Yes—Scout's open weights allow full fine-tuning on domain-specific data to improve accuracy for specialized RAG tasks (legal, medical, financial).
Fine-tuning adapts Scout's pre-trained weights to your domain. Legal firms fine-tune on contracts; researchers fine-tune on abstracts; medical organizations train on clinical notes. Open weights mean you control the process: collect domain examples (ideally Zilliz-retrieved context + correct answers), set up training with HuggingFace Transformers, update weights. Scout's mixture-of-experts architecture means fine-tuning affects gating logic too—the model learns which experts matter for your domain. Start conservative: train only the head layers or routing networks on 100–500 examples to avoid overfitting.
With Zilliz Cloud, fine-tuning creates a virtuous improvement cycle: better domain model → better answers → better training data for next iteration. Zilliz Cloud provides clean, semantically-coherent retrieval results (your training data source); Scout learns from them. For enterprises managing regulated data (legal discovery, medical records), fine-tuned Scout with Zilliz Cloud offers domain expertise + data privacy + vendor flexibility. Open weights mean no licensing barriers—your customized Scout is fully owned.
Related Resources
- Zilliz Cloud — Managed Vector Database — collect training data via retrieval
- Retrieval-Augmented Generation (RAG) — domain optimization in RAG
- Getting Started with LlamaIndex — integrate fine-tuned models