the model to perform specific tasks like multiple-choice selection, classification, and more.

Stages of LLM training

The third step, reinforcement learning with human feedback, further hones the model’s performance. Using reward models based on human feedback, the model fine-tunes its predictions to align more closely with human preferences. This helps reduce noise and increase the quality of responses.

RLHF - Reinforced learning with human feedback

Each step in this process improves the model’s performance and reduces uncertainty. It’s important to note that choosing the foundation model, dataset, and fine-tuning strategies depends on the specific use case.

Challenges of Closed Source LLMs and the Rise of Open Source Models

Closed-source LLMs, such as ChatGPT, Google Bard, and others, have demonstrated their effectiveness. However, they come with their share of challenges. These include concerns about data privacy, limited customization and control, high operational costs, and occasional unavailability.

Organizations and researchers have recognized the need for more accessible and customizable LLMs. In response, they have begun developing open-source models. These models are cost-effective, flexible, and can be tailored to specific requirements. They also eliminate concerns about sending sensitive data to external servers.

Open-source LLMs empower users to train their models and access the inner workings of the algorithms. This open ecosystem provides more control and transparency, making it a promising solution for various applications.

H2O’s LLM Ecosystem: Tools and Frameworks for Training LLMs Without Coding

H2O, a prominent player in the machine learning world, has developed a robust ecosystem for LLMs. Their tools and frameworks facilitate LLM training without the need for extensive coding expertise. Let’s explore some of these components.

H2O.ai ecosystem for LLM development: h2oGPT, LLM Studio, LLM DataStudio, H2O MLOPs, and Helium

h2oGPT

h2oGPT is a fine-tuned LLM that can be trained on your own data. The best part? It’s completely free to use. With h2oGPT, you can experiment with LLMs and even apply them commercially. This open-source model allows you to explore the capabilities of LLMs without financial barriers.

Deployment Tools

H2O.ai offers a range of tools for deploying your LLMs, ensuring that your models can be put into action effectively and efficiently. Whether you are building chatbots, data science assistants, or content generation tools, these deployment options provide flexibility.

LLM Training Frameworks

Training an LLM can be complex, but H2O’s LLM training frameworks simplify the task. With tools like Colossal and DeepSpeed, you can train your open-source models effectively. These frameworks support various foundation models and enable you to fine-tune them for specific tasks.

Demo: Preparing Data and Fine Tuning LLMs with H2O’s LLM DataStudio

Let’s now dive into a demonstration of how you can use H2O’s LLM ecosystem, specifically focusing on LLM DataStudio. This no-code solution allows you to prepare data for fine-tuning your LLM models. Whether you’re working with text, PDFs, or other data formats, LLM DataStudio streamlines the data preparation process, making it accessible to many users.

In this demo, we’ll walk through the steps of preparing data and fine-tuning LLMs, highlighting the user-friendly nature of these tools. By the end, you’ll have a clearer understanding of how to leverage H2O’s ecosystem for your own LLM projects.

H2O LLM DataStudio interface | H2O.ai

The world of LLMs and generative AI is evolving rapidly, and H2O’s contributions to this field are making it more accessible than ever before. With open-source models, deployment tools, and user-friendly frameworks, you can harness the power of LLMs for a wide range of applications without the need for extensive coding skills. The future of AI-driven content generation and interaction is here, and it’s exciting to be part of this transformative journey.

Introducing h2oGPT: A Multi-Model Chat Interface

In the world of artificial intelligence and natural language processing, there has been a remarkable evolution in the capabilities of language models. The advent of GPT-3 and similar models has paved the way for new possibilities in understanding and generating human-like text. However, the journey doesn’t end there. The world of language models is continually expanding and improving, and one exciting development is h2oGPT. This multi-model chat interface takes the concept of large language models to the next level.

h2oGPT is like a child of GPT, but it comes with a twist. Instead of relying on a single massive language model, h2oGPT harnesses the power of multiple language models running simultaneously. This approach provides users with a diverse range of responses and insights. When you ask a question, h2oGPT sends that query to various language models, including Llama 2, GPT-NeoX, Falcon 40 B, and others. Each of these models responds with its own unique answer. This diversity allows you to compare and contrast responses from different models to find the one that best suits your needs.

For example, if you ask a question like “What is statistics?” you will receive responses from various LLMs within h2oGPT. These different responses can offer valuable perspectives on the same topic. This powerful feature is incredibly useful and completely free to use.

h2oGPT interface | multiple AI answering 'What is statistics?'

Simplifying Data Curation with LLM DataStudio

To fine-tune a large language model effectively, you need high-quality curated data. Traditionally, this involved hiring people to craft prompts manually, gather comparisons, and generate answers, which could be a labor-intensive and time-consuming process. However, h2oGPT introduces a game-changing solution called LLM DataStudio that simplifies this data curation process.

LLM DataStudio allows you to create curated datasets from unstructured data effortlessly. Imagine you want to train or fine-tune an LLM to understand a specific document, like an H2O paper about h2oGPT. Normally, you’d have to read the paper and manually generate questions and answers. This process can be arduous, especially with a substantial amount of data.

