Fine Tuning Assistant (for gpt-3.5-turbo and other OpenAI models)

£65.00

Category:

Are you looking for a way to fine tune your AI models quickly and easily? Look no further than our Fine Tuning Assistant!

Our Fine Tuning Assistant takes the guesswork out of fine tuning by taking you through the process step by step.

Fine tuning is a technique in machine learning that involves taking a pre-trained model and adjusting its parameters to improve its performance on a new task. This can be done by training the AI model on a new dataset, or by adjusting the hyperparameters of the model. Fine tuning can be a very effective way to improve the performance of an AI model, especially if the new task is similar to the task that the model was originally trained on.

Fine tuning can improve the quality of results produced by an LLM in a number of ways. For example, it can help the model to better understand the nuances of a particular task, or to learn to generate more creative or informative text.

The process of fine tuning can be complex, but with its logical layout our Fine Tuning Assistant can help both fine-tuning newbies and experienced developers get started.

First, you’ll select a pre-trained base model or upload your own custom model. Then, you’ll import your training data – our app converts your data into the required JSONL format.  Next, you’ll adjust the hyperparameters of the fine tuning process. Finally, you’ll test the fine-tuned model on a validation set to assess its performance.

Overall, fine tuning can be a powerful tool for improving the performance of OpenAI models on specific tasks. Translators, for example, will appreciate that it can customise a “general” model for use in a specialist field like medicine. However, it is important to weigh the benefits of fine tuning against the costs before deciding whether to use it. Our Fine Tuning Assistant has a “Count tokens” feature that allows you to count the tokens (and estimate the costs) before running the fine-tuning operation.

Typical users of our Fine Tuning Assistant will be data scientists, machine learning engineers, business intelligence developers, research scientists, translation companies, healthcare professionals, financial analysts, but the future may see artists, writers,  teachers and musicians fine tuning AI models to assist their work in their specific use cases.

If you’re looking for a way to get the best results from your AI models, then our Fine Tuning Assistant is the perfect solution for you.

As of August 2023, gpt-3.5-turbo can be fine-tuned:in addition to the following legacy models:

Ada
Babbage
Curie
Davinci

These models are all based on the legancy GPT-3 family of LLMs.

Are you looking for a way to fine tune your AI models quickly and easily? Look no further than our Fine Tuning Assistant!

Our Fine Tuning Assistant takes the guesswork out of fine tuning by taking you through the process step by step.

Fine tuning is a technique in machine learning that involves taking a pre-trained model and adjusting its parameters to improve its performance on a new task. This can be done by training the AI model on a new dataset, or by adjusting the hyperparameters of the model. Fine tuning can be a very effective way to improve the performance of an AI model, especially if the new task is similar to the task that the model was originally trained on.

Fine tuning can improve the quality of results produced by an LLM in a number of ways. For example, it can help the model to better understand the nuances of a particular task, or to learn to generate more creative or informative text.

The process of fine tuning can be complex, but with its logical layout our Fine Tuning Assistant can help both fine-tuning newbies and experienced developers get started.

First, you’ll select a pre-trained base model or upload your own custom model. Then, you’ll import your training data – our app converts your data into the required JSONL format.  Next, you’ll adjust the hyperparameters of the fine tuning process. Finally, you’ll test the fine-tuned model on a validation set to assess its performance.

Overall, fine tuning can be a powerful tool for improving the performance of OpenAI models on specific tasks. Translators, for example, will appreciate that it can customise a “general” model for use in a specialist field like medicine. However, it is important to weigh the benefits of fine tuning against the costs before deciding whether to use it. Our Fine Tuning Assistant has a “Count tokens” feature that allows you to count the tokens (and estimate the costs) before running the fine-tuning operation.

Typical users of our Fine Tuning Assistant will be data scientists, machine learning engineers, business intelligence developers, research scientists, translation companies, healthcare professionals, financial analysts, but the future may see artists, writers,  teachers and musicians fine tuning AI models to assist their work in their specific use cases.

If you’re looking for a way to get the best results from your AI models, then our Fine Tuning Assistant is the perfect solution for you.

As of August 2023, gpt-3.5-turbo can now be fine-tuned in addition to the following legacy models::

Ada
Babbage
Curie
Davinci

These models are all based on the GPT-3 family of LLMs. .

Reviews

There are no reviews yet.

Be the first to review “Fine Tuning Assistant (for gpt-3.5-turbo and other OpenAI models)”

Your email address will not be published. Required fields are marked *