Akshara 8B Llama Multilingual V0.1

🚀 Introducing Akshara-8B: AI for India 🇮🇳✨

We're proud to unveil Akshara-8B, our cutting-edge AI fleet specifically built for India's diverse linguistic landscape. Akshara is designed to seamlessly understand and generate text in multiple Indian languages, making AI more accessible, powerful, and tailored to our nation's needs.

✨ Features

🌍 What is Akshara?

Akshara-8B is a highly optimized distilled version of SVECTOR’s flagship large-scale AI model (Akshara). While retaining the core intelligence and multilingual capabilities of its parent model, Akshara-8B is specifically designed for efficiency, speed, and accessibility. It leverages advanced distillation techniques to provide powerful AI performance while being lightweight and scalable. Akshara-8B embodies SVECTOR’s commitment to bringing cutting-edge AI to India, ensuring robust support for India’s diverse languages and applications. 🚀

Akshara can fluently understand and generate content in: ✅ Hindi ✅ Gujarati ✅ Marathi ✅ Tamil ✅ Telugu ✅ Kannada ✅ Punjabi ✅ English

🔥 Why Akshara?

🔹 Made in India, for India & Global 🇮🇳 🔹 Optimized for speed and efficiency ⚡ 🔹 Seamless multilingual processing 🗣️ 🔹 Balanced accuracy and creativity 🎨 🔹 Lightweight and scalable for real-world applications 🚀

📦 Installation

Install Dependencies

pip install transformers torch

💻 Usage Examples

Basic Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "SVECTOR-CORPORATION/Akshara-8B-Llama-Multilingual-V0.1"

# Load the model
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)

# Sample input
input_text = "भारत की सबसे बड़ी भाषा कौनसी है?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

# Generate response
output = model.generate(**input_ids, max_new_tokens=256)
response = tokenizer.decode(output[0], skip_special_tokens=True)

print(response)

Advanced Usage

Akshara supports multi-turn, dynamic conversations across languages.

messages = [
    {"role": "system", "content": "आप Akshara हैं, भारत के लिए बना एक AI, जो हिंदी, गुजराती, मराठी, तमिल, तेलुगु, कन्नड़, पंजाबी और अंग्रेजी में बातचीत कर सकता है।"},
    {"role": "user", "content": "नमस्ते! आप क्या कर सकते हैं?"}
]

input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")

outputs = model.generate(input_ids, max_new_tokens=256)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))

📚 Documentation

By embracing India’s linguistic diversity, Akshara represents a major step toward bridging the AI gap in our country. Whether it's education, research, customer service, content creation, or smart automation, Akshara is here to revolutionize multilingual AI interactions.

Join us as we shape the future of AI for India! 🇮🇳🚀

Akshara

📄 License

This project is licensed under the Apache-2.0 license.

📖 Citation

@misc{SVECTOR2025Akshara,
  title     = {Akshara: A Multilingual AI Model for India},
  author    = {SVECTOR},
  year      = {2025},
  url       = {https://svector.co.in},
  note      = {Developed by SVECTOR CORPORATION for multilingual AI Model},
}