What are the hardware requirements for Yes Or No 1.5B?

Yes Or No 1.5B requires approximately 1.6 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 Yes Or No 1.5B and how large is it?

Yes Or No 1.5B is developed by eventvoid, 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 Yes Or No 1.5B?

Yes Or No 1.5B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Yes Or No 1.5B by eventvoid — VRAM 1.6GB, 32K context

Name: Yes Or No 1.5B
Author: eventvoid

Yes Or No 1.5B is an open-source language model by eventvoid. Features: 1.5b LLM, VRAM: 1.6GB, Context: 32K, License: apache-2.0, Quantized, Instruction-Based, LLM Explorer Score: 0.32.

Base model:quantized:qwen/qwen... Base model:qwen/qwen2.5-1.5b-i... Calibration Conversational En Endpoints compatible Fact-checking Gguf Instruct Q8 Quantized Qwen2 Region:us Safetensors Uncertainty-quantification Yes-no

Model Card on HF 🤗: https://huggingface.co/eventvoid/yes-or-no-1.5b

Yes Or No 1.5B Parameters and Internals

LLM Name	Yes Or No 1.5B
Repository 🤗	https://huggingface.co/eventvoid/yes-or-no-1.5b
Base Model(s)	Qwen/Qwen2.5-1.5B-Instruct Qwen/Qwen2.5-1.5B-Instruct
Model Size	1.5b
Required VRAM	1.6 GB
Updated	2026-09-22
Maintainer	eventvoid
Model Type	qwen2
Instruction-Based	Yes
Model Files	3.1 GB 1.6 GB
Supported Languages	en
GGUF Quantization	Yes
Quantization Type	gguf\|q8
Model Architecture	Qwen2ForCausalLM
License	apache-2.0
Context Length	32768
Model Max Length	32768
Transformers Version	5.17.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151936
Errors	replace

Rank the Yes Or No 1.5B 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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