What are the hardware requirements for Jev Style Qwen3.5 2B Decision V2?

Jev Style Qwen3.5 2B Decision V2 requires approximately 3.8 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 Style Qwen3.5 2B Decision V2 and how large is it?

Jev Style Qwen3.5 2B Decision V2 is developed by chaoliangUNSW, a model with 2b 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 Style Qwen3.5 2B Decision V2?

Jev Style Qwen3.5 2B Decision V2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Jev Style Qwen3.5 2B Decision V2 by chaoliangUNSW — VRAM 3.8GB, 256K context

Name: Jev Style Qwen3.5 2B Decision V2
Author: chaoliangUNSW

Jev Style Qwen3.5 2B Decision V2 is an open-source language model by chaoliangUNSW. Features: 2b LLM, VRAM: 3.8GB, Context: 256K, License: apache-2.0, LLM Explorer Score: 0.33.

Base model:finetune:qwen/qwen3... Base model:qwen/qwen3.5-2b-bas... Calibration Classification Conversational Decision-model En Endpoints compatible Jev-style Qwen3.5 Qwen3 5 text Region:us Safetensors Single-prefill

Model Card on HF 🤗: https://huggingface.co/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2

Jev Style Qwen3.5 2B Decision V2 Parameters and Internals

LLM Name	Jev Style Qwen3.5 2B Decision V2
Repository 🤗	https://huggingface.co/chaoliangUNSW/Jev-Style-Qwen3.5-2B-Decision-v2
Base Model(s)	Qwen/Qwen3.5-2B-Base Qwen/Qwen3.5-2B-Base
Model Size	2b
Required VRAM	3.8 GB
Updated	2026-09-25
Maintainer	chaoliangUNSW
Model Type	qwen3_5_text
Model Files	3.8 GB
Supported Languages	en
Model Architecture	Qwen3_5ForCausalLM
License	apache-2.0
Context Length	262144
Model Max Length	262144
Transformers Version	5.17.0
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
Vocabulary Size	248320
Errors	replace

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Instruction Following and Task Automation
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Data Analysis and Insight Generation
Text Generation
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