How to tune LLMs in Generative AI Studio
TLDRThe video discusses how to enhance the quality of responses from large language models (LLMs) using tuning techniques. It explains the process of handcrafting prompts and the limitations of this approach. The video then introduces fine-tuning as a method to improve model performance but highlights the challenges associated with tuning large models due to their size and computational demands. As an alternative, the concept of parameter-efficient tuning is presented, which involves training only a small subset of the model's parameters. This approach is more manageable and cost-effective, making it suitable for scenarios with limited training data. The video provides a step-by-step guide on how to initiate a tuning job using Vertex Generative AI Studio, emphasizing the need for structured, supervised training data in a text-to-text format. It concludes by encouraging viewers to explore more about generative AI and large language models, and to share their projects in the comments.
Takeaways
- 🔍 **Prototype Quality**: Improving the quality of responses from large language models (LLMs) can go beyond just crafting prompts.
- 📝 **Prompt Design**: The text input (prompt) to the model can be an instruction or include examples, allowing for fast experimentation without ML expertise.
- ⚙️ **Tuning Models**: Tuning a model involves retraining a pre-trained model on a new, domain-specific dataset, starting from the learned weights.
- 🚧 **Challenges of Fine-Tuning**: Fine-tuning LLMs can be challenging due to their size, leading to long training times and increased computational costs.
- 🌟 **Parameter-Efficient Tuning**: An innovative approach that trains only a small subset of parameters to reduce the challenges associated with fine-tuning.
- 📚 **Research Area**: Determining the optimal methodology for parameter-efficient tuning is an active area of research.
- 🧩 **Adding Parameters**: This tuning approach may involve adding new layers or embeddings to the model, rather than retraining the entire model.
- 📈 **Serving Models**: Parameter-efficient tuning simplifies model serving by using the existing base model with additional tune parameters.
- 🔗 **Further Reading**: A summary paper on parameter-efficient tuning is available for those interested in more details.
- 🚀 **Generative AI Studio**: A platform where users can initiate a tuning job by providing a name and training data location.
- 📊 **Training Data**: Training data for tuning should be in a text-to-text format with input text and expected output for supervised learning.
- 🔧 **Tuning Process**: After specifying the data set, users can start the tuning job, monitor its status, and deploy the tuned model for serving.
Q & A
What is the main topic discussed in the video?
-The video discusses how to improve the quality of responses from large language models (LLMs) using tuning techniques, specifically focusing on parameter-efficient tuning and how to launch a tuning job from Vertex Generative AI Studio.
What is the purpose of a prompt when interacting with a large language model?
-A prompt is the text input that you pass to the model. It might look like an instruction and may include examples. The prompt is used to guide the model to take on the behavior that the user wants.
Why is prompt design important for working with LLMs?
-Prompt design is important because it allows for fast experimentation and customization without the need for writing complicated code. However, it can be tricky as small changes in wording or word order can impact the model's results in unpredictable ways.
What are the challenges associated with fine-tuning large language models?
-Fine-tuning LLMs can be challenging due to their large size, which means updating every weight would require a very long training job. Additionally, there are the hassle and cost associated with serving such a large model after fine-tuning.
What is parameter-efficient tuning and how does it differ from fine-tuning?
-Parameter-efficient tuning is an innovative approach that aims to reduce the challenges of fine-tuning LLMs by only training a small subset of parameters. This could be a subset of the existing model parameters or an entirely new set of parameters. Unlike fine-tuning, it does not require retraining the entire model and all of its weights.
How does parameter-efficient tuning simplify the process of serving models?
-Parameter-efficient tuning simplifies serving models by allowing the use of the existing base model with additional tune parameters, instead of having to serve an entirely new model that has been fully fine-tuned.
What kind of data is required for parameter-efficient tuning?
-Parameter-efficient tuning requires training data that is structured as a supervised training dataset in a text-to-text format. Each record or row in the data should contain the input text (prompt) followed by the expected output of the model.
What are the steps to start a tuning job in Vertex Generative AI Studio?
-To start a tuning job, you go to the language section of Vertex Generative AI Studio, select Tuning, provide a name for the tuned model, and point to the local or Cloud Storage location of your training data. Once the path to the dataset is specified, you can start the tuning job and monitor its status in the Cloud Console.
Where can one find more information about parameter-efficient tuning and different methods?
-For more information about parameter-efficient tuning and the different methods, there is a summary paper linked below the video for those who are extra curious.
What is the ideal amount of training data for parameter-efficient tuning?
-Parameter-efficient tuning is ideally suited for scenarios where you have modest amounts of training data, such as hundreds or maybe thousands of training examples.
What can you do with the tuned model once the tuning job is completed in Vertex Generative AI Studio?
-After the tuning job completes, the tuned model will be visible in the Vertex AI model registry. You can then deploy it to an endpoint for serving or test it out in Generative AI Studio.
How can one learn more about generative AI and large language models?
-To learn more about generative AI and large language models, one can check out the links provided below the video.
What is the viewer encouraged to do after watching the video?
-The viewer is encouraged to explore what they are building with generative AI and share their projects in the comments section of the video.
Outlines
📚 Introduction to Tuning Large Language Models
The video begins with an introduction to the process of improving the quality of responses from large language models (LLMs). Nikita Namjoshi discusses the concept of tuning a model to enhance its performance, beyond simply crafting prompts. The video outlines the use of Vertex Generative AI Studio for launching a tuning job. Tuning is contrasted with fine-tuning, which involves retraining a pre-trained model on a new dataset. The challenges of fine-tuning LLMs due to their size and computational costs are highlighted. An alternative approach, parameter-efficient tuning, is introduced as a method to overcome these challenges by training only a subset of the model's parameters, potentially adding new layers or embeddings. The benefits of this approach include reduced training requirements and simpler model serving. A summary paper on parameter-efficient tuning is mentioned for further reading.
Mindmap
Keywords
💡Large Language Model (LLM)
💡Prompt
💡Tuning
💡Fine-Tuning
💡Parameter-Efficient Tuning
💡Vertex Generative AI Studio
💡Training Data
💡Supervised Training
💡Model Registry
💡Deployment
💡Generative AI
Highlights
Prototyping with large language models can be improved by tuning beyond just crafting prompts.
Tuning a large language model involves launching a tuning job from Vertex Generative AI Studio.
The prompt is the text input given to the model, which can be an instruction or include examples.
Prompt design is crucial for fast experimentation and customization without needing ML expertise.
Small changes in wording or word order can unpredictably impact model results.
Tuning can help address inconsistencies in the quality of model responses.
Fine-tuning involves retraining a pre-trained model on a new, domain-specific dataset.
Fine-tuning large language models (LLMs) presents challenges due to their size and computational demands.
Parameter-efficient tuning is an innovative approach that trains only a small subset of parameters.
This method can involve adding additional layers or embeddings to the model or prompt.
Parameter-efficient tuning simplifies serving models by using the existing base model with added parameters.
The optimal methodology for parameter-efficient tuning is an active area of research.
Tuning is suited for scenarios with modest amounts of training data, structured in a text-to-text format.
Each record in the training data should contain the input text and the expected model output.
The tuning job can be started and monitored in the Cloud Console within Vertex Generative AI Studio.
Upon completion, the tuned model is available in the Vertex AI model registry for deployment or testing.
The process of parameter-efficient tuning and launching a tuning job in Vertex Generative AI Studio is outlined.
Additional resources on generative AI and large language models are provided for further learning.
The video encourages viewers to share their generative AI projects in the comments.