ErtasAI/LFM2.5-350M
LFM2.5-350M is a 350 million parameter hybrid model developed by Liquid AI, designed for efficient on-device deployment. It features extended pre-training on 28T tokens and large-scale multi-stage reinforcement learning, enabling high-quality AI in a compact size. This model excels at fast edge inference, supporting various platforms like llama.cpp, MLX, and vLLM, and is optimized for data extraction, structured outputs, and tool use.
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LFM2.5-350M: Optimized for On-Device AI
LFM2.5-350M, developed by Liquid AI, is a 350 million parameter hybrid model built upon the LFM2 architecture. It is specifically engineered for on-device deployment, offering high-quality AI performance in a compact footprint. The model benefits from significantly extended pre-training, now on 28 trillion tokens, combined with large-scale multi-stage reinforcement learning.
Key Capabilities & Features
- Best-in-Class Performance for Size: Achieves performance comparable to much larger models, making advanced AI accessible for edge devices.
- Fast Edge Inference: Delivers rapid decode speeds (e.g., 313 tok/s on AMD CPU, 188 tok/s on Snapdragon Gen4) and operates under 1GB of memory.
- Broad Platform Support: Offers day-one support for
llama.cpp,MLX, andvLLM, with optimized formats for various hardware including Apple Silicon and Intel platforms. - Tool Use: Supports function calling with a flexible mechanism for defining tools via JSON in the system prompt and interpreting tool outputs.
- Multilingual Support: Capable in English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, and Spanish.
Use Cases & Recommendations
LFM2.5-350M is particularly well-suited for:
- Data Extraction
- Structured Outputs
- Tool Use / Function Calling
It is not recommended for knowledge-intensive tasks or programming. The model's design prioritizes efficiency and performance for constrained environments, making it an excellent choice for integrating AI directly into applications and devices.