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. Lisez 3 points de vue avec leurs éléments à l’appui et les liens vers les sources.
Using only 38,340 examples keeps fine-tuning costs low—approximately $17.0—while larger datasets increase both cost and training time.
Lire le moment probant · Paragraphe 24The fine-tuning job takes approximately 25 minutes to complete.
Lire le moment probant · Paragraphe 43The Jev-like classifier was launched on Together’s serverless platform, using Qwen3.5 4B as the base model.
Lire le moment probant · Paragraphe 3Passages attribués et accompagnés du contexte nécessaire à leur vérification. Ouvrez le texte original pour vérifier la source.
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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.
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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.
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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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