What are the hardware requirements for Jev Decisions V1 Model?

Jev Decisions V1 Model requires approximately 1.5 GB of VRAM and supports a context window of 256K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Jev Decisions V1 Model and how large is it?

Jev Decisions V1 Model is developed by GhostScientist, a model with 0.8b 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 Jev Decisions V1 Model?

Jev Decisions V1 Model is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Jev Decisions V1 Model by GhostScientist — VRAM 1.5GB, 256K context

Name: Jev Decisions V1 Model
Author: GhostScientist

Jev Decisions V1 Model is an open-source language model by GhostScientist. Features: 0.8b LLM, VRAM: 1.5GB, Context: 256K, License: apache-2.0, LLM Explorer Score: 0.32.

Base model:finetune:qwen/qwen3... Base model:qwen/qwen3.5-0.8b Conversational Decision-model Endpoints compatible Jev-inspired Qwen3 5 text Region:us Safetensors Sft Synthetic Tev

Model Card on HF 🤗: https://huggingface.co/GhostScientist/jev-decisions-v1-model

Jev Decisions V1 Model Parameters and Internals

LLM Name	Jev Decisions V1 Model
Repository 🤗	https://huggingface.co/GhostScientist/jev-decisions-v1-model
Base Model(s)	Qwen/Qwen3.5-0.8B Qwen/Qwen3.5-0.8B
Model Size	0.8b
Required VRAM	1.5 GB
Updated	2026-10-06
Maintainer	GhostScientist
Model Type	qwen3_5_text
Model Files	1.5 GB 0.0 GB
Model Architecture	Qwen3_5ForCausalLM
License	apache-2.0
Context Length	262144
Model Max Length	262144
Transformers Version	5.18.0
Tokenizer Class	Qwen3_5Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	248320
Errors	replace

Rank the Jev Decisions V1 Model 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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