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How to automate fine tuning ChatGPT 3.5 Turbo

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How to automate fine tuning ChatGPT 3.5 Turbo

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How to automate fine tuning ChatGPT 3.5 Turbo

The advent of AI and machine learning has transform the wide variety of different areas, including the field of natural language processing. One of the most significant advancements in this area is the development and release of ChatGPT 3.5 Turbo, a language model developed by OpenAI. In this guide will delve into the process of automating the fine-tuning of GPT 3.5 Turbo for function calling using Python, with a particular focus on the use of the Llama Index.

OpenAI has announced the availability of fine-tuning for its GPT-3.5 Turbo model back in August 2023, with support for GPT-4 expected to be released this fall. This new feature allows developers to customize language models to better suit their specific needs, offering enhanced performance and functionality. Notably, early tests have shown that a fine-tuned version of GPT-3.5 Turbo can match or even outperform the base GPT-4 model in specialized tasks. In terms of data privacy, OpenAI ensures that all data sent to and from the fine-tuning API remains the property of the customer. This means that the data is not used by OpenAI or any other organization to train other models.

One of the key advantages of fine-tuning is improved steerability. Developers can make the model follow specific instructions more effectively. For example, the model can be fine-tuned to always respond in a particular language, such as German, when prompted to do so. Another benefit is the consistency in output formatting, which is essential for applications that require a specific response format, like code completion or generating API calls. Developers can fine-tune the model to reliably generate high-quality JSON snippets based on user prompts.

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How to automate fine tuning ChatGPT

The automation of fine-tuning GPT 3.5 Turbo involves a series of steps, starting with the generation of data classes and examples. This process is tailored to the user’s specific use case, ensuring that the resulting function description and fine-tuned model are fit for purpose. The generation of data classes and examples is facilitated by a Python file, which forms the first part of a six-file sequence.

Fine-tuning also allows for greater customization in terms of the tone of the model’s output, enabling it to better align with a business’s unique brand identity. In addition to these performance improvements, fine-tuning also brings efficiency gains. For instance, businesses can reduce the size of their prompts without losing out on performance. The fine-tuned GPT-3.5 Turbo models can handle up to 4k tokens, which is double the capacity of previous fine-tuned models. This increased capacity has the potential to significantly speed up API calls and reduce costs.

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The second file in the sequence leverages the Llama Index, a powerful tool that automates several processes. The Llama Index generates a fine-tuning dataset based on the list produced by the first file. This dataset is crucial for the subsequent fine-tuning of the GPT 3.5 Turbo model. The next step in the sequence extracts the function definition from the generated examples. This step is vital for making calls to the fine-tuned model. Without the function definition, the model would not be able to process queries effectively.

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The process then again utilizes the Llama Index, this time to fine-tune the GPT 3.5 Turbo model using the generated dataset. The fine-tuning process can be monitored from the Python development environment or from the OpenAI Playground, providing users with flexibility and control over the process.

Fine tuning ChatGPT 3.5 Turbo

Once the model has been fine-tuned, it can be used to make regular calls to GPT-4, provided the function definition is included in the call. This capability allows the model to be used in a wide range of applications, from answering complex queries to generating human-like text.

The code files for this project are available on the presenter’s Patreon page, providing users with the resources they need to automate the fine-tuning of GPT 3.5 Turbo for their specific use cases. The presenter’s website also offers a wealth of information, with a comprehensive library of videos that can be browsed and searched for additional guidance.

Fine-tuning is most effective when integrated with other techniques such as prompt engineering, information retrieval, and function calling. OpenAI has also indicated that it will extend support for fine-tuning with function calling and a 16k-token version of GPT-3.5 Turbo later this fall. Overall, the fine-tuning update for GPT-3.5 Turbo offers a versatile and robust set of features for developers seeking to tailor the model for specialized tasks. With the upcoming capability to fine-tune GPT-4 models, the scope for creating highly customized and efficient language models is set to expand even further.

The automation of fine-tuning GPT 3.5 Turbo for function calling using Python and the Llama Index is a complex but achievable process. By generating data classes and examples tailored to the user’s use case, leveraging the Llama Index to automate processes, and carefully extracting function definitions, users can create a fine-tuned model capable of making regular calls to GPT-4. This process, while intricate, offers significant benefits, enabling users to harness the power of GPT 3.5 Turbo for a wide range of applications.

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