What are the hardware requirements for Qwen2.5 0.5B Instruct ONNX?

Qwen2.5 0.5B Instruct ONNX requires a context window of 32K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen2.5 0.5B Instruct ONNX and how large is it?

Qwen2.5 0.5B Instruct ONNX is developed by JasonYANG170, 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 Qwen2.5 0.5B Instruct ONNX?

Qwen2.5 0.5B Instruct ONNX is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2.5 0.5B Instruct ONNX by JasonYANG170 — 32K context

Name: Qwen2.5 0.5B Instruct ONNX
Author: JasonYANG170

Qwen2.5 0.5B Instruct ONNX is an open-source language model by JasonYANG170. Features: 0.5b LLM, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.32.

Base model:quantized:qwen/qwen... Base model:qwen/qwen2.5-0.5b-i... Conversational Instruct Onnx Qwen2 Region:us Transformers.js Webgpu Zh

Model Card on HF 🤗: https://huggingface.co/JasonYANG170/Qwen2.5-0.5B-Instruct-ONNX

Qwen2.5 0.5B Instruct ONNX Parameters and Internals

LLM Name	Qwen2.5 0.5B Instruct ONNX
Repository 🤗	https://huggingface.co/JasonYANG170/Qwen2.5-0.5B-Instruct-ONNX
Base Model(s)	Qwen/Qwen2.5-0.5B-Instruct Qwen/Qwen2.5-0.5B-Instruct
Model Size	0.5b
Updated	2026-09-17
Maintainer	JasonYANG170
Model Type	qwen2
Instruction-Based	Yes
Supported Languages	zh
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.57.6
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Errors	replace

Rank the Qwen2.5 0.5B Instruct ONNX 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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