How to tune LLMs in Generative AI Studio

Google Cloud Tech
3 May 202304:34

TLDRIn this informative video, Nikita Namjoshi discusses the process of tuning large language models (LLMs) to enhance response quality using Vertex Generative AI Studio. She explains the importance of prompt design for customization and the challenges faced when fine-tuning such large models due to their size and computational demands. To overcome these, she introduces parameter-efficient tuning, a method that trains only a small subset of parameters, either existing or new, to refine the model's performance. This approach not only reduces the computational load but also simplifies model serving. The video provides a step-by-step guide on how to initiate a tuning job in Generative AI Studio, emphasizing the need for structured, supervised training data in a text-to-text format. The process concludes with monitoring the tuning job in the Cloud Console and deploying the tuned model for use. The video concludes with an invitation to explore more about generative AI and large language models, and to share projects built with generative AI in the comments.

Takeaways

  • 📝 **Prompt Design**: Crafting prompts is a way to guide the language model without needing to write complex code or be an ML expert.
  • 🔍 **Impact of Prompts**: Minor changes in wording or order can significantly affect the model's output, which can be unpredictable.
  • 🔄 **Fine Tuning**: Traditional fine-tuning involves retraining a pre-trained model on a new, domain-specific dataset, which is effective but challenging with large models.
  • 🚧 **Challenges with LLMs**: Large Language Models (LLMs) present difficulties in fine-tuning due to the extensive training time and computational resources required.
  • 🛠️ **Parameter-Efficient Tuning**: An innovative approach that involves training only a small subset of parameters to reduce the challenges associated with fine-tuning LLMs.
  • 📚 **Research Area**: Parameter-efficient tuning is an active research area aiming to optimize how to adjust LLMs without retraining the entire model.
  • 📈 **Serving Models**: This tuning method simplifies the serving of models by adding tune parameters to the existing base model instead of creating a new one.
  • 📁 **Training Data**: For parameter-efficient tuning, training data should be modest in size and structured in a text-to-text format with input and expected output.
  • 🔧 **Tuning Process**: In Vertex Generative AI Studio, you can initiate a tuning job by providing a name and the location of your training data.
  • 📊 **Monitoring**: The status of the tuning job can be monitored in the Cloud Console, and once completed, the tuned model is available in the Vertex AI model registry.
  • 🚀 **Deployment**: After tuning, the model can be deployed to an endpoint for serving or tested within Generative AI Studio.
  • 📘 **Further Learning**: For those interested, a summary paper on parameter-efficient tuning and different methods is provided for deeper understanding.

Q & A

  • What is the purpose of tuning a large language model?

    -Tuning a large language model aims to improve the quality of responses by adjusting the model's parameters to better suit specific use cases or tasks.

  • What is the difference between fine-tuning and parameter-efficient tuning?

    -Fine-tuning involves retraining a pre-trained model on a new, domain-specific dataset, updating every weight. Parameter-efficient tuning, on the other hand, trains only a small subset of parameters, either existing or new, to reduce the challenges and computational costs associated with fine-tuning large language models.

  • Why is fine-tuning a large language model not always the best option?

    -Fine-tuning a large language model is not always the best option due to the extensive training time required to update every weight in the model, as well as the increased computational resources and costs needed to serve the tuned model.

  • What is the role of the prompt in interacting with a language model?

    -The prompt is the text input provided to the model. It often includes instructions and examples that guide the model to exhibit the desired behavior for a specific task.

  • How can one start a tuning job in Vertex Generative AI Studio?

    -To start a tuning job in Vertex Generative AI Studio, navigate to the language section, select 'Tuning', provide a name for the tuned model, and specify the location of the training data in local or Cloud Storage.

  • What type of data is required for parameter-efficient tuning?

    -Parameter-efficient tuning requires structured, supervised training data in a text-to-text format, with each record containing the input text (prompt) and the expected output of the model.

  • What are the benefits of parameter-efficient tuning over fine-tuning?

    -Parameter-efficient tuning offers benefits such as reduced computational costs, simpler model serving since it uses the existing base model with additional tune parameters, and it does not require retraining the entire model.

  • How does the structure of training data affect the tuning process?

    -The structure of training data should be in a text-to-text format, with clear input prompts and corresponding expected outputs. This structure allows the model to be tuned effectively for text-to-text problems.

  • What happens after a tuning job is completed in Vertex Generative AI Studio?

    -After a tuning job is completed, the tuned model appears in the Vertex AI model registry. From there, it can be deployed to an endpoint for serving or tested in Generative AI Studio.

  • What are some challenges faced when trying to fine-tune large language models?

    -Challenges faced when fine-tuning large language models include the large computational resources required to update every weight, the long training times, and the increased costs and complexity of serving the fine-tuned model.

  • Why is prompt design important when working with large language models?

    -Prompt design is important because it allows for fast experimentation and customization without the need for complex coding. However, small changes in wording or word order can significantly impact the model's responses, making it a critical aspect of working with large language models.

  • What is the significance of the size of the training data for parameter-efficient tuning?

