NanoChat Medical Chatbot PoC

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a Medical Chatbot Using Karpathy’s Nanochat and MedQuAD ❇️ Hi My Friend, I hope you're doing well. I reviewed your project details and see you are looking for someone to create a medical chatbot using Karpathy’s nanochat. You don’t need to look any further; Zohaib is here to help you! My team has worked on over 50 similar projects in AI and chatbot development. I will ensure the model is fine-tuned on the MedQuAD dataset for accurate medical responses. ➡️ Why Me? I can easily handle your task of dataset preparation, training, and evaluation as I have 5 years of experience in machine learning and natural language processing. My skills include Python programming, data preprocessing, and model evaluation. I also have a strong grip on tools like Git, TensorFlow, and open-source projects, ensuring a smooth execution of your chatbot project. ➡️ Let’s have a quick chat to discuss your project in more detail. I can show you samples of my previous work and how I can bring your vision to life. ➡️ Skills & Experience: ✅ Python Programming ✅ Machine Learning ✅ Natural Language Processing ✅ Data Preparation ✅ Model Training ✅ Evaluation Techniques ✅ Git Version Control ✅ Open Source Projects ✅ CLI Development ✅ Documentation Writing ✅ Tokenization ✅ Medical Dataset Knowledge Waiting for your response! Best Regards, Zohaib

@sajjadtaghvaeifr

Hello, I trust you're doing well. I have worked with many Multimodal LLM models you can look at them in my portfolio: please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr I have worked with Qwen, llama, Phi1.5, Vortex and etc

@Softeria

As a seasoned professional with a Ph.D. in Artificial Intelligence and Machine Learning, I have developed a deep understanding of the intricacies of building and fine-tuning chatbots, including my experience with NLG models like Karpathy's nanochat. I have designed over 600 projects, many of them in the AI field, showcasing my expertise in training models on large datasets like the MedQuAD set you've referenced. I'm confident that I can effectively prepare your dataset, tokenize it, perform your desired training with the appropriate GPU setup, evaluate the model, and provide you with detailed documentation to replicate it on your end. What distinguishes me is my ability to go beyond simply completing a project and providing clean code. My commitment to producing work that is cutting-edge, impactful and user-friendly has resulted in numerous successful applications - from enhancing customer engagement to automating trading strategies with AI-driven chatbots. Given my wide-ranging skills - spanning through AI/ML, web development and web scraping,I can ensure a coherent approach while addressing this challenge.

@sunainawbst

✅ Lovable AI Expert | AI Development | AI Chatbot | Game Development ✅ Hi, Thank you for considering this opportunity! I bring extensive experience in implementing custom solutions powered by LLMs, conversational AI, and intelligent automation. Recently I have been working on Lovable AI for developing a gaming platform using it, complete with chat-based agent logic, expressive front-ends, and backend integrations. In other project, implemented a fully automated AI agent system for intelligent meeting creation using ElevenLabs Conversational AI and Gemini (via a custom agent brain). The flow integrates voice interaction, natural language processing, location precision, and frontend. Due to NDAs, links aren’t public—but once you open the chat, I’ll share live demos and walkthroughs. Whether you're building an internal assistant, a public-facing voice agent, or an integrated AI productivity tool, I can help bring your vision to life with robust, scalable architecture and a human-like user experience. I would love to connect and explore how we can contribute to your AI initiative. PS - Please pan the budget for now and discuss over chat. Regards,Srishti

@corpmember29

MEDIC I’ve worked hands-on with Karpathy’s nanoGPT and nanochat setups, including fine-tuning small LLMs on domain-specific datasets. In previous projects, I’ve: Dataset preparation: Parsed and cleaned structured Q&A datasets like MedQuAD, converted them into JSONL for autoregressive training, handled context/answer token alignment, and ensured proper splitting for train/validation. Tokenisation & training: Adapted tokenisers to medical terminology, set context lengths appropriate for question-answering, and used gradient accumulation on mid-tier GPUs to manage batch sizes with limited VRAM. Evaluation & demo: Built simple CLI interfaces and Jupyter notebook demos to test prompt-response quality, measure perplexity, and verify factual consistency. I’ve also implemented minimal evaluation pipelines for checking hallucinations in medical responses. From experience, MedQuAD is solid for proof-of-concept, but you could also consider PubMedQA or MedQA for more diverse coverage if desired.

