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 N-Bot-Int/ZoraBetaA2-Q16:F16
# Run inference directly in the terminal:
llama-cli -hf N-Bot-Int/ZoraBetaA2-Q16:F16
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf N-Bot-Int/ZoraBetaA2-Q16:F16
# Run inference directly in the terminal:
llama-cli -hf N-Bot-Int/ZoraBetaA2-Q16: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 N-Bot-Int/ZoraBetaA2-Q16:F16
# Run inference directly in the terminal:
./llama-cli -hf N-Bot-Int/ZoraBetaA2-Q16: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 N-Bot-Int/ZoraBetaA2-Q16:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf N-Bot-Int/ZoraBetaA2-Q16:F16
Use Docker
docker model run hf.co/N-Bot-Int/ZoraBetaA2-Q16:F16
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GGUF Version

GGUF with Quants! Allowing you to run models using KoboldCPP and other AI Environments!

Quantizations:

Quant Type Benefits Cons
Q16_0 ✅ Highest accuracy (closest to full model) ❌ Requires significantly more VRAM/RAM
✅ Best for complex reasoning & detailed outputs ❌ Slower inference compared to Q4 & Q5
✅ Suitable for high-end GPUs & serious workloads ❌ Larger file size (takes more storage)

Model Details:

Read the Model details on huggingface Model Detail Here

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