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README.md

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----
-library_name: transformers
-license: gpl-3.0
-language:
-- as
-- bn
-- brx
-- doi
-- gom
-- gu
-- en
-- hi
-- kn
-- ks
-- mai
-- ml
-- mni
-- mr
-- ne
-- or
-- pa
-- sa
-- sat
-- sd
-- ta
-- te
-- ur
-base_model:
-- google/gemma-3-4b-it
-base_model_relation: finetune
-pipeline_tag: translation
----
-
-# Sarvam-Translate
-<p align="center">
-  <a href="https://dashboard.sarvam.ai/translate"
-     target="_blank" rel="noopener noreferrer">
-    <img
-      src="https://img.shields.io/badge/🚀 Try on Sarvam&nbsp;Playground-1488CC?style=for-the-badge&logo=rocket"
-      alt="Try on Sarvam Playground"
-    />
-  </a>
-</p>
-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](https://www.sarvam.ai/blogs/sarvam-translate).
-
-## 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](https://pypi.org/project/sarvamai/).
-
-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",
-)
-```