Create README.md

README.md

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+# 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)
+- **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
+