What are the hardware requirements for Lura 1.0 500M?

Lura 1.0 500M requires approximately 1 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 Lura 1.0 500M and how large is it?

Lura 1.0 500M is developed by SEN-AGI, a model with 0.5b 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 Lura 1.0 500M?

Lura 1.0 500M is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Lura 1.0 500M by SEN-AGI — VRAM 1GB, 32K context

Name: Lura 1.0 500M
Author: SEN-AGI

Lura 1.0 500M is an open-source language model by SEN-AGI. Features: 0.5b LLM, VRAM: 1GB, Context: 32K, License: apache-2.0, LLM Explorer Score: 0.32.

Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-0.5b Code Conversational En Finetuned Lightweight Math Qwen2 Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/SEN-AGI/Lura-1.0-500m

Lura 1.0 500M Parameters and Internals

LLM Name	Lura 1.0 500M
Repository 🤗	https://huggingface.co/SEN-AGI/Lura-1.0-500m
Base Model(s)	Qwen/Qwen2.5-0.5B Qwen/Qwen2.5-0.5B
Model Size	0.5b
Required VRAM	1 GB
Updated	2026-08-26
Maintainer	SEN-AGI
Model Type	qwen2
Model Files	1.0 GB
Supported Languages	en
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	5.15.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Errors	replace

Rank the Lura 1.0 500M 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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...0.5B FT EnhancedMUSREnsembleV3	128K / 1 GB	32	1
....5B FT V4 MUSR ENSEMBLE Mathis	128K / 2 GB	30	1
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...PRYMMAL 0.5B FT V4 MUSR Mathis	128K / 1 GB	62	1
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ECE PRYMMAL 0.5B FT V3 MUSR	128K / 2 GB	17	0
ECE PRYMMAL 0.5B FT V4 MUSR	128K / 2 GB	10	0