What are the hardware requirements for Mergekit Slerp Kvkcnhb?

Mergekit Slerp Kvkcnhb 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 Kvkcnhb and how large is it?

Mergekit Slerp Kvkcnhb 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 Kvkcnhb?

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

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

Name: Mergekit Slerp Kvkcnhb
Author: mergekit-community

Mergekit Slerp Kvkcnhb is an open-source language model by mergekit-community. Features: 12b LLM, VRAM: 24.5GB, Context: 1000K, Merged, LLM Explorer Score: 0.16.

Merged Model Base model:crestf411/mn-slush Base model:elinas/chronos-gold... Conversational Endpoints compatible Mistral Region:us Safetensors Sharded Tensorflow

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

Mergekit Slerp Kvkcnhb Parameters and Internals

LLM Name	Mergekit Slerp Kvkcnhb
Repository 🤗	https://huggingface.co/mergekit-community/mergekit-slerp-kvkcnhb
Base Model(s)	elinas/Chronos-Gold-12B-1.0 MN Slush elinas/Chronos-Gold-12B-1.0 crestf411/MN-Slush
Merged Model	Yes
Model Size	12b
Required VRAM	24.5 GB
Updated	2026-08-04
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
Vocabulary Size	131072
Torch Data Type	bfloat16

Rank the Mergekit Slerp Kvkcnhb 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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Note: green Score (e.g. "73.2") means that the model is better than mergekit-community/mergekit-slerp-kvkcnhb.