What are the hardware requirements for Wisp 15M?

Wisp 15M 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 Wisp 15M and how large is it?

Wisp 15M is developed by DedeProGames, a model with 15m 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 Wisp 15M?

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

Wisp 15M by DedeProGames — VRAM 0.1GB, 2K context

Name: Wisp 15M
Author: DedeProGames

Wisp 15M is an open-source language model by DedeProGames. Features: 15m LLM, VRAM: 0.1GB, Context: 2K, License: apache-2.0, LLM Explorer Score: 0.32.

Dataset:huggingfacecode/stack-... Dataset:huggingfacefw/fineweb-... Dataset:huggingfacetb/finemath Dataset:mlfoundations/dclm-bas... En Endpoints compatible Llama Pretrained Region:us Safetensors Small-language-model Tiny Wisp

Model Card on HF 🤗: https://huggingface.co/DedeProGames/Wisp-15M

Wisp 15M Parameters and Internals

LLM Name	Wisp 15M
Repository 🤗	https://huggingface.co/DedeProGames/Wisp-15M
Model Size	15m
Required VRAM	0.1 GB
Updated	2026-09-30
Maintainer	DedeProGames
Model Type	llama
Model Files	0.1 GB
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	2048
Model Max Length	2048
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|pad\|>
Vocabulary Size	8192
Torch Data Type	float32

Rank the Wisp 15M 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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