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shafire/
Zero-Gemma4-E4B-OpenZero-GGUF

Name: Zero-Gemma4-E4B-OpenZero-GGUF
Availability: InStock

unsloth/gemma-4-E4B-it

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Zero OpenZero AI model

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:

bash

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

bash

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:

bash

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:

text

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

bash

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.

OpenZero

Zero Gemma is the compact member of the Zero model family: local-first, agent-oriented and built to verify before it boasts.

Model creator: shafire
OpenZero 7.1: openzero.talktoai.org
Related models: Agentic GGUF Models collection

Research. Code. Act. Verify.