The Yi model family has received a significant upgrade, now outperforming LLAMA-3 benchmarks. The best part? These models are released under the Apache 2.0 license, allowing for commercial use without limitations.

The Yi-1.5 series includes three models: a 6 billion, 9 billion, and 34 billion parameter version. All are upgraded from the original Yi models and have been trained on up to 4.1 trillion tokens. While the context window is limited to 4,000 tokens, the models can potentially expand on this in the future.

The 9 billion parameter model outperforms its peers, while the 34 billion version closely matches or even surpasses the LLAMA-370 billion model in performance. Beyond benchmarks, the Yi-1.5 models demonstrate strong capabilities in coding, math reasoning, and instruction following.

To test the models, the 34 billion version is available on Hugging Face, and the 9 billion version can be run locally. The models exhibit impressive reasoning abilities, handling complex scenarios and maintaining context throughout conversations.

In terms of coding, the Yi-1.5 models can identify and correct errors in simple Python programs. They can also generate code for tasks like file downloads from S3 buckets and creating interactive web pages with dynamic functionality.

While the models have some limitations, such as the fixed context window, the Yi-1.5 series represents a significant advancement in large language models. With their Apache 2.0 licensing, these models offer an exciting opportunity for commercial applications and further development.

The model demonstrates a nuanced approach to sensitive topics, providing educational information when asked about potentially illegal activities, while avoiding direct endorsement. It shows creativity in generating jokes, though the quality is variable. The model also exhibits strong reasoning and problem-solving abilities, as evidenced by its step-by-step responses to complex logic puzzles. However, it struggles with maintaining a complete mental model when dealing with multiple, rapidly changing scenarios.

The model's coding and math capabilities are impressive, accurately identifying errors in code samples and solving mathematical problems. Its ability to retrieve and summarize information from provided contexts suggests potential for use in research assistant tasks.

Overall, the model exhibits a balance of capabilities, with strengths in reasoning, coding, and math, but limitations in maintaining contextual awareness and generating truly novel content. Further development of the model's context window and training on more diverse datasets could help address these areas for improvement.

The YE model family has demonstrated impressive logical reasoning and problem-solving capabilities. The models were able to navigate complex scenarios and provide step-by-step reasoning to arrive at accurate conclusions.

When presented with a question about the number of siblings a character named Sally has, the model carefully analyzed the provided information and acknowledged the lack of sufficient details to determine the answer. It then walked through the possible scenarios, considering the relationships between the characters, to arrive at the correct response.

Similarly, the model displayed strong deductive reasoning skills when presented with a narrative about two hungry individuals. It logically deduced that the second person, Daniel, would likely also head to the kitchen in search of food, just as John had done.

The model's ability to track and recall multiple pieces of information was also tested, with mixed results. While it was able to accurately keep track of the sequence of events in some cases, it struggled to maintain a complete mental model in more complex scenarios, occasionally forgetting earlier details.

The model's performance on mathematical problems was impressive, demonstrating the ability to accurately solve a variety of calculations, from simple arithmetic to more complex expressions. This suggests strong numerical reasoning capabilities.

Additionally, the model was able to effectively retrieve and summarize information from a provided context, showcasing its potential for use in research and question-answering tasks. It acknowledged the context, demonstrated understanding, and provided accurate responses to follow-up questions.

Overall, the YE model family has exhibited a solid foundation in logical reasoning and problem-solving, with the potential for further improvements and expansion of its capabilities.

The model demonstrates impressive mathematical capabilities, accurately solving a variety of problems. When asked to calculate the probability of drawing a blue marble from a bag containing 5 red, 3 blue, and 2 green marbles, the model correctly determined the probability by adding the total number of marbles (10) and dividing the number of blue marbles (3) by the total. It also easily handled simple arithmetic operations like 3 + 100 and more complex expressions like 3x100x3 + 50x2.

The model's ability to retrieve information from provided context is also noteworthy. When given a hypothetical scientific paper on synthetic polys, the model was able to accurately summarize the context and answer follow-up questions based on the information given. This suggests the model could be useful for tasks like question-answering and retrieval-augmented generation.

