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Every amazing new feature in GPT-4 Turbo

By / Last updated on November 25, 2023

Great news – OpenAI just released GPT-4 Turbo, an upgraded version of the GPT-4 model with a context window up to 128K tokens – more than 300 pages of text, and a fourfold increase in regular GPT-4’s most powerful 32K context model.

The company made this known at its first-ever developer conference, touting a preview version of the model and promising a production-grade GPT-4 Turbo in the next few weeks.

Users will be able to have longer, more complex conversations with GPT-4 Turbo as there’ll be more room to remember more of what was said earlier in the chat.

DALLE-3 prompt: “A beautiful city with buildings made of different, bright, colorful candies and looks like a wondrous candy land”.
DALLE-3 prompt: “A beautiful city with buildings made of different, bright, colorful candies and looks like a wondrous candy land”

Also exciting to hear, GPT-4 Turbo is now trained on real-world knowledge and events up to April 2023, allowing us to build greater apps utilizing up-to-date data, without needing to manually keep it in the loop with custom data from embeddings and few-shot prompting.

Even better, the greater speed and efficiency of this new turbocharged model have made input tokens 3 times cheaper and slashed the cost of output tokens in half.

So, upgraded in capability, upgraded in knowledge, upgraded in speed, all with a fraction of the previous cost. That’s GPT-4 Turbo.

An innovative feature currently in preview, you can now pass image inputs to the GPT-4 model for processing, making it possible to perform tasks like generating captions, analyzing and classifying real-world images, and automated image moderation.

Then there’s the new DALL-E 3 API for automatically generating high-quality images and designs, and an advanced Text-to-speech (TTS) API capable of generating human-level speech with a variety of voices to choose from.

DALLE-3 outclasses Midjourney! Especially when it comes to creating complex images from highly detailed and creative prompts.

DALLE-3 (top) vs Midjourney (bottom). Prompt: "A vast landscape made entirely of various meats spreads out before the viewer. tender, succulent hills of roast beef, chicken drumstick trees, bacon rivers, and ham boulders create a surreal, yet appetizing scene. the sky is adorned with pepperoni sun and salami clouds".
DALLE-3 (top) vs Midjourney (bottom). Prompt: “A vast landscape made entirely of various meats spreads out before the viewer. tender, succulent hills of roast beef, chicken drumstick trees, bacon rivers, and ham boulders create a surreal, yet appetizing scene. the sky is adorned with pepperoni sun and salami clouds”. Source: DALL-E 3 vs. Midjourney: A Side by Side Quality Comparison

And we can’t forget the ambitious new Assistants API, aimed at helping devs build heavily customized AI agents with specific instructions that leverage extra knowledge and call models and tools to perform highly specialized tasks.

It’s always awesome to see these ground-breaking improvements in the world of AI, surely we can expect developers to take full advantage of these and produce even more intelligent and world-changing apps that improve the quality of life for everyone.

Fine-tuning for OpenAI’s GPT-3.5 Turbo model is finally here

By / Last updated on August 25, 2023

Some great news lately for AI developers from OpenAI.

Finally, you can now fine-tune the GPT-3.5 Turbo model using your own data. This gives you the ability to create customized versions of the OpenAI model that perform incredibly well at specific tasks and give responses in a customized format and tone, perfect for your use case.

For example, we can use fine-tuning to ensure that our model always responds in a JSON format, containing Spanish, with a friendly, informal tone. Or we could make a model that only gives one out of a finite set of responses, e.g., rating customer reviews as critical, positive, or neutral, according to how *we* define these terms.

As stated by OpenAI, early testers have successfully used fine-tuning in various areas, such as being able to:

  • Make the model output results in a more consistent and reliable format.
  • Match a specific brand’s style and messaging.
  • Improve how well the model follows instructions.

The company also claims that fine-tuned GPT-3.5 Turbo models can match and even exceed the capabilities of base GPT-4 for certain tasks.

Before now, fine-tuning was only possible with weaker, costlier GPT-3 models, like davinci-002 and babbage-002. Providing custom data for a GPT-3.5 Turbo model was only possible with techniques like few-shot prompting and vector embedding.

OpenAI also assures that any data used for fine-tuning any of their models belongs to the customer, and then don’t use it to train their models.

What is GPT-3.5 Turbo, anyway?

Launched earlier this year, GPT-3.5 Turbo is a model range that OpenAI introduced, stating that it is perfect for applications that do not solely focus on chat. It boasts the capability to manage 4,000 tokens at once, a figure that is twice the capacity of the preceding model. The company highlighted that preliminary users successfully shortened their prompts by 90% after applying fine-tuning on the GPT-3.5 Turbo model.

What can I use GPT-3.5 Turbo fine-tuning for?

  • Customer service automation: We can use a fine-tuned GPT model to make virtual customer service agents or chatbots that deliver responses in line with the brand’s tone and messaging.
  • Content generation: The model can be used for generating marketing content, blog posts, or social media posts. The fine-tuning would allow the model to generate content in a brand-specific style according to prompts given.
  • Code generation & auto-completion: In software development, such a model can provide developers with code suggestions and autocompletion to boost their productivity and get coding done faster.
  • Translation: We can use a fine-tuned GPT model for translation tasks, converting text from one language to another with greater precision. For example, the model can be tuned to follow specific grammatical and syntactical rules of different languages, which can lead to higher accuracy translations.
  • Text summarization: We can apply the model in summarizing lengthy texts such as articles, reports, or books. After fine-tuning, it can consistently output summaries that capture the key points and ideas without distorting the original meaning. This could be particularly useful for educational platforms, news services, or any scenario where digesting large amounts of information quickly is crucial.

How much will GPT-3.5 Turbo fine-tuning cost?

There’s the cost of fine-tuning and then the actual usage cost.

  • Training: $0.008 / 1K tokens
  • Usage input: $0.012 / 1K tokens
  • Usage output: $0.016 / 1K tokens

For example, a gpt-3.5-turbo fine-tuning job with a training file of 100,000 tokens that is trained for 3 epochs would have an expected cost of $2.40.

OpenAI, GPT 3.5 Turbo fine-tuning and API updates

When will fine-tuning for GPT-4 be available?

This fall.

OpenAI has announced that support for fine-tuning GPT-4, its most recent version of the large language model, is expected to be available later this year, probably during the fall season. This upgraded model has been proven to perform at par with humans across diverse professional and academic benchmarks. It surpasses GPT-3.5 in terms of reliability, creativity, and its capacity to deal with instructions that are more nuanced.