What are the hardware requirements for Distilabeled Marcoro14 7B Slerp?

Distilabeled Marcoro14 7B Slerp 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 Distilabeled Marcoro14 7B Slerp and how large is it?

Distilabeled Marcoro14 7B Slerp 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 Distilabeled Marcoro14 7B Slerp perform on standard benchmarks?

Distilabeled Marcoro14 7B Slerp has the following published scores: MMLU 65.22. Compare against reference models on this page or on the LLM Explorer leaderboards.

Distilabeled Marcoro14 7B Slerp by argilla — VRAM 14.4GB, 32K context

Name: Distilabeled Marcoro14 7B Slerp
Author: argilla

Distilabeled Marcoro14 7B Slerp is an open-source language model by argilla. Features: 7b LLM, VRAM: 14.4GB, Context: 32K, License: cc-by-nc-4.0, LLM Explorer Score: 0.11, Arc: 70.7, HellaSwag: 87.5, MMLU: 65.2, GSM8K: 71.2.

Base model:finetune:mlabonne/m... Base model:mlabonne/marcoro14-... Dataset:argilla/distilabel-int... Distilabel Dpo En Endpoints compatible Merge Mergekit Mistral Model-index Region:us Rlaif Rlhf Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp

Distilabeled Marcoro14 7B Slerp Parameters and Internals

LLM Name	Distilabeled Marcoro14 7B Slerp
Repository 🤗	https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp
Base Model(s)	Marcoro14 7B Slerp mlabonne/Marcoro14-7B-slerp
Model Size	7b
Required VRAM	14.4 GB
Updated	2026-08-07
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
Supported Languages	en
Model Architecture	MistralForCausalLM
License	cc-by-nc-4.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.36.2
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	float16

Rank the Distilabeled Marcoro14 7B Slerp 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 Distilabeled Marcoro14 7B Slerp

Model	Likes	Downloads	VRAM
...abeled Marcoro14 7B Slerp GGUF	0	44	2 GB

Best Alternatives to Distilabeled Marcoro14 7B Slerp

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 argilla/distilabeled-Marcoro14-7B-slerp.