Sarvam-Translate
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
Supported directions
The open-sourced checkpoint only supports:
- English to Indian languages
- Indian languages to English
Note: For Indic to Indic, please pivot via English (that is, translate Indic to English first, then English to Indic)
Quickstart
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)
vLLM Deployment
Server:
vllm serve sarvamai/sarvam-translate --port 8000 --dtype bfloat16 --max-model-len 8192
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",
)