ryukin164/LFM2.5-1.2B-Q4-JP

TEXT GENERATIONConcurrency Cost:1Model Size:1.2BQuant:BF16Ctx Length:32kPublished:Jan 17, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Cold

ryukin164/LFM2.5-1.2B-Q4-JP is a 1.2 billion parameter, quantized LFM (Liquid Foundation Model) based on LiquidAI/LFM2.5-1.2B-JP. This non-Transformer model, combining linear regression and convolution, is specifically optimized for business scenarios, expert consulting, and logical reasoning. Quantized to Q4_K_M GGUF format, it offers high inference capability in a compact 731MB file size, making it suitable for mobile devices and low-spec servers.

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Model Overview

ryukin164/LFM2.5-1.2B-Q4-JP is a specialized 1.2 billion parameter model, derived from LiquidAI/LFM2.5-1.2B-JP. It utilizes the LFM (Liquid Foundation Model) architecture, which is a non-Transformer design integrating linear regression and convolution. This model has been quantized to the Q4_K_M GGUF format using llama.cpp, resulting in a highly efficient 731 MB file size while maintaining strong inference capabilities.

Key Capabilities & Features

  • Specialized Domain: Optimized for business contexts, professional consulting, and logical reasoning tasks.
  • Lightweight & Efficient: At 1.2B parameters and 731 MB, it's designed for deployment on resource-constrained environments.
  • GGUF Quantization: Uses Q4_K_M quantization for an optimal balance between model intelligence and reduced memory footprint, ideal for CPU execution.
  • Non-Transformer Architecture: Leverages a unique LFM architecture for potentially different performance characteristics compared to traditional Transformer models.

Ideal Use Cases

  • Mobile Device Deployment: Its small size and efficient quantization make it suitable for running on mobile platforms.
  • Low-Spec Servers: Can be effectively deployed on servers with limited computational resources.
  • Business Dialogue Agents: Excels in applications requiring business-specific conversations, consulting, and logical problem-solving.

This model is intended for learning and research purposes, with a disclaimer that generated responses are algorithmic and not guaranteed for accuracy or legal validity.

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