Gemma3-En2Indic-NMT-270M API & Inference Endpoint

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

Table with columns: Property, Value
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

Table with columns: Language, Language Code
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

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 torchfrom 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 = Truemodel.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 = 256num_beams = 5do_sample = Falseuse_cache = Truepadding_side = "left"dtype = torch.bfloat16attn_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:

Table with columns: Language Code, Language, BLEU ↑, chrF2 ↑
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

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.

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

Table with columns: Property, Value
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

Table with columns: Language, Language Code
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

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 torchfrom 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 = Truemodel.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 = 256num_beams = 5do_sample = Falseuse_cache = Truepadding_side = "left"dtype = torch.bfloat16attn_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:

Table with columns: Language Code, Language, BLEU ↑, chrF2 ↑
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

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.

Gemma3-En2Indic-NMT-270M

README

Gemma-3-270M English → Indic Translator

Model Details

Supported Languages

Dataset

Prompt Format

Usage

Recommended Generation Settings

Evaluation

Known Limitations

Citation

Acknowledgements

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README

Gemma-3-270M English → Indic Translator

Model Details

Supported Languages

Dataset

Prompt Format

Usage

Recommended Generation Settings

Evaluation

Known Limitations

Citation

Acknowledgements