GGUF
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 PleIAs/Pleias-Pico-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf PleIAs/Pleias-Pico-GGUF:BF16
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf PleIAs/Pleias-Pico-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf PleIAs/Pleias-Pico-GGUF:BF16
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 PleIAs/Pleias-Pico-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf PleIAs/Pleias-Pico-GGUF:BF16
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 PleIAs/Pleias-Pico-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf PleIAs/Pleias-Pico-GGUF:BF16
Use Docker
docker model run hf.co/PleIAs/Pleias-Pico-GGUF:BF16
Quick Links

This model is the official GGUF version of [https://huggingface.co/PleIAs/Pleias-Pico Pleias-Pico].

The conversion is unquantized and should yield the same generation quality as the original model.

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GGUF
Model size
0.4B params
Architecture
llama
Hardware compatibility
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16-bit

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