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 Naphula-Archives/Savage-Sands-12B-Prototypes-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Naphula-Archives/Savage-Sands-12B-Prototypes-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Naphula-Archives/Savage-Sands-12B-Prototypes-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Naphula-Archives/Savage-Sands-12B-Prototypes-GGUF:Q8_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 Naphula-Archives/Savage-Sands-12B-Prototypes-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Naphula-Archives/Savage-Sands-12B-Prototypes-GGUF:Q8_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 Naphula-Archives/Savage-Sands-12B-Prototypes-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Naphula-Archives/Savage-Sands-12B-Prototypes-GGUF:Q8_0
Use Docker
docker model run hf.co/Naphula-Archives/Savage-Sands-12B-Prototypes-GGUF:Q8_0
Quick Links
A newer version of this model is available: DarkArtsForge/Savage-Sands-12B

UNSTABLE: USE THE DARKARTSFORGE RELEASE INSTEAD

Merge test for Savage Sands

All original donors, no ablated parts (some refusals)

  • A is full magnitude
  • B is higher SFT
  • C is higher ties
  • D is 2nd stage combo model_stock
  • E is 2nd stage combo karcher
  • F is 3rd stage combo qliphoth

All 6 prototypes are unstable and fail to properly generate EOS tokens. version G (final) won't be released until it is confirmed to be stable.

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GGUF
Model size
12B params
Architecture
llama
Hardware compatibility
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8-bit

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