NOTE: This model has been heavily vocab-sliced to reduce non-coding and non-English tokens. YMMV. (Mandarin, Korean, Vietnamese, Hindi, Arabic, Japanese, Russian)

==================================================
 PERPLEXITY QUALITY SCORECARD
 Base Model PPL      : 10.8559 (16-bit reference)
 Quantized Model PPL : 10.8594 (IQ4_NL target)
 Perplexity Drift    : +0.0035 (+0.03%)
 Quality Assessment  : [PASS] EXCELLENT (Near-Lossless / Gold Standard)
==================================================

A little over a month has passed since Spark-X2.5. In the rapidly evolving world of AI agents, that is enough time to rethink, refine, and move the frontier forward. Building on the Spark-X2.5-4B, we introduce Spark-X2.6-4B, the latest generation of our compact, general-purpose language models for on-device deployment.

Spark-X2.6-4B retains exactly the same architecture as Spark-X2.5-4B, achieving its capability gains through improved post-training. It delivers substantial improvements in coding, agent, long-context, multilingual, reasoning, etc. These advances enable more capable and practical on-device AI for developers and users tackling real-world tasks.

Technical Highlights:

Stronger Coding, Agentic, and Long-Context Capabilities: The models deliver substantial improvements in agentic workflows involving complex tool use, multi-step planning, and software engineering. They achieve state-of-the-art performance among similarly sized open-source models, matching or even surpassing models with approximately 9B parameters. Controllable Thinking Budget: The models provide three configurable thinking levels—none, low, high—allowing applications to balance reasoning quality, latency, and inference cost dynamically.

SOURCES:

• https://huggingface.co/XHToken/Spark-X2.6-4B

• https://huggingface.co/bartowski/Spark-X2.6-4B-GGUF

• https://huggingface.co/datasets/eaddario/imatrix-calibration