What are the hardware requirements for Mergekit Slerp Yvvcncu?

Mergekit Slerp Yvvcncu requires approximately 24.5 GB of VRAM and supports a context window of 1000K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Mergekit Slerp Yvvcncu and how large is it?

Mergekit Slerp Yvvcncu is developed by mergekit-community, a model with 12b 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 Mergekit Slerp Yvvcncu?

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

Mergekit Slerp Yvvcncu by mergekit-community — VRAM 24.5GB, 1000K context

Name: Mergekit Slerp Yvvcncu
Author: mergekit-community

Mergekit Slerp Yvvcncu is an open-source language model by mergekit-community. Features: 12b LLM, VRAM: 24.5GB, Context: 1000K, Merged, LLM Explorer Score: 0.17.

Merged Model Base model:azazelle/mn-halide-... Endpoints compatible Mistral Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/mergekit-community/mergekit-slerp-yvvcncu

Mergekit Slerp Yvvcncu Parameters and Internals

LLM Name	Mergekit Slerp Yvvcncu
Repository 🤗	https://huggingface.co/mergekit-community/mergekit-slerp-yvvcncu
Base Model(s)	Azazelle/MN-Halide-12b-v1.0 Mergekit Ties Wdlytza Azazelle/MN-Halide-12b-v1.0 mergekit-community/mergekit-ties-wdlytza
Merged Model	Yes
Model Size	12b
Required VRAM	24.5 GB
Updated	2026-08-05
Maintainer	mergekit-community
Model Type	mistral
Model Files	4.9 GB: 1-of-5 4.9 GB: 2-of-5 4.9 GB: 3-of-5 4.9 GB: 4-of-5 4.9 GB: 5-of-5
Model Architecture	MistralForCausalLM
Context Length	1024000
Model Max Length	1024000
Transformers Version	4.48.2
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<pad>
Vocabulary Size	131072
Torch Data Type	bfloat16

Rank the Mergekit Slerp Yvvcncu capabilities

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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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MN 12B LucidFaun RP RU	1000K / 24.5 GB	1005	6
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Note: green Score (e.g. "73.2") means that the model is better than mergekit-community/mergekit-slerp-yvvcncu.