What are the hardware requirements for Multi Verse Model GPTQ?

Multi Verse Model GPTQ requires approximately 4.2 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Multi Verse Model GPTQ and how large is it?

Multi Verse Model GPTQ is developed by MTSAIR, a model with 7.2b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Multi Verse Model GPTQ perform on standard benchmarks?

Multi Verse Model GPTQ has the following published scores: MMLU 64.4. Compare against reference models on this page or on the LLM Explorer leaderboards.

Multi Verse Model GPTQ by MTSAIR — VRAM 4.2GB, 32K context

Name: Multi Verse Model GPTQ
Author: MTSAIR

Multi Verse Model GPTQ is an open-source language model by MTSAIR. Features: 7.2b LLM, VRAM: 4.2GB, Context: 32K, License: other, Quantized, LLM Explorer Score: 0.12, Arc: 72.9, HellaSwag: 89.2, MMLU: 64.4, GSM8K: 71.3.

4-bit Conversational Endpoints compatible Gptq Mistral Pytorch Quantized Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/MTSAIR/multi_verse_model_GPTQ

Multi Verse Model GPTQ Parameters and Internals

Additional Notes

The model is described as an innovative idea developed through a groundbreaking training method. It aims to exhibit the capabilities of a novel approach and assist others by exploring its potential, illustrating the significance of ongoing learning and development.

Training Details

Methodology:

GPTQ quantization method

LLM Name	Multi Verse Model GPTQ
Repository 🤗	https://huggingface.co/MTSAIR/multi_verse_model_GPTQ
Model Size	7.2b
Required VRAM	4.2 GB
Updated	2026-07-21
Maintainer	MTSAIR
Model Type	mistral
Model Files	4.2 GB 4.2 GB
GPTQ Quantization	Yes
Quantization Type	gptq
Model Architecture	MistralForCausalLM
License	other
Context Length	32768
Model Max Length	32768
Transformers Version	4.34.0
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	32000
Torch Data Type	float16

Rank the Multi Verse Model GPTQ 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 Multi Verse Model GPTQ

Best Alternatives	Context / RAM	Downloads	Likes
Garrulus GPTQ	32K / 4.2 GB	28	3
HamSter 0.1 GPTQ	32K / 4.2 GB	7	2
Phoenix GPTQ	32K / 4.2 GB	12	1
Mistral Ft Optimized 1227 GPTQ	32K / 4.2 GB	19	2
...hat 3.5 1210 Seraph Slerp GPTQ	32K / 4.2 GB	10	2
...h Openchat 3.5 1210 Slerp GPTQ	32K / 4.2 GB	6	1
Metis 0.5 GPTQ	32K / 4.2 GB	6	1
XDAN L1 Chat RL V1 GPTQ	32K / 4.2 GB	15	3
Apricot Wildflower 20 GPTQ	32K / 4.2 GB	12	1
SAM GPTQ	32K / 4.2 GB	19	0