win10/EVE-27B-XENO-HAT

VISIONConcurrent Unit Cost:2Model Size:27BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Jul 14, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

win10/EVE-27B-XENO-HAT is a 27 billion parameter experimental language model created by win10, featuring a cross-architecture merge of a GPT OSS 120B model and a GRM-2.6-Plus-0628 27B model. Aligned to the QWEN 3.5 architecture, this model is designed for general usability with a 32768 token context length. Its unique merged architecture aims to combine strengths from different foundational models.

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Model Overview

win10/EVE-27B-XENO-HAT is an experimental 27 billion parameter language model developed by win10. This model represents a unique cross-architecture merge of two distinct foundational models: a GPT OSS 120B model and a GRM-2.6-Plus-0628 27B model. The resulting architecture is aligned with the QWEN 3.5 framework, providing a substantial context length of 32768 tokens.

Key Characteristics

  • Cross-Architecture Merge: Combines components from a GPT OSS 120B model and a GRM-2.6-Plus-0628 27B model.
  • QWEN 3.5 Alignment: The merged architecture is specifically aligned to the QWEN 3.5 framework.
  • Usability: Despite its experimental nature and complex merge, the model is confirmed to be usable for general tasks.
  • Context Length: Features a 32768 token context window, allowing for processing of longer inputs.

Potential Use Cases

This model is suitable for developers and researchers interested in:

  • Exploring merged architectures: Investigating the performance and characteristics of models created through cross-architecture merging.
  • General language tasks: Its usability suggests it can handle a variety of common NLP applications.
  • Applications requiring longer context: Benefiting from its 32768 token context length for tasks needing extensive input understanding.

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