Master ChatGPT Finetuning with Riku's No-Code Guide

Table of Contents:

• Introduction
• What is Fine-Tuning for Chat GPT?
• Benefits of Fine-Tuning Language Models
• How to Link OpenAI Key to Your Account
• Using the Fine-Tuning Studio in Riku
• Creating a Data Set for Fine-Tuning
• Converting a CSV File to JsonL Format
• Fine-Tuning with a Small Data Set
• Fine-Tuning with a Large Data Set
• Deploying the Fine-Tuned Model in a Chat App
• Conclusion

Introduction

In recent news, OpenAI has made a significant announcement about the introduction of fine-tuning for ChatGPT. This new feature, ChatGPT 3.5 Turbo, allows users to fine-tune the language model for specific chat-Based tasks. The upcoming release of GPT-4 will provide even more advanced capabilities in the future. The goal is to make it easier for individuals and businesses to utilize large language models for their specific needs. Riku, an AI platform, has also launched a no-code builder for data sets that can be used in the fine-tuning process, making it accessible to anyone without coding knowledge.

What is Fine-Tuning for Chat GPT?

Fine-tuning is a process that enhances the performance of a pre-trained language model by training it on specific data sets. With ChatGPT, fine-tuning involves providing examples of conversational exchanges, including both user inputs and model outputs. This additional information helps the model generate more contextually appropriate and accurate responses during chat interactions. Fine-tuning allows users to customize and optimize the language model to perform better in specific chat-based applications.

Benefits of Fine-Tuning Language Models

Fine-tuning offers several benefits when using language models like ChatGPT:

1. Customization: Fine-tuning allows users to adapt the language model to their specific needs, whether it is for customer support, content generation, or any other chat-based application.

2. Improved Accuracy: By training on task-specific data, fine-tuned models can provide more accurate and Context-aware responses, reducing the chances of irrelevant or misleading answers.

3. Enhanced Performance: Fine-tuning helps optimize the model's performance by aligning it with the desired output of the task at HAND. This results in better conversational experiences.

4. Efficient Learning: Fine-tuning improves the efficiency of language models by training them on a narrow task instead of training from scratch, saving time and resources.

How to Link OpenAI Key to Your Account

To begin the fine-tuning process, You need to link your OpenAI key to your Riku account. If you don't have an OpenAI key, sign up for free on their Website to receive a key. Once you have the key, follow these steps:

1. Go to Riku's dashboard and click on your name at the bottom.
2. Select "API keys" and paste your OpenAI key in the designated field.
3. Click "Update API key" to link your OpenAI key to your Riku account.

Once the process is complete, you're ready to start using Riku's fine-tuning capabilities.

Using the Fine-Tuning Studio in Riku

Riku provides a user-friendly Fine-Tuning Studio that simplifies the process of fine-tuning language models. From the Riku dashboard, click on "Fine-Tuning Studio". Here, you can manage your fine-tuning jobs and Create data sets for training. The Fine-Tuning Studio interface allows you to:

• View past fine-tuning jobs and their details.
• Access existing data sets for reuse and modification.
• Create new fine-tuning jobs from scratch or with existing data sets.

To create a fine-tuning job, click on "Create Fine-Tune" in the top-right corner. This will open the Fine-Tuning Data Studio.

Creating a Data Set for Fine-Tuning

In the Fine-Tuning Data Studio, you can create data sets for fine-tuning by defining system messages, user inputs, and corresponding model outputs. The system message provides context for the model, while user inputs simulate different chat interactions.

To create a data set:

1. Enter the system message, such as "Marv is a factual chatbot that is also sarcastic".
2. Add user inputs and their respective model outputs as pairs. For example, "What is the capital of France?" and "Paris".
3. Click "Add entry" after each input-output example.
4. Ensure your data set has a minimum of 10 examples for successful fine-tuning.

You can also modify and download existing data sets or convert CSV files to JsonL format for easy import into the Fine-Tuning Data Studio.

Converting a CSV File to JsonL Format

To convert a CSV file to JsonL format, follow these steps:

1. Organize your CSV file with three columns: system, user, and AI.
2. Ensure the system column contains the same system message for all entries.
3. Enter user inputs in the user column and corresponding model outputs in the AI column.
4. Download the CSV file and go to the Riku Fine-Tuning Studio.
5. Click on the "CSV to JsonL" button and enter a name for the data set.
6. Upload the CSV file and click "Run conversion".
7. After the conversion process, you can download the resulting JsonL file for use in fine-tuning.

