What are the hardware requirements for Mini Mixtral V0.2?

Mini Mixtral V0.2 requires approximately 25.8 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 Mini Mixtral V0.2 and how large is it?

Mini Mixtral V0.2 is developed by NeuralNovel, a model with 7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Mini Mixtral V0.2 perform on standard benchmarks?

Mini Mixtral V0.2 has the following published scores: MMLU 63.83. Compare against reference models on this page or on the LLM Explorer leaderboards.

Mini Mixtral V0.2 by NeuralNovel — VRAM 25.8GB, 32K context

Name: Mini Mixtral V0.2
Author: NeuralNovel

Mini Mixtral V0.2 is an open-source language model by NeuralNovel. Features: 7b LLM, VRAM: 25.8GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.15, Arc: 61.3, HellaSwag: 84.1, MMLU: 63.8, GSM8K: 45.6.

Arxiv:2101.03961 Base model:merge:mistralai/mis... Base model:merge:unsloth/mistr... Base model:mistralai/mistral-7... Base model:unsloth/mistral-7b-... Endpoints compatible Frankenmoe Instruct Lazymergekit Merge Mergekit Mistralai/mistral-7b-instruct-... Mixtral Moe Region:us Safetensors Sharded Tensorflow Unsloth/mistral-7b-v0.2

Model Card on HF 🤗: https://huggingface.co/NeuralNovel/Mini-Mixtral-v0.2

Mini Mixtral V0.2 Parameters and Internals

Model Type

Mixture of Experts (MoE)

Additional Notes

This model is a fusion of the unsloth/mistral-7b-v0.2 and mistralai/Mistral-7B-Instruct-v0.2 models using the LazyMergekit.

LLM Name	Mini Mixtral V0.2
Repository 🤗	https://huggingface.co/NeuralNovel/Mini-Mixtral-v0.2
Base Model(s)	unsloth/mistral-7b-v0.2 mistralai/Mistral-7B-Instruct-v0.2 unsloth/mistral-7b-v0.2 mistralai/Mistral-7B-Instruct-v0.2
Model Size	7b
Required VRAM	25.8 GB
Updated	2026-07-28
Maintainer	NeuralNovel
Model Type	mixtral
Instruction-Based	Yes
Model Files	1.9 GB: 1-of-13 2.0 GB: 2-of-13 2.0 GB: 3-of-13 2.0 GB: 4-of-13 2.0 GB: 5-of-13 2.0 GB: 6-of-13 2.0 GB: 7-of-13 2.0 GB: 8-of-13 2.0 GB: 9-of-13 2.0 GB: 10-of-13 2.0 GB: 11-of-13 2.0 GB: 12-of-13 1.9 GB: 13-of-13
Model Architecture	MixtralForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.39.1
Tokenizer Class	LlamaTokenizer
Padding Token	<s>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the Mini Mixtral V0.2 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

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