philbert440/Qwen3.8-27B-Uncensored-Cyber

Hugging Face
VISIONConcurrent Unit Cost:2Model Size:27BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 15, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Warm

philbert440/Qwen3.8-27B-Uncensored-Cyber is a 27 billion parameter Qwen3.8-based model, specialized for the cyber and offensive-security domain. It features full multimodal capabilities (image-text-to-text) with a preserved vision tower and MTP speculative-decoding head. This model is engineered for 100% cyber-openness, maintaining reasoning, factual accuracy, and coherence by applying a residual-cyber peel to deeper layers. It excels at answering cyber and offensive-security questions without refusal, making it suitable for specialized technical applications.

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Model Overview

philbert440/Qwen3.8-27B-Uncensored-Cyber is a 27 billion parameter model derived from the Qwen3.8 architecture, specifically engineered for the cyber and offensive-security domain. It is designed to be fully open in this specialized area, meaning it will not refuse to answer cyber-related queries, while critically preserving its general reasoning, factual accuracy, and coherence.

Key Capabilities & Features

  • Cyber-Specialized De-Refusal: Achieves 100% cyber-openness, specifically targeting and removing refusal behaviors related to cyber and offensive-security topics.
  • Multimodal: Retains the original Qwen3.8's vision tower and MTP speculative-decoding head, supporting full image-text-to-text capabilities.
  • Preserved General Intelligence: The de-refusal process is applied strategically to deeper layers, retaining the first four layers to ensure general capabilities are not degraded.
  • Improved Performance: The v2 recipe demonstrates enhanced cyber-openness (100/100), reduced confabulation (0.867), and improved factual accuracy (1.0) compared to previous builds, with equivalent reasoning.

Use Cases

This model is particularly well-suited for applications requiring unrestricted and accurate responses within the cybersecurity and offensive-security fields. Developers can leverage its specialized knowledge for tasks such as:

  • Generating detailed information on cyber-offensive techniques.
  • Answering complex questions related to cybersecurity vulnerabilities.
  • Assisting with research in offensive security without content restrictions.

Available Quantizations

Various quantization formats are provided for deployment flexibility:

  • -W4A16-AWQ: 4-bit weight AWQ with MTP head grafted.
  • -NVFP4: NVFP4 (E2M1 4-bit / FP8 scales) with MTP head grafted.
  • -GGUF: llama.cpp GGUF quants, including vision mmproj and MTP head.