LLM Fine-Tuning for Wider Coverage

I’ve built a hallucination-resistant LLM platform that already marries FAISS-based retrieval, answer cross-checking and modular knowledge-base support. The last piece that still feels brittle is the lightweight fine-tuning component. Today it performs well on the narrow domain data I trained on, yet I need it to generalize confidently across both technical subjects and varied industry-specific information without bloating compute costs. Here’s what I’m after: • Refresh the current LoRA/PEFT workflow or suggest an alternative that can stretch the model’s knowledge boundaries while keeping the footprint “lightweight.” • Curate or synthesize balanced tuning and evaluation sets that cover the two priority areas (deep technical content and sector-focused material) so we can measure genuine cross-topic lift. • Implement an automated evaluation loop (exact-match, BLEU, factuality scoring against retrieved context) so we can prove the improvement instead of eyeballing it. • Return reproducible notebooks or scripts plus the updated checkpoint so I can drop the new module straight into my existing pipeline. Success for me looks like a fine-tuned model that retains its hallucination safeguards yet now answers questions that jump between coding intricacies, manufacturing specs, fintech jargon and more all with the same reliability I see in its original niche. If this sounds like your kind of challenge, tell me how you’d approach the data mix and tuning strategy and let’s get started.

Project ID: 40276305