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 Fibogacci/Qra-1B-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Fibogacci/Qra-1B-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Fibogacci/Qra-1B-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Fibogacci/Qra-1B-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 Fibogacci/Qra-1B-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Fibogacci/Qra-1B-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 Fibogacci/Qra-1B-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Fibogacci/Qra-1B-GGUF:Q8_0
Use Docker
docker model run hf.co/Fibogacci/Qra-1B-GGUF:Q8_0
Quick Links

Qra is a series of LLMs adapted to the Polish language, resulting from a collaboration between the National Information Processing Institute (OPI) and Gdańsk University of Technology (PG).

Original base model can be found on HuggingFace here: https://huggingface.co/OPI-PG/Qra-1b

This GGUF file was quantized using Colab Notebook: https://colab.research.google.com/github/adithya-s-k/LLM-Alchemy-Chamber/blob/main/Quantization/GGUF_Quantization.ipynb

This is my first convertion of model. I don't know if whole process was correct (I mean model/gguf file gives strange answers, maybe I'm configuring it or setting not properly), but I'm fresh learner.

Pierwsze boty za płoty, jak to mówią.

Gratuluję twórcom, miejmy nadzieję, że będzie to Qra znosząca złote jajka.

Pozdro!

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GGUF
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Architecture
llama
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