Quickstart

Get started with NexaAI Python SDK - choose your platform and explore AI capabilities.

What is NexaAI Python SDK?

The NexaAI Python SDK provides a comprehensive API for on-device AI inference across multiple modalities. It supports:

• Large Language Models (LLM): Text generation and conversation
• Vision-Language Models (VLM): Multimodal understanding and generation
• Embedder: Text vectorization and similarity computation
• Reranker: Document reranking
• ASR (Automatic Speech Recognition): Speech-to-text transcription
• CV (Computer Vision): OCR/text recognition
• TTS (Text-to-Speech): Text-to-speech synthesis
• ImageGen: Image generation from text prompts
• Diarize: Speaker diarization

Choose Your Platform

The SDK supports multiple platforms with optimized backends. Select your platform for detailed setup instructions:

macOS

Apple Silicon optimized
Python 3.10 • MLX backend • Metal acceleration

Windows x64

CPU/GPU acceleration
Python 3.10 • GGUF models • CUDA support

Windows ARM64

NPU acceleration
Python 3.11-3.13 ARM64 • Snapdragon X Elite

Quick Overview

Installation

Each platform has specific installation requirements. Follow your platform guide for detailed instructions:

• macOS: pip install 'nexaai[mlx]'
• Windows x64: pip install nexaai
• Windows ARM64: pip install nexaai

Authentication

Set up your NexaAI token from https://sdk.nexa.ai/:

# Linux/macOS
export NEXA_TOKEN="key/your_token_here"

# Windows
$env:NEXA_TOKEN="key/your_token_here"

Basic Usage

Here’s a simple example to get you started:

from nexaai import LLM, GenerationConfig, ModelConfig, LlmChatMessage

# Initialize model (platform-specific)
model_name = "NexaAI/Qwen3-1.7B-4bit-MLX"  # Example for macOS
config = ModelConfig()
llm = LLM.from_(model=model_name, config=config)

# Create conversation
conversation = [
    LlmChatMessage(role="system", content="You are a helpful assistant."),
    LlmChatMessage(role="user", content="Hello, how are you?")
]

# Generate response
prompt = llm.apply_chat_template(conversation)
for token in llm.generate_stream(prompt, GenerationConfig(max_tokens=100)):
    print(token, end="", flush=True)

This is a simplified example. For complete setup instructions, model recommendations, and platform-specific optimizations, please refer to your platform guide above.

Next Steps

1. Choose your platform and follow the detailed setup guide
2. Explore the API Reference for comprehensive documentation
3. Check out platform-specific examples in your chosen guide

Was this page helpful?

Yes

No