Instructions to use cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF",
	filename="huihui-qwen3.5-35b-a3b-abliterated-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-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 cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-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 cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-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 cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-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 cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio

How to use cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-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 cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-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 cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF to start chatting

How to use cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-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": "cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-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 cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-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 cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF with Docker Model Runner:
```
docker model run hf.co/cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF:Q4_K_M
```

Lemonade

How to use cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF-Q4_K_M

List all available models

lemonade list

A significantly improved version of this model is available.

This repo quantizes huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated — the standard abliteration. A newer fine-tune of the same architecture, trained in the style of Claude 4.6 Opus, has since been released and produces noticeably richer, more expressive outputs.

➡️ Recommended upgrade: Huihui-Qwen3.5-35B-A3B-Claude-4.6-Opus-abliterated-Q4_K_M-GGUF

Same architecture, same Q4_K_M quantization, same VRAM footprint — just a better fine-tune. This repo will remain available for reference.

Huihui-Qwen3.5-35B-A3B-abliterated — Q4_K_M GGUF

This is a Q4_K_M GGUF quantization of huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated.

Refer to the original model card for full details, usage warnings, and licensing information.

Details

Property	Value
Source model	huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated
Architecture	qwen35moe (35B total params, ~3B active; 256 experts, 8 active per token)
Quantization	Q4_K_M (~4.8 BPW)
File size	~21 GB
Quantized with	llama.cpp

Usage with llama.cpp

llama-cli \
  --hf-repo cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF \
  --hf-file huihui-qwen3.5-35b-a3b-abliterated-Q4_K_M.gguf \
  -p "Tell me about the universe"

llama-server \
  --hf-repo cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF \
  --hf-file huihui-qwen3.5-35b-a3b-abliterated-Q4_K_M.gguf \
  -c 8192

Usage with Ollama

Requires Ollama with qwen35moe support. See PR #14506 for the architecture patch.

ollama run hf.co/cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF

Credits

Abliteration by huihui-ai — Huihui-Qwen3.5-35B-A3B-abliterated
Base model by Qwen — Qwen3.5-35B-A3B
Quantization by cesarsal1nas

Downloads last month: 108

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

Inference Providers NEW

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Model tree for cesarsal1nas/Huihui-Qwen3.5-35B-A3B-abliterated-Q4_K_M-GGUF

Base model

Qwen/Qwen3.5-35B-A3B-Base

Finetuned

Qwen/Qwen3.5-35B-A3B

Finetuned

huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated

Quantized

(20)

this model