XHToken/Spark-X2.6-4B-Base

text-generationtransformersUpstream checksumstransformerssafetensorsspark2_5text-generationllmsparkx2_5

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Spark-X2.6-4B: Advancing the Frontier of On-Device Agents for Real-World Tasks

[blocked]

[!Note] This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format.

Introduction

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.

Fine-Tuning

We recommend using Llama-Factory to fine-tune the model.

License

The Spark-X2.6 model series is licensed under the Apache 2.0 License [blocked].

Citation

If you find our work helpful, feel free to give us a cite.

Introduction

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.

Fine-Tuning

We recommend using Llama-Factory to fine-tune the model.

Citation

If you find our work helpful, feel free to give us a cite.