What are the hardware requirements for MoE Merging?

MoE Merging requires approximately 48.3 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 MoE Merging and how large is it?

MoE Merging is developed by Cartinoe5930, a model with 24.2b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does MoE Merging perform on standard benchmarks?

MoE Merging has the following published scores: MMLU 61.31. Compare against reference models on this page or on the LLM Explorer leaderboards.

MoE Merging by Cartinoe5930 — VRAM 48.3GB, 32K context

Name: MoE Merging
Author: Cartinoe5930

MoE Merging is an open-source language model by Cartinoe5930. Features: 24.2b LLM, VRAM: 48.3GB, Context: 32K, License: apache-2.0, MoE, Instruction-Based, LLM Explorer Score: 0.13, Arc: 65.4, HellaSwag: 84.6, MMLU: 61.3, GSM8K: 54.2.

Arxiv:1910.09700 Conversational Endpoints compatible Instruct Mixtral Moe Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/Cartinoe5930/MoE-Merging

MoE Merging Benchmarks

ARC: 65.44 vs 96.7 (so35)^-32.3%

HellaSwag: 84.58 vs 95.3 (gpt4)^-11.2%

MMLU: 61.31 vs 88.3 (so35)^-30.6%

GSM8K: 54.21 vs 96.4 (so35)^-43.8%

LLME Score: 0.12908

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

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MoE Merging Parameters and Internals

LLM Name	MoE Merging
Repository 🤗	https://huggingface.co/Cartinoe5930/MoE-Merging
Model Size	24.2b
Required VRAM	48.3 GB
Updated	2026-04-28
Maintainer	Cartinoe5930
Model Type	mixtral
Instruction-Based	Yes
Model Files	9.9 GB: 1-of-5 10.0 GB: 2-of-5 10.0 GB: 3-of-5 10.0 GB: 4-of-5 8.4 GB: 5-of-5
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.37.0
Tokenizer Class	LlamaTokenizer
Padding Token	<s>
Vocabulary Size	32000
Torch Data Type	float16

Best Alternatives to MoE Merging

Best Alternatives	Context / RAM	Downloads	Likes
Proto Athena 4x7B	32K / 48.4 GB	7	0
Proto Athena V0.2 4x7B	32K / 48.4 GB	7	0
...erges MoE 4x7b V10 Mixtralv0.3	32K / 48.3 GB	7	0
Sixtyoneeighty 4x7B V1	32K / 48.3 GB	66	0
MixtureofMerges MoE 4x7bRP V11	32K / 48.3 GB	10	1
Eclipse Mistral 4x7b	32K / 48.5 GB	16	1
West Ramen 7Bx4	32K / 48.7 GB	63	0
Boundary Mistral 4x7b MoE	32K / 48.7 GB	117	1
...tral Fusion 4x7B Instruct V0.1	32K / 48.5 GB	15	18
... Extraction 4x7B Instruct V0.1	32K / 48.5 GB	9	5

Note: green Score (e.g. "73.2") means that the model is better than Cartinoe5930/MoE-Merging.

Rank the MoE Merging Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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Release v20260328a

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MoE Merging by Cartinoe5930

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MoE Merging Benchmarks

MoE Merging Parameters and Internals

Best Alternatives to MoE Merging

Rank the MoE Merging Capabilities

What open-source LLMs or SLMs are you in search of? 53999 in total.