What are the hardware requirements for HyperAutoGGUF Q4?

HyperAutoGGUF Q4 requires approximately 4.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed HyperAutoGGUF Q4 and how large is it?

HyperAutoGGUF Q4 is developed by HyperdustProtocol, 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 HyperAutoGGUF Q4?

HyperAutoGGUF Q4 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

HyperAutoGGUF Q4 by HyperdustProtocol — VRAM 4.1GB

Name: HyperAutoGGUF Q4
Author: HyperdustProtocol

HyperAutoGGUF Q4 is an open-source language model by HyperdustProtocol. Features: 7b LLM, VRAM: 4.1GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.12.

4bit Base model:quantized:unsloth/l... Base model:unsloth/llama-2-7b-... En Endpoints compatible Gguf Llama Q4 Quantized Region:us Unsloth

Model Card on HF 🤗: https://huggingface.co/HyperdustProtocol/HyperAutoGGUF-q4

HyperAutoGGUF Q4 Parameters and Internals

Model Type

text-generation-inference, transformers, unsloth, llama, gguf

Additional Notes

This Llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

LLM Name	HyperAutoGGUF Q4
Repository 🤗	https://huggingface.co/HyperdustProtocol/HyperAutoGGUF-q4
Base Model(s)	Llama 2 7B Bnb 4bit unsloth/llama-2-7b-bnb-4bit
Model Size	7b
Required VRAM	4.1 GB
Updated	2026-08-06
Maintainer	HyperdustProtocol
Model Type	llama
Model Files	4.1 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf\|q4\|4bit
Model Architecture	AutoModel
License	apache-2.0

Rank the HyperAutoGGUF Q4 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

Best Alternatives to HyperAutoGGUF Q4

Best Alternatives	Context / RAM	Downloads	Likes
Pixel	8K / 4.4 GB	17	0
Qwen2 7B Alpacha Q8 0	0K / 9.1 GB	23	0
...ral 7B Instruct V0.2 Llamafile	0K / 14.5 GB	8850	25
Conversely Mistral 7B	0K / 0.2 GB	132	0
Mistral 7B Instruct V0.3 GGUF	0K / 1.6 GB	91691	147
CleverBoi 7B V2	0K / 0.1 GB	586	0
WizardLM 2 7B GGUF	0K / 2.7 GB	93547	83
Qwen2 7B Instruct GGUF	0K / 1.9 GB	56414	10
...hemeng Qwen Math 7b 24 1 100 1	0K / 15.2 GB	25	0
Mistral 7B Instruct V0.2 GGUF	0K / 3.1 GB	70243	511

Note: green Score (e.g. "73.2") means that the model is better than HyperdustProtocol/HyperAutoGGUF-q4.