Instructions to use josharsh/harshell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use josharsh/harshell with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="josharsh/harshell", filename="harsh-shell-q8_0.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use josharsh/harshell with 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 josharsh/harshell:Q8_0 # Run inference directly in the terminal: llama cli -hf josharsh/harshell:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf josharsh/harshell:Q8_0 # Run inference directly in the terminal: llama cli -hf josharsh/harshell: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 josharsh/harshell:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf josharsh/harshell: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 josharsh/harshell:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf josharsh/harshell:Q8_0
Use Docker
docker model run hf.co/josharsh/harshell:Q8_0
- LM Studio
- Jan
- vLLM
How to use josharsh/harshell with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "josharsh/harshell" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "josharsh/harshell", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/josharsh/harshell:Q8_0
- Ollama
How to use josharsh/harshell with Ollama:
ollama run hf.co/josharsh/harshell:Q8_0
- Unsloth Studio
How to use josharsh/harshell with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for josharsh/harshell to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for josharsh/harshell to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for josharsh/harshell to start chatting
- Pi
How to use josharsh/harshell with Pi:
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 the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "josharsh/harshell:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use josharsh/harshell with Hermes Agent:
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 Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default josharsh/harshell:Q8_0
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use josharsh/harshell with 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"
- Docker Model Runner
How to use josharsh/harshell with Docker Model Runner:
docker model run hf.co/josharsh/harshell:Q8_0
- Lemonade
How to use josharsh/harshell with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull josharsh/harshell:Q8_0
Run and chat with the model
lemonade run user.harshell-Q8_0
List all available models
lemonade list
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-healthRun OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"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
- Download the GGUF and Modelfile from this repo
- Create the model:
ollama create harshell -f Modelfile
- 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.ggufโ The quantized model (Q8_0, ~1.5GB)Modelfileโ 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.)
- Downloads last month
- 8
8-bit
Model tree for josharsh/harshell
Base model
Qwen/Qwen2.5-1.5B
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