What are the hardware requirements for Monarch 7B?

Monarch 7B 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 Monarch 7B and how large is it?

Monarch 7B is developed by mlabonne, 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 Monarch 7B perform on standard benchmarks?

Monarch 7B has the following published scores: MMLU 64.41. Compare against reference models on this page or on the LLM Explorer leaderboards.

Monarch 7B by mlabonne — VRAM 14.4GB, 32K context

Name: Monarch 7B
Author: mlabonne

Monarch 7B is an open-source language model by mlabonne. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, License: cc-by-nc-4.0, Merged, LLM Explorer Score: 0.12, Arc: 73, HellaSwag: 89, MMLU: 64.4, GSM8K: 69.1.

Merged Model Base model:mlabonne/neubeagle-... Base model:mlabonne/neuralomni... Base model:mlabonne/omnitruthy... Endpoints compatible Mistral Model-index Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/mlabonne/Monarch-7B

Monarch 7B Parameters and Internals

Model Type

text generation

Use Cases

Areas:

research, academic evaluation

Applications:

language generation tasks, academic benchmarks

Primary Use Cases:

text generation, answering academic questions

Limitations:

Not suitable for commercial use., Performance may vary based on input modalities.

Considerations:

Performance on academic datasets varies; benchmark evaluations should be consulted.

Additional Notes

Monarch-7B leverages merged models for enhanced performance on text generation tasks.

Training Details

Data Sources:

AI2 Reasoning Challenge, HellaSwag, MMLU, TruthfulQA, Winogrande, GSM8k

Methodology:

Training involved merging models using LazyMergekit.

Input Output

Input Format:

chat messages in role-content format

Accepted Modalities:

text

Output Format:

generated text

LLM Name	Monarch 7B
Repository 🤗	https://huggingface.co/mlabonne/Monarch-7B
Base Model(s)	OmniTruthyBeagle 7B V0 NeuBeagle 7B NeuralOmniBeagle 7B mlabonne/OmniTruthyBeagle-7B-v0 mlabonne/NeuBeagle-7B mlabonne/NeuralOmniBeagle-7B
Merged Model	Yes
Model Size	7b
Required VRAM	14.4 GB
Updated	2026-07-24
Maintainer	mlabonne
Model Type	mistral
Model Files	9.9 GB: 1-of-2 4.5 GB: 2-of-2
Model Architecture	MistralForCausalLM
License	cc-by-nc-4.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.35.2
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Monarch 7B 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

Quantized Models of the Monarch 7B

Model	Likes	Downloads	VRAM
NeuralMonarch 7B GPTQ	0	5	4 GB

Best Alternatives to Monarch 7B

Best Alternatives	Context / RAM	Downloads	Likes
...Nemo Instruct 2407 Abliterated	1000K / 24.5 GB	220	20
MegaBeam Mistral 7B 512K	512K / 14.4 GB	8470	54
SpydazWeb AI HumanAI RP	512K / 14.4 GB	16	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	8018	17
Hebrew Mistral 7B 200K	256K / 30 GB	23	15
Astral 256K 7B	250K / 14.4 GB	9	0
Astral 256K 7B V2	250K / 14.4 GB	8	0

Note: green Score (e.g. "73.2") means that the model is better than mlabonne/Monarch-7B.