LFM2-1.2B
Run LFM2-1.2B on Qualcomm NPU with NexaSDK.
Quickstart
Install NexaSDK and create a free account at sdk.nexa.ai
Activate your device with your access token:
nexa config set license '<access_token>'Run the model locally in one line:
nexa infer NexaAI/LFM2-1.2B-npu
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].
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