Best practices for building and fine-tuning large language models with Gemini-like architectures

25 Tem 09:00

I'm planning a project that leverages a Gemini‑style transformer for a domain‑specific chatbot. Looking for a solid workflow: data collection, preprocessing, tokenization strategy, and fine‑tuning tricks that work well at scale. Also curious about evaluation metrics that capture both factual accuracy and conversational fluency. Any recommendations on handling hallucinations, optimizing inference latency, or integrating RLHF loops? How do you balance model size versus performance in production environments? Would love to hear your go‑to methods and pitfalls to avoid.