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

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

Qwen2.5 0.5B Rag Thinking is developed by cnmoro, 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 Rag Thinking?

Qwen2.5 0.5B Rag Thinking 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 Rag Thinking by cnmoro — VRAM 1GB, 32K context

Name: Qwen2.5 0.5B Rag Thinking
Author: cnmoro

Qwen2.5 0.5B Rag Thinking is an open-source language model by cnmoro. Features: 0.5b LLM, VRAM: 1GB, Context: 32K, License: mit, Instruction-Based, LLM Explorer Score: 0.16.

Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-0.5b-i... Conversational Instruct Pt Qwen2 Region:us Safetensors

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

Qwen2.5 0.5B Rag Thinking Parameters and Internals

LLM Name	Qwen2.5 0.5B Rag Thinking
Repository 🤗	https://huggingface.co/cnmoro/Qwen2.5-0.5B-Rag-Thinking
Base Model(s)	Qwen/Qwen2.5-0.5B-Instruct Qwen/Qwen2.5-0.5B-Instruct
Model Size	0.5b
Required VRAM	1 GB
Updated	2026-08-03
Maintainer	cnmoro
Model Type	qwen2
Instruction-Based	Yes
Model Files	1.0 GB
Supported Languages	pt
Model Architecture	Qwen2ForCausalLM
License	mit
Context Length	32768
Model Max Length	32768
Transformers Version	4.47.1
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|vision_pad\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the Qwen2.5 0.5B Rag Thinking 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 Rag Thinking

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 cnmoro/Qwen2.5-0.5B-Rag-Thinking.