What are the hardware requirements for Mergekit Linear Jyaduup?

Mergekit Linear Jyaduup requires approximately 16 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Mergekit Linear Jyaduup and how large is it?

Mergekit Linear Jyaduup is developed by djuna-test-lab, a model with 8b 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 Linear Jyaduup?

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

Mergekit Linear Jyaduup by djuna-test-lab — VRAM 16GB, 128K context

Name: Mergekit Linear Jyaduup
Author: djuna-test-lab

Mergekit Linear Jyaduup is an open-source language model by djuna-test-lab. Features: 8b LLM, VRAM: 16GB, Context: 128K, LLM Explorer Score: 0.17.

Arxiv:2203.05482 Base model:allenai/llama-3.1-t... Base model:huihui-ai/deepseek-... Base model:merge:allenai/llama... Base model:merge:huihui-ai/dee... Conversational Endpoints compatible Llama Merge Mergekit Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/djuna-test-lab/mergekit-linear-jyaduup

Mergekit Linear Jyaduup Parameters and Internals

LLM Name	Mergekit Linear Jyaduup
Repository 🤗	https://huggingface.co/djuna-test-lab/mergekit-linear-jyaduup
Base Model(s)	...1 Distill Llama 8B Abliterated allenai/Llama-3.1-Tulu-3.1-8B huihui-ai/DeepSeek-R1-Distill-Llama-8B-abliterated allenai/Llama-3.1-Tulu-3.1-8B
Model Size	8b
Required VRAM	16 GB
Updated	2026-01-15
Maintainer	djuna-test-lab
Model Type	llama
Model Files	1.1 GB: 1-of-9 1.9 GB: 2-of-9 2.0 GB: 3-of-9 1.9 GB: 4-of-9 2.0 GB: 5-of-9 1.9 GB: 6-of-9 2.0 GB: 7-of-9 1.9 GB: 8-of-9 1.3 GB: 9-of-9
Model Architecture	LlamaForCausalLM
Context Length	131072
Model Max Length	131072
Transformers Version	4.49.0
Tokenizer Class	LlamaTokenizerFast
Padding Token	<｜end▁of▁sentence｜>
Vocabulary Size	128256
Torch Data Type	bfloat16

Rank the Mergekit Linear Jyaduup 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
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Code Generation
Multi-Language Support and Translation

Best Alternatives to Mergekit Linear Jyaduup

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...otron 8B UltraLong 4M Instruct	4192K / 32.1 GB	1135	125
UltraLong Thinking	4192K / 16.1 GB	2	3
...a 3.1 8B UltraLong 4M Instruct	4192K / 32.1 GB	176	24
...a 3.1 8B UltraLong 2M Instruct	2096K / 32.1 GB	875	9
...otron 8B UltraLong 2M Instruct	2096K / 32.1 GB	124	18
Cthulhu 8B V1.4	1048K / 16.1 GB	10	10
...raLong 1M Instruct Abliterated	1048K / 32.1 GB	4	9
...a 3.1 8B UltraLong 1M Instruct	1048K / 32.1 GB	1387	29
...otron 8B UltraLong 1M Instruct	1048K / 32.1 GB	702	59
...xis Bookwriter Llama3.1 8B Sft	1048K / 16.1 GB	25	4

Note: green Score (e.g. "73.2") means that the model is better than djuna-test-lab/mergekit-linear-jyaduup.