---
license: apache-2.0
language:
- en
- zh
library_name: transformers
pipeline_tag: text-generation
tags:
- llm
- sparkx2_5
---


# Spark-X2.6-4B: Advancing the Frontier of On-Device Agents for Real-World Tasks
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> [!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](https://github.com/XHToken/LlamaFactory) to fine-tune the model.


## License

The Spark-X2.6 model series is licensed under the [Apache 2.0 License](https://huggingface.co/XHToken/Spark-X2.6-4B-Base/blob/main/LICENSE).

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

```bibtex
@misc{sparkx2.6,
    title  = {Spark-X2.6-4B&1.7B: Advancing the Frontier of On-Device Agents for Real-World Tasks},
    author = {SparkLLM Team},
    year   = {2026}
}
```