LLM Fine-Tuning for Wider Coverage
Budget: ₹750 – ₹1,250 INR
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.
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.