What are the hardware requirements for Mergekit Ties Svidyqt?

Mergekit Ties Svidyqt requires approximately 16.1 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 Ties Svidyqt and how large is it?

Mergekit Ties Svidyqt is developed by mergekit-community, 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 Ties Svidyqt?

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

Mergekit Ties Svidyqt by mergekit-community — VRAM 16.1GB, 128K context

Name: Mergekit Ties Svidyqt
Author: mergekit-community

Mergekit Ties Svidyqt is an open-source language model by mergekit-community. Features: 8b LLM, VRAM: 16.1GB, Context: 128K, Instruction-Based, Merged, LLM Explorer Score: 0.15.

Merged Model Arxiv:2306.01708 Autotrain compatible Base model:arcee-ai/llama-3.1-... Base model:huihui-ai/meta-llam... Base model:unsloth/meta-llama-... Endpoints compatible Instruct Llama Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/mergekit-community/mergekit-ties-svidyqt

Mergekit Ties Svidyqt Parameters and Internals

LLM Name	Mergekit Ties Svidyqt
Repository 🤗	https://huggingface.co/mergekit-community/mergekit-ties-svidyqt
Base Model(s)	huihui-ai/Meta-Llama-3.1-8B-Instruct-abliterated unsloth/Meta-Llama-3.1-8B arcee-ai/Llama-3.1-SuperNova-Lite huihui-ai/Meta-Llama-3.1-8B-Instruct-abliterated unsloth/Meta-Llama-3.1-8B arcee-ai/Llama-3.1-SuperNova-Lite
Merged Model	Yes
Model Size	8b
Required VRAM	16.1 GB
Updated	2025-09-15
Maintainer	mergekit-community
Model Type	llama
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-4 5.0 GB: 2-of-4 4.9 GB: 3-of-4 1.2 GB: 4-of-4
Model Architecture	LlamaForCausalLM
Context Length	131072
Model Max Length	131072
Transformers Version	4.46.2
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|finetune_right_pad_id\|>
Vocabulary Size	128256
Torch Data Type	bfloat16

Rank the Mergekit Ties Svidyqt capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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 Ties Svidyqt

Best Alternatives	Context / RAM	Downloads	Likes
...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
Zero Llama 3.1 8B Beta6	1048K / 16.1 GB	7	1

Note: green Score (e.g. "73.2") means that the model is better than mergekit-community/mergekit-ties-svidyqt.