How to use from
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 aasdasdasdas/Qwen3.6-27B-Fable-Fusion-711-MTP-Q6K-RunPod:Q6_K
# Run inference directly in the terminal:
llama cli -hf aasdasdasdas/Qwen3.6-27B-Fable-Fusion-711-MTP-Q6K-RunPod:Q6_K
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aasdasdasdas/Qwen3.6-27B-Fable-Fusion-711-MTP-Q6K-RunPod:Q6_K
# Run inference directly in the terminal:
llama cli -hf aasdasdasdas/Qwen3.6-27B-Fable-Fusion-711-MTP-Q6K-RunPod:Q6_K
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 aasdasdasdas/Qwen3.6-27B-Fable-Fusion-711-MTP-Q6K-RunPod:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf aasdasdasdas/Qwen3.6-27B-Fable-Fusion-711-MTP-Q6K-RunPod:Q6_K
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 aasdasdasdas/Qwen3.6-27B-Fable-Fusion-711-MTP-Q6K-RunPod:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aasdasdasdas/Qwen3.6-27B-Fable-Fusion-711-MTP-Q6K-RunPod:Q6_K
Use Docker
docker model run hf.co/aasdasdasdas/Qwen3.6-27B-Fable-Fusion-711-MTP-Q6K-RunPod:Q6_K
Quick Links

Qwen3.6 27B Fable Fusion 711 MTP Q6_K โ€” RunPod cache mirror

Single-file cache mirror for RunPod Serverless. The model artifact is copied from Iambackup/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF revision 1eceb23dba99800c73c39f269e982394cc2273f6.

No weights were modified. This repository intentionally contains one quantization so RunPod does not stage unused variants.

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GGUF
Model size
27B params
Architecture
qwen35
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