What are the hardware requirements for Notus 7B V1?

Notus 7B V1 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 Notus 7B V1 and how large is it?

Notus 7B V1 is developed by argilla, 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 Notus 7B V1 perform on standard benchmarks?

Notus 7B V1 has the following published scores: MMLU 61.8. Compare against reference models on this page or on the LLM Explorer leaderboards.

Notus 7B V1 by argilla — VRAM 14.4GB, 32K context

Name: Notus 7B V1
Author: argilla

Notus 7B V1 is an open-source language model by argilla. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, License: mit, LLM Explorer Score: 0.18, Arc: 80.4, HellaSwag: 58.5, MMLU: 61.8, GSM8K: 39.9.

Base model:alignment-handbook/... Base model:finetune:alignment-... Conversational Dataset:argilla/ultrafeedback-... Dpo En Endpoints compatible Mistral Model-index Preference Region:us Rlaif Safetensors Sharded Tensorboard Tensorflow Ultrafeedback

Model Card on HF 🤗: https://huggingface.co/argilla/notus-7b-v1

Notus 7B V1 Benchmarks

ARC: 80.4 vs 96.7 (so35)^-16.9%

HellaSwag: 58.5 vs 95.3 (gpt4)^-38.6%

MMLU: 61.8 vs 88.3 (so35)^-30%

GSM8K: 39.9 vs 96.4 (so35)^-58.6%

OpenCompas: 45.3 vs 73.3 (gpt4)^-38.2%

Exam: 62.2 vs 77.2 (gpt4)^-19.4%

Language: 21.1 vs 62 (gpt4)^-66%

Knowledge: 48.4 vs 73.5 (gpt4)^-34.1%

Understand: 50.5 vs 70 (gpt4)^-27.9%

Reasoning: 44.7 vs 74.4 (gpt4)^-39.9%

LLME Score: 0.18143

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

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Notus 7B V1 Parameters and Internals

Model Type

GPT-like, text-generation

Supported Languages

English (Mainly)

Training Details

Data Sources:

github.com/argilla-io/distilabel, openbmb/UltraFeedback

Methodology:

Direct Preference Optimization (DPO) fine-tuning

Hardware Used:

8 x A100 40GB

LLM Name	Notus 7B V1
Repository 🤗	https://huggingface.co/argilla/notus-7b-v1
Base Model(s)	Zephyr 7B Sft Full alignment-handbook/zephyr-7b-sft-full
Model Size	7b
Required VRAM	14.4 GB
Updated	2026-04-19
Maintainer	argilla
Model Type	mistral
Model Files	4.9 GB: 1-of-3 5.0 GB: 2-of-3 4.5 GB: 3-of-3 0.0 GB
Supported Languages	en
Model Architecture	MistralForCausalLM
License	mit
Context Length	32768
Model Max Length	32768
Transformers Version	4.35.0
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000
Torch Data Type	bfloat16

Quantized Models of the Notus 7B V1

Model	Likes	Downloads	VRAM
Notus 7B V1 GGUF	23	1196	3 GB
Notus 7B V1 GGUF	0	144	2 GB
Notus 7B V1 GPTQ	2	17	4 GB
Notus 7B V1 AWQ	3	21	4 GB

Best Alternatives to Notus 7B V1

Best Alternatives	Context / RAM	Downloads	Likes
...Nemo Instruct 2407 Abliterated	1000K / 24.5 GB	254	20
MegaBeam Mistral 7B 512K	512K / 14.4 GB	8623	54
SpydazWeb AI HumanAI RP	512K / 14.4 GB	24	1
SpydazWeb AI HumanAI 002	512K / 14.4 GB	18	1
...daz Web AI ChatML 512K Project	512K / 14.5 GB	12	0
MegaBeam Mistral 7B 300K	282K / 14.4 GB	3779	16
MegaBeam Mistral 7B 300K	282K / 14.4 GB	8131	17
Hebrew Mistral 7B 200K	256K / 30 GB	1378	15
Astral 256K 7B V2	250K / 14.4 GB	5	0
Astral 256K 7B	250K / 14.4 GB	5	0

Note: green Score (e.g. "73.2") means that the model is better than argilla/notus-7b-v1.

Rank the Notus 7B V1 Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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Notus 7B V1 by argilla