Abstract: To address the challenges faced by general-purpose large language models (LLMs) in mineral exploration, including scarcity of domain corpora, insufficient coverage of domain terminology and register adaptation, and pronounced factual hallucinations. We constructed a mineral-exploration corpus of approximately 25 million tokens and, on this basis, proposed a curriculum-based continual pre-training strategy, which organizes training data into three stages: terminology, mechanisms, and cases. Coupled with gradual unfreezing of Transformer blocks and learning-rate scheduling, we conducted continual pre-training of Qwen3-1.7B to achieve stage-wise domain adaptation, resulting in a mineral-exploration-oriented LLM, Geo-MineLLM. During inference, we integrated a Hybrid RAG framework, leveraging hybrid retrieval and evidence-constrained generation to enhance factual consistency. Human evaluation indicates that Geo-MineLLM substantially improves domain question-answering performance relative to the base model and larger-parameter models within the same family. With Hybrid RAG enabled, overall domain QA performance approaches that of GPT-4.1. The proposed training–inference integrated framework provides a lightweight pathway for building mineral-exploration LLMs and enabling reliable domain-specific question answering.