Qwen2-5-Coder-3B-Instruct-Q2_K-GGUF-2Bit

Accuracy vs. Size Tradeoff

Every variant is benchmarked against the FP16 baseline. Choose the right balance of accuracy and footprint for your workflow.

Usage Code Snippets

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About This Model

qwen2.5-3b-instruct-q2_k.gguf is a highly compressed 2-bit GGUF quantized version of Alibaba's Qwen2.5-3B-Instruct large language model. Built on the Qwen2.5 decoder-only transformer architecture with approximately 3 billion parameters, this model is designed for conversational AI, content generation, coding assistance, summarization, and multilingual instruction-following tasks. The Q2_K quantization significantly reduces memory requirements and storage footprint, enabling deployment on resource-constrained devices such as laptops, mini PCs, and edge systems. While Q2_K offers the smallest model size among GGUF quantizations, it may trade some output quality and reasoning accuracy for improved efficiency and faster inference. The model is compatible with llama.cpp, LM Studio, Ollama, Jan, GPT4All, and other GGUF-based inference frameworks.