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README.md

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+---
+license: mit
+datasets:
+- skypro1111/ubertext-2-news-verbalized
+language:
+- uk
+base_model:
+- skypro1111/m2m100-ukr-verbalization
+pipeline_tag: text2text-generation
+library_name: transformers
+widget:
+- text: >-
+    Очікувалось, що цей застосунок буде запущено о 11 ранку 22.08.2025, але
+    розробники затягнули святкування і запуск був відкладений на 2 тижні.
+---
+
+# m2m100-ukr-verbalization-ct2
+
+This is the CTranslate2-optimized version of [skypro1111/m2m100-ukr-verbalization](https://huggingface.co/skypro1111/m2m100-ukr-verbalization).
+
+## About CTranslate2 Optimization
+
+CTranslate2 is an optimized inference engine for Transformer models that provides much faster inference than standard implementations. This repository contains the m2m100-418M model for Ukrainian verbalization, optimized for production deployment.
+
+Key advantages:
+- **Faster inference**: 3-5x speed improvement over the standard Transformers implementation
+- **Lower memory footprint**: Uses efficient int8/float16 quantization
+- **Better hardware utilization**: Optimized for both CPU and GPU
+- **Efficient batching**: Improved batch processing for production workloads
+
+## Usage
+
+```python
+import os
+import ctranslate2
+from transformers import M2M100Tokenizer
+
+def process_sentence(translator, tokenizer, sentence: str):
+    # Tokenize input
+    source = tokenizer.convert_ids_to_tokens(tokenizer.encode(sentence))
+    target_prefix = [tokenizer.lang_code_to_token["uk"]]
+    
+    # Run inference
+    results = translator.translate_batch(
+        [source],
+        target_prefix=[target_prefix],
+        beam_size=1,
+        num_hypotheses=1,
+        use_vmap=True,
+    )
+    
+    # Get target tokens and decode
+    target = results[0].hypotheses[0][1:]  # Remove language token
+    return tokenizer.decode(tokenizer.convert_tokens_to_ids(target))
+
+# Initialize translator with optimizations
+translator = ctranslate2.Translator(
+    "path/to/m2m100-ukr-verbalization-ct2",
+    device="cuda",
+    compute_type="int8_float16",  # Options: int8, int8_float16, float16, float32
+    intra_threads=16,
+)
+
+# Load tokenizer from the original model
+tokenizer = M2M100Tokenizer.from_pretrained("skypro1111/m2m100-ukr-verbalization")
+tokenizer.src_lang = "uk"
+
+# Example
+text = "Моя бабуся народилася 07.11.1919, у важкий післявоєнний час."
+result = process_sentence(translator, tokenizer, text)
+print(result)
+# Output: "Моя бабуся народилася сьомого листопада тисяча дев'ятсот дев'ятнадцятого року, у важкий післявоєнний час."
+```
+
+## Performance Comparison
+
+| Metric | Original Model | CTranslate2 Model |
+|--------|---------------|-------------------|
+| Inference time (avg) | ~300ms | ~70ms |
+| Memory usage | ~2.5GB | ~900MB |
+| Batch processing (10) | ~2.8s | ~0.7s |
+
+*Measurements taken on NVIDIA T4 GPU with int8_float16 quantization
+
+## Integration
+
+This model can be easily integrated into production systems using:
+- Python with CTranslate2 library
+- C++ applications via CTranslate2 C++ API
+- REST API services
+- Docker containers
+
+## Model Information
+
+For detailed information about the model capabilities, training data, and usage examples, please refer to the original model: [skypro1111/m2m100-ukr-verbalization](https://huggingface.co/skypro1111/m2m100-ukr-verbalization)
+
+## License
+
+This model is released under the MIT License, in line with the original m2m100-ukr-verbalization model.