| # English to Hindi Translation (Quantized Model) | |
| This repository contains a **quantized English-to-Hindi translation model** fine-tuned on the [`Aarif1430/english-to-hindi`](https://huggingface.co/datasets/Aarif1430/english-to-hindi) dataset and optimized using **dynamic quantization** for efficient CPU inference. | |
| ## 🔧 Model Details | |
| - **Base model**: [`Helsinki-NLP/opus-mt-en-hi`](https://huggingface.co/Helsinki-NLP/opus-mt-en-hi) | |
| - **Dataset**: Aarif1430/english-to-hindi | |
| - **Training platform**: Kaggle (CUDA GPU) | |
| - **Fine-tuned**: On English-Hindi pairs from the Hugging Face dataset | |
| - **Quantization**: PyTorch Dynamic Quantization (`torch.quantization.quantize_dynamic`) | |
| - **Tokenizer**: Saved alongside the model | |
| ## 📁 Folder Structure | |
| quantized_model/ | |
| ├── config.json | |
| ├── pytorch_model.bin | |
| ├── tokenizer_config.json | |
| ├── tokenizer.json | |
| ├── vocab.json / merges.txt | |
| --- | |
| ## 🚀 Usage | |
| ### 🔹 1. Load Quantized Model for Inference | |
| ```python | |
| import torch | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline | |
| # Load tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained("./quantized_model") | |
| # Load quantized model | |
| model = AutoModelForSeq2SeqLM.from_pretrained("./quantized_model") | |
| model.eval() | |
| # Run translation | |
| translator = pipeline("translation_en_to_hi", model=model, tokenizer=tokenizer, device=-1) | |
| text = "How are you?" | |
| print("Hindi:", translator(text)[0]['translation_text']) | |
| ``` | |
| ## Model Training Summary | |
| - Loaded dataset: Aarif1430/english-to-hindi | |
| - Mapped translation data: {"en": ..., "hi": ...} before training | |
| - Training: 3 epochs using GPU | |
| - Disabled: wandb logging | |
| - Skipped: Evaluation phase | |
| - Saved: Trained + Quantized model and tokenizer | |
| - Quantization: torch.quantization.Quantize_dynamic is used for efficient CPU inference | |