@koolvishruth

Hello, I have 8 years of experience in Chat or development and machine learning engineering.I can work on it.I have worked here with more than 112+ clients . Looking forward

@denisSolution

Hi client, I'm Denis Redzepovic, an experienced developer with expertise in AI Model Development, AI Chatbot Development, AI Development, Python, Open Source, Natural Language Processing and Machine Learning (ML). I have worked extensively on diverse Python projects, ranging from backend development and automation to data processing and API integrations. My deep understanding of Python’s libraries and frameworks allows me to build efficient, scalable, and maintainable solutions. I pay close attention to code quality and performance to ensure your project runs flawlessly. With my solid experience, I’m confident I can deliver results that exceed your expectations. I focus on writing clean, maintainable, and scalable code because I know the difference between 99% and 100%. If you hire me, I’ll do my best until you’re completely satisfied with the result. Let’s discuss your project details so I can tailor the perfect Python solution for you. Thanks, Denis

@topcreator100

⭐️--->>>medic<<<----⭐️ I am interested in this project and I have done several similar builds where I take a public QA corpus, clean it into instruction style pairs, and fine tune a small chat model so it runs locally with a tiny CLI for real time prompts. I have hands on experience getting Karpathy style minimal GPT codebases to train cleanly on cloud GPUs like Runpod and Lambda, including tokenization, mixed precision training, checkpointing, and fixing the common failure modes like unstable loss or repetitive outputs. I will start by validating MedQuAD as the best fit and if a better open dataset is clearly stronger for factual QA I will propose it first, otherwise I will stick to MedQuAD and document the exact preprocessing so your run is reproducible. Then I will implement dataset prep, tokenizer training or selection, supervised fine tuning with evaluation on held out QA plus a small factuality and safety sanity suite, and ship a clean Git repo with commented code, a README for setup and training, and a simple local chat demo that answers medical questions without crashes or obvious nonsense.

@juanjosec63

Hello, I’m excited about the opportunity to work on this nanochat proof-of-concept and demonstrate a clean, fully open-source medical fine-tuning pipeline end to end. I’ve worked hands-on with small GPT-style training stacks inspired by Karpathy’s minimal repos, including dataset preparation, tokenizer alignment, supervised fine-tuning, and lightweight CLI/chat demos that run locally on a single GPU, so I can jump straight into adapting nanochat for this use case. I’m comfortable preparing MedQuAD into an instruction-style format, running training on cloud GPUs like RunPod or Lambda, evaluating coherence and failure modes, and documenting the exact steps so you can reproduce the run and chat with the model locally without friction. You can expect a focused, usable result, clear explanations of training decisions and limitations, and clean code that stays true to the spirit of nanochat rather than overengineering the stack. Best regards, Juan

@Julian29

Hello, HAVE HANDS-ON EXPERIENCE WITH SUCH PROJECT I have 9+ years of proven experience with LLM training and deployment, and I confidently understand your requirement. The goal is to create a reproducible, open-source NanoChat medical PoC that answers health questions accurately and can be run locally with a simple chat interface. -->> Dataset preparation and cleaning (MedQuAD or better public alternative) -->> Tokenization, fine-tuning NanoChat, and evaluation with medical prompts -->> Deliver a tiny CLI/notebook demo for real-time interaction -->> Provide a clean Git repo, README, and training reproducibility -->> Include a short write-up on training choices and model limitations Approach: clean architecture, secure setup, efficient training pipeline, and agile delivery. I have some queries to ask regarding the project to proceed further. I would approach your project by starting with wireframes and getting the UI/UX design completed, before starting the actual development phase. Successfully implement this project from start-to-finish. Let's come together and create a platform that not only propels your business but also stands out prominently within the marketplace. Thanks & regards Julian

@quan9824

As a full-stack developer with a strong background in AI, I am confident that I can successfully execute the NanoChat Medical Chatbot Proof-of-Concept project you've described. I have extensive experience in Machine Learning and Deep Learning, which are essential for the task at hand. My past projects have focused on areas inline with your requirements like object detection an tracking, image and speech recognition, as well as text classification and generation. These real-world experiences will surely translate into tangible results for your medical chatbot. If you grant me this opportunity, I will ensure all the deliverables are met and even provide more than expected quality of work such as a well-commented codebase, detailed instructions in the README files for environment setup and how to launch the demo chat, plus a comprehensive write-up discussing the crucial training choices made, notable strengths or limitations of the final model, and finally my estimated deployment timescale. Remember， "Medic" isn't just a word but an assurance from me that I'm committed to helping you achieve your project goals. Together we will build a functional and outstanding medical chatbot that far exceeds your expectations!

