How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sleeping-ai/Intellect-2-TQ2-0:TQ2_0
# Run inference directly in the terminal:
llama-cli -hf sleeping-ai/Intellect-2-TQ2-0:TQ2_0
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sleeping-ai/Intellect-2-TQ2-0:TQ2_0
# Run inference directly in the terminal:
llama-cli -hf sleeping-ai/Intellect-2-TQ2-0:TQ2_0
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 sleeping-ai/Intellect-2-TQ2-0:TQ2_0
# Run inference directly in the terminal:
./llama-cli -hf sleeping-ai/Intellect-2-TQ2-0:TQ2_0
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 sleeping-ai/Intellect-2-TQ2-0:TQ2_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sleeping-ai/Intellect-2-TQ2-0:TQ2_0
Use Docker
docker model run hf.co/sleeping-ai/Intellect-2-TQ2-0:TQ2_0
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We have quantised the model in 2-bit to make it inferenceable in low-end GPU cards at scale. It was achieved thanks to llama.cpp library.

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