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davron04
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Model Details
Model Provider
davron04
Model Tree
google/gemma-3-270m
Input Modalities
Output Modalities
Supported Functionality
en), Uzbek (uz)The model was fine-tuned on a combination of the following datasets:
| Dataset | Source |
|---|---|
wikimedia-v20230407 | OPUS |
MLDataScientist/SlimOrca-Dedup-English-Uzbek | Hugging Face |
ML-Jonibek/English-Uzbek-Translation-1 | Hugging Face |
Jonibek21/English-Uzbek-Translation | Hugging Face |
The model uses a simple prompt format with language tags (<english> / <uzbek>) to indicate the source and target languages, and generates a translation in a decoder-only, causal-LM fashion.
python
from transformers import AutoTokenizer, AutoModelForCausalLM MODEL = "davron04/gemma-3-270m-dueta"ENGLISH_TAG = "<english>"UZBEK_TAG = "<uzbek>" def load_tokenizer_and_model(model_name: str): tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto") return tokenizer, model def translate_text(source_text: str, source_lang: str, target_lang: str, tokenizer, model) -> str: model_input = f"{source_lang}: {source_text}\n{target_lang}:" token_ids = tokenizer.encode(model_input, return_tensors="pt").to(model.device) input_token_count = token_ids.shape[1] output = model.generate(token_ids) generated_token_ids = output[0][input_token_count:] translated_text = tokenizer.decode(generated_token_ids, skip_special_tokens=True) return translated_text tokenizer, model = load_tokenizer_and_model(MODEL)source_text = "Hello, world! What can I do for you today?"translated_text = translate_text(source_text, ENGLISH_TAG, UZBEK_TAG, tokenizer, model)print(f"Translated text: {translated_text}") """Salom, dunyo! Bugun siz uchun nima qilishim mumkin?"""To translate from Uzbek to English, simply swap the source_lang and target_lang arguments:
python
translated_text = translate_text(source_text, UZBEK_TAG, ENGLISH_TAG, tokenizer, model)from transformers import AutoTokenizer, AutoModelForCausalLM MODEL = "davron04/gemma-3-270m-dueta"ENGLISH_TAG = "<english>"UZBEK_TAG = "<uzbek>" def load_tokenizer_and_model(model_name: str): tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto") return tokenizer, model def translate_text(source_text: str, source_lang: str, target_lang: str, tokenizer, model) -> str: model_input = f"{source_lang}: {source_text}\n{target_lang}:" token_ids = tokenizer.encode(model_input, return_tensors="pt").to(model.device) input_token_count = token_ids.shape[1] output = model.generate(token_ids) generated_token_ids = output[0][input_token_count:] translated_text = tokenizer.decode(generated_token_ids, skip_special_tokens=True) return translated_text tokenizer, model = load_tokenizer_and_model(MODEL)source_text = "Hello, world! What can I do for you today?"translated_text = translate_text(source_text, ENGLISH_TAG, UZBEK_TAG, tokenizer, model)print(f"Translated text: {translated_text}") """Salom, dunyo! Bugun siz uchun nima qilishim mumkin?"""