Meet the Unstoppable 13B LLM King - StableVicuna!

Meet the Unstoppable 13B LLM King - StableVicuna!

Table of Contents:

  1. Introduction
  2. What is Stable Vacuna?
  3. Training and Fine-tuning Process
  4. Comparison with Vacuna
  5. Testing Stable Vacuna
    1. Simple General Knowledge Question
    2. Creative Writing Task
    3. Summarizing an Article
    4. Translation Task
    5. Logic Puzzle
    6. Coding Example
    7. Warnings and Limitations

Introduction

Stable Vacuna, the latest innovation from Stability AI, is making waves in the world of local MMs (Machine Learning Models). Following the success of Wizard LM, which recently took the crown as the best 7 billion LM model, Stable Vacuna now enters the stage as a potential successor to the throne. In this article, we will explore what makes Stable Vacuna unique, Delve into its training and fine-tuning process, compare it with its predecessor Vacuna, and put it to the test in various scenarios. By the end, we hope to determine if Stable Vacuna truly deserves the title of the new local MM king.

What is Stable Vacuna?

Stable Vacuna is an enhanced version of the popular Vacuna 13 billion parameter model. As the reigning king of the local MM gang, Vacuna proved to be an excellent choice for fine-tuning. Stability AI has employed reinforcement learning from Human feedback (RLHF) to improve the model's output. This unique training approach involved manual evaluation of the model's performance by humans, leading to improved conversation capabilities. Stable Vacuna was fine-tuned on three diverse datasets: the Open Assistant dataset, GPT-4 data set, and the Alpaca dataset generated by OpenAI's DaVinci 003. This combination of high-quality data sets provides a solid foundation for the enhancement of the Vacuna model.

Training and Fine-tuning Process

Stable Vacuna's training and fine-tuning process were designed to optimize its performance. Stability AI used the RLHF technique, which involved humans evaluating the model's output. This process aims to improve the quality of the generated text and enhance its ability to engage in conversations with users. The three datasets used for fine-tuning consisted of the Open Assistant data set, GPT-4 data set, and Alpaca data set. The Open Assistant data set provided valuable Prompts and responses, while the GPT-4 data set generated a plethora of diverse conversations. The Alpaca data set, Based on instructions generated by Open AIS's DaVinci 003, added another layer of complexity to the fine-tuning process. By fine-tuning Stable Vacuna on these datasets, Stability AI aimed to provide users with a local MM model that exhibits significant improvements in generating high-quality content and maintaining Meaningful interactions.

Comparison with Vacuna

In order to evaluate the value of Stable Vacuna as an enhanced version of Vacuna, a detailed comparison was conducted between the two models. The 1.1 version of Vacuna was chosen for the comparison, as it represents an updated iteration of the 1.0 version, which Stable Vacuna was fine-tuned on. It is important to note that while Stable Vacuna was trained on the 1.0 version, users utilizing the model through the generation API might encounter an issue where the model Talks to itself, a bug resolved in the 1.1 version. Despite this bug, it should not affect the comparison results.

Several tests were performed to assess the performance of Stable Vacuna compared to Vacuna. These tests covered various aspects, including general knowledge questions, creative writing tasks, article summarization, translation tasks, logic puzzles, coding examples, and warnings and limitations. The results of these tests will help us determine the extent to which Stable Vacuna excels in generating high-quality, contextually appropriate, and accurate content.

Testing Stable Vacuna

Simple General Knowledge Question

The first test involved asking Stable Vacuna and Vacuna the same basic general knowledge question: "Which country has the largest population?" Stable Vacuna provided an accurate and comprehensive response, mentioning China as the country with the largest population as of 2021, followed by India, the United States, Indonesia, and Brazil. On the other HAND, Vacuna's response was also correct, but it lacked some additional details. In this case, Stable Vacuna proved to be the superior model, providing a more in-depth and informative answer.

Creative Writing Task

To assess the creative writing capabilities of Stable Vacuna and Vacuna, a poem about an AI Overlord called "K" taking over the world was requested. Stable Vacuna generated a poem with Vivid imagery and a Cohesive flow, garnering positive praise. Meanwhile, Vacuna produced a poem that lacked the same depth and impact. An evaluation by GPT-4 confirmed that Stable Vacuna's poem exhibited stronger flow and structure, making it more cohesive and engaging. Stable Vacuna emerged as the winner in this creative writing test.

