What are the hardware requirements for Qwen2.5 VL 3B FakeBench LoRA V2?

Qwen2.5 VL 3B FakeBench LoRA V2 requires approximately 0.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen2.5 VL 3B FakeBench LoRA V2 and how large is it?

Qwen2.5 VL 3B FakeBench LoRA V2 is developed by NTX88666, a model with 3b 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 Qwen2.5 VL 3B FakeBench LoRA V2?

Qwen2.5 VL 3B FakeBench LoRA V2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2.5 VL 3B FakeBench LoRA V2 by NTX88666 — VRAM 0.1GB

Name: Qwen2.5 VL 3B FakeBench LoRA V2
Author: NTX88666

Qwen2.5 VL 3B FakeBench LoRA V2 is an open-source language model by NTX88666. Features: 3b LLM, VRAM: 0.1GB, Instruction-Based, LLM Explorer Score: 0.28.

Adapter Ai-generated-image-detection Base model:adapter:qwen/qwen2.... Base model:qwen/qwen2.5-vl-3b-... Conversational Fakebench Finetuned Image-forensics Image-text-to-text Instruct Lora Out-of-distribution Peft Qwen2.5-vl Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/NTX88666/Qwen2.5-VL-3B-FakeBench-LoRA-V2

Qwen2.5 VL 3B FakeBench LoRA V2 Parameters and Internals

LLM Name	Qwen2.5 VL 3B FakeBench LoRA V2
Repository 🤗	https://huggingface.co/NTX88666/Qwen2.5-VL-3B-FakeBench-LoRA-V2
Base Model(s)	Qwen/Qwen2.5-VL-3B-Instruct Qwen/Qwen2.5-VL-3B-Instruct
Model Size	3b
Required VRAM	0.1 GB
Updated	2026-10-17
Maintainer	NTX88666
Instruction-Based	Yes
Model Files	0.1 GB
Model Architecture	Adapter
Model Max Length	131072
Is Biased	none
Tokenizer Class	Qwen2Tokenizer
Padding Token	<\|endoftext\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	o_proj\|gate_proj\|k_proj\|down_proj\|v_proj\|q_proj\|up_proj
LoRA Alpha	32
LoRA Dropout	0.05
R Param	16
Errors	replace

Rank the Qwen2.5 VL 3B FakeBench LoRA V2 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

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