What are the hardware requirements for Bruphin Lambda?

Bruphin Lambda requires approximately 14.4 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 Lambda and how large is it?

Bruphin Lambda 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 Lambda perform on standard benchmarks?

Bruphin Lambda has the following published scores: MMLU 64.9. Compare against reference models on this page or on the LLM Explorer leaderboards.

Bruphin Lambda by nbeerbower — VRAM 14.4GB, 32K context

Name: Bruphin Lambda
Author: nbeerbower

Bruphin Lambda is an open-source language model by nbeerbower. Features: 7.2b LLM, VRAM: 14.4GB, Context: 32K, License: apache-2.0, Merged, LLM Explorer Score: 0.12, Arc: 72.4, HellaSwag: 88.2, MMLU: 64.9, GSM8K: 70.7.

Merged Model Base model:chihoonlee10/t3q-mi... Base model:nbeerbower/bruphin-... Conversational Endpoints compatible Mistral Region:us Safetensors Sharded Tensorflow

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

Bruphin Lambda Parameters and Internals

Additional Notes

This model was created using MergeKit with the SLERP merge method.

LLM Name	Bruphin Lambda
Repository 🤗	https://huggingface.co/nbeerbower/bruphin-lambda
Base Model(s)	T3Q Mistral Orca Math DPO Bruphin Kappa chihoonlee10/T3Q-Mistral-Orca-Math-DPO nbeerbower/bruphin-kappa
Merged Model	Yes
Model Size	7.2b
Required VRAM	14.4 GB
Updated	2026-07-06
Maintainer	nbeerbower
Model Type	mistral
Model Files	9.9 GB: 1-of-2 4.5 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 Lambda 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-lambda.