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gemma-4-E4B-it-The-DECKARD-HERETIC-UNCENSORED-Thinking-GGUF

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Abiray/gemma-4-E4B-it-The-DECKARD-HERETIC-UNCENSORED-Thinking-GGUF is an uncensored open-source model hosted on Hugging Face.

Text GenerationGGUFEnglishLicense: gemmallama-cppgemma4uncensoredreasoningthinkinghereticdeckardroleplayconversational
README.md说明 / 使用方法

language:

  • en license: gemma base_model: DavidAU/gemma-4-E4B-it-The-DECKARD-HERETIC-UNCENSORED-Thinking tags:
  • gguf
  • llama-cpp
  • gemma4
  • uncensored
  • reasoning
  • thinking
  • heretic
  • deckard
  • roleplay pipeline_tag: text-generation

Gemma 4 E4B Instruct - The DECKARD HERETIC UNCENSORED Thinking (GGUF)

This repository contains GGUF format quantizations for DavidAU's Gemma-4-E4B-it-The-DECKARD-HERETIC-UNCENSORED-Thinking.

These files allow for highly efficient local inference on CPUs and Apple Silicon, as well as VRAM-constrained GPUs, utilizing llama.cpp and compatible frontends.

🧠 About the Base Model

The base model is a specialized, heavily fine-tuned version of Google's Gemma 4 E4B (Effective 4B parameters).

  • HERETIC / DECKARD Fine-Tune: This model has undergone the "HERETIC" abliteration and fine-tuning process by DavidAU. It is completely uncensored, unrestricted, and heavily calibrated for complex creative writing, roleplay, and deep fiction generation.
  • Reasoning & Thinking: It includes augmented reasoning/thinking capabilities, allowing the model to internally chain logic before outputting text, making its responses deeper and more contextually accurate.
  • Context Window: The Gemma 4 architecture supports extended context windows (up to 256k in base), making it highly capable for long-form conversations.

📦 Available Quantization Formats

This repository provides multiple levels of quantization to help you balance VRAM/RAM usage, generation speed, and model fidelity.

File NameBit ResolutionRecommended Use
Q3_K_M3-bitUltra-low RAM usage. Noticeable perplexity degradation but runs on very constrained hardware.
Q4_K_M4-bitRecommended. The sweet spot for local LLMs. Great balance of speed, low memory footprint, and quality.
Q5_K_M5-bitHigher precision. Use this if you have the memory to spare and want slightly better reasoning.
Q6_K6-bitVery high fidelity. Close to unquantized performance with a larger memory footprint.
Q8_08-bitExtremely close to FP16 baseline. Best for users who want maximum precision and have ample RAM/VRAM.
HUGGING FACE

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来源与版本

作者
Abiray
最近更新
2026/04/08
最新创建
2026/04/08
HF 趋势
未知
数据快照
2026/09/16
版本
未知
许可证
gemma