Instructions to use shafire/Zero-Gemma4-E4B-OpenZero-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shafire/Zero-Gemma4-E4B-OpenZero-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="shafire/Zero-Gemma4-E4B-OpenZero-GGUF",
	filename="Zero-Gemma4-E4B-OpenZero-Q5_K_M-F16-Merged.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-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 Studio

How to use shafire/Zero-Gemma4-E4B-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-Gemma4-E4B-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-Gemma4-E4B-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-Gemma4-E4B-OpenZero-GGUF to start chatting

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 @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-Gemma4-E4B-OpenZero-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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 new

OpenClaw new

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"

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

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.

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.