Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF

🤗 Hugging Face 来源apache-2.0激活 3B152 GBGGUF✓ 9 个校验和今天更新
一条命令提交

在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对,然后提交。你只需开始做种,并粘贴你账户中的密钥。它只读取你的文件,绝不修改。如果愿意,可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF ./model-folder
需要做种者 →

Huihui Qwen3.6-35B A3B Abliterated (GGUF)

This repository provides GGUF format quantizations for the huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated model.

Because this model has been fully "abliterated" to bypass alignment and safety refusals, it acts as a highly capable engine for unrestricted creative writing, dynamic storytelling, and immersive roleplay scenarios.

Available Quantizations

File Bit Size Description
huihui-35B-Q8_0.gguf 8-bit Highest quality quant, virtually indistinguishable from F16.
huihui-35B-Q6_K.gguf 6-bit Excellent quality with a noticeably reduced memory footprint.
huihui-35B-Q5_K_M.gguf 5-bit Great balance between reasoning performance and RAM usage.
huihui-35B-Q4_K_M.gguf 4-bit Recommended. The optimal sweet spot for speed and quality.
huihui-35B-Q4_K_S.gguf 4-bit Slightly smaller than K_M, allowing for faster inference on constrained setups.
huihui-35B-Q3_K_M.gguf 3-bit Lowest resource requirement, though perplexity loss becomes more noticeable.

Quick Start (llama.cpp)

These models are designed to be run directly via llama.cpp. The following commands are standard for local Linux environments (such as Linux Mint or Ubuntu).

1. Clone and compile via CMake:

git clone [https://github.com/ggerganov/llama.cpp](https://github.com/ggerganov/llama.cpp)
cd llama.cpp
cmake -B build
cmake --build build --config Release