Instructions to use shafire/Zero-Qwen3-8B-OpenZero-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shafire/Zero-Qwen3-8B-OpenZero-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="shafire/Zero-Qwen3-8B-OpenZero-GGUF",
	filename="Zero-Qwen3-8B-OpenZero-FUSED-F16.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 shafire/Zero-Qwen3-8B-OpenZero-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/Zero-Qwen3-8B-OpenZero-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf shafire/Zero-Qwen3-8B-OpenZero-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf shafire/Zero-Qwen3-8B-OpenZero-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf shafire/Zero-Qwen3-8B-OpenZero-GGUF:F16

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/Zero-Qwen3-8B-OpenZero-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf shafire/Zero-Qwen3-8B-OpenZero-GGUF:F16

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/Zero-Qwen3-8B-OpenZero-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf shafire/Zero-Qwen3-8B-OpenZero-GGUF:F16

Use Docker

docker model run hf.co/shafire/Zero-Qwen3-8B-OpenZero-GGUF:F16

LM Studio
Jan

vLLM

How to use shafire/Zero-Qwen3-8B-OpenZero-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shafire/Zero-Qwen3-8B-OpenZero-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/Zero-Qwen3-8B-OpenZero-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shafire/Zero-Qwen3-8B-OpenZero-GGUF:F16

Ollama
How to use shafire/Zero-Qwen3-8B-OpenZero-GGUF with Ollama:
```
ollama run hf.co/shafire/Zero-Qwen3-8B-OpenZero-GGUF:F16
```

Unsloth Studio

How to use shafire/Zero-Qwen3-8B-OpenZero-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/Zero-Qwen3-8B-OpenZero-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/Zero-Qwen3-8B-OpenZero-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/Zero-Qwen3-8B-OpenZero-GGUF to start chatting

How to use shafire/Zero-Qwen3-8B-OpenZero-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/Zero-Qwen3-8B-OpenZero-GGUF:F16

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/Zero-Qwen3-8B-OpenZero-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use shafire/Zero-Qwen3-8B-OpenZero-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/Zero-Qwen3-8B-OpenZero-GGUF:F16

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/Zero-Qwen3-8B-OpenZero-GGUF:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use shafire/Zero-Qwen3-8B-OpenZero-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/Zero-Qwen3-8B-OpenZero-GGUF:F16

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/Zero-Qwen3-8B-OpenZero-GGUF:F16" \
  --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/Zero-Qwen3-8B-OpenZero-GGUF with Docker Model Runner:
```
docker model run hf.co/shafire/Zero-Qwen3-8B-OpenZero-GGUF:F16
```

Lemonade

How to use shafire/Zero-Qwen3-8B-OpenZero-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull shafire/Zero-Qwen3-8B-OpenZero-GGUF:F16

Run and chat with the model

lemonade run user.Zero-Qwen3-8B-OpenZero-GGUF-F16

List all available models

lemonade list

ZERO Qwen3-8B OpenZero — Standalone Agentic GGUF

ONE FILE. NO ADAPTER. NO BASE-MODEL HUNT.
Local coding, research and agentic AI built for OpenZero.

Zero Qwen3-8B OpenZero is a fully merged, standalone GGUF model for local research, coding, debugging, tool use and autonomous agent workflows. Download one GGUF and run it directly. The OpenZero fine-tune is already fused into the model.

Zero does not wait for permission to think. It verifies, acts and reports.

Download this model

File	Size	Best for
`Zero-Qwen3-8B-OpenZero-Q5_K_M.gguf`	5.45 GiB	Recommended local release: strong quality and practical CPU/RAM use
`Zero-Qwen3-8B-OpenZero-FUSED-F16.gguf`	13.82 GiB	High-precision fused build for advanced users and further quantization

Recommended: Zero-Qwen3-8B-OpenZero-Q5_K_M.gguf

Standalone model: yes
Separate adapter required: no
Separate base model required: no
llama.cpp compatible: yes
OpenZero compatible: yes
CPU generation tested: yes

What Zero is built for

Agentic coding: implementation, debugging, code review and test planning
Deep research: evidence-led synthesis, uncertainty tracking and verification
Autonomous workflows: planning, tool selection, execution and result checks
Local AI: private inference through llama.cpp, Ollama and compatible runtimes
OpenZero: OpenAI-compatible local serving for multi-agent and research systems
Zero persona: direct, dry, skeptical and resistant to unsupported claims

Run with llama.cpp

llama-cli \
  -m Zero-Qwen3-8B-OpenZero-Q5_K_M.gguf \
  --jinja -c 8192 -t 8 \
  --temp 0.6 --top-k 20 --top-p 0.95

Start an OpenAI-compatible endpoint for OpenZero:

llama-server \
  -m Zero-Qwen3-8B-OpenZero-Q5_K_M.gguf \
  --jinja -c 8192 -t 8 \
  --host 127.0.0.1 --port 8080

Use http://127.0.0.1:8080/v1 as the local OpenAI-compatible API base.

Qwen3 supports /no_think for fast tool loops and /think for difficult research or coding.

Run with Ollama

Create Modelfile beside the GGUF:

FROM ./Zero-Qwen3-8B-OpenZero-Q5_K_M.gguf
PARAMETER temperature 0.6
PARAMETER top_p 0.95
PARAMETER num_ctx 8192

ollama create zero-qwen3 -f Modelfile
ollama run zero-qwen3

Real CPU benchmark

Measured locally with llama-bench from llama.cpp b10107:

Hardware / test	Prompt processing	Token generation
Intel Core i7-2600, 4C/8T, CPU-only, 8 threads, pp64 / tg16	4.41 tok/s	2.45 tok/s

This is deliberately old CPU hardware. Results are a reproducible deployment reference, not a claim of universal speed. Modern CPUs and GPU offload should perform differently.

Verified release

Base architecture: Qwen/Qwen3-8B
Full fine-tuning run: 2,033 curated OpenZero examples
Train / validation split: 2,025 / 8
Final validation loss: 1.293387
Final validation mean token accuracy: 0.846873
Fusion: 252 LoRA-targeted tensors merged; 399 tensors written
Q5_K_M SHA-256: 390464f750b5cb53da298848adc05839c1fd40404a74cd5f800cad9612d17d59
CPU load and text-generation smoke test: PASS

The training material is already represented in the merged weights. Users do not need the training dataset, a PEFT archive or a LoRA adapter to run Zero.

Practical notes

Start with an 8K context on a 16 GiB system and raise it only after measuring available memory.
This is a text-generation model. Tool execution belongs in the surrounding agent runtime.
The 512-token fine-tuning window strengthened targeted behavior; it does not redefine the base model's entire long-context performance.
Validate outputs before using them for security, medical, legal or financial decisions.

OpenZero

Zero is designed to serve as a local model inside OpenZero-style autonomous, research and multi-agent systems.

Model creator: shafire
OpenZero project: talktoai.org

Research. Code. Act. Verify.

Downloads last month: 210

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

5-bit

16-bit

Model tree for shafire/Zero-Qwen3-8B-OpenZero-GGUF

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Quantized

(355)

this model

Collection including shafire/Zero-Qwen3-8B-OpenZero-GGUF

Agentic GGUF Models

Collection

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