What are the hardware requirements for Aura Tinyllama Rag?

Aura Tinyllama Rag requires approximately 2.2 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 Aura Tinyllama Rag and how large is it?

Aura Tinyllama Rag is developed by praveenhasavathu, a model with 1.1b 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 Aura Tinyllama Rag?

Aura Tinyllama Rag is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Aura Tinyllama Rag by praveenhasavathu — VRAM 2.2GB, 2K context

Name: Aura Tinyllama Rag
Author: praveenhasavathu

Aura Tinyllama Rag is an open-source language model by praveenhasavathu. Features: 1.1b LLM, VRAM: 2.2GB, Context: 2K, LLM Explorer Score: 0.32.

Arxiv:1910.09700 Conversational Endpoints compatible Llama Region:us Safetensors Sft Trl

Model Card on HF 🤗: https://huggingface.co/praveenhasavathu/aura-tinyllama-rag

Aura Tinyllama Rag Parameters and Internals

LLM Name	Aura Tinyllama Rag
Repository 🤗	https://huggingface.co/praveenhasavathu/aura-tinyllama-rag
Model Size	1.1b
Required VRAM	2.2 GB
Updated	2026-09-15
Maintainer	praveenhasavathu
Model Type	llama
Model Files	2.2 GB
Model Architecture	LlamaForCausalLM
Context Length	2048
Model Max Length	2048
Transformers Version	5.16.1
Tokenizer Class	LlamaTokenizer
Padding Token	</s>
Vocabulary Size	32000

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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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