Instructions to use josharsh/harshell with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- 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
- 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
- Atomic Chat
| license: apache-2.0 | |
| language: | |
| - en | |
| library_name: gguf | |
| tags: | |
| - gguf | |
| - shell | |
| - macos | |
| - terminal | |
| - command-line | |
| - qwen2 | |
| - lora | |
| - ollama | |
| base_model: Qwen/Qwen2.5-1.5B | |
| model_name: harshell | |
| pipeline_tag: text-generation | |
| quantized_by: josharsh | |
| # 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 — 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: | |
| ```bash | |
| ollama create harshell -f Modelfile | |
| ``` | |
| 3. Run it: | |
| ```bash | |
| 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 → macOS shell command pairs | |
| - **Quantization**: Converted to GGUF Q8_0 using llama.cpp | |
| ## Files | |
| - `harsh-shell-q8_0.gguf` — The quantized model (Q8_0, ~1.5GB) | |
| - `Modelfile` — 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.) | |