    -Parameter-efficient tuning is well-suited for scenarios with modest amounts of training data, such as hundreds or thousands of examples. This approach is more manageable and efficient than fine-tuning, which can be computationally intensive with large datasets.

Outlines

00:00

🚀 Introduction to Tuning Large Language Models

Nikita Namjoshi introduces the concept of improving the quality of responses from large language models (LLMs) beyond crafting prompts. The video discusses the process of tuning a model to achieve desired behavior, which involves retraining a pretrained model on a domain-specific dataset. The challenges of fine-tuning LLMs due to their size are highlighted, and an alternative approach called parameter-efficient tuning is presented. This method focuses on training a small subset of parameters, either existing or new, to reduce computational challenges and simplify model serving. The video also provides a link to a summary paper for those interested in learning more about parameter-efficient tuning.

Mindmap

Keywords

💡Large Language Model (LLM)

A Large Language Model (LLM) refers to an artificial intelligence model that is designed to process and understand large volumes of human language data. These models are typically pre-trained on vast corpora of text and are capable of generating human-like text when given a prompt. In the context of the video, LLMs are the focus of tuning to improve their performance for specific tasks or domains.

💡Generative AI Studio

Generative AI Studio is a platform mentioned in the video that allows users to work with and tune large language models. It provides an interface for launching tuning jobs and managing the process of improving the model's performance on specific tasks. The studio is part of the broader Vertex AI suite of tools.

💡Prompt

In the context of language models, a prompt is a text input that guides the model to generate a specific output. It can be an instruction or a question, and may include examples to help the model understand the desired behavior. The design of prompts is crucial for directing the model's responses and is a key aspect of working with LLMs.

💡Tuning

Tuning in the context of LLMs refers to the process of adjusting or optimizing the model's parameters to improve its performance on a specific task or dataset. This can involve techniques such as fine-tuning or parameter-efficient tuning, which aim to enhance the model's ability to generate responses that are more aligned with the user's needs.

💡Fine-Tuning

Fine-tuning is a technique where a pre-trained model is further trained on a smaller, more specific dataset to adapt it to a particular task. While effective, fine-tuning LLMs can be computationally expensive and challenging due to the size of the models. The video discusses this as a traditional approach and contrasts it with parameter-efficient tuning.

💡Parameter-Efficient Tuning

Parameter-efficient tuning is an innovative approach to adjusting LLMs that involves training only a small subset of the model's parameters or adding new parameters, rather than retraining the entire model. This method is highlighted in the video as a more efficient and cost-effective way to tune LLMs, especially when dealing with modest amounts of training data.

💡Training Data

Training data refers to the dataset used to train or tune a machine learning model. In the context of tuning LLMs, the training data should be structured in a text-to-text format with input prompts and their corresponding expected outputs. This data is crucial for teaching the model how to generate the desired responses for specific tasks.

💡Model Registry

The Vertex AI model registry is a component within the Vertex AI platform where trained and tuned models are stored, managed, and versioned. Once a model is tuned and registered, it can be easily deployed to endpoints for serving predictions or further testing.

💡Endpoints

Endpoints in the context of Vertex AI are points of access where a deployed model can be used to make predictions or generate outputs. After a model is tuned and registered, it can be associated with an endpoint, allowing users to interact with the model and leverage its capabilities.

💡Supervised Training

Supervised training is a type of machine learning where the model is provided with input data and the corresponding correct outputs. The model learns to map inputs to outputs by being trained on this labeled data. In the video, tuning LLMs involves supervised training where the model is given prompts and the desired responses.

💡Text-to-Text Problem

A text-to-text problem is a type of machine learning task where the model is trained to convert input text into output text. This is relevant to the tuning process described in the video, as the training data for tuning LLMs should present the model with input prompts and their expected text outputs.

Highlights

Prototyping with large language models can be improved beyond handcrafted prompts through tuning.

Tuning a large language model involves adjusting the model's parameters to enhance response quality.

The prompt is the text input given to the model, which can be an instruction or include examples.

Prompt design allows for fast experimentation and customization without ML expertise.

Small changes in wording or word order can significantly impact model results.

Fine-tuning LLMs presents challenges due to their large size and the extensive training required.

Parameter-efficient tuning is an innovative approach that trains only a small subset of parameters.

This technique can add new parameters or modify existing ones to improve model performance.

Parameter-efficient tuning reduces the challenges associated with fine-tuning large models.

It simplifies serving models by using the base model with additional tune parameters.

Tuning is suitable for scenarios with modest amounts of training data, structured in a text-to-text format.

Each record in the training data contains the input text and the expected model output.

The tuning job can be monitored in the Cloud Console and once completed, the model is available in the Vertex AI model registry.

Tuned models can be deployed to an endpoint for serving or tested in Generative AI Studio.

There is ongoing research to determine the optimal methodology for parameter-efficient tuning.

A summary paper on parameter-efficient tuning methods is available for further reading.

Generative AI Studio provides a platform to initiate and manage tuning jobs for large language models.

Tuning can enhance the consistency and quality of model responses for specific use cases.

Viewers are encouraged to explore generative AI on Vertex and share their projects in the comments.

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