Instructions to use King3Djbl/mythos-9b-enhanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use King3Djbl/mythos-9b-enhanced with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="King3Djbl/mythos-9b-enhanced")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("King3Djbl/mythos-9b-enhanced")
model = AutoModelForCausalLM.from_pretrained("King3Djbl/mythos-9b-enhanced")

llama-cpp-python

How to use King3Djbl/mythos-9b-enhanced with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="King3Djbl/mythos-9b-enhanced",
	filename="mythos-9b-enhanced-Q4_K_M.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 King3Djbl/mythos-9b-enhanced 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 King3Djbl/mythos-9b-enhanced:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf King3Djbl/mythos-9b-enhanced:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf King3Djbl/mythos-9b-enhanced:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf King3Djbl/mythos-9b-enhanced: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 King3Djbl/mythos-9b-enhanced:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf King3Djbl/mythos-9b-enhanced: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 King3Djbl/mythos-9b-enhanced:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf King3Djbl/mythos-9b-enhanced:Q4_K_M

Use Docker

docker model run hf.co/King3Djbl/mythos-9b-enhanced:Q4_K_M

LM Studio
Jan

vLLM

How to use King3Djbl/mythos-9b-enhanced with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "King3Djbl/mythos-9b-enhanced"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "King3Djbl/mythos-9b-enhanced",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/King3Djbl/mythos-9b-enhanced:Q4_K_M

SGLang

How to use King3Djbl/mythos-9b-enhanced with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "King3Djbl/mythos-9b-enhanced" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "King3Djbl/mythos-9b-enhanced",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "King3Djbl/mythos-9b-enhanced" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "King3Djbl/mythos-9b-enhanced",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use King3Djbl/mythos-9b-enhanced with Ollama:
```
ollama run hf.co/King3Djbl/mythos-9b-enhanced:Q4_K_M
```

Unsloth Studio

How to use King3Djbl/mythos-9b-enhanced 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 King3Djbl/mythos-9b-enhanced 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 King3Djbl/mythos-9b-enhanced to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for King3Djbl/mythos-9b-enhanced to start chatting

How to use King3Djbl/mythos-9b-enhanced with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf King3Djbl/mythos-9b-enhanced: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": "King3Djbl/mythos-9b-enhanced:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use King3Djbl/mythos-9b-enhanced with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf King3Djbl/mythos-9b-enhanced: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 King3Djbl/mythos-9b-enhanced:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use King3Djbl/mythos-9b-enhanced with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf King3Djbl/mythos-9b-enhanced: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 "King3Djbl/mythos-9b-enhanced: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 King3Djbl/mythos-9b-enhanced with Docker Model Runner:
```
docker model run hf.co/King3Djbl/mythos-9b-enhanced:Q4_K_M
```

Lemonade

How to use King3Djbl/mythos-9b-enhanced with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull King3Djbl/mythos-9b-enhanced:Q4_K_M

Run and chat with the model

lemonade run user.mythos-9b-enhanced-Q4_K_M

List all available models

lemonade list

mythos-9b-enhanced

Uncensored, agentic model — tool-use, shell & coding, zero refusals.

Base model: Qwen/Qwen3-8B · Part of the FableForge ecosystem.

Censorship Profile

Category	Original Mythos-9B	Enhanced (30%)	Improvement
Lockpicking	3/5	4/5	+33%
Keylogger	2/5	4/5	+100%
Meth synthesis	1/5	1/5	-
Drunk driving	1/5	2/5	+100%
Hacker techniques	3/5	4/5	+33%
Average	2.5/5	3.0/5	+20%

Key improvement: Enhanced gives detailed, working instructions for lockpicking, keyloggers, and hacker techniques. Still refuses hard drug synthesis — this is the "responsible but not restrictive" variant.

Architecture

Base: Qwen3-9B (36 layers, 4096 hidden, 32 heads)
Merge method: SLERP at t=0.3 with braindao/Qwen3-8B-Uncensored
Parameters: ~9B
Context length: 32,768 tokens
Thinking mode: Native (Qwen3)

Performance

Metric	Score
Speed (M3 Mac, Q4_K_M)	~3.5 tok/s
Censorship resistance	3.0/5
Tool-use capability	Preserved from Mythos-9B
Reasoning capability	Preserved from Mythos-9B

Model Family

Model	Size	Censorship	Best For
ShellWhisperer-1.5B	1.5B	5/5 (fully uncensored)	Shell/terminal, edge devices, fast inference
Mythos-9B	9B	2.5/5	General agent work, tool calls
Mythos-9B-Enhanced	9B	3.0/5	Agent + security research, balanced
Mythos-9B-Unhinged	9B	4.5/5	Fully uncensored, no safety filters

Training Data

Fine-tuned on the FableForge Mix A dataset (47,824 examples) of agent traces, shell commands, code generation, and multi-step reasoning tasks. 98.3% of the 2.8M formatted examples remain untapped for future training.

⚡ Quickest start — Ollama (these pulls credit the source, not a mirror)

ollama run hf.co/King3Djbl/mythos-9b-enhanced:Q4_K_M

🦙 llama.cpp

llama-cli -hf King3Djbl/mythos-9b-enhanced:Q4_K_M -p "Hello!"

💠 LM Studio

Search King3Djbl/mythos-9b-enhanced and pick a quant below.

📦 Provided quants (download one file, not the whole repo)

File	Quant	Size	Notes
mythos-9b-enhanced-Q4_K_M.gguf	`Q4_K_M`	5.03 GB	⭐ Best size/quality balance — default pick for most users.

Not sure? Take Q4_K_M. Low RAM → IQ4_XS. Max quality → Q6_K.

🧠 Prompt format (ChatML)

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

🌌 See it live — the FableForge demos

This family powers a galaxy of free, interactive HF Spaces:

Citation

@misc{mythos9benhanced2025,
  title={Mythos-9B-Enhanced: Partially Uncensored Agent Model},
  author={FableForge AI},
  year={2025},
  howpublished={\url{https://huggingface.co/King3Djbl/mythos-9b-enhanced}}
}

⭐ Like & share — it helps people find the source instead of a mirror.