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

Name: Zero-Qwen3-8B-OpenZero-GGUF
Availability: InStock

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Zero OpenZero AI model

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.

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`	Zero-Gemma4-E4B-OpenZero-GGUF
OpenZero Qwen3 8B Q5_K_M	`zero-qwen3-q5:latest`	this repository
OpenZero Qwen3 8B F16	`zero-qwen3-f16:latest`	this repository

Run directly from Hugging Face with Ollama:

bash

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

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-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

bash

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:

bash

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:

text

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

bash

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 7.1: openzero.talktoai.org
Related models: Agentic GGUF Models collection

Research. Code. Act. Verify.