OpenAI adds fine-tuning to GPT-3.5 Turbo, letting developers customize models with their own data to make them perform better for their use cases for a fee

OpenAI customers can now bring custom data to the lightweight version of GPT-3.5, GPT-3.5 Turbo — making it easier to improve …

TechCrunch 2023-08-23 Kyle Wiggers

Context & Ripple Effects

OpenAI had already made GPT-3.5 Turbo more useful for application builders through function calling and lower base pricing. This adds a paid customization layer, shifting the offering from a general-purpose API toward models adapted to particular workflows.

The move foreshadows a broader customization ladder: OpenAI later extended fine-tuning to GPT-4o and introduced assisted fine-tuning with data pipelines and evaluation systems.

First-order effects

Developers can adapt GPT-3.5 Turbo to their own examples rather than relying solely on prompting, creating a new paid path to improve task-specific behavior.
OpenAI gains a higher-value service around its lower-cost model, while customers take on the work of preparing useful training data.

Second-order effects

Fine-tuned models can reduce the need to use a larger general model for every task, making model choice increasingly about performance per useful task rather than headline capability.
The feature raises the value of data preparation and evaluation tooling; OpenAI's later assisted fine-tuning program suggests those services become part of the product surface as customization grows.

Third-order effects

If customization becomes standard, competition among model providers will increasingly center on how easily enterprises can turn proprietary data into reliable workflow behavior, not just on base-model quality.
Paid tuning services can deepen customer integration with a model platform, while lower underlying model prices such as later GPT-3.5 Turbo price cuts keep pressure on the cost of deployment.

The trend: Foundation-model vendors are commercializing customization layers that turn general APIs into workflow-specific systems built around customers' data.

Discussion

@drjimfan @drjimfan on x
OpenAI's most significant product update since the App Store: GPT-3.5 finetuning API. This will be the largest LoRA-as-a-service ever. GPT-4 ft is coming in a few months. Pricing: inference (output tokens) is 2x more expensive than training tokens. API is quite simple: submit a..…
@mbusigin Matt Busigin on x
This will significantly boost the quality of the next generation of LLM apps. The most interesting thing to me so far is how few examples they need.
@minimaxir Max Woolf on x
OpenAI announced ChatGPT finetuning, but generating from a finetuned GPT 3.5 Turbo is *8x* the cost of generating from the base model, so you really have to be in the “reduce prompt size by 90%” bucket they mention to get cost effectiveness out of it. https://openai.com/...
@nathanbenaich Nathan Benaich on x
bye bye a bunch of startups 😔
@drivelinekyle Kyle Boddy on x
Pretty great to see movement on this front. 8x the cost of the base model, however, so eke out all you can with better prompts. We'll probably wait until GPT-4 fine tuning is available since we're not building on 3.5-turbo at all, though who knows!
@officiallogank @officiallogank on x
Big news: Fine-tuning support for GPT-3.5 Turbo is here 🔥 Starting today, developers can create fine-tuned models for their use cases. Some important details: - Early testers have reduced prompt size by up to 90% by fine-tuning instructions into the model itself, speeding up...
@josephjacks_ @josephjacks_ on x
Now open weights it.
@marktenenholtz Mark Tenenholtz on x
You can now fine-tune GPT-3.5-Turbo! Seems like inference is significantly more expensive (8x more) though. My guess is that anyone with the ability to deploy their own models won't be swayed by this.
r/MachineLearning r on reddit
OpenAI launches fine-tuning for GPT-3.5 Turbo [N]

Chronicles