Instructions to use Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF",
	filename="Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated.Q3_K_L.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-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 Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-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 Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-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-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-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-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF:Q4_K_M

Ollama
How to use Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF with Ollama:
```
ollama run hf.co/Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF:Q4_K_M
```

Unsloth Studio

How to use Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-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-Qwythos-9B-Claude-Mythos-5-1M-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-Qwythos-9B-Claude-Mythos-5-1M-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-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF to start chatting

How to use Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-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 Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-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-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-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 Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-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-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-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 Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-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 "Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-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 Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF:Q4_K_M
```

Lemonade

How to use Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF-Q4_K_M

List all available models

lemonade list

Abiray/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated-GGUF

This repository contains GGUF format quantizations of huihui-ai/Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated, which is an uncensored version of empero-ai's Qwythos-9B-Claude-Mythos-5-1M model stripped of its refusal vectors via abliteration techniques.

Additionally, this repository includes the necessary multimodal projection files (mmproj) required to utilize the model's vision/image-to-text capabilities locally.

Provided Files & Quantization Details

File Name	Size	Description
`Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated.Q3_K_L.gguf`	5.05 GB	3-bit ultra-light quantization, long variant.
`Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated.Q3_K_M.gguf`	4.74 GB	3-bit medium quantization. Lowest resource footprint.
`Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated.Q4_K_M.gguf`	5.78 GB	4-bit medium quantization. Recommended balance of speed and quality.
`Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated.Q4_K_S.gguf`	5.49 GB	4-bit small variant footprint.
`Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated.Q5_K_M.gguf`	6.64 GB	5-bit medium quantization. High quality preservation.
`Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated.Q6_K.gguf`	7.56 GB	6-bit quantization. Extremely near-lossless performance.
`Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated.Q8_0.gguf`	9.79 GB	8-bit standard quantization. Maximum precision text execution.
`Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated.mmproj-Q8_0.gguf`	624 MB	Quantized vision projector adapter (required for vision capabilities).
`Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated.mmproj-f16.gguf`	918 MB	Unquantized 16-bit float vision projector adapter for absolute vision fidelity.

Deployment & Usage Instructions

Ensure you are utilizing a recent build of llama.cpp to natively handle modern Qwen 3.5 architectures and context window stretching constraints.

1. Pure Text Mode (CLI Inference)

To interact with the model purely over terminal text-generation pipelines:

./llama-cli \
  -m Huihui-Qwythos-9B-Claude-Mythos-5-1M-abliterated.Q4_K_M.gguf \
  -ngl 99 \
  -c 32768 \
  -fa on \
  -p "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nWrite a short story about an AI break-out.<|im_end|>\n<|im_start|>assistant\n"