Instructions to use juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-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 juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-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 juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf:Q4_K_M

Use Docker

docker model run hf.co/juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf with Ollama:
```
ollama run hf.co/juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf:Q4_K_M
```

Unsloth Studio

How to use juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-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 juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-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 juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-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 juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf to start chatting

How to use juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-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": "juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf with Docker Model Runner:
```
docker model run hf.co/juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf:Q4_K_M
```

Lemonade

How to use juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-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 juanml82/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

GGUF quants for Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated

I've recreated them after the late December 2025 llama.cpp update which speeds up Qwen 3 Next, so these quants should perform better than the early quants for this model. I've uploaded three quants:

iQ3_M – should fit (tight) in systems with 32gb of ram plus an 8-12gb gpu with ram offloading. Possibly lowest useful quant.

MXFP4_MOE – a tight fit for systems with 32gb of ram plus a 16gb or more gpu. Or to fully load it in system ram, with cpu_moe, in systems with 64gb of ram

Q6K – will work well with systems with 64gb of ram plus ram offloading. Quality is supposed to very almost indistinguishable from Q8

I didn't do a Q8. it could be a tight fit in systems with 64gb of ram and a 24gb vram gpu, but I have that system and it's freezing when I try to load it.

The q4_m file is older and slower than these new three quants, so I see no reason to use it instad of the mxfp4_moe

Enjoy!

license: apache-2.0 language: - en - zh base_model: - huihui-ai/Huihui-Qwen3-Next-80B-A3B-Thinking-abliterated pipeline_tag: text-generation tags: - abliterated - uncensored

Downloads last month: 312

GGUF

Model size

80B params

Architecture

qwen3next

Hardware compatibility

3-bit

4-bit

6-bit

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