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

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

Qwen2.5 0.5B Instruct is developed by Qwen, 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?

Qwen2.5 0.5B Instruct 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 by Qwen — VRAM 1GB, 32K context

Name: Qwen2.5 0.5B Instruct
Author: Qwen

Qwen2.5 0.5B Instruct is an open-source language model by Qwen. Features: 0.5b LLM, VRAM: 1GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.34.

Arxiv:2407.10671 Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-0.5b Chat Conversational Deploy:azure En Endpoints compatible Instruct Qwen2 Region:us Safetensors

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

Qwen2.5 0.5B Instruct Benchmarks

LLME Score: 0.33599

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Qwen2.5 0.5B Instruct (Qwen/Qwen2.5-0.5B-Instruct)

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Qwen2.5 0.5B Instruct Parameters and Internals

Model Type

Causal Language Model

Use Cases

Areas:

Chat applications, Multilingual text generation

Applications:

Role-play chatbots, Long text generation

Primary Use Cases:

Coding, Mathematics

Limitations:

Limited understanding of unsupported languages

Supported Languages

English (High), Chinese (High), French (Medium), Spanish (Medium), Portuguese (Medium), German (Medium), Italian (Medium), Russian (Medium), Japanese (Medium), Korean (Medium), Vietnamese (Medium), Thai (Medium), Arabic (Medium)

Training Details

Methodology:

Pretraining & Post-training

Context Length:

32768

Model Architecture:

Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings

Input Output

Input Format:

Chat template with system and user roles

Accepted Modalities:

text

Output Format:

Text

Performance Tips:

Use the latest `transformers` library for optimal performance.

LLM Name	Qwen2.5 0.5B Instruct
Repository 🤗	https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct
Base Model(s)	Qwen/Qwen2.5-0.5B Qwen/Qwen2.5-0.5B
Model Size	0.5b
Required VRAM	1 GB
Updated	2026-04-29
Maintainer	Qwen
Model Type	qwen2
Instruction-Based	Yes
Model Files	1.0 GB
Supported Languages	en
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	4.43.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Quantized Models of the Qwen2.5 0.5B Instruct

Model	Likes	Downloads
Flowscribe Qwen2.5 0.5B V2	0	24
...0.5B Instruct Unsloth Bnb 4bit	4	56487
Qwen2.5 0.5B Instruct Bnb 4bit	4	13044
Qwen2.5 0.5B Instruct AWQ	11	13724
...wen2.5 0.5B Instruct GPTQ Int4	9	3179
...wen2.5 0.5B Instruct GPTQ Int8	10	715

Best Alternatives to Qwen2.5 0.5B Instruct

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2 0.5B Abyme Merge3	128K / 1.3 GB	23	1
Qwen2 0.5B Abyme Merge2	128K / 0.3 GB	22	0
Qwen2.5 0.5B Instruct	64K / 1 GB	7	0
...5 0.5B Instruct Abliterated Ru	32K / 2 GB	432	0
Q SS 0.5B Reasoning Math	32K / 1 GB	59	2
BoyBarley Sparky V3	32K / 1 GB	225	0
Qwen2 0.5B Instruct	32K / 1 GB	1440477	200
DAC5.4 0.5B	32K / 1 GB	34	0
...ext Aware Abstention Qwen 0.5B	32K / 2 GB	48	0
Qwen Math SFT	32K / 2 GB	5	0

Rank the Qwen2.5 0.5B Instruct Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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Qwen2.5 0.5B Instruct by Qwen