But with LLM DataStudio, the process becomes significantly more straightforward. You can upload various types of data, such as PDFs, Word documents, web pages, audio data, and more. The system will automatically parse this information, extract relevant pieces of text, and create question-and-answer pairs. This means you can create high-quality datasets without the need for manual data entry.

LLM DataStudio by H2O.ai - features & uses | training using h2oGPT

Cleaning and Preparing Datasets Without Coding

Cleaning and preparing datasets are critical steps in training a language model, and LLM DataStudio simplifies this task without requiring coding skills. The platform offers a range of options to clean your data, such as removing white spaces, URLs, profanity, or controlling the response length. It even allows you to check the quality of prompts and answers. All of this is achieved through a user-friendly interface, so you can clean your data effectively without writing a single line of code.

Moreover, you can augment your datasets with additional conversational systems, questions, and answers, giving your LLM even more context. Once your dataset is ready, you can download it in JSON or CSV format for training your custom language model.

Training Your Custom LLM with H2O LLM Studio

Now that you have your curated dataset, it’s time to train your custom language model, and H2O LLM Studio is the tool to help you do that. This platform is designed for training language models without requiring any coding skills.

H2O LLM Studio interface | H2O.ai

The process begins by importing your dataset into LLM Studio. You specify which columns contain the prompts and responses, and the platform provides an overview of your dataset. Next, you create an experiment, name it and select a backbone model. The choice of backbone model depends on your specific use case, as different models excel in various applications. You can select from a range of options, each with varying numbers of parameters to suit your needs.

Process of model training using H2O LLM Studio | AI training

You can configure parameters like the number of epochs, low-rank approximation, task probability, temperature, and more during the experiment setup. If you’re not well-versed in these settings, don’t worry; LLM Studio offers best practices to guide you. Additionally, you can use GPT from OpenAI as a metric to evaluate your model’s performance, though alternative metrics like BLEU are available if you prefer not to use external APIs.

Once your experiment is configured, you can start the training process. LLM Studio provides logs and graphs to help you monitor your model’s progress. After successful training, you can enter a chat session with your custom LLM, test its responses, and even download the model for further use.

Conclusion

In this captivating journey through the world of Large Language Models (LLMs) and generative AI, we’ve uncovered the transformative potential of these models. The emergence of open-source LLMs, exemplified by H2O’s ecosystem, has made this technology more accessible than ever. We’re witnessing a revolution in AI-driven content generation and interaction with user-friendly tools, flexible frameworks, and diverse models like h2oGPT.

h2oGPT, LLM DataStudio, and H2O LLM Studio represent a powerful trio of tools that empower users to work with large language models, curate data effortlessly, and train custom models without the need for coding expertise. This comprehensive resource suite simplifies the process and makes it accessible to a wider audience, ushering in a new era of AI-driven natural language understanding and generation. Whether you’re a seasoned AI practitioner or just starting, these tools allow you to explore the fascinating world of language models and their applications.

Key Takeaways:

  • Generative AI, powered by LLMs, allows machines to create new information from existing data, opening up possibilities beyond traditional predictive models.
  • Open-source LLMs like h2oGPT provide users with cost-effective, customizable, and transparent solutions, eliminating data privacy and control concerns.
  • H2O’s ecosystem offers a range of tools and frameworks, such as LLM DataStudio and H2O LLM Studio, that stand as a no-code solution for training LLMs.

Frequently Asked Questions

Q1. What are LLMs, and how do they differ from traditional predictive AI?

Ans. LLMs, or Large Language Models, empower machines to generate content rather than just predict outcomes based on historical data patterns. They can create text, summarize information, classify data, and more, expanding the capabilities of AI.

Q2. Why are open-source LLMs like h2oGPT gaining popularity?

Ans. Open-source LLMs are gaining traction due to their cost-effectiveness, customizability, and transparency. Users can tailor these models to their specific needs, eliminating data privacy and control concerns.

Q3. How can I train LLMs without extensive coding skills?

Ans. H2O’s ecosystem offers user-friendly tools and frameworks, such as LLM DataStudio and H2O LLM Studio, that simplify the training process. These platforms guide users through data curation, model setup, and training, making AI more accessible to a wider audience.

About the Author: Favio Vazquez

Favio Vazquez is a leading Data Scientist and Solutions Engineer at H2O.ai, one of the world’s biggest machine-learning platforms. Living in Mexico, he leads the operations in all of Latin America and Spain. Within this role, he is instrumental in developing cutting-edge data science solutions tailored for LATAM customers. His mastery of Python and its ecosystem, coupled with his command over H2O Driverless AI and H2O Hybrid Cloud, empowers him to create innovative data-driven applications. Moreover, his active participation in private and open-source projects further solidifies his commitment to AI.

DataHour Page: https://community.analyticsvidhya.com/c/datahour/datahour-training-your-own-llm-without-coding

LinkedIn: https://www.linkedin.com/in/faviovazquez/

guest_blog 23 Oct 2023

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