What are the hardware requirements for Bruphin Iota?

Bruphin Iota requires approximately 14.5 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 Bruphin Iota and how large is it?

Bruphin Iota is developed by nbeerbower, 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 Bruphin Iota perform on standard benchmarks?

Bruphin Iota has the following published scores: MMLU 65.02. Compare against reference models on this page or on the LLM Explorer leaderboards.

Bruphin Iota by nbeerbower — VRAM 14.5GB, 32K context

Name: Bruphin Iota
Author: nbeerbower

Bruphin Iota is an open-source language model by nbeerbower. Features: 7.2b LLM, VRAM: 14.5GB, Context: 32K, License: apache-2.0, Merged, LLM Explorer Score: 0.12, Arc: 68.4, HellaSwag: 86.6, MMLU: 65, GSM8K: 67.8.

Merged Model Base model:nbeerbower/bruphin-... Base model:pabloce/dolphin-2.8... Endpoints compatible Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/nbeerbower/bruphin-iota

Bruphin Iota Parameters and Internals

Additional Notes

This model was created by merging pre-trained language models using the SLERP merge method and configuration provided.

LLM Name	Bruphin Iota
Repository 🤗	https://huggingface.co/nbeerbower/bruphin-iota
Base Model(s)	Bruphin Theta pabloce/Dolphin-2.8-slerp nbeerbower/bruphin-theta pabloce/Dolphin-2.8-slerp
Merged Model	Yes
Model Size	7.2b
Required VRAM	14.5 GB
Updated	2026-07-21
Maintainer	nbeerbower
Model Type	mistral
Model Files	9.8 GB: 1-of-2 4.7 GB: 2-of-2
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.37.1
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Bruphin Iota 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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Note: green Score (e.g. "73.2") means that the model is better than nbeerbower/bruphin-iota.