gemma-3-270m-dueta/README.md

library_name, license, datasets, language, base_model, pipeline_tag

library_name	license	datasets	language	base_model	pipeline_tag
transformers	mit	MLDataScientist/SlimOrca-Dedup-English-Uzbek ML-Jonibek/English-Uzbek-Translation-1 Jonibek21/English-Uzbek-Translation davron04/wikimedia_v20230407_en_uz	uz en	google/gemma-3-270m	translation

gemma-3-270m-dueta

DUETA — a Decoder-only, Uzbek–English Transformer-based model for machine trAnslation.

This model is a fine-tuned version of google/gemma-3-270m, adapted for bidirectional English ↔ Uzbek translation using a decoder-only architecture. The approach follows the methodology described in DIETA: A Decoder-only transformer-based model for Italian–English machine TrAnslation, applying the same decoder-only translation paradigm to the English–Uzbek language pair.

Model Details

• Base model: google/gemma-3-270m
• Architecture: Decoder-only transformer
• Languages: English (en), Uzbek (uz)
• Task: Machine translation (En→Uz and Uz→En)
• Reference paper: DIETA: A Decoder-only transformer-based model for Italian–English machine TrAnslation

Training Data

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

How to Use

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.

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:

translated_text = translate_text(source_text, UZBEK_TAG, ENGLISH_TAG, tokenizer, model)

Limitations

• Context length: Avoid feeding the model very long context. Performance degrades with long inputs; it is recommended to split long text into individual sentences before translation and process them one at a time (or in short chunks) rather than passing entire paragraphs or documents at once.
• As with any machine translation model, performance may vary across domains, informal/colloquial text, and low-resource constructs not well represented in the training data.
• The model has not been evaluated for factual accuracy preservation in translation; it should not be used for translating sensitive or critical content without human review.