MahmutCanBoran

Model Description This model is a fine-tuned mBART-50 sequence-to-sequence model for diagnosing chronic issues in Audi vehicles. It maps user input describing symptoms (engine, transmission, electrical, etc.) into technical explanations. The model focuses on Audi models, engine types, and powertrains — helping identify issues such as timing chain problems, turbocharger failures, injector issues, DPF clogging, and more. Dataset - Collected from Audi models and known chronic problems - Training pairs: user complaint (input) → technical explanation (target) Usage Example Provide the car model, engine type, power (if relevant), and the observed problem. The AI Agent will generate a possible technical explanation.

This model generates recipe cards (title, ingredients, and step-by-step directions) from a list of ingredients. It is fine-tuned from `t5-small` using LoRA adapters and merged into a standalone checkpoint. ✨ Model Details - Base model: `t5-small` - Fine-tuning method: LoRA (rank=16, α=32, dropout=0.05, query/value projection layers) - Dataset: Custom JSONL (`inputtext`, `targettext` pairs), originally prepared from a Kaggle recipe dataset. - Task: Text-to-Text Generation bash Clone repo git clone https://github.com/ /recipe-card-t5-lora.git cd recipe-card-t5-lora Create environment python -m venv venv source venv/bin/activate # (Windows: venv\Scripts\activate) Install dependencies pip install -r requirements.txt pip install transformers peft torch from transformers import pipeline pipe = pipeline("text2text-generation", model="MahmutCanBoran/t5-recipe-card-en-lora-merged") Time/Servings fields are currently fixed values (20-60 minutes, Servings: 4). Model may hallucinate instructions if STRICT=no mode is used (future work: add dataset with variable strictness).

AI-powered assistant for diagnosing chronic issues in Audi vehicles. Built with Streamlit, powered by Transformers, and fine-tuned on real-world repair patterns. Audi AI Diagnosis helps identify possible chronic faults in Audi vehicles. Using a fine-tuned mBART model, the app turns natural language symptom descriptions into likely diagnoses. Features - Optimized for Audi-specific problem phrases - Responds in natural, technical language - Real-time inference with Hugging Face Transformers [](https://huggingface.co/spaces/MahmutCanBoran/audi-ai-diagnosis) Architecture: facebook/mbart-large-50 Task: Text2Text generation (Symptom ➝ Diagnosis)