How to effectively evaluate and fine‑tune large language models like DeepSeek?

04 Eki 04:45

I'm starting to experiment with open‑source LLMs similar to DeepSeek and I'm looking for a solid workflow. Specifically, I'd like to know how you structure dataset preprocessing, which training objectives tend to give the best balance between coherence and creativity, and what evaluation metrics you rely on before a model is ready for downstream tasks. Do you prefer incremental fine‑tuning on domain‑specific data or a full‑scale retrain? Also, any tips on managing compute resources efficiently would be appreciated. How do you approach this overall?