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 unsloth, 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 unsloth — VRAM 1GB, 32K context

Name: Qwen2.5 0.5B Instruct
Rating: 3 (1 reviews)
Author: unsloth

Qwen2.5 0.5B Instruct is an open-source language model by unsloth. Features: 0.5b LLM, VRAM: 1GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.18.

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

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

Qwen2.5 0.5B Instruct Parameters and Internals

Model Type

Causal Language Models

Additional Notes

Significant improvements in knowledge, coding, mathematics, and instruction following abilities. Multilingual support for more than 29 languages. Long-context support up to 128K tokens, generating up to 8K tokens. Robust against diverse system prompts, enhancing chatbot capabilities.

Supported Languages

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

Training Details

Context Length:

32768

Model Architecture:

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

Input Output

Input Format:

Structured input using predefined roles in JSON format

Accepted Modalities:

text

Output Format:

JSON

LLM Name	Qwen2.5 0.5B Instruct
Repository 🤗	https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct
Base Model(s)	Qwen/Qwen2.5-0.5B-Instruct Qwen/Qwen2.5-0.5B-Instruct
Model Size	0.5b
Required VRAM	1 GB
Updated	2026-06-28
Maintainer	unsloth
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	4.49.0.dev0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|vision_pad\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the Qwen2.5 0.5B Instruct capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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

Best Alternatives to Qwen2.5 0.5B Instruct

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2 0.5B Abyme Merge3	128K / 1.3 GB	32	1
Qwen2 0.5B Abyme Merge2	128K / 0.3 GB	26	0
Qwen2.5 0.5B Instruct	64K / 1 GB	16	0
Qwen2.5 0.5B Abliterated	32K / 1.3 GB	1450	1
Qwen2.5 0.5B Instruct	32K / 1 GB	6324790	572
Seger Qwen2.5 0.5B	32K / 1 GB	599	0
Qwen2.5 0.5B Grounded Sft	32K / 1 GB	728	1
Qwen2.5 0.5B Grounded Rloo	32K / 1 GB	705	1
Sawyer 0.5B	32K / 1 GB	474	3
Parchi V2	32K / 1 GB	538	0

Note: green Score (e.g. "73.2") means that the model is better than unsloth/Qwen2.5-0.5B-Instruct.