LFM2-1.2B-npu-mobile

LFM2-1.2B

Run LFM2-1.2B on Android devices powered by Qualcomm NPU.

Quickstart

See Documentation

Model Description

LFM2-1.2B is part of Liquid AI’s second-generation LFM2 family, designed specifically for on-device and edge AI deployment.
With 1.2 billion parameters, it strikes a balance between compact size, strong reasoning, and efficient compute utilization—ideal for running on CPUs, GPUs, or NPUs.

LFM2 introduces a hybrid Liquid architecture with multiplicative gates and short convolutions, enabling faster convergence and improved contextual reasoning.
It demonstrates up to 3× faster training and 2× faster inference on CPU compared to Qwen3, while maintaining superior accuracy across multilingual and instruction-following benchmarks.

Features

• ⚡ Speed & Efficiency – 2× faster inference and prefill].
• 🧠 Hybrid Liquid Architecture – Combines multiplicative gating with convolutional layers for better reasoning and token reuse.
• 🌍 Multilingual Competence – Supports diverse languages for global use cases.
• 🛠 Flexible Deployment – Runs efficiently on CPU, GPU, and NPU hardware.
• 📈 Benchmark Performance – Outperforms similarly-sized models in math, knowledge, and reasoning tasks.

Use Cases

• Edge AI assistants and voice agents
• Offline reasoning and summarization on mobile or automotive devices
• Local code and text generation tools
• Lightweight multimodal or RAG pipelines
• Domain-specific fine-tuning for vertical applications (e.g., finance, robotics)

Inputs and Outputs

Input

• Text prompts or structured instructions (tokenized sequences for API use).

Output

• Natural-language or structured text generations.
• Optionally: logits or embeddings for advanced downstream integration.

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].