What are the hardware requirements for Mergekit Della Linear Vmeykci?

Mergekit Della Linear Vmeykci requires approximately 16.2 GB of VRAM and supports a context window of 986K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Mergekit Della Linear Vmeykci and how large is it?

Mergekit Della Linear Vmeykci is developed by Sakalti, 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 Mergekit Della Linear Vmeykci?

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

Mergekit Della Linear Vmeykci by Sakalti — VRAM 16.2GB, 986K context

Name: Mergekit Della Linear Vmeykci
Author: Sakalti

Mergekit Della Linear Vmeykci is an open-source language model by Sakalti. Features: 7b LLM, VRAM: 16.2GB, Context: 986K, Instruction-Based, Merged, LLM Explorer Score: 0.17.

Merged Model Base model:axcxept/qwen2.5-mat... Base model:bunnycore/qwen2.5-7... Conversational Endpoints compatible Instruct Qwen2 Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Sakalti/mergekit-della_linear-vmeykci

Mergekit Della Linear Vmeykci Parameters and Internals

LLM Name	Mergekit Della Linear Vmeykci
Repository 🤗	https://huggingface.co/Sakalti/mergekit-della_linear-vmeykci
Base Model(s)	AXCXEPT/Qwen2.5-Math-7B-Instruct-jp-EZO_OREO bunnycore/Qwen2.5-7B-RRP-1M AXCXEPT/Qwen2.5-Math-7B-Instruct-jp-EZO_OREO bunnycore/Qwen2.5-7B-RRP-1M
Merged Model	Yes
Model Size	7b
Required VRAM	16.2 GB
Updated	2026-07-02
Maintainer	Sakalti
Model Type	qwen2
Instruction-Based	Yes
Model Files	1.1 GB: 1-of-30 1.1 GB: 2-of-30 0.5 GB: 3-of-30 0.5 GB: 4-of-30 0.5 GB: 5-of-30 0.5 GB: 6-of-30 0.5 GB: 7-of-30 0.5 GB: 8-of-30 0.5 GB: 9-of-30 0.5 GB: 10-of-30 0.5 GB: 11-of-30 0.5 GB: 12-of-30 0.5 GB: 13-of-30 0.5 GB: 14-of-30 0.5 GB: 15-of-30 0.5 GB: 16-of-30 0.5 GB: 17-of-30 0.5 GB: 18-of-30 0.5 GB: 19-of-30 0.5 GB: 20-of-30 0.5 GB: 21-of-30 0.5 GB: 22-of-30 0.5 GB: 23-of-30 0.5 GB: 24-of-30 0.5 GB: 25-of-30 0.5 GB: 26-of-30 0.5 GB: 27-of-30 0.5 GB: 28-of-30 0.5 GB: 29-of-30 0.5 GB: 30-of-30
Model Architecture	Qwen2ForCausalLM
Context Length	1010000
Model Max Length	1010000
Transformers Version	4.46.3
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151665
Torch Data Type	float16
Errors	replace

Rank the Mergekit Della Linear Vmeykci 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

Best Alternatives to Mergekit Della Linear Vmeykci

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2.5 7B Instruct 1M	986K / 15.4 GB	52534	373
Hush Qwen2.5 7B RP V1.4 1M	986K / 15.2 GB	10	2
Qwen2.5 7B RRP 1M	986K / 15.2 GB	5	6
Qwen2.5 7B CelestialHarmony 1M	986K / 14.8 GB	8	7
Q2.5 Instruct 1M Harmony	986K / 15.2 GB	4	2
Impish QWEN 7B 1M	986K / 15.2 GB	4	6
COCO 7B Instruct 1M	986K / 15.2 GB	9	4
Apollo 7B	986K / 15.2 GB	8	0
Graph R1 7B	986K / 15.3 GB	7	3
Qwen2.5 7B Sky R1 Mini	986K / 15.2 GB	6	0

Note: green Score (e.g. "73.2") means that the model is better than Sakalti/mergekit-della_linear-vmeykci.