What are the hardware requirements for Mergekit Slerp Fgoimpq?

Mergekit Slerp Fgoimpq requires approximately 15.2 GB of VRAM and supports a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Mergekit Slerp Fgoimpq and how large is it?

Mergekit Slerp Fgoimpq is developed by mergekit-community, 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 Slerp Fgoimpq?

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

Mergekit Slerp Fgoimpq by mergekit-community — VRAM 15.2GB, 32K context

Name: Mergekit Slerp Fgoimpq
Author: mergekit-community

Mergekit Slerp Fgoimpq is an open-source language model by mergekit-community. Features: 7b LLM, VRAM: 15.2GB, Context: 32K, Instruction-Based, Merged, LLM Explorer Score: 0.16.

Merged Model Base model:huihui-ai/qwen2.5-7... Base model:maywell/qwen2-7b-mu... Conversational Endpoints compatible Instruct Qwen2 Region:us Safetensors Sharded Tensorflow

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

Mergekit Slerp Fgoimpq Parameters and Internals

LLM Name	Mergekit Slerp Fgoimpq
Repository 🤗	https://huggingface.co/mergekit-community/mergekit-slerp-fgoimpq
Base Model(s)	huihui-ai/Qwen2.5-7B-Instruct-1M-abliterated Qwen2 7B Multilingual RP huihui-ai/Qwen2.5-7B-Instruct-1M-abliterated maywell/Qwen2-7B-Multilingual-RP
Merged Model	Yes
Model Size	7b
Required VRAM	15.2 GB
Updated	2026-07-30
Maintainer	mergekit-community
Model Type	qwen2
Instruction-Based	Yes
Model Files	5.0 GB: 1-of-4 4.9 GB: 2-of-4 5.0 GB: 3-of-4 0.3 GB: 4-of-4
Model Architecture	Qwen2ForCausalLM
Context Length	32768
Model Max Length	32768
Transformers Version	4.48.2
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	152064
Torch Data Type	bfloat16
Errors	replace

Rank the Mergekit Slerp Fgoimpq 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 Slerp Fgoimpq

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 mergekit-community/mergekit-slerp-fgoimpq.