Text Generation
Transformers
Burmese
English
myanmar
burmese
llm
chat
instruction-following
conversational
autoregressive
Instructions to use amkyawdev/myanmar-ghost with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amkyawdev/myanmar-ghost with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="amkyawdev/myanmar-ghost") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("amkyawdev/myanmar-ghost", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use amkyawdev/myanmar-ghost with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "amkyawdev/myanmar-ghost" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "amkyawdev/myanmar-ghost", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/amkyawdev/myanmar-ghost
- SGLang
How to use amkyawdev/myanmar-ghost with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "amkyawdev/myanmar-ghost" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "amkyawdev/myanmar-ghost", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "amkyawdev/myanmar-ghost" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "amkyawdev/myanmar-ghost", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use amkyawdev/myanmar-ghost with Docker Model Runner:
docker model run hf.co/amkyawdev/myanmar-ghost
| """Back-translation augmentation for Myanmar text. | |
| Translates text to another language and back to create | |
| paraphrased versions for data augmentation. | |
| """ | |
| import logging | |
| from pathlib import Path | |
| from typing import Dict, List, Optional, Tuple | |
| logger = logging.getLogger(__name__) | |
| class BackTranslator: | |
| """Back-translation augmentation using translation APIs.""" | |
| def __init__( | |
| self, | |
| translator_api: Optional[object] = None, | |
| target_lang: str = "en", | |
| source_lang: str = "my", | |
| ): | |
| """ | |
| Args: | |
| translator_api: Translation API instance | |
| target_lang: Target language for translation | |
| source_lang: Source language | |
| """ | |
| self.translator_api = translator_api | |
| self.target_lang = target_lang | |
| self.source_lang = source_lang | |
| def translate( | |
| self, | |
| text: str, | |
| direction: str = "forward", | |
| ) -> Optional[str]: | |
| """Translate text. | |
| Args: | |
| text: Text to translate | |
| direction: "forward" (src->tgt) or "backward" (tgt->src) | |
| Returns: | |
| Translated text or None if failed | |
| """ | |
| if self.translator_api is None: | |
| # Simulate translation for testing | |
| return self._simulate_translation(text, direction) | |
| try: | |
| if direction == "forward": | |
| return self.translator_api.translate( | |
| text, | |
| src=self.source_lang, | |
| tgt=self.target_lang, | |
| ) | |
| else: | |
| return self.translator_api.translate( | |
| text, | |
| src=self.target_lang, | |
| tgt=self.source_lang, | |
| ) | |
| except Exception as e: | |
| logger.error(f"Translation failed: {e}") | |
| return None | |
| def _simulate_translation( | |
| self, | |
| text: str, | |
| direction: str, | |
| ) -> str: | |
| """Simulate translation for testing without API. | |
| In real use, this would call a translation service. | |
| """ | |
| # This is a placeholder - real implementation would use | |
| # Google Translate, DeepL, or similar API | |
| # For testing, just return the original text | |
| # with a marker to indicate it was "translated" | |
| marker = "[EN]" if direction == "forward" else "[MY]" | |
| return f"{marker}{text}{marker}" | |
| def back_translate( | |
| self, | |
| text: str, | |
| ) -> Tuple[Optional[str], Optional[str], Optional[str]]: | |
| """Translate text to target language and back. | |
| Args: | |
| text: Myanmar text | |
| Returns: | |
| (forward_translation, back_translation, final_text) | |
| """ | |
| # Forward translation | |
| forward = self.translate(text, "forward") | |
| if forward is None: | |
| return None, None, None | |
| # Back translation | |
| back = self.translate(forward, "backward") | |
| if back is None: | |
| return forward, None, None | |
| return forward, back, back | |
| def augment_dataset( | |
| self, | |
| samples: List[Dict], | |
| batch_size: int = 10, | |
| ) -> List[Dict]: | |
| """Augment dataset using back-translation. | |
| Args: | |
| samples: List of sample dictionaries | |
| batch_size: Batch size for API calls | |
| Returns: | |
| List of augmented samples | |
| """ | |
| augmented = [] | |
| for i, sample in enumerate(samples): | |
| text = sample.get("text", "") | |
| forward, back, final = self.back_translate(text) | |
| if final and final != text: | |
| aug_sample = sample.copy() | |
| aug_sample["text"] = final | |
| aug_sample["forward_translation"] = forward | |
| aug_sample["back_translation"] = back | |
| aug_sample["augmentation_type"] = "back_translation" | |
| aug_sample["is_augmented"] = True | |
| augmented.append(aug_sample) | |
| if (i + 1) % batch_size == 0: | |
| logger.info(f"Processed {i + 1}/{len(samples)} samples") | |
| return augmented | |
| class TranslationAugmenter: | |
| """Advanced translation-based augmentation.""" | |
| def __init__( | |
| self, | |
| translator_api: Optional[object] = None, | |
| languages: Optional[List[str]] = None, | |
| ): | |
| """ | |
| Args: | |
| translator_api: Translation API instance | |
| languages: List of intermediate languages for multi-hop translation | |
| """ | |
| self.translator_api = translator_api | |
| self.languages = languages or ["en", "zh", "ja", "ko"] | |
| def multi_hop_translate( | |
| self, | |
| text: str, | |
| intermediate_langs: Optional[List[str]] = None, | |
| ) -> str: | |
| """Translate through multiple intermediate languages. | |
| Args: | |
| text: Text to translate | |
| intermediate_langs: Languages to translate through | |
| Returns: | |
| Final translated text | |
| """ | |
| if intermediate_langs is None: | |
| intermediate_langs = random.sample( | |
| self.languages, | |
| k=min(2, len(self.languages)) | |
| ) | |
| current_text = text | |
| for lang in intermediate_langs: | |
| # Translate to intermediate language | |
| if self.translator_api: | |
| current_text = self.translator_api.translate( | |
| current_text, | |
| src="my", | |
| tgt=lang, | |
| ) | |
| # Translate back to Myanmar | |
| if self.translator_api: | |
| current_text = self.translator_api.translate( | |
| current_text, | |
| src=lang, | |
| tgt="my", | |
| ) | |
| return current_text | |
| def paraphrase_with_context( | |
| self, | |
| text: str, | |
| context: str, | |
| ) -> str: | |
| """Paraphrase text while maintaining context. | |
| Args: | |
| text: Text to paraphrase | |
| context: Additional context to help translation | |
| Returns: | |
| Paraphrased text | |
| """ | |
| # Combine text with context | |
| combined = f"{context}: {text}" | |
| # Translate and back-translate | |
| translator = BackTranslator(self.translator_api) | |
| _, _, paraphrased = translator.back_translate(combined) | |
| return paraphrased if paraphrased else text | |
| def create_back_translator( | |
| translator_api: Optional[object] = None, | |
| target_lang: str = "en", | |
| ) -> BackTranslator: | |
| """Factory function to create back translator.""" | |
| return BackTranslator( | |
| translator_api=translator_api, | |
| target_lang=target_lang, | |
| ) | |
| if __name__ == "__main__": | |
| print("BackTranslator loaded") | |
| print("For production use, integrate with translation APIs like:") | |
| print(" - Google Cloud Translation") | |
| print(" - DeepL API") | |
| print(" - transformers.TranslationPipeline") | |