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LatestXHTokenX2.6-4B
XHTokenText / LLM

XHToken Releases Spark-X2.6-4B Bilingual Model

A compact 4-billion-parameter instruction model targets English and Chinese tasks under a permissive Apache 2.0 license.

Oct 19, 2026
UpdateApache 2.0
Spark-X2.6-4B

XHToken has published Spark-X2.6-4B, a 4-billion-parameter instruction-tuned language model built to handle both English and Chinese. The release is available now on Hugging Face under the permissive Apache 2.0 license.

As a dense (non-mixture-of-experts) model in the 4B range, Spark-X2.6-4B sits in the increasingly crowded small-model tier, where developers trade raw capability for lower memory use, faster inference, and the ability to run on modest hardware. Models of this size are well suited to on-device deployment, fine-tuning experiments, and cost-sensitive production workloads.

Why it matters

Bilingual coverage is the model's headline feature. A single checkpoint that handles both English and Chinese instruction-following is useful for teams building for cross-border audiences without maintaining separate models.

  • Size: 4B parameters, dense architecture
  • Languages: English and Chinese
  • License: Apache 2.0, permitting commercial use and modification
  • Modality: Text-only instruction following

The release notes on Hugging Face remain light on published benchmarks and context-length details, so prospective users will want to validate performance against their own tasks. Still, the permissive licensing and compact footprint make Spark-X2.6-4B an easy candidate for hands-on evaluation.

Sources

  • XHToken/Spark-X2.6-4B

    Hugging Face

    Visit
OlderBria's Fibo 2.0 Brings Structured Prompts to Open Image AIBriaai · Text → Image · last weekNewerQwen-Audio-3.1-ASR Adds Speaker-Aware TranscriptionQwen · Alibaba · Speech → Text · last week

Get the model

Hugging Face

Specs

Parameters4B
Context window1.0M tokens
Size8.2 GB
PrecisionBF16
ArchitectureSpark2_5ForCausalLM
LicenseAPACHE-2.0
Downloads32.7K
Likes1.4K

Can you run it?

Runs on a laptop — about 4.7 GB at 4-bit.

  • BF16 (as published)

    12 GB GPU (RTX 3060 / 4070) · 16 GB Mac

    10.4 GB
  • 8-bit

    8 GB GPU (RTX 4060) · 16 GB laptop or Mac

    6.6 GB
  • 4-bit

    8 GB GPU (RTX 4060) · 16 GB laptop or Mac

    4.7 GB
Your machine
GGUF builds
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text / LLM
2 versions — view changelog

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