What are the hardware requirements for Depguard Jev Style 1.5B?

Depguard Jev Style 1.5B requires approximately 6.2 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 Depguard Jev Style 1.5B and how large is it?

Depguard Jev Style 1.5B is developed by JackKozmo29, 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 Depguard Jev Style 1.5B?

Depguard Jev Style 1.5B is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Depguard Jev Style 1.5B by JackKozmo29 — VRAM 6.2GB, 32K context

Name: Depguard Jev Style 1.5B
Author: JackKozmo29

Depguard Jev Style 1.5B is an open-source language model by JackKozmo29. Features: 1.5b LLM, VRAM: 6.2GB, Context: 32K, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.37.

Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-1.5b-i... Conversational En Endpoints compatible Enterprise Instruct Qwen2 Region:us Safetensors Security Supply-chain

Model Card on HF 🤗: https://huggingface.co/JackKozmo29/depguard-jev-style-1.5b

Depguard Jev Style 1.5B Parameters and Internals

LLM Name	Depguard Jev Style 1.5B
Repository 🤗	https://huggingface.co/JackKozmo29/depguard-jev-style-1.5b
Base Model(s)	Qwen/Qwen2.5-1.5B-Instruct Qwen/Qwen2.5-1.5B-Instruct
Model Size	1.5b
Required VRAM	6.2 GB
Updated	2026-10-09
Maintainer	JackKozmo29
Model Type	qwen2
Instruction-Based	Yes
Model Files	6.2 GB
Supported Languages	en
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

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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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