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Model: davron04/gemma-3-270m-dueta
Source: Original Platform

README.md

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+---
+library_name: transformers
+license: mit
+datasets:
+- MLDataScientist/SlimOrca-Dedup-English-Uzbek
+- ML-Jonibek/English-Uzbek-Translation-1
+- Jonibek21/English-Uzbek-Translation
+- davron04/wikimedia_v20230407_en_uz
+language:
+- uz
+- en
+base_model:
+- google/gemma-3-270m
+pipeline_tag: translation
+---
+
+# gemma-3-270m-dueta
+
+**DUETA** — a **D**ecoder-only, **U**zbek–**E**nglish **T**ransformer-based model for machine tr**A**nslation.
+
+This model is a fine-tuned version of [google/gemma-3-270m](https://huggingface.co/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](https://huggingface.co/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](https://opus.nlpl.eu/) |
+| `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.
+
+```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)
+```
+
+## 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.