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sarvamai

sarvamai/sarvam-translate

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Model Documentation

Sarvam-Translate

Try on Sarvam Playground

Sarvam-Translate is an advanced translation model built by Sarvam AI in partnership with AI4Bharat, specifically designed for comprehensive, document-level translation across the 22 official Indian languages, built on Gemma3-4B-IT. It addresses modern translation needs by moving beyond isolated sentences to handle long-context inputs, diverse content types, and various formats. Sarvam-Translate aims to provide high-quality, contextually aware translations for Indian languages, which have traditionally lagged behind high-resource languages in LLM performance.

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

Key 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. Maximum context length: 8k tokens
  • 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

    Quickstart

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



    vLLM Deployment



    Server:

    bash
vllm serve sarvamai/sarvam-translate --port 8000 --dtype bfloat16 --max-model-len 8192


    Client:

    python
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:

    python
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",
)

    Files & Weights

    FilenameSizeAction
    model-00001-of-00002.safetensors 4.62 GB
    model-00002-of-00002.safetensors 3.39 GB