What are the hardware requirements for Minueza 32M Base?

Minueza 32M Base requires approximately 0.1 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Minueza 32M Base and how large is it?

Minueza 32M Base is developed by Felladrin, a model with 32m parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Minueza 32M Base perform on standard benchmarks?

Minueza 32M Base has the following published scores: MMLU 24.8. Compare against reference models on this page or on the LLM Explorer leaderboards.

Minueza 32M Base by Felladrin — VRAM 0.1GB, 2K context

Name: Minueza 32M Base
Author: Felladrin

Minueza 32M Base is an open-source language model by Felladrin. Features: 32m LLM, VRAM: 0.1GB, Context: 2K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.18, Arc: 21.3, HellaSwag: 26.4, MMLU: 24.8, GSM8K: 0.4.

Conversational Dataset:c4 Dataset:cohereforai/aya datase... Dataset:databricks/databricks-... Dataset:euclaise/reddit-instru... Dataset:izumi-lab/open-text-bo... Dataset:skylion007/openwebtext Dataset:tiiuae/falcon-refinedw... Dataset:togethercomputer/redpa... Dataset:wikimedia/wikipedia En Endpoints compatible Instruct Mistral Model-index Pretrained Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/Felladrin/Minueza-32M-Base

Minueza 32M Base Parameters and Internals

LLM Name	Minueza 32M Base
Repository 🤗	https://huggingface.co/Felladrin/Minueza-32M-Base
Model Size	32m
Required VRAM	0.1 GB
Updated	2026-06-29
Maintainer	Felladrin
Model Type	mistral
Instruction-Based	Yes
Model Files	0.1 GB
Supported Languages	en
Model Architecture	MistralForCausalLM
License	apache-2.0
Context Length	2048
Model Max Length	2048
Transformers Version	4.38.0.dev0
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32002
Torch Data Type	float32

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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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