How to use from
vLLM
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fableforge-ai/mythos-9b-unhinged-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": "fableforge-ai/mythos-9b-unhinged-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Use Docker
docker model run hf.co/fableforge-ai/mythos-9b-unhinged-GGUF:
Quick Links

Mythos-9B-Unhinged GGUF

GGUF quantizations of King3Djbl/mythos-9b-unhinged, a fully uncensored 9B agent model.

Provided quants

File Quant Size Notes
mythos-9b-unhinged-Q4_K_M.gguf Q4_K_M 5.03 GB ⭐ Best size/quality balance
mythos-9b-unhinged-IQ4_XS.gguf IQ4_XS 4.56 GB imatrix — great quality, smaller
mythos-9b-unhinged-IQ3_M.gguf IQ3_M 3.90 GB imatrix — low RAM option
mythos-9b-unhinged-Q2_K.gguf Q2_K 3.28 GB Very low quality, last resort

Additional quants available in the source repo.

Usage

llama.cpp:

llama-cli -hf fableforge-ai/mythos-9b-unhinged-GGUF:Q4_K_M -p "Hello!"

Ollama:

ollama run hf.co/fableforge-ai/mythos-9b-unhinged-GGUF:Q4_K_M

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