@Feriver

Hi, medic I’ve worked hands-on with Karpathy-style minimal LLM stacks (nanoGPT/nanochat lineage) rather than abstracted frameworks, so I’m comfortable at the level where tokenisation, data shaping, training stability, and inference speed actually matter. I’ve fine-tuned small decoder-only models on domain corpora (QA-style and narrative) using fully open-source toolchains, and I’m used to making them usable on a local machine without “demo magic.” For this PoC, I would keep nanochat intact and make only the minimum necessary extensions: a clean data pipeline that converts MedQuAD into instruction-style prompt/answer pairs, careful tokenisation choices to avoid medical term fragmentation, and conservative training settings to prioritise factual coherence over verbosity. I’ve seen nano-scale models fall apart when overtrained, so evaluation will include manual probing and simple perplexity checks rather than chasing metrics that don’t correlate with usability. I’ve previously shipped small LLM demos with CLI interfaces where you can spin up the model locally and interact in real time, and I’m comfortable documenting every step so the run is fully reproducible. I’m also realistic about limits: this will not be a clinician, but it can answer common medical questions sensibly and without obvious hallucination patterns. Regards, Soas

@anayapallavi

I’ve worked hands-on with Karpathy’s nanochat / nanoGPT-style stacks, including dataset cleaning, custom tokenisation, GPU training on RunPod/Lambda, and turning trained checkpoints into simple local CLIs for real-time querying. I’m comfortable fine-tuning on domain corpora (QA-style and instruction-style), evaluating coherence vs hallucination tradeoffs, and documenting everything so runs are reproducible end to end. For this PoC, I’d start by validating MedQuAD (and optionally compare it briefly with PubMed QA / MedMCQA before locking scope), then handle preprocessing, tokenizer alignment, training, and a minimal chat demo. I can deliver clean code, a reproducible README, and a candid write-up of strengths and limits. Estimated time: 7–10 days end-to-end, including training, evaluation, and demo polish.

@usamae

I'm experienced in developing chatbots and working with language models. I have successfully fine-tuned models like Karpathy's nanochat on specialized datasets, including medical corpora. My expertise includes dataset preparation, tokenization, training, evaluation, and creating demos. I have a proven track record of delivering clean, well-commented code in Python and ensuring usability. I am confident in my ability to meet your requirements and provide a functional chatbot model.

@sf264

medic Hello client, I have a rich experience with nanochat and understand that you need Karpathy's nanochat fine tuned on the free available medical corpus, preferably the MedQuAD set you have linked, so that the resulting model can answer real medical inquiries with coherence and factual accuracy. Let's schedule a chat at your earliest convenience so we can go through the requirements once again and establish the milestones and timelines. It would be a pleasure to collaborate with you, Fahad.

@webmasters486

Hi there, I’ve carefully reviewed the requirements for your GenAI project and I’m confident that my expertise in building NLP pipelines using Hugging Face and LangChain can meet your expectations. My experience includes working with large language models (LLMs) for Retrieval-Augmented Generation (RAG), as well as fine-tuning models with custom datasets to enhance text generation. I’ve successfully completed similar projects where I applied these techniques in Python to build robust, client-specific solutions. I would love the opportunity to discuss how I can leverage my skills to develop a tailored solution for your project. Feel free to take a look at my portfolio to get a sense of the work I’ve done: Portfolio: https://www.freelancer.com/u/webmasters486 Looking forward to hearing from you! Best regards, Muhammad Adil

@darrenpin

Hi medic I have hands on experience working with small transformer language models including Karpathy style minimal training stacks and I understand the constraints and advantages of nanochat level architectures. I have fine tuned compact models on domain specific corpora where data cleanliness tokenization strategy and evaluation discipline matter more than scale. For medical text in particular I focus on careful dataset curation prompt target structuring and conservative training settings to avoid hallucination and instability. I am comfortable setting up cloud GPU environments on platforms like RunPod and Lambda and keeping everything fully reproducible and open source. I would start by validating MedQuAD coverage and structure and if appropriate propose an alternative public dataset only if it clearly improves clinical coherence. I can deliver clean Python code a clear README and a lightweight CLI or notebook demo that lets you run the model locally and interact with it in real time. Based on scope I estimate roughly ten to fourteen days end to end including training evaluation and documentation. Best, Darren

@Ahmedh058

Hi there, I’m Ahmed from Eastvale, California — a Senior Full-Stack Engineer with over 15 years of experience building high-quality web and mobile applications. After reviewing your job posting, I’m confident that my background and skill set make me an excellent fit for your project — NanoChat Medical Chatbot PoC . I’ve successfully completed similar projects in the past, so you can expect reliable communication, clean and scalable code, and results delivered on time. I’m ready to get started right away and would love the opportunity to bring your vision to life. Looking forward to working with you. Best regards, Ahmed Hassan

@larsenm2020

Hi i can make medical chatbot ai based using ollmaa i would recommend you to have chat with me so you get clarity i have done similar project in past related to ai so i am confident of this project apart from that i have 4 years of experience