What are the hardware requirements for Mergekit Slerp Ijgjytz?

Mergekit Slerp Ijgjytz 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 Ijgjytz and how large is it?

Mergekit Slerp Ijgjytz 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 Ijgjytz?

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

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

Name: Mergekit Slerp Ijgjytz
Author: mergekit-community

Mergekit Slerp Ijgjytz 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:davidau/mn-dark-pla... Endpoints compatible Mistral Region:us Safetensors Sharded Tensorflow

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

Mergekit Slerp Ijgjytz Parameters and Internals

LLM Name	Mergekit Slerp Ijgjytz
Repository 🤗	https://huggingface.co/mergekit-community/mergekit-slerp-ijgjytz
Base Model(s)	Mergekit Model Stock Izmzpot MN Dark Planet TITAN 12B mergekit-community/mergekit-model_stock-izmzpot DavidAU/MN-Dark-Planet-TITAN-12B
Merged Model	Yes
Model Size	12b
Required VRAM	24.5 GB
Updated	2026-08-02
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	4.49.0
Tokenizer Class	PreTrainedTokenizer
Vocabulary Size	131072
Torch Data Type	bfloat16

Rank the Mergekit Slerp Ijgjytz 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-ijgjytz.