ErtasAI/LFM2.5-1.2B-Instruct
LFM2.5-1.2B-Instruct is a 1.17 billion parameter instruction-tuned causal language model developed by Liquid AI, designed for efficient on-device deployment. This hybrid model, built on the LFM2 architecture, features extended pre-training on 28T tokens and large-scale multi-stage reinforcement learning, supporting a 32,768-token context length. It delivers best-in-class performance for its size, rivaling larger models, and offers fast edge inference on CPUs and mobile NPUs, making it suitable for agentic tasks, data extraction, and RAG in resource-constrained environments. The model supports multiple languages including English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish, and includes advanced features like tool use and a ChatML-like chat template.
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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 optimized for on-device deployment. It is part of the LFM2.5 family of hybrid models, building on the LFM2 architecture with significantly extended pre-training (28T tokens) and multi-stage reinforcement learning.
Key Capabilities & Features
- Best-in-Class Performance: Achieves performance comparable to much larger models, enabling high-quality AI in resource-constrained environments.
- Fast Edge Inference: Designed for rapid execution, demonstrating 239 tok/s decode on AMD CPU and 82 tok/s on mobile NPU, while operating under 1GB of memory.
- Broad Compatibility: Offers day-one support for popular inference frameworks like llama.cpp, MLX, and vLLM, with quantized formats (GGUF, ONNX, MLX) for diverse hardware.
- Multilingual Support: Capable in English, Arabic, Chinese, French, German, Japanese, Korean, and Spanish.
- Advanced Functionality: Supports a ChatML-like chat template and robust tool use capabilities, including Pythonic function calls and JSON output options.
- Extensive Context: Features a 32,768-token context length and a 65,536-token vocabulary size.
Performance Highlights
Benchmarks show LFM2.5-1.2B-Instruct outperforming other sub-2B models like Qwen3-1.7B and Gemma 3 1B IT across various metrics, including GPQA, MMLU-Pro, IFEval, and BFCLv3. Its efficient design also translates to superior inference speeds and lower memory consumption compared to similar-sized models on mobile CPUs and NPUs.
Recommended Use Cases
- Agentic tasks
- Data extraction
- Retrieval-Augmented Generation (RAG)
It is not recommended for knowledge-intensive tasks or programming.