Sarvam Translate

🚀 Sarvam-Translate

Sarvam-Translate is an advanced translation model developed by Sarvam AI. Built on Gemma3 - 4B - IT, it is designed for comprehensive, document - level translation across 22 official Indian languages. It can handle long - context inputs, diverse content types, and various formats, aiming to provide high - quality, contextually aware translations for Indian languages.

Learn more about Sarvam-Translate in our detailed blog post.

🚀 Quick Start

The following code snippet demonstrates how to use Sarvam-Translate using Transformers.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "sarvamai/sarvam-translate"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to('cuda:0')

# Translation task
tgt_lang = "Hindi"
input_txt = "Be the change you wish to see in the world."

# Chat-style message prompt
messages = [
    {"role": "system", "content": f"Translate the text below to {tgt_lang}."},
    {"role": "user", "content": input_txt}
]

# Apply chat template to structure the conversation
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

# Tokenize and move input to model device
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# Generate the output
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=1024,
    do_sample=True,
    temperature=0.01,
    num_return_sequences=1
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
output_text = tokenizer.decode(output_ids, skip_special_tokens=True)

print("Input:", input_txt)
print("Translation:", output_text)

✨ Features

• Comprehensive Indian Language Support: Focus on the 22 official Indian languages, ensuring nuanced and accurate translations.
• Advanced Document-Level Translation: Translates entire documents, web pages, speeches, textbooks, and scientific articles, not just isolated sentences.
• Versatile Format Handling: Processes a wide array of input formats, including markdown, digitized content (handling OCR errors), documents with embedded math and chemistry equations, and code files (translating only comments).
• Context-Aware & Inclusive: Engineered to respect different contexts, formats, styles (formal/informal), and ensure inclusivity (e.g., appropriate gender attribution).

📦 Supported languages list

Assamese, Bengali, Bodo, Dogri, Gujarati, English, Hindi, Kannada, Kashmiri, Konkani, Maithili, Malayalam, Manipuri, Marathi, Nepali, Odia, Punjabi, Sanskrit, Santali, Sindhi, Tamil, Telugu, Urdu

💻 Usage Examples

vLLM Deployment

Server:

vllm serve sarvamai/sarvam-translate --port 8000 --dtype bfloat16

Client:

from openai import OpenAI

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id


tgt_lang = 'Hindi'
input_txt = 'Be the change you wish to see in the world.'
messages = [{"role": "system", "content": f"Translate the text below to {tgt_lang}."}, {"role": "user", "content": input_txt}]


response = client.chat.completions.create(model=model, messages=messages, temperature=0.01)
output_text = response.choices[0].message.content

print("Input:", input_txt)
print("Translation:", output_text)

With Sarvam APIs

Refer our python client documentation.

Sample code:

from sarvamai import SarvamAI
client = SarvamAI()
response = client.text.translate(
    input="Be the change you wish to see in the world.",
    source_language_code="en-IN",
    target_language_code="hi-IN",
    speaker_gender="Male",
    model="sarvam-translate:v1",
)

📄 License

This project is licensed under the GPL - 3.0 license.