What are the hardware requirements for Regulus Qwen3 R1 Llama Distill GGUF?

Regulus Qwen3 R1 Llama Distill GGUF requires approximately 0.8 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Regulus Qwen3 R1 Llama Distill GGUF and how large is it?

Regulus Qwen3 R1 Llama Distill GGUF is developed by prithivMLmods, a model with 70b 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 Regulus Qwen3 R1 Llama Distill GGUF?

Regulus Qwen3 R1 Llama Distill GGUF is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Regulus Qwen3 R1 Llama Distill GGUF by prithivMLmods — VRAM 0.8GB

Name: Regulus Qwen3 R1 Llama Distill GGUF
Author: prithivMLmods

Regulus Qwen3 R1 Llama Distill GGUF is an open-source language model by prithivMLmods. Features: 70b LLM, VRAM: 0.8GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.2.

Base model:prithivmlmods/regul... Base model:quantized:prithivml... Code Conversational Dataset:magpie-align/magpie-re... En Endpoints compatible Gguf Math Quantized Qwen3 Reasoning Region:us Trl

Model Card on HF 🤗: https://huggingface.co/prithivMLmods/Regulus-Qwen3-R1-Llama-Distill-GGUF

Regulus Qwen3 R1 Llama Distill GGUF Parameters and Internals

LLM Name	Regulus Qwen3 R1 Llama Distill GGUF
Repository 🤗	https://huggingface.co/prithivMLmods/Regulus-Qwen3-R1-Llama-Distill-GGUF
Base Model(s)	prithivMLmods/Regulus-Qwen3-R1-Llama-Distill-1.7B prithivMLmods/Regulus-Qwen3-R1-Llama-Distill-1.7B
Model Size	70b
Required VRAM	0.8 GB
Updated	2026-06-17
Maintainer	prithivMLmods
Model Type	qwen3
Model Files	3.5 GB 3.5 GB 6.9 GB 0.8 GB 1.0 GB 0.9 GB 0.9 GB 1.1 GB 1.1 GB 1.1 GB 1.1 GB 1.1 GB 1.2 GB 1.3 GB 1.3 GB 1.3 GB 1.2 GB 1.4 GB 1.8 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	AutoModel
License	apache-2.0

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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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