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.
If you find our work helpful, feel free to give us a cite.
@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}
}
Files
20 files · 8.24 GBWeights 8.22 GB (safetensors). Sizes and sha256 from the signed manifest.
import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("XHToken/Spark-X2.6-4B-Base", revision="ec7af5e19b293a6fbe9e87080e36b83e20d3243f")
model = AutoModelForCausalLM.from_pretrained("XHToken/Spark-X2.6-4B-Base", revision="ec7af5e19b293a6fbe9e87080e36b83e20d3243f", device_map="auto")