• author: AI FOCUS

H2O GPT: An Open Source Language Model Exposing its Secret Formula


Language models (LLMs) have become an integral part of the artificial intelligence landscape. Developed by big players like OpenAI, Microsoft, and Google, these LLMs have proven to be incredibly powerful and are transforming the world as we know it. However, they suffer from a major flaw - their training data is shrouded in secrecy, just like the secret formula of the infamous Krabby Patty from the cartoon SpongeBob SquarePants.

But what if there was an open-source LLM that not only matched the capabilities of the big players but also exposed its own secret formula? In this article, we will explore H2O GPT, an open-source LLM that aims to democratize AI and push innovation in the space. We will delve into its features, its uniqueness, and even put it to the test against the big, closed-source LLMs. So without further ado, let's dive in!

The Limitations of Closed-Source LLMs

While closed-source LLMs like OpenAI's GPT-4 are undeniably remarkable in their language skills, they come with their own set of limitations and concerns. Two major limitations are the issues of unauthorized use of copyrighted data and the potential for biased and harmful text generation.

Addressing these concerns, H2O AI, a company known for its commitment to open source, has developed an open-source LLM called H2O GPT. H2O AI has a strong background in building world-class machine learning, deep learning, and AI platforms using open-source software over the past decade. This commitment to open source led to the creation of H2O GPT, an LLM that opens up its training data and aims to lower the barriers to entry in the AI space.

The Philosophy behind H2O GPT

H2O AI believes that LLMs should be accessible and applicable across various domains, such as healthcare and education, to drive innovation. To fulfill this vision, they have made their suite of Open Source Code repositories for H2O GPT available, under Apache 2.0 licenses. This suite includes seven fine-tuned models, ranging from 7 to 40 billion parameters, ready for commercial use.

In addition to the models, H2O GPT introduces a powerful feature called "private document search." This feature allows users to search through private documents using natural language queries. By applying natural language processing to sensitive documents, H2O AI aims to strike a balance between privacy and the utility of AI.

Advantages of Open Source LLMs

H2O AI highlights four main reasons why open source LLMs like H2O GPT have an advantage over their closed-source counterparts:

  1. Data Privacy and Security: Closed-source LLMs require users to send their data to external servers, potentially raising concerns regarding data privacy and security. In contrast, open-source LLMs allow for local deployment, ensuring data confidentiality.

  2. Dependency and Customization: Closed-source LLMs typically have limited flexibility when it comes to customization and integration with existing systems. Open-source LLMs, like H2O GPT, provide the freedom to customize and modify the code, allowing for seamless integration with diverse infrastructures.

  3. Cost and Scalability: Open-source LLMs are generally more cost-effective, as they eliminate the need to pay additional fees to a service provider. They also offer scalability without the worries of additional costs.

  4. Downtime and Availability: Closed-source LLMs may suffer from downtime, potentially disrupting access. Open-source models can be hosted privately, ensuring uninterrupted availability.

With these advantages in mind, it becomes clear why open-source LLMs like H2O GPT are gaining popularity and proving to be a viable alternative to closed-source solutions.

The Secret Formula of H2O GPT Revealed

Now, let's unravel the secret formula behind H2O GPT. The journey of H2O GPT begins with foundational models, the largest being the GPT Neo x20b and Falcon 40b. These models serve as the starting point for fine-tuning, where input and output pairs are provided to make the model more sophisticated.

During the fine-tuning process, the model gradually learns the style and context of the provided prompts. Data filtering and pre-processing steps are then performed to remove profanity, long dialogues, and incomplete sentences. H2O AI has made approximately 1800 lines of code available on their GitHub repository for data processing and cleaning, ensuring transparency and allowing users to explore and fine-tune models on their own.

H2O GPT's training heavily relies on the OpenAI GPT base models, using the OpenAI "InstructGPT" and "ChatGPT" models. H2O AI's conversational data collection process involves using high-quality conversational data from humans, creating conversational responses that match the prompts.

The Extensive H2O Ecosystem

Apart from the impressive training process, H2O GPT comes with a vast ecosystem of tools and resources. These tools expand the possibilities of what users can achieve and enhance their overall experience. Here are some of the key components of the H2O ecosystem:

  1. Fully Usable Code, Data, and Models: H2O AI provides users with access to all the code, data, and models associated with H2O GPT. These models can be found in the Hugging Face model hub, ensuring ease of use and compatibility with other Hugging Face models.

  2. State-of-the-Art Fine-Tuning Code: H2O GPT provides users with fine-tuning code, enabling them to further enhance and customize the model based on their specific needs.

  3. Chatbot Framework: H2O GPT includes a chatbot framework with a sleek user interface comparable to GPT-4. This framework allows users to deploy the chatbot on GPU servers, facilitating interactive conversational experiences.

  4. Natural Language Document Search System: H2O GPT incorporates a document search system that leverages natural language understanding. Users can provide source content as context when querying the system, ensuring accurate and context-aware responses.

  5. No-Code Fine-Tuning Framework: H2O GPT offers a no-code fine-tuning framework, making it accessible to anyone without prior coding knowledge. This framework includes a user-friendly interface for non-code fine-tuning and a command-line interface for more advanced use cases. Users can leverage various fine-tuning techniques, such as Lora, and visually track and compare model performance.

Performance Evaluation of H2O GPT

While H2O GPT excels in many areas, it does have certain limitations. Common sense reasoning tasks, mathematics and logic, and code completion are areas where the model shows room for improvement. However, it performs impressively in tasks related to creativity, summarization, general chat, private document chat, and rephrasing.

To assess its performance, a series of tests were conducted. The private document search feature yielded accurate and factual summaries based on the provided context. Simple factual questions were answered correctly by the majority of the models, although a couple did occasionally exhibit errors. Algebraic questions were generally answered correctly, while logical reasoning problems showed consistent accuracy, with only

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