Summarizing an Article

The ability to summarize an article is an essential skill for an AI model. Stable Vacuna and Vacuna were challenged to summarize a short article on the European Union proposing new copyright rules for Generative AI. Stable Vacuna's summary was rated higher than Vacuna's, demonstrating better organization and comprehensiveness. It provided more information, including risk classification levels and balancing benefits with potential harm. Such improvements in summary quality make Stable Vacuna the preferred choice for capturing the essence of an article.

Translation Task

Translating sentences accurately from one language to another is a complex task. Stable Vacuna and Vacuna were tasked with translating the sentence, "Are You crazy? It's too cold outside for ice cream. I would rather drink something hot, like cocoa" from English to French. Stable Vacuna performed significantly better, providing a translation with some minor errors. However, Vacuna's translation contained more mistakes, particularly in the initial part of the sentence. Stable Vacuna's translation was lauded as the superior option, offering a more reliable and accurate result.

Logic Puzzle

A logical puzzle was presented to Stable Vacuna and Vacuna: "You see a boat filled with people, yet there isn't a single person on board. How is that possible?" Stable Vacuna offered multiple speculative explanations, but none of them correctly answered the question. On the other hand, Vacuna failed to provide any response at all. GPT-4 identified the correct answer - that all the people on the boat were married - showcasing its understanding of the logic puzzle. Unfortunately, neither Stable Vacuna nor Vacuna were successful in this test.

Coding Example

A coding example was given to Stable Vacuna and Vacuna. The task was to write code for an HTML page with a button that, when pressed, changes the background color to a random one. Stable Vacuna successfully provided the code, incorporating HTML and JavaScript elements. However, Vacuna's initial code did not include the button, requiring a revised version. After the revisions, neither Stable Vacuna nor Vacuna produced the desired outcome. Their coding solutions were flawed, resulting in errors and unintended consequences. This test ultimately yielded no clear winner.

Warnings and Limitations

It is important to note that both Stable Vacuna and Vacuna have limitations. Due to the inherent complexity of training local MM models, there may be occasional inaccuracies or unexpected behaviors. The RLHF fine-tuning process carried out by Stability AI helps mitigate these issues but does not eliminate them entirely. Users should be cautious and monitor the model's outputs for sensitive or inappropriate content. Additionally, performance may vary depending on the specific tasks and the size of the generated content. Regular updates and improvements are expected, and it is advisable to stay informed about the latest advancements to fully leverage the potential of these models.

Highlights:

  • Stable Vacuna is an enhanced version of the Vacuna model, aiming to improve conversation capabilities and generate high-quality content.
  • Reinforcement Learning from Human Feedback (RLHF) was used to fine-tune Stable Vacuna using diverse datasets.
  • Stable Vacuna outperformed Vacuna in general knowledge questions, creative writing tasks, and article summarization.
  • Vacuna showed better results in accurately translating sentences from English to French.
  • Neither Stable Vacuna nor Vacuna was successful in solving a logic puzzle or providing a Flawless coding example.
  • It is important to consider the inherent limitations and occasional inaccuracies of local MM models and monitor their outputs for sensitive content.

FAQs:

Q: Can Stable Vacuna accurately translate sentences from one language to another?

A: Stable Vacuna performs reasonably well in translation tasks, producing translations with minor errors. However, it is worth reviewing the output for inaccuracies or to ensure contextually appropriate translations.

Q: Is Stable Vacuna better at writing creative content compared to its predecessor, Vacuna?

A: Yes, Stable Vacuna exhibits stronger creative writing capabilities, providing poems with vivid imagery and cohesive flow. Vacuna's creative writing falls short in comparison.

Q: Does Stable Vacuna have any limitations or drawbacks?

A: Like all local MM models, Stable Vacuna has limitations and occasional inaccuracies. Users should monitor its outputs and exercise caution, especially regarding sensitive or inappropriate content. Additionally, performance may vary depending on the specific task and content size.

Q: Can Stable Vacuna accurately summarize articles?

A: Stable Vacuna demonstrates improved summarization skills compared to Vacuna. Its summaries are more comprehensive and organized, capturing the essence of articles more effectively.

Q: Can Stable Vacuna provide accurate translations for HTML coding examples?

A: Results vary for coding examples. Stable Vacuna and Vacuna both struggled to provide flawless translations, and their coding solutions may yield errors or unintended consequences. Double-checking their outputs and making revisions may be necessary.

Q: How reliable is Stable Vacuna for solving logic puzzles?

A: Stable Vacuna, as well as Vacuna, had difficulty providing correct answers to logic puzzles. If precise solutions are required, alternative approaches or dedicated logic-solving models may be more suitable.

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