Instructions to use Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF",
	filename="Huihui-Qwen3.6-35B-A3B-abliterated-Q3_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF with Ollama:
```
ollama run hf.co/Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF:Q4_K_M
```

Unsloth Studio new

How to use Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF to start chatting

Pi new

How to use Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF:Q4_K_M
```

Lemonade

How to use Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Abiray/Huihui-Qwen3.6-35B-A3B-abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Huihui-Qwen3.6-35B-A3B-abliterated-GGUF-Q4_K_M

List all available models

lemonade list

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

Downloads last month: 8,716

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

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

Base model

Qwen/Qwen3.6-35B-A3B

Finetuned

huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated

Quantized

(13)

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

Qwen3.6-35B-A3B

Collection

10 items • Updated Apr 18