Together AI Blog ·
Hassan El Mghari describes fine-tuning a Jev-like classifier based on Qwen3.5 4B and deploying it on Together’s serverless platform. The guide gives the example dataset size, estimated training cost, and approximate training time. Lies 3 Standpunkte mit Belegen und Links zu den Originalquellen.
Using only 38,340 examples keeps fine-tuning costs low—approximately $17.0—while larger datasets increase both cost and training time.
Unterstützendes Moment lesen · Absatz 24The fine-tuning job takes approximately 25 minutes to complete.
Unterstützendes Moment lesen · Absatz 43The Jev-like classifier was launched on Together’s serverless platform, using Qwen3.5 4B as the base model.
Unterstützendes Moment lesen · Absatz 3Zugeordnete Passagen mit dem Kontext zur Überprüfung. Öffnen Sie den Originaltext, um die Quelle zu prüfen.
Originalauszug
The training job will take roughly 25 minutes to complete, and once it does we’ll have a model that is ready to do classification.
Originalauszug
We only use 38,340 examples to keep our fine-tuning costs low. Training against a dataset of this size will only cost about $17.0, while larger datasets are more expensive and time-consuming to train against.
Originalauszug
We just launched our own Jev-like classifier, together/Tev1-4B-experimental , on top of Qwen3.5 4B on Together’s serverless platform. In this blog post we’ll show you how to fine-tune your own version!
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