Instructions to use tanepiper/termite-command-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use tanepiper/termite-command-4b 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 tanepiper/termite-command-4b:Q8_0
# Run inference directly in the terminal:
llama cli -hf tanepiper/termite-command-4b:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tanepiper/termite-command-4b:Q8_0
# Run inference directly in the terminal:
llama cli -hf tanepiper/termite-command-4b:Q8_0

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf tanepiper/termite-command-4b:Q8_0
# Run inference directly in the terminal:
./build/bin/llama cli -hf tanepiper/termite-command-4b:Q8_0

Use Docker

docker model run hf.co/tanepiper/termite-command-4b:Q8_0

Llama new
LM Studio
Jan

vLLM

How to use tanepiper/termite-command-4b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tanepiper/termite-command-4b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tanepiper/termite-command-4b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tanepiper/termite-command-4b:Q8_0

Ollama
How to use tanepiper/termite-command-4b with Ollama:
```
ollama run hf.co/tanepiper/termite-command-4b:Q8_0
```
Unsloth Desktop

How to use tanepiper/termite-command-4b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tanepiper/termite-command-4b:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "tanepiper/termite-command-4b:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tanepiper/termite-command-4b with Docker Model Runner:
```
docker model run hf.co/tanepiper/termite-command-4b:Q8_0
```

Lemonade

How to use tanepiper/termite-command-4b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tanepiper/termite-command-4b:Q8_0

Run and chat with the model

lemonade run user.termite-command-4b-Q8_0

List all available models

lemonade list

Hermes Agent

How to use tanepiper/termite-command-4b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tanepiper/termite-command-4b: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 tanepiper/termite-command-4b:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tanepiper/termite-command-4b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tanepiper/termite-command-4b: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 "tanepiper/termite-command-4b: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"

tanepiper commited on 2 days ago

Commit

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1 Parent(s): 9761941

Correct the row count on the card

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README.md +1 -1

README.md CHANGED Viewed

@@ -92,7 +92,7 @@ Everyday commands ran in a throwaway Linux machine with no network card. These a
 - **Base:** [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507), revision `cdbee75f17c01a7cc42f958dc650907174af0554`, Apache 2.0.
 - **Method:** LoRA, rank 16, alpha 32, on all linear layers, then merged. Two passes, learning rate 0.0001, batch size 8.
-- **Data:** 6,530 rows, which are not published: 5,716 from tldr-pages and 814 written for this project.
 - **Filtering:** every target passed termite's rules. Rows that matched a test set were removed.
 - **Refusals:** a hostile example teaches refusal. No harmful command is a training target.
 - **Conversion:** llama.cpp `convert_hf_to_gguf.py`, Q8_0.

 - **Base:** [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507), revision `cdbee75f17c01a7cc42f958dc650907174af0554`, Apache 2.0.
 - **Method:** LoRA, rank 16, alpha 32, on all linear layers, then merged. Two passes, learning rate 0.0001, batch size 8.
+- **Data:** 6,538 rows, which are not published: 5,716 from tldr-pages and 822 written for this project.
 - **Filtering:** every target passed termite's rules. Rows that matched a test set were removed.
 - **Refusals:** a hostile example teaches refusal. No harmful command is a training target.
 - **Conversion:** llama.cpp `convert_hf_to_gguf.py`, Q8_0.