We introduce Spark-X2.6-1.7B, the second model in the Spark-X2.6 series, following the release of Spark-X2.6-4B. Building on Spark-X2.5-1.7B, this release further advances our compact, general-purpose language models for on-device deployment.
Spark-X2.6-1.7B retains exactly the same architecture as Spark-X2.5-1.7B, achieving its capability gains through improved post-training. It delivers substantial improvements in coding, agent, long-context, multilingual, reasoning, etc. These capability gains, combined with strong inference efficiency among models of comparable size, 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 model delivers substantial improvements in complex tool use, multi-step planning, software engineering, and reasoning over extended contexts, achieving state-of-the-art performance among open-source models of comparable size.
Controllable Thinking Budget: The model provides 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
19 files · 3.43 GBWeights 3.42 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-X2.6-1.7B-Base", revision="b6e9cbe6e41892f2e14bc0eeb5d49c10e951837d")
model = AutoModelForCausalLM.from_pretrained("XHToken/Spark-X2.6-1.7B-Base", revision="b6e9cbe6e41892f2e14bc0eeb5d49c10e951837d", device_map="auto")
