Instructions to use shafire/Zero-Gemma4-E4B-OpenZero-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/Zero-Gemma4-E4B-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-Gemma4-E4B-OpenZero-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf shafire/Zero-Gemma4-E4B-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-Gemma4-E4B-OpenZero-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf shafire/Zero-Gemma4-E4B-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-Gemma4-E4B-OpenZero-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf shafire/Zero-Gemma4-E4B-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-Gemma4-E4B-OpenZero-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf shafire/Zero-Gemma4-E4B-OpenZero-GGUF:F16

Use Docker

docker model run hf.co/shafire/Zero-Gemma4-E4B-OpenZero-GGUF:F16

LM Studio
Jan

vLLM

How to use shafire/Zero-Gemma4-E4B-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-Gemma4-E4B-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-Gemma4-E4B-OpenZero-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shafire/Zero-Gemma4-E4B-OpenZero-GGUF:F16

Ollama
How to use shafire/Zero-Gemma4-E4B-OpenZero-GGUF with Ollama:
```
ollama run hf.co/shafire/Zero-Gemma4-E4B-OpenZero-GGUF:F16
```
Unsloth Desktop

How to use shafire/Zero-Gemma4-E4B-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-Gemma4-E4B-OpenZero-GGUF:F16

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": "shafire/Zero-Gemma4-E4B-OpenZero-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use shafire/Zero-Gemma4-E4B-OpenZero-GGUF with Docker Model Runner:
```
docker model run hf.co/shafire/Zero-Gemma4-E4B-OpenZero-GGUF:F16
```

Lemonade

How to use shafire/Zero-Gemma4-E4B-OpenZero-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull shafire/Zero-Gemma4-E4B-OpenZero-GGUF:F16

Run and chat with the model

lemonade run user.Zero-Gemma4-E4B-OpenZero-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use shafire/Zero-Gemma4-E4B-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-Gemma4-E4B-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-Gemma4-E4B-OpenZero-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use shafire/Zero-Gemma4-E4B-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-Gemma4-E4B-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-Gemma4-E4B-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"

ZERO Gemma 4 E4B OpenZero — Standalone Compact Agentic GGUF

COMPACT ZERO. FULL STANDALONE MODEL. ONE DOWNLOAD.
Local research, coding and autonomous workflows without adapter setup.

Zero Gemma 4 E4B OpenZero is a standalone, merged GGUF model for compact local AI deployments. The trained OpenZero weights are already fused into the model. There is no separate LoRA adapter and no second base-model download.

Small enough to run locally. Sharp enough to be Zero.

OpenZero 7.1 demo

▶ Watch “OpenZero 7.1 — 16 Agents, 1 LLM” on YouTube

The demo shows the local-first OpenZero control panel running a 16-agent autonomy configuration around one local LLM.

OpenZero local model family

This release is one of the three local model choices shown in the current OpenZero and ZeroThink interfaces:

Model	Local runtime alias	Hugging Face
OpenZero Gemma 4 E4B	`openzerogemma:latest`	this repository
OpenZero Qwen3 8B Q5_K_M	`zero-qwen3-q5:latest`	Zero-Qwen3-8B-OpenZero-GGUF
OpenZero Qwen3 8B F16	`zero-qwen3-f16:latest`	Zero-Qwen3-8B-OpenZero-GGUF

Run directly from Hugging Face with Ollama:

ollama run hf.co/shafire/Zero-Gemma4-E4B-OpenZero-GGUF
ollama run hf.co/shafire/Zero-Qwen3-8B-OpenZero-GGUF:Q5_K_M

OpenZero 7.1 can select the installed aliases locally. ZeroThink can route through its OpenZero Local provider when connected to an OpenZero node. Alias availability depends on which quantizations are installed on the machine.

OpenZero in action

_{OpenZero 7.1 — Ultra autonomy with the 16-agent setting.}	_{ZeroThink Studio — the three current OpenZero local model choices.}
_{Local Agent Zero response inside the OpenZero control panel.}
_{OpenZero agentic workflow and local privacy controls.}

Download this model

File	Size	Best for
`Zero-Gemma4-E4B-OpenZero-Q5_K_M-F16-Merged.gguf`	5.46 GiB	Recommended one-file release for local coding, research and agents

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

The file retains the Q5_K_M base tensors while preserving the 66 trained attention tensors in F16. That keeps the fine-tuned tensors at higher precision without forcing a second lossy quantization pass across the whole model.

What Zero is built for

Compact agentic AI: planning, structured execution and verification
Coding: debugging, implementation guidance, review and test design
Research: careful synthesis, evidence checks and explicit uncertainty
Tool workflows: deciding what to inspect, change and verify next
Local privacy: CPU-friendly inference without a hosted model dependency
OpenZero: OpenAI-compatible local serving for autonomous systems

Run with llama.cpp

llama-cli \
  -m Zero-Gemma4-E4B-OpenZero-Q5_K_M-F16-Merged.gguf \
  --jinja -c 8192 -t 8

Start an OpenAI-compatible endpoint for OpenZero:

llama-server \
  -m Zero-Gemma4-E4B-OpenZero-Q5_K_M-F16-Merged.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.

Run with Ollama

Create Modelfile beside the GGUF:

FROM ./Zero-Gemma4-E4B-OpenZero-Q5_K_M-F16-Merged.gguf
PARAMETER temperature 0.7
PARAMETER top_p 0.95
PARAMETER num_ctx 8192

ollama create zero-gemma4 -f Modelfile
ollama run zero-gemma4

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	2.70 tok/s	2.37 tok/s

This old-CPU result is a reproducible deployment reference, not a universal speed claim. Modern CPUs and GPU offload should perform differently.

Verified release

Base: unsloth/gemma-4-E4B-it
Full fine-tuning run: 2,033 curated OpenZero examples
Train / validation split: 1,972 / 61
Final reported train loss: 1.66231323
Validation loss at step 50: 1.287308
Validation mean token accuracy at step 50: 0.681814
Trainable LoRA parameters: 2,269,184
Fusion: 66 trained tensors merged; 720 tensors written
SHA-256: 84fd62ff6c5f0abe14dd2c6135e56800df4bc4a0b9d4cd8d9f26c36b28aa190b
CPU load and text-generation smoke test: PASS

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

Practical notes

Start with an 8K context on a 16 GiB system and increase it only after measuring available memory.
This release is text-only. A multimodal projector is not included or required.
Tool execution is provided by the surrounding OpenZero or agent runtime.
The 512-token fine-tuning window strengthened targeted behavior; it does not redefine all long-context behavior inherited from the base.
Validate high-stakes outputs independently.