A compact encoder–decoder Transformer trained from scratch on 10,000 Sanskrit–English pairs (NLU Assignment 2), with a joint SentencePiece BPE vocabulary. It is small and fast — meant to be efficient rather than to match large pretrained models. This is not a Transformers architecture, so it ships with a self-contained modeling.py. Files: pytorchmodel.bin (weights), spm.model (tokenizer), config.json (hyperparameters), modeling.py (model + load/translate helpers). pip install gradio first. To host it, create a Hugging Face Space (SDK: Gradio) with an app.py (the load + respond code) and a requirements.txt of torch sentencepiece huggingfacehub gradio. Trained only on the provided dataset…