What are the hardware requirements for Marcoro14 7B Slerp?

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

Marcoro14 7B Slerp is developed by yanng1242, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Marcoro14 7B Slerp?

Marcoro14 7B Slerp is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Marcoro14 7B Slerp by yanng1242 — VRAM 14.5GB, 32K context

Name: Marcoro14 7B Slerp
Author: yanng1242

Marcoro14 7B Slerp is an open-source language model by yanng1242. Features: 7b LLM, VRAM: 14.5GB, Context: 32K, License: apache-2.0, Merged, LLM Explorer Score: 0.22.

Merged Model Mistral Openpipe/mistral-ft-optimized-... Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/yanng1242/Marcoro14-7B-slerp

Marcoro14 7B Slerp Parameters and Internals

LLM Name	Marcoro14 7B Slerp
Repository 🤗	https://huggingface.co/yanng1242/Marcoro14-7B-slerp
Merged Model	Yes
Model Size	7b
Required VRAM	14.5 GB
Updated	2026-07-06
Maintainer	yanng1242
Model Type	mistral
Model Files	4.9 GB: 1-of-3 4.9 GB: 2-of-3 4.7 GB: 3-of-3
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.48.0
Tokenizer Class	LlamaTokenizer
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Marcoro14 7B Slerp capabilities

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Instruction Following and Task Automation
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Data Analysis and Insight Generation
Text Generation
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