Fine Tune Education LLM Accuracy

I have a large-language model trained for the education sector, yet its answers still fall short of the accuracy teachers and students expect. Your assignment is to craft and execute a fine-tuning strategy that raises factual correctness and reduces hallucinations while keeping existing speed intact.

You will receive:
• The current checkpoint of the model.
• A domain-specific corpus of lesson plans, quizzes and annotated Q&A pairs.
• A held-out validation set mirroring real classroom queries.

Deliverables
• A fine-tuned model ready for deployment.
• Reproducible training scripts, config files and a concise hand-off guide.
• A report comparing pre- vs post-tuning accuracy with clearly stated metrics.

Acceptance criteria
• Measurable accuracy improvement on the provided validation set.
• No significant regression in response time or existing capabilities.
• Complete, well-documented code so my internal team can rerun the process.

Please submit a detailed project proposal outlining your methodology, toolchain (e.g., Hugging Face Transformers, PEFT/LoRA, RLHF or other approaches you deem fit), expected timeline and resource requirements. I will evaluate proposals on how convincingly they tackle the accuracy challenge within the education domain.