Instructions to use shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF 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 shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF:Q4_K_M

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 shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF:Q4_K_M

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 shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF:Q4_K_M

Use Docker

docker model run hf.co/shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF:Q4_K_M

Ollama
How to use shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF with Ollama:
```
ollama run hf.co/shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF:Q4_K_M
```

Unsloth Studio

How to use shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF 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 shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF 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 shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF to start chatting

How to use shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF:Q4_K_M

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": "shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF: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 "shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF: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"

Docker Model Runner
How to use shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF with Docker Model Runner:
```
docker model run hf.co/shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF:Q4_K_M
```

Lemonade

How to use shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OpenZero-Qwen3-1.7B-Agentic-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF: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 shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenZero Qwen3-1.7B Agentic — Standalone GGUF

ONE FILE. NO ADAPTER. NO BASE-MODEL HUNT.

OpenZero Qwen3-1.7B Agentic is a compact local GGUF model fine-tuned for coding, research, debugging and tool-use workflows. Choose the verified Q4_K_M, Q8_0 or F16 build and run it with llama.cpp-compatible tooling.

What is included

File	Purpose
`OpenZero-Qwen3-1.7B-Agentic-Q4_K_M.gguf`	Recommended balance of size and quality
`OpenZero-Qwen3-1.7B-Agentic-Q8_0.gguf`	Higher fidelity, larger download
`OpenZero-Qwen3-1.7B-Agentic-F16.gguf`	Reference full-precision GGUF

Standalone model: yes
Separate adapter required: no
Separate base model required: no
Base architecture: Qwen/Qwen3-1.7B
Fine-tuning: 2,606 training examples; 137 held-out evaluation examples
Final held-out loss: 2.270656
GGUF conversion and CPU load test: passed for all three files with llama.cpp b10333

Run with llama.cpp

hf download shafire/OpenZero-Qwen3-1.7B-Agentic-GGUF OpenZero-Qwen3-1.7B-Agentic-Q4_K_M.gguf --local-dir .

llama-cli -m OpenZero-Qwen3-1.7B-Agentic-Q4_K_M.gguf --jinja -c 8192 -t 8 --temp 0.6 --top-p 0.95

For a local OpenAI-compatible endpoint:

llama-server -m OpenZero-Qwen3-1.7B-Agentic-Q4_K_M.gguf --jinja -c 8192 -t 8 --host 127.0.0.1 --port 8080

Positioning

Built for private local inference, practical code work, evidence-aware research and agent runtimes. Tool execution belongs to the surrounding runtime; validate outputs before acting on them.

Verified release

Training adapter: QLoRA specialist run, one epoch
Train loss: 2.949538
Held-out evaluation loss: 2.270656
Q4_K_M — 1,107,408,576 bytes — SHA-256 1d43348dc10a4b97ec733cc435e398393cd235f7f6088b0cced2382ed8c9b1b7
Q8_0 — 1,834,426,048 bytes — SHA-256 b95cedcf23e5698fd2e6368caa18e8f38ec953de5a31f449f0f18bd9993cfb0a
F16 — 3,447,348,928 bytes — SHA-256 4d0de653af5b248dfbcb0a75afcc9a8d30c261e34a98efa1208bf1e51498fc70

Provenance and reproducibility

The V10 LoRA adapter was trained for one epoch on 2,606 OpenZero instruction examples. A separate 137-row held-out set was used only for final evaluation. The adapter was merged into Qwen/Qwen3-1.7B revision 70d244cc86ccca08cf5af4e1e306ecf908b1ad5e, converted with llama.cpp b10333 / commit 08659901c43b51de735740f1cf61bb82fbe0c4e4, and Q8_0 and Q4_K_M were independently quantized from that F16 source. Every file passed a bounded one-shot CPU text load test (-c 128 -n 1 -ngl 0 --no-conversation --single-turn --simple-io --no-warmup) and its SHA-256 was compared with the remote Hugging Face LFS/Xet object.

This is a practical specialist fine-tune, not a claim of superiority over the base model. Tool calls are text emitted for an agent runtime to validate and execute; the GGUF does not itself access a shell, browser, network or private data.

This model is an independent fine-tune based on Qwen and is not affiliated with or endorsed by Qwen. The Qwen base is Apache-2.0. OpenZero training materials and resulting community release are subject to the OpenZero Community Source terms; do not describe this release as OSI-approved open source. Review both upstream and OpenZero terms before redistribution or commercial use.