What are the hardware requirements for PhysicsOnBooks?

PhysicsOnBooks 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 PhysicsOnBooks and how large is it?

PhysicsOnBooks is developed by RomanNeobutov, 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 PhysicsOnBooks?

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

PhysicsOnBooks by RomanNeobutov — VRAM 3.1GB, 32K context

Name: PhysicsOnBooks
Author: RomanNeobutov

PhysicsOnBooks is an open-source language model by RomanNeobutov. Features: 1.5b LLM, VRAM: 3.1GB, Context: 32K, License: other, Instruction-Based, LLM Explorer Score: 0.18.

Autotrain Base model:finetune:vikhrmodel... Base model:vikhrmodels/qvikhr-... Conversational Dataset:romanneobutov/physicss... Endpoints compatible Instruct Peft Qwen2 Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/RomanNeobutov/PhysicsOnBooks

PhysicsOnBooks Parameters and Internals

LLM Name	PhysicsOnBooks
Repository 🤗	https://huggingface.co/RomanNeobutov/PhysicsOnBooks
Base Model(s)	Vikhrmodels/QVikhr-2.5-1.5B-Instruct-r Vikhrmodels/QVikhr-2.5-1.5B-Instruct-r
Model Size	1.5b
Required VRAM	3.1 GB
Updated	2026-08-05
Maintainer	RomanNeobutov
Model Type	qwen2
Instruction-Based	Yes
Model Files	3.1 GB 0.0 GB
Model Architecture	Qwen2ForCausalLM
License	other
Context Length	32768
Model Max Length	32768
Transformers Version	4.48.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	151665
Torch Data Type	float16
Errors	replace

Rank the PhysicsOnBooks 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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