Hey Product Hunt! Fine-tuning a model is only one part of the job. Preparing good training data, checking whether the model actually improved, and getting it into an app take just as much care. We built Tensorant to bring those steps into one workspace: - Turn documents or Hugging Face datasets into training examples you can review and version. - Fine-tune models on your own GPUs (We already support Runpod). - Compare the tuned model against the base model before deploying. - Connect it to your app through an OpenAI-compatible API. You bring your own compute and storage, so your datasets and model weights stay in your account. We’d love to hear from developers and teams exploring fine-tuning: where do you get stuck today - preparing data, training, evaluating results, or deployment? Give Tensorant a try and let us know what would make it more useful for your workflow. We’re here for your questions and feedback!

Tensorant

Fine-tuning and LLM Inference Provider

Fine-tuning and LLM Inference Provider