Discover What's New In Gemma 1.1 Update: New 2B & 7B Instruction Tuned models

Sam Witteveen
7 Apr 202418:06

TLDRGoogle has released an update for the Gemma models, named Gemma 1.1, focusing on the 7 billion and 2 billion instruction tuned models. The update, although not officially detailed, has shown improvements in quality, coding capabilities, factuality, and multi-turn conversation quality. The model remains censored but has altered its response patterns, hinting at instruction fine-tuning. Users are encouraged to test the model for their specific applications. The video also discusses the potential of Gemma for structured prompting and its understanding of various languages, as well as its capability for React prompting and tool usage, with the 7 billion model showing more promise than the 2 billion model.

Takeaways

  • 🚀 Google has released an update for the Gemma models, named Gemma 1.1, specifically for the 7 billion and 2 billion instruction tuned models.
  • 🔍 The update details are not officially documented extensively, but the changes in model responses and improvements are noticeable.
  • 📈 Gemma 1.1 was trained using a novel RLHF method, showing substantial gains in quality, coding capabilities, factuality, instruction following, and multi-turn conversation quality.
  • 🔓 Despite improvements, the model remains heavily censored, addressing ongoing concerns about content restrictions.
  • 📝 The release notes suggest that while the new version is an improvement for most use cases, users should test it with their specific applications.
  • 🌐 The old version of the model is still available for download, alongside the new 1.1 version.
  • 📚 Google recently held a Gemma Developer Day in London, with videos now available for those interested in learning more about the platform.
  • 🛠️ The script discusses the technical aspects of using the Gemma model, including system roles and chat templates.
  • 📊 The updated Gemma model demonstrates a step-by-step reasoning approach in its responses, possibly due to fine tuning with structured prompts and responses.
  • 🔑 The script highlights the impact of system prompts on the model's output, showing that altering these prompts can yield significantly different results.
  • 🌍 The model exhibits an understanding of multiple languages, indicating potential for fine-tuning in specific languages.

Q & A

  • What is the main topic of the video transcript?

    -The main topic of the video transcript is the release and analysis of Google's new update for the Gemma models, specifically Gemma 1.1 for the 7 billion and 2 billion instruction tuned models.

  • How does the video creator describe the changes in Gemma 1.1 compared to the previous versions?

    -The video creator describes the changes in Gemma 1.1 as having substantial gains on quality, coding capabilities, factuality, instruction following, and multi-turn conversation quality, achieved through a novel RLHF method.

  • What are some of the key improvements the Gemma 1.1 model has over its predecessors?

    -Key improvements in the Gemma 1.1 model include better quality of responses, enhanced coding capabilities, increased factuality, improved instruction following, and superior multi-turn conversation quality.

  • What does the video creator suggest about the censorship in the Gemma models?

    -The video creator suggests that while the Gemma 1.1 model has made changes in how it responds, it is still heavily censored, addressing the complaints about the models being censored.

  • How does the video creator demonstrate the effectiveness of the new Gemma model?

    -The video creator demonstrates the effectiveness of the new Gemma model by comparing the responses of the 1.1 version with the previous 1.0 version on various prompts, highlighting the differences and improvements in the model's outputs.

  • What is the significance of the system role in the Gemma model as discussed in the video?

    -In the Gemma model, the system role is significant because it provides guidance or context for the model's responses. However, the Gemma model does not have a designated system role, so the video creator treats it similarly to other models by inserting the system prompt into the user content.

  • What is the 'meditation mantra' technique mentioned in the video?

    -The 'meditation mantra' technique is a prompt strategy used with the Gemma model where the model is addressed as a 'word math genius' and is instructed to slow down and think through the math in the question, then write out the reasoning step by step.

  • How does the video creator evaluate the performance of the Gemma model in understanding and responding to different languages?

    -The video creator evaluates the Gemma model's performance in understanding and responding to different languages by asking it to provide directions to a place in Thai, and noting that while the model treats it as a translation task, it demonstrates a good understanding of many languages other than English.

