Update README.md

README.md

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 ---
+language:
+- multilingual
+pipeline_tag: translation
+tags:
+- universal-translation
+- nmt
+- transformer
+- encoder-decoder
+- pytorch
 license: apache-2.0
+datasets:
+- code-with-zeeshan/UTS-Datasets
+library_name: universal-translation-system
 ---
+
+# Universal Translation System
+
+A compact, production-ready multilingual neural machine translation model supporting **20 languages** (190 language pairs). Trained on curated OPUS-100 data with synthetic augmentation, knowledge distillation, and neural quality filtering.
+
+## Model Architecture
+
+| Component | Configuration |
+|-----------|--------------|
+| Encoder | 6-layer Transformer, 512 hidden dim, 8 heads |
+| Decoder | 8-layer Transformer, 768 hidden dim, 12 heads |
+| Vocab | 32K tokens, script-grouped (latin, cjk, arabic, devanagari, cyrillic, thai) |
+| Params | ~40MB (compact), ~150M total |
+| Precision | BF16 mixed-precision training |
+
+## Supported Languages
+
+| Group | Languages |
+|-------|-----------|
+| Latin | en, es, fr, de, it, pt, nl, sv, pl, id, vi, tr |
+| CJK | zh, ja, ko |
+| Arabic | ar |
+| Devanagari | hi |
+| Cyrillic | ru, uk |
+| Thai | th |
+
+## Usage
+
+### Via the CLI (`uts`)
+
+```bash
+# Translate a sentence
+uts serve --config config/base.yaml
+curl -X POST http://localhost:8000/translate \
+  -H "Content-Type: application/json" \
+  -d '{"text": "Hello world", "source": "en", "target": "es"}'
+```
+
+### Via Python
+
+```python
+from runtime.encoder.universal_encoder import UniversalEncoder
+from runtime.cloud_decoder import OptimizedUniversalDecoder
+
+encoder = UniversalEncoder.from_pretrained("code-with-zeeshan/Universal-Translation-System")
+decoder = OptimizedUniversalDecoder.from_pretrained("code-with-zeeshan/Universal-Translation-System")
+# See docs/API.md for full inference examples
+```
+
+### Via Hugging Face Hub
+
+```python
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+model = AutoModelForSeq2SeqLM.from_pretrained("code-with-zeeshan/Universal-Translation-System")
+tokenizer = AutoTokenizer.from_pretrained("code-with-zeeshan/Universal-Translation-System")
+```
+
+## Training
+
+The model was trained using the [Universal Translation System](https://github.com/code-with-zeeshan/universal-translation-system) pipeline:
+
+1. **Data pipeline** — OPUS-100 download, sampling, augmentation (false friends, idioms, backtranslation), COMET quality filtering
+2. **Knowledge distillation** — NLLB-3.3B teacher → compact student
+3. **Vocabulary** — Script-grouped SentencePiece tokenizer (32K per group)
+4. **Training** — BF16 mixed-precision, dynamic batch sizing, gradient checkpointing. ~10 epochs with cosine LR schedule.
+
+## Evaluation
+
+| Metric | Score |
+|--------|-------|
+| BLEU (average across 190 pairs) | *Coming soon* |
+| COMET (average) | *Coming soon* |
+
+## Files
+
+- `encoder/` — Universal encoder weights
+- `decoder/` — Optimized decoder weights
+- `vocab/` — Script-grouped vocabulary packs
+- `config.yaml` — Training configuration used for this model
+
+## License
+
+Apache 2.0