Fine-Tuning with a Small Data Set

Even with a small data set, fine-tuning can still yield noticeable improvements in the performance of ChatGPT. While it is recommended to have at least 100 examples for significant impact, a smaller data set can be used to experiment and fine-tune the model.

For example, with a data set like "Marv is a factual chatbot that is also sarcastic" consisting of 10 pairs of user inputs and model outputs, the fine-tuned model can generate contextually appropriate responses based on the nature of the chatbot.

Fine-Tuning with a Large Data Set

To achieve the best performance with fine-tuning, it is recommended to use a large data set consisting of at least 100 examples or more. A larger data set allows the model to learn from a variety of scenarios, resulting in improved context awareness and accuracy.

When working with a large data set, it is essential to ensure that the data covers a diverse range of possible chat interactions and includes different nuances Relevant to the desired task.

Deploying the Fine-Tuned Model in a Chat App

After successfully fine-tuning the model, you can deploy it within a chat application. Riku provides a chat app builder that allows you to create and customize chat interfaces easily. To deploy the fine-tuned model, follow these steps:

1. Access the Chat App Studio from the Riku dashboard.
2. Customize the chat interface by providing a greeting message, prompt, and setting options, such as temperature.
3. Select the fine-tuned model, such as "Marvin Maestro", for the chat app.
4. Save and publish the chat application, choosing different privacy settings and deployment options.

Once deployed, the chat app will utilize the fine-tuned model to generate responses specific to your customizations and data set.

Conclusion

Fine-tuning language models like ChatGPT can greatly enhance their performance and make them more suitable for specific chat-based applications. With Riku's fine-tuning capabilities, anyone can create, customize, and deploy fine-tuned models without the need for coding. By using chat app builders and data set management tools, users can unlock the full potential of large language models and create conversational experiences tailored to their unique needs. Start fine-tuning your language models with Riku and explore the power of AI in chat-based interactions.

Highlights

• OpenAI introduces fine-tuning for ChatGPT, empowering users to optimize language models for specific chat-based tasks.
• Riku's no-code builder and Fine-Tuning Studio make it effortless to create data sets and fine-tune models without coding.
• Fine-tuning enhances performance, customization, and accuracy of language models for different chat applications.
• Link your OpenAI key to your Riku account to access fine-tuning capabilities seamlessly.
• Convert CSV files to JsonL format using Riku's CSV to JsonL converter for easy import into the Fine-Tuning Studio.
• Fine-tuning can be done with both small and large data sets, depending on the desired impact and use case.
• Deploy fine-tuned models in custom chat applications using Riku's Chat App Studio, providing personalized conversational experiences.
• Riku simplifies the entire process of fine-tuning language models, making it accessible to individuals and businesses alike.
• Fine-tuning enables users to bypass the limitations of generic language models and create content specific to their brand or purpose.
• Explore the possibilities of fine-tuning with Riku and leverage AI to revolutionize chat-based interactions.

FAQ

Q: Can I fine-tune ChatGPT using my existing work in CSV format?

A: Yes, Riku allows you to convert your existing work in CSV format to JsonL format for easy import into the Fine-Tuning Studio. This enables you to utilize your previous data sets without hassle.

Q: How many examples do I need in my data set for successful fine-tuning?

A: It is recommended to have a minimum of 10 examples in your data set for successful fine-tuning. However, for better performance and more substantial improvements, it is advised to have at least 100 examples or more.

Q: Can I combine multiple data sets into one for fine-tuning?

A: Yes, Riku allows you to combine different data sets into one data set, giving you the flexibility to include diverse examples from various sources. This helps create a comprehensive training set for fine-tuning.

Q: Can I edit my data sets after creating them in Riku?

A: Currently, Riku provides the option to download and delete data sets. However, the ability to edit data sets directly within Riku is an upcoming feature. This will allow you to add more entries and make modifications easily.

Q: How long does the fine-tuning process take?

A: The duration of the fine-tuning process depends on the size of your data set. A larger data set may take longer to train the model. It is recommended to be patient and let the process complete, as the result will be an optimized language model for your chat-based application.