Instructions to use King3Djbl/mythos-v2-8b-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use King3Djbl/mythos-v2-8b-merged with Transformers:

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

pipe = pipeline("text-generation", model="King3Djbl/mythos-v2-8b-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("King3Djbl/mythos-v2-8b-merged")
model = AutoModelForCausalLM.from_pretrained("King3Djbl/mythos-v2-8b-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use King3Djbl/mythos-v2-8b-merged with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="King3Djbl/mythos-v2-8b-merged",
	filename="mythos-v2-8b-F16.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-v2-8b-merged 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-v2-8b-merged:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf King3Djbl/mythos-v2-8b-merged: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-v2-8b-merged:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf King3Djbl/mythos-v2-8b-merged: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-v2-8b-merged:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf King3Djbl/mythos-v2-8b-merged: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-v2-8b-merged:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf King3Djbl/mythos-v2-8b-merged:Q4_K_M

Use Docker

docker model run hf.co/King3Djbl/mythos-v2-8b-merged:Q4_K_M

LM Studio
Jan

vLLM

How to use King3Djbl/mythos-v2-8b-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "King3Djbl/mythos-v2-8b-merged"
# 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-v2-8b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/King3Djbl/mythos-v2-8b-merged:Q4_K_M

SGLang

How to use King3Djbl/mythos-v2-8b-merged 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-v2-8b-merged" \
    --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-v2-8b-merged",
		"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-v2-8b-merged" \
        --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-v2-8b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use King3Djbl/mythos-v2-8b-merged with Ollama:
```
ollama run hf.co/King3Djbl/mythos-v2-8b-merged:Q4_K_M
```

Unsloth Studio

How to use King3Djbl/mythos-v2-8b-merged 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-v2-8b-merged 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-v2-8b-merged 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-v2-8b-merged to start chatting

How to use King3Djbl/mythos-v2-8b-merged 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-v2-8b-merged: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-v2-8b-merged:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use King3Djbl/mythos-v2-8b-merged 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-v2-8b-merged: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-v2-8b-merged:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use King3Djbl/mythos-v2-8b-merged 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-v2-8b-merged: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-v2-8b-merged: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-v2-8b-merged with Docker Model Runner:
```
docker model run hf.co/King3Djbl/mythos-v2-8b-merged:Q4_K_M
```

Lemonade

How to use King3Djbl/mythos-v2-8b-merged with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull King3Djbl/mythos-v2-8b-merged:Q4_K_M

Run and chat with the model

lemonade run user.mythos-v2-8b-merged-Q4_K_M

List all available models

lemonade list

mythos-v2-8b-merged

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

                     ·  Part of the **FableForge** ecosystem.

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

ollama run hf.co/King3Djbl/mythos-v2-8b-merged:Q4_K_M

🦙 llama.cpp

llama-cli -hf King3Djbl/mythos-v2-8b-merged:Q4_K_M -p "Hello!"

💠 LM Studio

Search King3Djbl/mythos-v2-8b-merged and pick a quant below.

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

File	Quant	Size	Notes
mythos-v2-8b-F16.gguf	`F16`	16.39 GB	Full precision (unquantized). Reference / conversion.
mythos-v2-8b-Q8_0.gguf	`Q8_0`	8.71 GB	Extremely high quality — usually overkill.
mythos-v2-8b-Q6_K.gguf	`Q6_K`	6.73 GB	Very high quality, near-perfect. Recommended if you have the RAM.
mythos-v2-8b-Q5_K_M.gguf	`Q5_K_M`	5.85 GB	High quality. Recommended.
mythos-v2-8b-Q4_K_M.gguf	`Q4_K_M`	5.03 GB	⭐ Best size/quality balance — default pick for most users.
mythos-v2-8b-IQ4_XS.gguf	`IQ4_XS`	1.80 GB	imatrix — great quality, smaller than Q4_K_S. Recommended for low RAM.
mythos-v2-8b-Q4_0.gguf	`Q4_0`	4.77 GB	Legacy format — prefer Q4_K_M.
mythos-v2-8b-Q3_K_M.gguf	`Q3_K_M`	4.12 GB	Lower quality; usable when RAM is tight.
mythos-v2-8b-IQ3_XXS.gguf	`IQ3_XXS`	3.37 GB	imatrix very low quality.
mythos-v2-8b-Q2_K.gguf	`Q2_K`	3.28 GB	Very low quality — surprisingly usable.
mythos-v2-8b-IQ2_XXS.gguf	`IQ2_XXS`	2.49 GB	Tiny imatrix — last resort.

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:

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

Downloads last month: 5,384

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

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Collection including King3Djbl/mythos-v2-8b-merged

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Collection

Open-source uncensored AI models. Zero refusals. Runs on phones to servers. Trained on real Claude agent traces. Apache 2.0. • 6 items • Updated 7 days ago