Do not write a response that is autogenerated - I want to know more about your experiences with LLM. I want to see Karpathy’s nanochat fine-tuned on a freely available medical corpus, preferably the MedQuAD set I linked, so that the resulting model can answer real medical inquiries with coherence and factual accuracy. This is a true proof-of-concept, yet the end result must be usable: after you finish I should be able to spin up the model locally, type a health-related question, and receive a sensible response in real time. Ths would be trained on a cloud instance with proper gpu setup (think runpods, lambda) Your task covers the dataset preparation, tokenisation, training, evaluation, and a tiny CLI or notebook demo—while keeping everything strictly open-source. If you believe there is a better public dataset than MedQuAD, propose it first; otherwise stick with MedQuAD. Please walk me through your prior experience with nanochat when you reply, because I need to gauge how quickly you can jump in. Deliverables • Clean, well-commented source code in a Git repo (Python, nanochat, any helper scripts). • A README explaining environment setup, training steps, and how to launch the demo chat. • Brief write-up of key training choices and the final model’s observed strengths/limitations. To bid, write medic so that I know you read the description. And again, no ai responses - just your thoughts, experience, and how long do you think you will need for this project. I will consider the job complete once I can reproduce your training run on my machine and chat with the model about medical topics without crashes or obvious nonsense answers.