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 dev@nexa.ai.
