Instructions to use govailabs/govai 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 govailabs/govai 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 govailabs/govai:Q4_K_M # Run inference directly in the terminal: llama cli -hf govailabs/govai:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf govailabs/govai:Q4_K_M # Run inference directly in the terminal: llama cli -hf govailabs/govai:Q4_K_M
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 govailabs/govai:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf govailabs/govai:Q4_K_M
Use Docker
docker model run hf.co/govailabs/govai:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use govailabs/govai with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "govailabs/govai" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "govailabs/govai", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/govailabs/govai:Q4_K_M
- Ollama
How to use govailabs/govai with Ollama:
ollama run hf.co/govailabs/govai:Q4_K_M
- Unsloth Desktop
- Pi
How to use govailabs/govai with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf govailabs/govai:Q4_K_M
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": "govailabs/govai:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use govailabs/govai with Docker Model Runner:
docker model run hf.co/govailabs/govai:Q4_K_M
- Lemonade
How to use govailabs/govai with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull govailabs/govai:Q4_K_M
Run and chat with the model
lemonade run user.govai-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use govailabs/govai with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf govailabs/govai:Q4_K_M
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 govailabs/govai:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use govailabs/govai with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf govailabs/govai:Q4_K_M
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 "govailabs/govai:Q4_K_M" \ --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"
govai answers in plain words, writes full step-by-step guides when you ask for detail, solves maths and science problems, and can think before answering hard questions. New in 2.4: tool calling. Give govai tools (search your files, a calculator, any API) and it decides by itself when to call one, and answers directly when none fits. One 1.3 GB file, fully offline.
| Size | ~2B parameters ยท govai-2.4-q4_k_m.gguf ยท 1.31 GB (Q4_K_M) |
| Context | 16,384 tokens recommended |
| Tools | OpenAI-style function definitions (Ollama tools, llama.cpp --jinja) |
| Languages | English (best), Hindi, Telugu |
| Speed | ~150 tokens/s on a gaming GPU; usable on CPU |
| License | Apache 2.0 ยท by govai labs |
Benchmarks
All scores: the Q4_K_M file above, temperature 0, unfinished answers counted as wrong, and no test question used in training (8-word overlap check).
| Tool calling | govai 2.3 | govai 2.4 |
|---|---|---|
| BFCL v3 ยท one call (100) | 21% | 85% |
| BFCL v3 ยท pick the right tool (100) | 38% | 85% |
| BFCL v3 ยท several calls at once (100) | 6% | 78% |
| BFCL v3 ยท no tool fits, don't call (100) | 95% | 85% |
| BFCL v3 ยท average | 40.0% | 83.2% |
| Your files: right tool (search / list / read, 20) | 12/20 | 20/20 |
| Your files: answer uses what the tool returned (20) | 8/20 | 19/20 |
The file test uses made-up computers; half of its questions use file kinds that never appeared in training (insurance policy, salary slip, blood test).
| Same questions, thinking off | govai 2.3 | govai 2.4 |
|---|---|---|
| GSM8K ยท school maths (250) | 82.8% | 82.4% |
| ARC-Challenge ยท science (300) | 83.0% | 83.3% |
| MMLU ยท knowledge (400) | 61.5% | 61.8% |
Maths, science and knowledge stay level with govai 2.3 (differences are within ยฑ5 points).
Use it
ollama run hf.co/govailabs/govai:Q4_K_M # Ollama
llama-cli -hf govailabs/govai --jinja # llama.cpp
Tool calling (Ollama, Python)
import ollama
tools = [{"type": "function", "function": {
"name": "search_files",
"description": "Search the user's files by words in the file name.",
"parameters": {"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]}}}]
reply = ollama.chat(model="hf.co/govailabs/govai:Q4_K_M", think=False, tools=tools,
messages=[{"role": "user", "content": "Where is my resume?"}])
print(reply.message.tool_calls) # [search_files(query="resume")]
# Run the tool, add {"role": "tool", "content": <result>} to the messages, and ask again for the answer.
Python (llama-cpp-python)
from llama_cpp import Llama
llm = Llama.from_pretrained(repo_id="govailabs/govai", filename="govai-2.4-q4_k_m.gguf", n_ctx=16384)
reply = llm.create_chat_completion(
messages=[
{"role": "system", "content": "You are govai, a friendly and helpful AI assistant. Answer clearly in simple words."},
{"role": "user", "content": "Explain how vaccines work."},
],
temperature=0.3, top_p=0.9, top_k=20,
)
print(reply["choices"][0]["message"]["content"])
Settings: temperature 0.3 (0 for maths and tool calls), top_p 0.9, top_k 20. Turn thinking on
(think: true in Ollama) for hard problems and give it room (4,096+ tokens). Ask "explain fully" or
"step by step" for a complete guide; short questions get short answers.
govai for Windows
Download govai-setup-windows.exe (183 MB, Windows 10/11 64-bit). Chat with this model fully on your PC, with web search and sources, your own files (govai can find, list and read documents, PDFs and spreadsheets in your personal folders when a question needs it: read-only, and it shows which files it opened), a built-in calculator, memory, light and dark themes, and updates inside the app. The installer isn't code-signed yet: if Windows says "Windows protected your PC", click More info โ Run anyway.
SHA-256: 963ebdd1b1e311a67bf1a77892fd3ef66d6f0edb1789025afb0d4206eab56b24
Limitations
- A small model: it can get facts wrong, especially about people, places, dates and recent events. The govai app searches the web for those; on its own, check important facts.
- Tool calls can still be wrong or missing an argument; check what a tool will do before running it, and give govai read-only tools where you can.
- Hindi and Telugu answers are weaker than English ones.
- Very hard problems (competition maths, graduate science) need thinking on and a long budget.
- Not for professional medical, legal or financial advice.
License
Apache 2.0 (LICENSE), provided "as is". Open-source notices are in NOTICE.
Citation
@misc{govai2026,
title = {govai 2.4: a small, fast, private AI assistant with tool calling},
author = {govai labs},
year = {2026},
url = {https://huggingface.co/govailabs/govai}
}
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