What are the hardware requirements for Rifa Nano 0.5B?

Rifa Nano 0.5B 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 Rifa Nano 0.5B and how large is it?

Rifa Nano 0.5B is developed by smshahbaj, 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 Rifa Nano 0.5B?

Rifa Nano 0.5B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Rifa Nano 0.5B by smshahbaj — VRAM 1GB, 32K context

Name: Rifa Nano 0.5B
Author: smshahbaj

Rifa Nano 0.5B is an open-source language model by smshahbaj. Features: 0.5b LLM, VRAM: 1GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.32.

Bangla Banglish Base model:adapter:qwen/qwen2.... Base model:qwen/qwen2.5-0.5b-i... Bengali Bn Coding Conversational En Endpoints compatible Finetuned Instruct Instruction-tuned Lora Qwen2 Region:us Rifa Rifa-nano Safetensors

Model Card on HF 🤗: https://huggingface.co/smshahbaj/Rifa-Nano-0.5B

Rifa Nano 0.5B Parameters and Internals

LLM Name	Rifa Nano 0.5B
Repository 🤗	https://huggingface.co/smshahbaj/Rifa-Nano-0.5B
Base Model(s)	Qwen/Qwen2.5-0.5B-Instruct Qwen/Qwen2.5-0.5B-Instruct
Model Size	0.5b
Required VRAM	1 GB
Updated	2026-08-29
Maintainer	smshahbaj
Model Type	qwen2
Instruction-Based	Yes
Model Files	1.0 GB
Supported Languages	en bn
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.45.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	float16
Errors	replace

Rank the Rifa Nano 0.5B 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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