---
license: apache-2.0

language:
  - en
  - hi
  - bn
  - gu
  - kn
  - ml
  - mr
  - or
  - pa
  - ta
  - te
  - as
  - ur
  - ks
  - sa
library_name: transformers
pipeline_tag: translation
base_model: google/gemma-3-270m-it
tags:
  - translation
  - machine-translation
  - english-to-indic
  - indic
  - multilingual
  - gemma-3
  - gemma3
  - bpcc
  - ai4bharat
  - flash-attention
  - bfloat16
  - transformers
---

# Gemma-3-270M English → Indic Translator

A fine-tuned version of **Gemma-3-270m-it** for multilingual machine translation from **English to 14 Indic languages**.

This model is designed for lightweight, fast, and high-quality translation from English into major Indic languages while maintaining the conversational capabilities inherited from Gemma-3.


# Model Details

| Property | Value |
|----------|-------|
| **Base Model** | google/gemma-3-270m-it |
| **Architecture** | Gemma 3 |
| **Parameters** | 270 Million |
| **Task** | English → Indic Machine Translation |
| **Framework** | Hugging Face Transformers |
| **Precision** | bfloat16 |
| **Attention** | Flash Attention 2 |
| **Generation** | Beam Search |
| **Beam Size** | 5 |
| **Sampling** | Disabled (`do_sample=False`) |
| **Padding Side** | Left |
| **Cache** | Enabled (`use_cache=True`) |


# Supported Languages

| Language | Language Code |
|------------|---------------|
| Assamese | asm_Beng |
| Bengali | ben_Beng |
| Gujarati | guj_Gujr |
| Hindi | hin_Deva |
| Kannada | kan_Knda |
| Kashmiri | kas_Arab |
| Malayalam | mal_Mlym |
| Marathi | mar_Deva |
| Odia | ory_Orya |
| Punjabi | pan_Guru |
| Sanskrit | san_Deva |
| Tamil | tam_Taml |
| Telugu | tel_Telu |
| Urdu | urd_Arab |

Input language is always **English**.


# Dataset

The model was fine-tuned using the **AI4Bharat BPCC (bpcc-seed-v2)** multilingual parallel corpus.

The dataset contains parallel English–Indic sentence pairs covering multiple domains and language families.


# Prompt Format

The model expects prompts in the following format:

```text
Translate to {Target Language}:

{English Sentence}
```

Example:

```text
Translate to Telugu:

Artificial Intelligence is changing healthcare.
```


# Usage

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL_NAME = "ManiKumarAdapala/Gemma3-En2Indic-NMT-270M"

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
tokenizer.padding_side = "left"

model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map={"": 0},
)

model.config.use_cache = True
model.eval()

EOT = tokenizer.convert_tokens_to_ids("<end_of_turn>")


@torch.inference_mode()
def translate(sentences, language):

    if isinstance(sentences, str):
        sentences = [sentences]

    prompts = [
        tokenizer.apply_chat_template(
            [
                {
                    "role": "user",
                    "content": f"Translate to {language}:\n\n{s}",
                }
            ],
            tokenize=False,
            add_generation_prompt=True,
        )
        for s in sentences
    ]

    inputs = tokenizer(
        prompts,
        return_tensors="pt",
        padding=True,
        add_special_tokens=False,
    ).to(model.device)

    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=False,
        num_beams=5,
        eos_token_id=[
            tokenizer.eos_token_id,
            EOT,
        ],
        pad_token_id=tokenizer.pad_token_id,
    )

    generated = outputs[:, inputs["input_ids"].shape[1]:]

    return tokenizer.batch_decode(
        generated,
        skip_special_tokens=True,
    )


sentence = "Artificial Intelligence is transforming agriculture."

translation = translate(sentence, "Hindi")

print(translation[0])
```


# Recommended Generation Settings

```python
max_new_tokens = 256
num_beams = 5
do_sample = False
use_cache = True
padding_side = "left"
dtype = torch.bfloat16
attn_implementation = "flash_attention_2"
```

These settings are the same as those used in the provided inference notebook and are recommended for obtaining deterministic, high-quality translations.


# Evaluation

The model was evaluated using sentence pairs from the BPCC dataset.

Metrics used:

- BLEU
- chrF2

Average benchmark results:

| Language Code | Language | BLEU ↑ | chrF2 ↑ |
|:--------------|:---------|-------:|--------:|
| asm_Beng | Assamese | **44.1** | **48.2** |
| ben_Beng | Bengali | **55.3** | **54.3** |
| guj_Gujr | Gujarati | **54.1** | **53.5** |
| hin_Deva | Hindi | **61.9** | **59.5** |
| kan_Knda | Kannada | **41.0** | **46.6** |
| kas_Arab | Kashmiri | **8.4** | **31.6** |
| mal_Mlym | Malayalam | **45.3** | **46.7** |
| mar_Deva | Marathi | **50.7** | **50.6** |
| ory_Orya | Odia | **40.6** | **44.3** |
| pan_Guru | Punjabi | **48.7** | **49.2** |
| san_Deva | Sanskrit | **38.4** | **41.2** |
| tam_Taml | Tamil | **48.9** | **49.2** |
| tel_Telu | Telugu | **49.8** | **49.7** |
| urd_Arab | Urdu | **33.7** | **55.2** |
| **Average** | **14 Languages** | **44.4** | **48.6** |


# Known Limitations

Like most compact multilingual translation models, this model has a few limitations.

- Numerical values may occasionally change during translation.
- Rarely, Latin characters may appear within Indic script outputs.
- Translation quality varies across languages, with lower-resource languages generally being more challenging.
- Not intended for legal, medical, or other safety-critical translation tasks without human verification.


# Citation

```bibtex
@misc{Gemma3-En2Indic-NMT-270M,
  title = {Gemma3-En2Indic-NMT-270M: English to Indic Neural Machine Translation},
  author = {Adapala, Mani Kumar},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/ManiKumarAdapala/Gemma3-En2Indic-NMT-270M}
}
```


# Acknowledgements

This work builds upon:

- **Google** for the Gemma-3 model.
- **AI4Bharat** for the BPCC multilingual parallel corpus.
- **Hugging Face** for the Transformers ecosystem.
- The open-source community for tools and libraries that enabled this work.