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. Lee 3 puntos de vista con sus evidencias y enlaces a las fuentes.
Using only 38,340 examples keeps fine-tuning costs low—approximately $17.0—while larger datasets increase both cost and training time.
Ver el momento de apoyo · Párrafo 24The fine-tuning job takes approximately 25 minutes to complete.
Ver el momento de apoyo · Párrafo 43The Jev-like classifier was launched on Together’s serverless platform, using Qwen3.5 4B as the base model.
Ver el momento de apoyo · Párrafo 3Pasajes atribuidos con contexto para verificarlos. Abra el texto original para comprobar la fuente.
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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!
Estas perspectivas enlazan a sus fuentes originales. Las paráfrasis están identificadas y no son citas textuales.
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