What are the hardware requirements for VisionPsy Nano 460M?

VisionPsy Nano 460M requires approximately 2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed VisionPsy Nano 460M and how large is it?

VisionPsy Nano 460M is developed by qvac, a model with 460m 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 VisionPsy Nano 460M?

VisionPsy Nano 460M is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

VisionPsy Nano 460M by qvac — VRAM 2GB

Name: VisionPsy Nano 460M
Author: qvac

VisionPsy Nano 460M is an open-source language model by qvac. Features: 460m LLM, VRAM: 2GB, License: apache-2.0, LLM Explorer Score: 0.32.

Base model:finetune:lusxvr/nan... Base model:lusxvr/nanovlm-460m... Conversational Custom code Edge En Ext 8k Feature-extraction Image-text-to-text Multimodal Nanovlm On-device Region:us Safetensors Vision Vision-language-model Visionpsynano Vqa

Model Card on HF 🤗: https://huggingface.co/qvac/VisionPsy-Nano-460M

VisionPsy Nano 460M Parameters and Internals

LLM Name	VisionPsy Nano 460M
Repository 🤗	https://huggingface.co/qvac/VisionPsy-Nano-460M
Base Model(s)	NanoVLM 460M 8K lusxvr/nanoVLM-460M-8k
Model Size	460m
Required VRAM	2 GB
Updated	2026-08-09
Maintainer	qvac
Model Type	visionpsynano
Model Files	2.0 GB
Supported Languages	en
Context Length	8k
Model Architecture	VisionPsyNanoForConditionalGeneration
License	apache-2.0
Model Max Length	8192
Transformers Version	5.14.1
Tokenizer Class	GPT2Tokenizer
Padding Token	<\|im_end\|>
Vocabulary Size	49152
Torch Data Type	float32
Errors	replace

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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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