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 AryaYT/nl2shell-0.8b:
# Run inference directly in the terminal:
llama cli -hf AryaYT/nl2shell-0.8b:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AryaYT/nl2shell-0.8b:
# Run inference directly in the terminal:
llama cli -hf AryaYT/nl2shell-0.8b:
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 AryaYT/nl2shell-0.8b:
# Run inference directly in the terminal:
./llama-cli -hf AryaYT/nl2shell-0.8b:
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 AryaYT/nl2shell-0.8b:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AryaYT/nl2shell-0.8b:
Use Docker
docker model run hf.co/AryaYT/nl2shell-0.8b:
Quick Links

NL2Shell 0.8B — Natural Language to Shell Commands

Fine-tuned Qwen3.5-0.8B for converting natural language to shell commands.

Quick Start

ollama run hf.co/AryaYT/nl2shell-0.8b

Training

  • Method: QLoRA (r=16, alpha=32, 4-bit NF4, all linear layers)
  • Data: NL2Bash (8,130 pairs) + 40 macOS synthetic
  • Epochs: 3 | Loss: 0.6338 | Hardware: A100 40GB
  • Architecture: Hybrid DeltaNet (75% linear + 25% softmax attention)

Prompt Format (ChatML)

<|im_start|>system
You are an expert shell programmer. Given a natural language request, output ONLY the corresponding shell command. No explanations.<|im_end|>
<|im_start|>user
find all Python files modified today<|im_end|>
<|im_start|>assistant

License

MIT | Built by Arya Teja | CloudAGI

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