Question: Can we train local LLMs with Nexus/KB data?
Answer: YES - with fine-tuning, not full training.
Full Training vs Fine-Tuning: - Full training: Billions of parameters, massive compute, weeks/months - Fine-tuning: Take existing model (Qwen, Llama, Mistral), adapt to specific domain
How It Works: 1. Export KB/Documents/Context as training dataset (JSONL format) 2. Structure: {instruction, input, output} pairs 3. Use LoRA/QLoRA for efficient fine-tuning (fraction of compute) 4. Result: Model learns domain-specific knowledge
Nexus Advantage: - KB hierarchical structure = clean training data - Documents already parsed and organized - Context provides Q&A pairs - Track shows workflows and patterns
Implementation Path: 1. Create export tool: kb.export_training_data() 2. Format as instruction-tuning dataset 3. Use Hugging Face transformers + PEFT 4. Fine-tune on client's GPU server 5. Deploy fine-tuned model with Nexus MCP access
Result: LLM knows company data IN its weights + has Memory layer for current data