What are the hardware requirements for Veyra2 Mango 30M Base?

Veyra2 Mango 30M Base requires approximately 0.1 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Veyra2 Mango 30M Base and how large is it?

Veyra2 Mango 30M Base is developed by veyra-ai, a model with 30m 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 Veyra2 Mango 30M Base?

Veyra2 Mango 30M Base is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Veyra2 Mango 30M Base by veyra-ai — VRAM 0.1GB, 4K context

Name: Veyra2 Mango 30M Base
Author: veyra-ai

Veyra2 Mango 30M Base is an open-source language model by veyra-ai. Features: 30m LLM, VRAM: 0.1GB, Context: 4K, License: apache-2.0, LLM Explorer Score: 0.28.

Base-model En Endpoints compatible Llama Model-index Region:us Safetensors Small-language-model Veyra

Model Card on HF 🤗: https://huggingface.co/veyra-ai/Veyra2-Mango-30M-Base

Veyra2 Mango 30M Base Parameters and Internals

LLM Name	Veyra2 Mango 30M Base
Repository 🤗	https://huggingface.co/veyra-ai/Veyra2-Mango-30M-Base
Model Size	30m
Required VRAM	0.1 GB
Updated	2026-08-12
Maintainer	veyra-ai
Model Type	llama
Model Files	0.1 GB
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	4096
Model Max Length	4096
Transformers Version	4.56.2
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|pad\|>
Vocabulary Size	8192

Rank the Veyra2 Mango 30M Base 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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