Additionally, the model demonstrated competence in identifying and correcting errors in a simple Python program, showcasing its coding abilities. It was able to identify and fix multiple issues in the provided code, indicating potential usefulness for code review and debugging tasks.

Overall, the model's strong performance in mathematics, information retrieval, and coding tasks highlights its versatility and the breadth of its capabilities.

The model demonstrated strong coding capabilities by successfully identifying and correcting errors in a provided Python program. When presented with a simple Python script containing a few bugs, the model was able to pinpoint the specific issues and suggest the appropriate fixes.

The model's ability to understand basic programming constructs and syntax allowed it to accurately diagnose the problems in the code. It highlighted the incorrect variable names, missing function definitions, and other logical errors, providing clear explanations for each issue.

Furthermore, the model was able to generate the corrected code, ensuring that the program would function as intended. This showcases the model's proficiency in translating its understanding of programming concepts into practical solutions.

While the model's performance on a more complex coding task, such as writing a Python function to download files from an S3 bucket, was also satisfactory, it did exhibit some limitations in generating a fully functional solution. This suggests that the model's coding abilities, while impressive, may still have room for improvement, particularly when dealing with more intricate programming challenges.

Overall, the model's strong coding competence, as demonstrated by its ability to identify and fix code errors, highlights its potential usefulness in software development and programming-related tasks.

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The model was able to generate a simple HTML web page with a button that changes the background color and displays a random joke. The code is as follows:

<!DOCTYPE html>
<html>
<head>
  <title>Random Joke Generator</title>
  <style>
    body {
      font-family: Arial, sans-serif;
      text-align: center;
      padding: 20px;
    }
    button {
      padding: 10px 20px;
      font-size: 16px;
      background-color: #4CAF50;
      color: white;
      border: none;
      cursor: pointer;
    }
  </style>
</head>
<body>
  <h1>Random Joke Generator</h1>
  <button onclick="changeBackgroundColor(); getRandomJoke();">Click me for a joke!</button>
  <p id="joke">Joke goes here</p>

  <script>
    function changeBackgroundColor() {
      var randomColor = '#' + Math.floor(Math.random() * 16777215).toString(16);
      document.body.style.backgroundColor = randomColor;
    }

    function getRandomJoke() {
      // Code to fetch a random joke from an API and display it
      var jokes = [
        "Why don't scientists trust atoms? Because they make up everything.",
        "What do you call a fake noodle? An Impasta.",
        "Why can't a bicycle stand up by itself? It's two-tired."
      ];
      var randomIndex = Math.floor(Math.random() * jokes.length);
      document.getElementById("joke").textContent = jokes[randomIndex];
    }
  </script>
</body>
</html>

The key features of this web page are:

1. A button that, when clicked, changes the background color of the page to a random color and displays a random joke.
2. The changeBackgroundColor() function generates a random hexadecimal color code and applies it to the body's background.
3. The getRandomJoke() function selects a random joke from a predefined array and displays it on the page.
4. The HTML structure includes a button and a paragraph element to display the joke.
5. The CSS styles the button and the page layout.

While the random number generator for the jokes does not seem to be working correctly, the overall functionality of the web page is implemented as expected.

The new YE model family from 01 AI represents a significant upgrade, outperforming many existing large language models on various benchmarks. The key highlights of this release include:

• Three model sizes available: 6 billion, 9 billion, and 34 billion parameters, all under the Apache 2.0 license for commercial use.
• Impressive performance, with the 34 billion version rivaling the capabilities of the larger GPT-4 model.
• Strong performance in areas like coding, math reasoning, and instruction following.
• Limitations in the current 4,000 token context window, but the potential to expand this in future versions.
• Availability of the 34 billion model on Hugging Face for testing and evaluation.

Overall, the YE models demonstrate the continued progress in large language model development, providing a compelling alternative to other prominent models like GPT-3 and LLaMA. While further testing and comparison are needed, this release from 01 AI is an exciting development in the field of open-source, high-performance language models.