What are the hardware requirements for Void Zero?

Void Zero requires approximately 9.8 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Void Zero and how large is it?

Void Zero is developed by error404dev, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Void Zero?

Void Zero is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Void Zero by error404dev — VRAM 9.8GB

Name: Void Zero
Author: error404dev

Void Zero is an open-source language model by error404dev. Features: 7b LLM, VRAM: 9.8GB, License: apache-2.0, Quantized, Instruction-Based, Code Generating, LLM Explorer Score: 0.32.

Base model:quantized:qwen/qwen... Base model:qwen/qwen2.5-coder-... Codegen Conversational Endpoints compatible Generated from trainer Gguf Instruct Lora Q8 Quantized Qwen2 Region:us Safetensors Sft Sharded Tensorflow Trl

Model Card on HF 🤗: https://huggingface.co/error404dev/void-zero

Void Zero Parameters and Internals

LLM Name	Void Zero
Repository 🤗	https://huggingface.co/error404dev/void-zero
Model Name	void-zero-output
Base Model(s)	Qwen/Qwen2.5-Coder-7B-Instruct Qwen/Qwen2.5-Coder-7B-Instruct
Model Size	7b
Required VRAM	9.8 GB
Updated	2026-10-31
Maintainer	error404dev
Instruction-Based	Yes
Model Files	0.1 GB 4.9 GB: 1-of-4 4.9 GB: 2-of-4 15.2 GB 7.7 GB
GGUF Quantization	Yes
Quantization Type	gguf\|q8
Generates Code	Yes
Model Architecture	AutoModelForCausalLM
License	apache-2.0
Model Max Length	32768
Is Biased	none
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|PAD_TOKEN\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	down_proj\|v_proj\|up_proj\|o_proj\|gate_proj\|k_proj\|q_proj
LoRA Alpha	32
LoRA Dropout	0.05
R Param	16
Errors	replace

Rank the Void Zero capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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