Create README.md

README.md

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+## English to French Translation AI Model
+
+This demonstrates training, quantization, and inference of a text translation model from **English to French** using Hugging Face Transformers on CUDA-enabled devices.
+
+## 🧠 Model Overview
+
+- **Base Model**: `Helsinki-NLP/opus-mt-tc-big-en-fr`
+- **Task**: English to French text translation
+- **Dataset**: [`FrancophonIA/english_french`](https://huggingface.co/datasets/FrancophonIA/english_french)
+- **Framework**: Hugging Face Transformers & Datasets
+- **Accelerator**: CUDA (GPU)
+
+---
+
+## 📦 Dependencies
+
+Install all required Python packages:
+
+```python
+ pip install torch transformers datasets evaluate sentencepiece
+```
+
+# Load Dataset
+
+
+```python
+ from datasets import load_dataset
+ dataset = load_dataset("FrancophonIA/english_french")
+ dataset["train"] = dataset["train"].shuffle(seed=42).select(range(60000))
+```
+
+## ⚙️ Training Configuration
+ Training is done using Seq2SeqTrainer with the following configuration:
+
+ - **batch_size**: **8**
+
+ - **epochs**: **3**
+
+ - **fp16**: **Mixed precision enabled**
+
+ - **save_strategy**: **Disabled to reduce I/O**
+
+ - **report_to**: **Disabled (no Weights & Biases)**
+
+## 🧊 Model Quantization (CPU Inference)
+
+ We apply dynamic quantization on the trained model to reduce size and enable CPU inference:
+
+```python
+quantized_model = torch.quantization.quantize_dynamic(
+    model.cpu(), {torch.nn.Linear}, dtype=torch.qint8
+)
+```
+
+## 📏 Evaluation (Optional)
+The BLEU score section is commented out but can be enabled by:
+
+```python
+from evaluate import load
+
+metric = load("sacrebleu")
+score = metric.compute(predictions=predictions, references=references)
+print(f"BLEU Score: {score['score']}")
+```