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

Mailguard 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 Mailguard Jev Style 1.5B and how large is it?

Mailguard 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 Mailguard Jev Style 1.5B?

Mailguard 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.

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

Name: Mailguard Jev Style 1.5B
Author: JackKozmo29

Mailguard 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: 0.32.

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

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

Mailguard Jev Style 1.5B Parameters and Internals

LLM Name	Mailguard Jev Style 1.5B
Repository 🤗	https://huggingface.co/JackKozmo29/mailguard-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	2026-10-03
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

Rank the Mailguard Jev Style 1.5B capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

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

Best Alternatives to Mailguard Jev Style 1.5B

Best Alternatives	Context / RAM	Downloads	Likes
Qwen2.5 1.5B 1.3M Stretched	1280K / 3.1 GB	27	0
Merge Cord V1	128K / 3.1 GB	155	1
Gte Qwen2 1.5B Instruct	128K / 7.1 GB	518818	238
AceInstruct 1.5B	128K / 3.5 GB	139	22
... Abliterated TIES Qwen2.5 1.5B	128K / 3.5 GB	15	0
Saba1 1.8B	128K / 3.6 GB	6	1
Miniclaus Qw1.5B UNAMGS	128K / 3.5 GB	12	8
Bellatrix 1.5B XElite	128K / 3.5 GB	19	3
GCIRS Reasoning 1.5B R1	128K / 3.5 GB	9	1
...illed Scout 1.5B Instruct Gen1	128K / 3.5 GB	10	0