The idea for Commissioned started taking shape during a hackathon for Software Engineers that me and Gabe participated in. And like many of the best ideas that come out of hackathons, it wasn’t the project we submitted but a product of a problem we noticed. Out of all the dozens of impressive projects at that AI themed hackathon, we were the only ones using fine-tuning.
That was quite surprising to us because we knew how valuable it can be. Even in the hackathon, our project’s eligibility model had gotten a 35% boost in accuracy from fine-tuning. But something else also became obvious: fine-tuning is intimidating even for seasoned developers.
When we looked closer, the reasons became clear. People experimenting with fine-tuning kept running into daunting data formatting requirements, brittle pipelines, and painful infrastructure setup. These time-consuming, low-level tasks have shaped the perception of fine-tuning as something slow, expensive, and reserved for large companies with dedicated ML teams.
We built Commissioned to change that. We want marketers to fine-tune models that write in their voice without having to know what a JSONL is. We want ops teams to get better labeling and classification models by simply uploading a CSV. And we want developers to focus on building differentiated products instead of rewriting the same integration logic for every provider.
Our goal is simple: make fine-tuning accessible enough that anyone can experience the value it can bring in minutes not weeks.
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Show HN: Simple, Fast, Accessible Fine-tuning · HackerTrans
That was quite surprising to us because we knew how valuable it can be. Even in the hackathon, our project’s eligibility model had gotten a 35% boost in accuracy from fine-tuning. But something else also became obvious: fine-tuning is intimidating even for seasoned developers.
When we looked closer, the reasons became clear. People experimenting with fine-tuning kept running into daunting data formatting requirements, brittle pipelines, and painful infrastructure setup. These time-consuming, low-level tasks have shaped the perception of fine-tuning as something slow, expensive, and reserved for large companies with dedicated ML teams.
We built Commissioned to change that. We want marketers to fine-tune models that write in their voice without having to know what a JSONL is. We want ops teams to get better labeling and classification models by simply uploading a CSV. And we want developers to focus on building differentiated products instead of rewriting the same integration logic for every provider.
Our goal is simple: make fine-tuning accessible enough that anyone can experience the value it can bring in minutes not weeks.