Granite-4.0-h-350M-NPU

Granite-4.0-h-350M

<p align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6851901ea43b4824f79e27a9/vBAkkCukOQ3CHlT2GBwvI.png" width="350" height="350"> </p>

Run Granite-4.0-h-350M optimized for Qualcomm Hexagon NPUs with NexaSDK.

Quickstart

1. Install NexaSDK

2. Activate your device with your access token for free at sdk.nexa.ai

   nexa config set license '<access_token>'
   

3. Run the model on Qualcomm NPU in one line:

   nexa infer NexaAI/Granite-4.0-h-350M-NPU
   

Model Description

Granite-4.0-h-350M is a 350-million-parameter transformer model from IBM’s Granite 4.0 family — designed for efficient inference, low-latency edge deployment, and instruction following at compact scale.
It shares the same data quality, architecture design, and alignment pipeline as larger Granite 4.0 models but is optimized for lightweight environments where performance per watt and model size are critical.

Built on the Granite 4.0 foundation, this model continues IBM’s commitment to open, responsible AI, offering transparency and adaptability for developers, researchers, and embedded AI applications.

Features

• Compact yet capable: Delivers high-quality generation and reasoning with just 350M parameters.
• Instruction-tuned: Follows natural language instructions for diverse tasks.
• Low-latency performance: Ideal for CPU, GPU, and NPU inference.
• Efficient deployment: Runs smoothly on edge and resource-constrained devices.
• Open and transparent: Released under IBM’s open model governance framework.

Use Cases

• On-device assistants and chatbots
• Edge AI and IoT inference
• Document and text summarization
• Education and lightweight reasoning tasks
• Prototype fine-tuning for domain adaptation

Inputs and Outputs

Input:

• Text prompt (instruction or question)

Output:

• Generated text response completing or following the input prompt

License

This model is released under the Creative Commons Attribution–NonCommercial 4.0 (CC BY-NC 4.0) license.
Non-commercial use, modification, and redistribution are permitted with attribution.
For commercial licensing, please contact [email protected].