How to use from
OpenClaw
Start the llama.cpp server
# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf josharsh/harshell:Q8_0
Configure OpenClaw
# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "josharsh/harshell:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Quick Links

Harshell - Natural Language to macOS Shell Commands

Harshell is a fine-tuned Qwen 2.5 1.5B model that converts natural language into macOS shell commands. It returns only the command โ€” no explanations, no markdown, just the shell command you need.

Model Details

Property Value
Base Model Qwen 2.5 1.5B
Fine-tuning LoRA (rank 8, 1000 iterations)
Quantization Q8_0 GGUF
File Size ~1.5 GB
License Apache 2.0

Quick Start with Ollama

  1. Download the GGUF and Modelfile from this repo
  2. Create the model:
ollama create harshell -f Modelfile
  1. Run it:
ollama run harshell "list all pdf files in my downloads folder"

Example Usage

Input Output
list all pdf files in downloads find ~/Downloads -name "*.pdf"
show disk usage of current folder du -sh .
kill the process on port 3000 lsof -ti:3000 | xargs kill
compress this folder into a zip zip -r archive.zip .
show my ip address ifconfig | grep "inet " | grep -v 127.0.0.1

System Prompt

The model uses this system prompt:

You are a macOS terminal assistant. Convert natural language into safe shell commands. Return only the command, nothing else.

Ollama Modelfile

The included Modelfile configures:

  • Temperature: 0.3 (low for deterministic command output)
  • Top-p: 0.9
  • Max tokens: 128
  • Chat template: ChatML format (<|im_start|> / <|im_end|>)

Training Details

  • Method: LoRA (Low-Rank Adaptation)
  • LoRA Rank: 8
  • Training iterations: 1000
  • Base model: Qwen/Qwen2.5-1.5B
  • Dataset: Curated natural language โ†’ macOS shell command pairs
  • Quantization: Converted to GGUF Q8_0 using llama.cpp

Files

  • harsh-shell-q8_0.gguf โ€” The quantized model (Q8_0, ~1.5GB)
  • Modelfile โ€” Ollama configuration file

Limitations

  • Optimized for macOS commands; Linux/Windows commands may be less accurate
  • Best for single-line commands; complex multi-line scripts may not generate correctly
  • Always review generated commands before running them, especially destructive operations (rm, mv, etc.)
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GGUF
Model size
2B params
Architecture
qwen2
Hardware compatibility
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