  • What is React prompting as discussed in the video?

    -React prompting, as discussed in the video, is a technique where the model is given a set of tools such as Wikipedia, web search, calculator, and weather API, and is prompted to decide which tool to use for a given task, and then provide the appropriate input for that tool.

  • What are the video creator's recommendations for users interested in the Gemma models?

    -The video creator recommends that users interested in the Gemma models should test out the new version for their particular applications, explore the notebooks provided in the video description, and share their findings, improvements, and issues in the comments section.

  • What is the main takeaway from the video regarding the Gemma 2 billion model?

    -The main takeaway from the video regarding the Gemma 2 billion model is that while it has shown improvement compared to its previous version, it is still significantly behind the 7 billion model in terms of performance, especially in React prompting and reasoning tasks.

Outlines

00:00

🚀 Google's Gemma 1.1 Update Overview

The video begins with the announcement of an unexpected update to Google's Gemma models, specifically the 7 billion and 2 billion instruction-tuned models, now referred to as Gemma 1.1. Despite the lack of an official announcement or detailed blog post, the update appears to introduce significant changes in how the models respond to prompts and produce better results. The speaker notes that while censorship remains an issue, the update has altered the models' responses in intriguing ways, hinting at the methods used for instruction fine-tuning. The release notes mention improvements in quality, coding capabilities, factuality, and conversation quality achieved through a new RLHF method. The video will explore these updates in depth, comparing the old and new versions of the model and discussing the potential for further customization and learning from the changes.

05:03

📝 Analyzing Gemma 1.1's Response Patterns

This paragraph delves into the specifics of how Gemma 1.1 has altered its response patterns. The speaker observes that the model now provides more structured and step-by-step reasoning in its answers, which is evident when writing analogies or explaining concepts. The use of system prompts seems to influence the model's output significantly, as seen when writing an email or responding to questions with a modified system prompt. The speaker also notes that the model's performance in creative writing and code generation remains largely unchanged. However, the step-by-step reasoning approach is highlighted as a key area of improvement, suggesting that the model has been fine-tuned to respond in this manner. The speaker encourages viewers to experiment with different prompts to optimize the model's performance.

10:05

🧠 Enhancing Gemma's Math and Language Capabilities

The speaker discusses strategies for improving Gemma 1.1's performance in word math problems and its understanding of different languages. By using a 'meditation mantra' to prompt the model to think through math problems step-by-step, the speaker achieves better results. Similarly, the model shows a good understanding of non-English languages, treating translation tasks effectively. The speaker also explores the potential of React prompting, where the model suggests the use of specific tools (like Wikipedia or web search) to answer questions, although it's noted that the model may hallucinate observations since it's not connected to the internet. This section highlights the model's adaptability and the possibility of further fine-tuning for specific functions.

15:06

🤖 Testing Gemma 1.1's React Prompting and Function Calling

The speaker continues to experiment with Gemma 1.1's capabilities, focusing on React prompting and function calling. By providing the model with a set of tools (Wikipedia, web search, calculator, weather API), the model is prompted to identify which tool is needed to answer a question and what input that tool would require. The speaker notes that while the 7 billion model performs well with this type of prompting, the 2 billion model is less responsive and does not exhibit the same level of reasoning or step-by-step approach. The video emphasizes the potential of Gemma 1.1 for future applications involving React prompting and function calling, especially if further fine-tuned for these purposes.

📈 Comparing Gemma 1.1 and 2 Billion Models, and Conclusion

The speaker concludes the video by comparing the performance of Gemma 1.1 and the 2 billion model. While the 2 billion model has improved from its previous version, it still lags behind the 7 billion model in terms of React prompting and reasoning capabilities. The speaker encourages viewers to interact with the models themselves, experiment with different prompts, and share their findings. The video ends with a call to action for viewers to leave comments, likes, and subscriptions, and to look forward to future videos exploring these topics further.

