What are the hardware requirements for LoRA Qwen2.5 14B Instruct Abliterated V2?

LoRA Qwen2.5 14B Instruct Abliterated V2 requires approximately 0.6 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed LoRA Qwen2.5 14B Instruct Abliterated V2 and how large is it?

LoRA Qwen2.5 14B Instruct Abliterated V2 is developed by ngxson, a model with 14b 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 LoRA Qwen2.5 14B Instruct Abliterated V2?

LoRA Qwen2.5 14B Instruct Abliterated V2 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

LoRA Qwen2.5 14B Instruct Abliterated V2 by ngxson — VRAM 0.6GB

Name: LoRA Qwen2.5 14B Instruct Abliterated V2
Author: ngxson

LoRA Qwen2.5 14B Instruct Abliterated V2 is an open-source language model by ngxson. Features: 14b LLM, VRAM: 0.6GB, Instruction-Based, LLM Explorer Score: 0.16.

Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-14b Endpoints compatible Instruct Lora Mergekit Peft Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/ngxson/LoRA-Qwen2.5-14B-Instruct-abliterated-v2

LoRA Qwen2.5 14B Instruct Abliterated V2 Parameters and Internals

LLM Name	LoRA Qwen2.5 14B Instruct Abliterated V2
Repository 🤗	https://huggingface.co/ngxson/LoRA-Qwen2.5-14B-Instruct-abliterated-v2
Base Model(s)	Qwen/Qwen2.5-14B huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2 Qwen/Qwen2.5-14B huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2
Model Size	14b
Required VRAM	0.6 GB
Updated	2026-07-30
Maintainer	ngxson
Instruction-Based	Yes
Model Files	0.6 GB
Model Architecture	AutoModel
Is Biased	none
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	q_proj\|o_proj\|k_proj\|gate_proj\|down_proj\|embed_tokens\|v_proj\|lm_head\|up_proj
LoRA Alpha	64
LoRA Dropout	0
R Param	64

Rank the LoRA Qwen2.5 14B Instruct Abliterated V2 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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