Spark-X2.6-4B-GGUF: 4B Model with 9B-Level Performance for On-Device Agents

Spark-X2.6-4B is a small general-purpose language model designed to run in on-device environments. It maintains the same architecture as Spark-X2.5-4B while improving the post-training process to boost performance. Significant improvements over the previous generation are evident in coding, agent workflows, long-context processing, multilingual capabilities, and reasoning.

It delivers top-tier performance among open-source models of the same size, matching or even exceeding 9B-parameter models in certain areas. It excels at agent tasks that require complex tool use and multi-step planning. In particular, it allows setting a thinking budget at three levels: 'none', 'low', and 'high', enabling developers to adjust response quality, latency, and reasoning costs according to the situation.

Provided in GGUF format, it is compatible with various local inference engines such as llama.cpp, Ollama, and LM Studio. It is distributed under the Apache-2.0 license and is optimized for English and Chinese. It is suitable for developers looking to implement real-time AI features while reducing cloud dependency and maintaining privacy.