What are the hardware requirements for Llama3merge6 15B MoE?

Llama3merge6 15B MoE requires approximately 27.5 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama3merge6 15B MoE and how large is it?

Llama3merge6 15B MoE is developed by allknowingroger, 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 Llama3merge6 15B MoE?

Llama3merge6 15B MoE is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama3merge6 15B MoE by allknowingroger — VRAM 27.5GB, 8K context

Name: Llama3merge6 15B MoE
Author: allknowingroger

Llama3merge6 15B MoE is an open-source language model by allknowingroger. Features: 8b LLM, VRAM: 27.5GB, Context: 8K, License: apache-2.0, LLM Explorer Score: 0.12.

Autotrain compatible Base model:imone/llama-3-8b-fi... Base model:kukedlc/neuralllami... Base model:merge:imone/llama-3... Base model:merge:kukedlc/neura... Conversational Endpoints compatible Frankenmoe Imone/llama-3-8b-fixed-special... Kukedlc/neuralllamita-3-8b-v0.... Lazymergekit Merge Mergekit Mixtral Moe Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/allknowingroger/Llama3merge6-15B-MoE

Llama3merge6 15B MoE Parameters and Internals

Model Type

Mixture of Experts (MoE)

Additional Notes

The model is created using LazyMergekit from two base models: Kukedlc/NeuralLlamita-3-8B-v0.2 and imone/Llama-3-8B-fixed-special-embedding. It uses Mixture of Experts strategy by assigning positive prompts.

LLM Name	Llama3merge6 15B MoE
Repository 🤗	https://huggingface.co/allknowingroger/Llama3merge6-15B-MoE
Base Model(s)	Kukedlc/NeuralLlamita-3-8B-v0.2 ...a 3 8B Fixed Special Embedding Kukedlc/NeuralLlamita-3-8B-v0.2 imone/Llama-3-8B-fixed-special-embedding
Model Size	8b
Required VRAM	27.5 GB
Updated	2025-09-22
Maintainer	allknowingroger
Model Type	mixtral
Model Files	1.1 GB: 1-of-15 2.0 GB: 2-of-15 2.0 GB: 3-of-15 2.0 GB: 4-of-15 2.0 GB: 5-of-15 2.0 GB: 6-of-15 2.0 GB: 7-of-15 2.0 GB: 8-of-15 2.0 GB: 9-of-15 2.0 GB: 10-of-15 2.0 GB: 11-of-15 2.0 GB: 12-of-15 2.0 GB: 13-of-15 2.0 GB: 14-of-15 0.4 GB: 15-of-15
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	8192
Model Max Length	8192
Transformers Version	4.40.0
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|begin_of_text\|>
Vocabulary Size	128258
Torch Data Type	bfloat16

Rank the Llama3merge6 15B MoE 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 Llama3merge6 15B MoE

Best Alternatives	Context / RAM	Downloads	Likes
Lamma3merge3 15B MoE	8K / 27.5 GB	11	1
Lamma3merge2 15B MoE	8K / 27.5 GB	10	0
Llama3merge7 15B MoE	8K / 27.5 GB	7	0
Mergkit 1	8K / 22.6 GB	5	0
Llama 3 8B Shisa 2x8B	8K / 7.4 GB	3	2
Llama3merge8 15B MoE	8K / 27.5 GB	5	0
...8B Finetune All V6 Epoch2 V0.1	2K / 18 GB	9	1
...oE 8B Pretrain 0520 Iter134999	2K / 18 GB	15	0
...Storm V1.15 4x8B B 8 0bpw EXL2	8K / 25.2 GB	6	0
... SnowStorm 4x8B 6.5bpw H8 EXL2	8K / 21 GB	5	2

Note: green Score (e.g. "73.2") means that the model is better than allknowingroger/Llama3merge6-15B-MoE.