What are the hardware requirements for MultiverseMath 12B MoE?

MultiverseMath 12B MoE requires approximately 25.8 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 MultiverseMath 12B MoE and how large is it?

MultiverseMath 12B MoE is developed by allknowingroger, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does MultiverseMath 12B MoE perform on standard benchmarks?

MultiverseMath 12B MoE has the following published scores: MMLU 64.36. Compare against reference models on this page or on the LLM Explorer leaderboards.

MultiverseMath 12B MoE by allknowingroger — VRAM 25.8GB, 32K context

Name: MultiverseMath 12B MoE
Author: allknowingroger

MultiverseMath 12B MoE is an open-source language model by allknowingroger. Features: 7b LLM, VRAM: 25.8GB, Context: 32K, License: apache-2.0, LLM Explorer Score: 0.12, Arc: 70.5, HellaSwag: 88.1, MMLU: 64.4, GSM8K: 72.5.

Allknowingroger/multiverseex26... Autotrain compatible Base model:allknowingroger/mul... Base model:dt12the/math-mixtra... Base model:merge:allknowingrog... Base model:merge:dt12the/math-... Dt12the/math-mixtral-7b Endpoints compatible Frankenmoe Lazymergekit Merge Mergekit Mixtral Moe Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/allknowingroger/MultiverseMath-12B-MoE

MultiverseMath 12B MoE Parameters and Internals

Model Type

Mixture of Experts, text generation

Additional Notes

Model made using LazyMergekit. It is a Mixture of Experts (MoE) model.

LLM Name	MultiverseMath 12B MoE
Repository 🤗	https://huggingface.co/allknowingroger/MultiverseMath-12B-MoE
Base Model(s)	MultiverseEx26 7B Slerp DT12the/Math-Mixtral-7B allknowingroger/MultiverseEx26-7B-slerp DT12the/Math-Mixtral-7B
Model Size	7b
Required VRAM	25.8 GB
Updated	2025-09-23
Maintainer	allknowingroger
Model Type	mixtral
Model Files	1.9 GB: 1-of-13 2.0 GB: 2-of-13 2.0 GB: 3-of-13 2.0 GB: 4-of-13 2.0 GB: 5-of-13 2.0 GB: 6-of-13 2.0 GB: 7-of-13 2.0 GB: 8-of-13 2.0 GB: 9-of-13 2.0 GB: 10-of-13 2.0 GB: 11-of-13 2.0 GB: 12-of-13 1.9 GB: 13-of-13
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.39.3
Tokenizer Class	LlamaTokenizer
Padding Token	<s>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the MultiverseMath 12B MoE 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 MultiverseMath 12B MoE

Best Alternatives	Context / RAM	Downloads	Likes
Multimaster 7B V6	32K / 142.5 GB	8108	2
Mini Mixtral V0.2	32K / 25.8 GB	3923	4
MultiverseBuddy 15B MoE	32K / 25.8 GB	9	0
Lumina 2	32K / 37.1 GB	6	0
OpenMistral MoE	32K / 48.3 GB	1218	0
RogerWizard 12B MoE	32K / 25.8 GB	4	1
Rawr	32K / 93.5 GB	1302	0
Merged Model MoE	32K / 53.3 GB	7	1
StarlingMaths 12B MoE	32K / 25.8 GB	5	0
Lumina 3	32K / 37.1 GB	45	0

Note: green Score (e.g. "73.2") means that the model is better than allknowingroger/MultiverseMath-12B-MoE.