tttt111/test-tmp3
LFM2.5-1.2B-Instruct by Liquid AI is a 1.17 billion parameter instruction-tuned hybrid model from the LFM2.5 family, featuring a 32,768 token context length. It is specifically designed for efficient on-device deployment, offering fast inference speeds on various hardware including CPUs and NPUs. This model excels in agentic tasks, data extraction, and RAG, supporting tool use and multilingual capabilities across 8 languages.
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LFM2.5-1.2B-Instruct: On-Device AI
LFM2.5-1.2B-Instruct, developed by Liquid AI, is a 1.17 billion parameter instruction-tuned model built on the LFM2 architecture. It features an extended pre-training of 28 trillion tokens and large-scale multi-stage reinforcement learning. This model is optimized for on-device deployment, delivering high performance comparable to much larger models while maintaining a low memory footprint (under 1GB).
Key Capabilities
- Efficient On-Device Inference: Achieves 239 tok/s on AMD CPU and 82 tok/s on mobile NPU, with day-one support for llama.cpp, MLX, and vLLM. Optimized versions are available for Apple Silicon and various hardware.
- Long Context Window: Supports a context length of 32,768 tokens.
- Multilingual Support: Capable in English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish.
- Tool Use: Supports function calling with a Pythonic format for agentic workflows, allowing for function definition, execution, and interpretation.
- Strong Performance: Benchmarks show competitive results against other sub-2B models, particularly in IFEval (86.23) and Multi-IF (60.98), and significantly faster inference speeds compared to Qwen3-1.7B on mobile CPUs.
Good For
- Agentic tasks
- Data extraction
- Retrieval Augmented Generation (RAG)
- Edge deployment scenarios on mobile devices, laptops, and embedded systems.