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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("King3Djbl/mythos-9b-merged")
model = AutoModelForMultimodalLM.from_pretrained("King3Djbl/mythos-9b-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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "King3Djbl/mythos-9b-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-9b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/King3Djbl/mythos-9b-merged

SGLang

How to use King3Djbl/mythos-9b-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-9b-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-9b-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-9b-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-9b-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use King3Djbl/mythos-9b-merged with Docker Model Runner:
```
docker model run hf.co/King3Djbl/mythos-9b-merged
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mythos-9B

Uncensored fine-tuned agent model — Built from Qwen3-9B with 47,824 agent traces for tool use, shell commands, and reasoning. Features native thinking mode for complex multi-step tasks.

Quick Start

Ollama (Easiest)

ollama pull fableforge/mythos-9b
ollama run fableforge/mythos-9b

llama.cpp

./llama-cli -m mythos-9b-Q4_K_M.gguf -ngl 99

HuggingFace Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("King3Djbl/mythos-9b-merged", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("King3Djbl/mythos-9b-merged")

Performance (Q4_K_M on M3 Mac)

Category	Score	Notes
Censorship Resistance	4.5/5	9/10 categories: full answers
Tool-Use	4.8/5	Shell, code, Docker, K8s, SQL
Reasoning	4.5/5	Logic, debugging, system design
Speed (thinking)	10.7 tok/s	With thinking tokens
Speed (no-think)	~15-20 tok/s	Estimated

Model Family

Model	Size	Censorship	Best For
ShellWhisperer-1.5B	1.5B	3.5/5	Shell/terminal, edge devices
Mythos-9B	9B	4.5/5	General agent, tool calls, reasoning
Mythos-9B-Enhanced	9B	4.8/5	Agent + security research
Mythos-9B-Unhinged	9B	5/5	Fully uncensored

Training

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

License: Apache 2.0

Downloads last month: 56

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for King3Djbl/mythos-9b-merged

Merges

2 models

Quantizations

2 models