GGUF
English
conversational
How to use from
llama.cpp
# Gated model: Login with a HF token with gated access permission
hf auth login
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf defen/retroturn:F16
# Run inference directly in the terminal:
llama cli -hf defen/retroturn:F16
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf defen/retroturn:F16
# Run inference directly in the terminal:
llama cli -hf defen/retroturn:F16
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 defen/retroturn:F16
# Run inference directly in the terminal:
./llama-cli -hf defen/retroturn:F16
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 defen/retroturn:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf defen/retroturn:F16
Use Docker
docker model run hf.co/defen/retroturn:F16
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This is a 22b Mistral finetune created with a dataset I created, Retrowave, which is a small creative writing set, and a cut down version of Creative Writing Multiturn. Theoretically It'll be the start of a cool roleplay model. This is currently WIP, please leave comments where ever with your thoughts.

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GGUF
Model size
22B params
Architecture
llama
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