What are the hardware requirements for Qwen 2.5 1.5B Slang Explainer V1?

Qwen 2.5 1.5B Slang Explainer V1 requires approximately 3.1 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 Qwen 2.5 1.5B Slang Explainer V1 and how large is it?

Qwen 2.5 1.5B Slang Explainer V1 is developed by Raobotnix, a model with 1.5b 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 Qwen 2.5 1.5B Slang Explainer V1?

Qwen 2.5 1.5B Slang Explainer V1 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen 2.5 1.5B Slang Explainer V1 by Raobotnix — VRAM 3.1GB, 32K context

Name: Qwen 2.5 1.5B Slang Explainer V1
Author: Raobotnix

Qwen 2.5 1.5B Slang Explainer V1 is an open-source language model by Raobotnix. Features: 1.5b LLM, VRAM: 3.1GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.32.

Base model:finetune:unsloth/qw... Base model:unsloth/qwen2.5-1.5... Conversational En Endpoints compatible Instruct Qwen2 Region:us Safetensors Unsloth

Model Card on HF 🤗: https://huggingface.co/Raobotnix/Qwen-2.5-1.5B-Slang-Explainer-V1

Qwen 2.5 1.5B Slang Explainer V1 Parameters and Internals

LLM Name	Qwen 2.5 1.5B Slang Explainer V1
Repository 🤗	https://huggingface.co/Raobotnix/Qwen-2.5-1.5B-Slang-Explainer-V1
Base Model(s)	unsloth/Qwen2.5-1.5B-Instruct unsloth/Qwen2.5-1.5B-Instruct
Model Size	1.5b
Required VRAM	3.1 GB
Updated	2026-09-30
Maintainer	Raobotnix
Model Type	qwen2
Instruction-Based	Yes
Model Files	3.1 GB
Supported Languages	en
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	5.5.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|vision_pad\|>
Vocabulary Size	151936
Errors	replace

Rank the Qwen 2.5 1.5B Slang Explainer V1 capabilities

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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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