Instructions to use SVECTOR-CORPORATION/Akshara-8B-Llama-Multilingual-V0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SVECTOR-CORPORATION/Akshara-8B-Llama-Multilingual-V0.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SVECTOR-CORPORATION/Akshara-8B-Llama-Multilingual-V0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SVECTOR-CORPORATION/Akshara-8B-Llama-Multilingual-V0.1")
model = AutoModelForCausalLM.from_pretrained("SVECTOR-CORPORATION/Akshara-8B-Llama-Multilingual-V0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SVECTOR-CORPORATION/Akshara-8B-Llama-Multilingual-V0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SVECTOR-CORPORATION/Akshara-8B-Llama-Multilingual-V0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SVECTOR-CORPORATION/Akshara-8B-Llama-Multilingual-V0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SVECTOR-CORPORATION/Akshara-8B-Llama-Multilingual-V0.1

SGLang

How to use SVECTOR-CORPORATION/Akshara-8B-Llama-Multilingual-V0.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SVECTOR-CORPORATION/Akshara-8B-Llama-Multilingual-V0.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SVECTOR-CORPORATION/Akshara-8B-Llama-Multilingual-V0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SVECTOR-CORPORATION/Akshara-8B-Llama-Multilingual-V0.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SVECTOR-CORPORATION/Akshara-8B-Llama-Multilingual-V0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SVECTOR-CORPORATION/Akshara-8B-Llama-Multilingual-V0.1 with Docker Model Runner:
```
docker model run hf.co/SVECTOR-CORPORATION/Akshara-8B-Llama-Multilingual-V0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

We’re proud to unveil Akshara-8B, our cutting-edge AI fleet built exclusively 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.

🌍 What is Akshara?

Akshara-8B is a highly optimized distilled version of SVECTOR’s flagship large-scale AI model (Akshara). While it retains 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 🚀

🛠️ Usage Guide

Install Dependencies

pip install transformers torch

Load the Model

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)

💬 Multi-turn Conversation Support

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))

🌟 Akshara: Built for the Future of AI in India

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! 🇮🇳🚀

@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},
}

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Tensor type

BF16

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Collection

🚀 Introducing Akshara—AI built for India! 🇮🇳✨ Bringing powerful, language-first AI to every region. Shaping the future of AI for Bharat! • 2 items • Updated Nov 5, 2025 • 6