Mindmap

Keywords

💡Gemma models

The Gemma models refer to a series of AI language models developed by Google. In the context of the video, the focus is on the Gemma 7 billion and 2 billion instruction-tuned models, which have been updated to version 1.1. These models are designed to improve upon the original releases, with enhancements in quality, coding capabilities, factuality, and conversation quality.

💡Instruction tuning

Instruction tuning is a process in machine learning where the AI model is trained to follow specific instructions or prompts more effectively. In the video, it is mentioned that the Gemma 1.1 models were trained using a novel RLHF (Reinforcement Learning from Human Feedback) method, leading to significant gains in instruction following and multi-turn conversation quality.

💡Censorship

Censorship in the context of AI models refers to the restriction or filtering of certain outputs to adhere to guidelines or avoid generating inappropriate content. The video mentions that despite updates, the Gemma models still maintain a level of censorship to prevent the generation of undesirable responses.

💡Quality

Quality in AI models pertains to the accuracy, coherence, and overall effectiveness of the model's responses. The video highlights that the Gemma 1.1 update has led to substantial improvements in quality, which includes better factuality and more relevant outputs.

💡Multi-turn conversation

Multi-turn conversation refers to the ability of an AI model to engage in a dialogue that involves multiple exchanges or turns, understanding and building upon the context of previous interactions. The Gemma 1.1 models have been specifically trained to improve in this area, enhancing the natural flow and coherence of conversations.

💡Hugging Face

Hugging Face is a platform that provides a wide range of AI models, including the Gemma models discussed in the video. It is a community and hub for AI developers and researchers to share, discover, and utilize various pre-trained models for different applications.

💡System role

In the context of AI models, the system role typically refers to the role that the model assumes when generating responses. The Gemma models, as discussed in the video, do not have a system role, which means they do not differentiate between system-generated content and user input in the same way other models might.

💡Markdown

Markdown is a lightweight markup language that allows for formatting of text, such as making it bold or italic, creating lists, and inserting links. In the context of the video, the Gemma models use Markdown to format their outputs, making the information more readable and organized.

💡ReAct prompting

ReAct prompting is a method of interacting with AI models where the model is given a set of tools or actions it can 'take' in response to a prompt, and it decides which tool to use based on the context of the question. This approach simulates a more dynamic and interactive conversation where the model can suggest using external resources or actions to answer a query.

💡Fine-tuning

Fine-tuning in machine learning involves training a model on a specific task or dataset to improve its performance for that particular task. In the context of the video, fine-tuning is discussed as a potential method to enhance the Gemma models for specific applications or languages.

Highlights

Google released a new update for the Gemma models, named Gemma 1.1.

The update is specifically for the 7 billion and 2 billion instruction-tuned models.

No official blog post with detailed information about the update was found initially.

The new Gemma 1.1 model shows interesting updates in how it reacts to certain prompts and delivers better results.

The model remains heavily censored, addressing ongoing complaints about censorship.

The release notes suggest that Gemma 1.1 was trained using a novel RLHF method, improving quality, coding capabilities, factuality, and conversation quality.

Users are encouraged to test the new model with their specific applications as it may represent an improvement for most use cases.

The old version of the model is still available for download.

A new notebook has been created to compare the new versus old models.

Google hosted a Gemma Developer Day in London, with videos now available for public viewing.

The updated Gemma model uses a Hugging Face chat template without a system role.

The model now provides structured responses with step-by-step reasoning when prompted accordingly.

The model's responses can be influenced by changing the system prompt or preamble before the main prompt.

Gemma 1.1 shows an improved ability to understand and respond to prompts in different languages.

The model exhibits potential for fine-tuning to improve performance in specific areas, such as code generation.

Gemma 1.1 demonstrates the ability to engage in React prompting, suggesting future potential for function calling and customized tool usage.

The 2 billion model has improved but still lags behind the 7 billion model in terms of React prompting and reasoning capabilities.

The video encourages viewers to experiment with their own prompts and share their findings.

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