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 UmbrellaInc/Neptune.3.2-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf UmbrellaInc/Neptune.3.2-1B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf UmbrellaInc/Neptune.3.2-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf UmbrellaInc/Neptune.3.2-1B-GGUF:Q4_K_M
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 UmbrellaInc/Neptune.3.2-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf UmbrellaInc/Neptune.3.2-1B-GGUF:Q4_K_M
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 UmbrellaInc/Neptune.3.2-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf UmbrellaInc/Neptune.3.2-1B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/UmbrellaInc/Neptune.3.2-1B-GGUF:Q4_K_M
Quick Links

Neptune-3.2-1B

  • Good fluency

  • Strong personality

  • Still somewhat conversational

  • Structural reasoning

  • Syntactic correctness

  • Code consistency

  • Zero hesitation

  • Zero politeness

  • Zero verbosity

Ideal Inference Configuration (Code-Only Mode)

temperature: 0.3
top_p: 0.9
do_sample: true
max_new_tokens: 1024
repetition_penalty: 1.05

Optional but Highly Effective

System prompt:

“Respond with code only. No explanations. No comments unless explicitly requested.”
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GGUF
Model size
1B params
Architecture
llama
Hardware compatibility
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4-bit

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