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

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

Qwen2.5 0.5B 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?

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

Name: Qwen2.5 0.5B
Author: unsloth

Qwen2.5 0.5B is an open-source language model by unsloth. Features: 0.5b LLM, VRAM: 1GB, Context: 32K, License: apache-2.0, LLM Explorer Score: 0.19.

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

Qwen2.5 0.5B Parameters and Internals

Model Type

Causal Language Models

Use Cases

Areas:

Research, Commercial applications

Applications:

Text generation, Multilingual translation, Coding assistance, Mathematical computations

Limitations:

Not recommended for conversational models without post-training such as SFT, RLHF, etc.

Considerations:

Post-training is recommended for specialized conversational use cases.

Additional Notes

Qwen2.5 features include long-context support up to 128K tokens and can generate up to 8K tokens. It has significantly improved capabilities in instruction following, coding, and mathematics.

Supported Languages

languages_supported (:[), Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more. ({{"languages_supported":""{"), description":"Supported languages ":null , null ] (), proficiency_level (N/A)

Training Details

Data Sources:

Multiple expert data sources in coding, mathematics, and multilingual domains

Methodology:

Pre-training with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings

Context Length:

32768

Model Architecture:

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

LLM Name	Qwen2.5 0.5B
Repository 🤗	https://huggingface.co/unsloth/Qwen2.5-0.5B
Base Model(s)	Qwen/Qwen2.5-0.5B Qwen/Qwen2.5-0.5B
Model Size	0.5b
Required VRAM	1 GB
Updated	2026-07-19
Maintainer	unsloth
Model Type	qwen2
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 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

Best Alternatives	Context / RAM	Downloads	Likes
Reader Lm 0.5B	250K / 1 GB	188	150
Qwen2 0.5B	128K / 1 GB	1216780	169
Qwen2 0.5B Finetune Exp 1	128K / 1 GB	15	0
...0.5B FT EnhancedMUSREnsembleV3	128K / 1 GB	32	1
....5B FT V4 MUSR ENSEMBLE Mathis	128K / 2 GB	30	1
...0.5B FT MUSR ENSEMBLE V2Mathis	128K / 1 GB	25	0
...PRYMMAL 0.5B FT V4 MUSR Mathis	128K / 1 GB	62	1
Qwen2 0.5B Stf Imdb	128K / 1 GB	7	0
ECE PRYMMAL 0.5B FT V3 MUSR	128K / 2 GB	17	0
ECE PRYMMAL 0.5B FT V4 MUSR	128K / 2 GB	10	0

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