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

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

Emotion Classifier Qwen2.5 0.5B is developed by madhanbv12345, 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 Emotion Classifier Qwen2.5 0.5B?

Emotion Classifier 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.

Emotion Classifier Qwen2.5 0.5B by madhanbv12345 — VRAM 1GB, 32K context

Name: Emotion Classifier Qwen2.5 0.5B
Author: madhanbv12345

Emotion Classifier Qwen2.5 0.5B is an open-source language model by madhanbv12345. Features: 0.5b LLM, VRAM: 1GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.32.

4bit Base model:finetune:unsloth/qw... Base model:unsloth/qwen2.5-0.5... Conversational En Endpoints compatible Instruct Quantized Qwen2 Region:us Safetensors Unsloth

Model Card on HF 🤗: https://huggingface.co/madhanbv12345/emotion-classifier-qwen2.5-0.5b

Emotion Classifier Qwen2.5 0.5B Parameters and Internals

LLM Name	Emotion Classifier Qwen2.5 0.5B
Repository 🤗	https://huggingface.co/madhanbv12345/emotion-classifier-qwen2.5-0.5b
Base Model(s)	unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit
Model Size	0.5b
Required VRAM	1 GB
Updated	2026-09-30
Maintainer	madhanbv12345
Model Type	qwen2
Instruction-Based	Yes
Model Files	1.0 GB
Supported Languages	en
Quantization Type	4bit
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|vision_pad\|>
Vocabulary Size	151936
Torch Data Type	bfloat16
Errors	replace

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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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