- author: Matthew Berman
H2O GPT: A Free, Open-Source AI Solution
In a previous video, we reviewed the Falcon 40b model, which was unusably slow. However, we now have an incredible implementation of it by H2O.ai, a relatively unknown company in the consumer AI space. In this article, we will review their version of Falcon 40b, called H2O GPT, and explore its features.
H2O.ai provides a range of solutions for artificial intelligence, primarily serving large enterprise companies. However, they also offer tooling for individuals, and they have recently released their own models, hosted for free and open source. They also offer a version where users can load up their own documents and chat with them.
H2O GPT: Features
H2O GPT is essentially H2O.ai's competitor to OpenAI, but it's completely free and uses open source models. They offer three separate models, including Falcon 7B, Falcon 40b, and Open Llama 7B. While the Falcon 7B model is incredibly fast, we will focus on the Falcon 40b model today, as it recently took the number one spot in the LLM leaderboards.
The H2O GPT interface looks similar to that of Chat CPT, so users should feel comfortable using it right away. They have a range of features, including "Share this conversation," and users can also provide feedback by giving a thumbs up or thumbs down to the model's responses.
Testing H2O GPT
We will be using the LLM Rubric to test the H2O GPT model. First, we wrote a python script to output numbers 1 to 100, and the interface gave us an option to copy the code quickly and easily. Next, we attempted to write the game snake in Python, but the model was slower than GPT 3.5 and stopped before completing the task.
However, we successfully wrote a poem about AI with exactly 50 words. Although it was more than 50 words, the model passed the test. We also asked it a fact-based question about the US president in 1996, and it correctly identified William Jefferson Clinton.
We also gave the model a logic problem about drying shirts in the sun, and it got the math wrong. However, it successfully solved a simple math problem and a slightly harder one.
Using Machine Learning to Test AI Capabilities
Machine learning techniques have come a long way in recent years and they have been increasingly used to test the capabilities of AI systems. In this article, we will discuss some experiments conducted to test the limits of AI systems using machine learning techniques. We will also explore the use of a website, Falcon.h2o.ai, which is specifically designed to assist in testing the capabilities of AI systems.
Here are some of the experiments conducted to test the limits of AI systems:
Testing the Limits of Logic
In this experiment, a logical question is posed: "If Jane is faster than Joe, and Joe is faster than Sam, can Sam be faster than Jane?" The AI system is then asked to generate a response. The answer would be "no," but the AI system fails to provide the correct answer.
Testing Mathematical Aptitude
In this experimental setting, math problems of varying difficulty are posed to the AI system. The AI system is expected to solve these problems. The system does well on simple problems like 4+4=8, but struggles with more complex ones like 25-4x2=17+3.
Hybrid Writing and Planning Challenge
In this experiment, the AI system is asked to put together a healthy meal plan. The system is given prompts like "Greek yogurt," "whole grains," and "green tea." The AI system does a good job, but the formatting is slightly off.
Crime Mystery Challenge
In this experiment, the AI system is asked to solve a crime mystery. A riddle is posed: "If there are three killers in a room, someone enters the room and kills one of them, nobody leaves the room, how many killers are left in the room?" The AI system is expected to understand that there are still two killers in the room, but it fails to provide the correct answer.
In this challenge, the AI system is given a prompt and is asked to provide a summary. This AI system is expected to generate a bullet-point summary of the first six pages of the book Harry Potter and the Philosopher's Stone. The system only talks about one character, Mr. Dursley, and the experiment is considered a failure.
Chatting with Documents
Falcon.h2o.ai is a website designed to test the capabilities of AI systems, and it allows you to upload your own files or URLs to test. The website uses the Gradio library to access data sources. In an example of chatting with documents, the AI system is asked to give a summary of the implementation of transformers in the attention is all you need paper from the AR XIV link. The system successfully provides a description of how Transformers work for large language models.
InH2o gpt is an impressive, free, open-source ai solution. with its range of features and ease of use, it's a great alternative to openai. while it still has some limitations, it's a promising option for those who need a reliable ai tool.
, with the help of machine learning techniques and dedicated websites like Falcon.h2o.ai, we can test the limits of AI systems. These tests help us understand the capabilities and limitations of AI systems and assist in further advancements to make AI systems more efficient in their tasks.