What are the hardware requirements for Qwen2 Simple Arguments?

Qwen2 Simple Arguments requires approximately 0.4 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen2 Simple Arguments and how large is it?

Qwen2 Simple Arguments is developed by cris177, 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 Simple Arguments?

Qwen2 Simple Arguments is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2 Simple Arguments by cris177 — VRAM 0.4GB, 128K context

Name: Qwen2 Simple Arguments
Author: cris177

Qwen2 Simple Arguments is an open-source language model by cris177. Features: 0.5b LLM, VRAM: 0.4GB, Context: 128K, License: apache-2.0, Quantized, LLM Explorer Score: 0.13.

4bit Conversational En Endpoints compatible Gguf Model-index Quantized Qwen2 Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/cris177/Qwen2-Simple-Arguments

Qwen2 Simple Arguments Parameters and Internals

Model Type

LLM

Supported Languages

English (NLP)

Training Details

Data Sources:

synthetic data based on various types of logical arguments

Data Volume:

50k arguments created for training and 100 for testing

Methodology:

The data was converted to the Alpaca chat format before feeding it into the model. We used unsloth for memory reduced sped up training. We trained for one epoch.

Training Time:

2.5 hours

Hardware Used:

Less than 2.5 GB of VRAM were used for training

LLM Name	Qwen2 Simple Arguments
Repository 🤗	https://huggingface.co/cris177/Qwen2-Simple-Arguments
Model Size	0.5b
Required VRAM	0.4 GB
Updated	2026-08-03
Maintainer	cris177
Model Type	qwen2
Model Files	1.0 GB 0.4 GB 1.0 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	4bit\|gguf
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	131072
Model Max Length	131072
Transformers Version	4.42.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|PAD_TOKEN\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

Rank the Qwen2 Simple Arguments 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 Simple Arguments

Best Alternatives	Context / RAM	Downloads	Likes
...sh English Raspberry Pi5 16bit	128K / 1 GB	18	0
Apex Code Roblox Luau 0.5B	32K / 0.4 GB	721	1
Kitty Bash Llm	32K / 0.4 GB	351	1
Soulbox Cbt Therapy 0.5B	32K / 0.4 GB	81	0
DigitalAhmed V3 Qwen2.5 0.5B	32K / 0.5 GB	128	0
Flowscribe Qwen2.5 0.5B V2	32K / 0.4 GB	45	0
Qwen2.5 Coder 0.5B Merged	32K / 1.3 GB	29	0
QuadConnect2.5 0.5B V0.0.9B	32K / 0.5 GB	47	0
Qwen2 0.5B Instruct GGUF	32K / 0.3 GB	212	0
Qwen2 0.5B Instruct GGUF	32K / 0.3 GB	106	1

Note: green Score (e.g. "73.2") means that the model is better than cris177/Qwen2-Simple-Arguments.