What are the hardware requirements for LoRA Qwen2.5 7B Instruct Abliterated V3?

LoRA Qwen2.5 7B Instruct Abliterated V3 requires approximately 0.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed LoRA Qwen2.5 7B Instruct Abliterated V3 and how large is it?

LoRA Qwen2.5 7B Instruct Abliterated V3 is developed by ngxson, a model with 7b 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 7B Instruct Abliterated V3?

LoRA Qwen2.5 7B Instruct Abliterated V3 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

LoRA Qwen2.5 7B Instruct Abliterated V3 by ngxson — VRAM 0.2GB

Name: LoRA Qwen2.5 7B Instruct Abliterated V3
Author: ngxson

LoRA Qwen2.5 7B Instruct Abliterated V3 is an open-source language model by ngxson. Features: 7b LLM, VRAM: 0.2GB, Instruction-Based, LLM Explorer Score: 0.16.

Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-7b-ins... Endpoints compatible Instruct Lora Mergekit Peft Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/ngxson/LoRA-Qwen2.5-7B-Instruct-abliterated-v3

LoRA Qwen2.5 7B Instruct Abliterated V3 Parameters and Internals

LLM Name	LoRA Qwen2.5 7B Instruct Abliterated V3
Repository 🤗	https://huggingface.co/ngxson/LoRA-Qwen2.5-7B-Instruct-abliterated-v3
Base Model(s)	Qwen/Qwen2.5-7B-Instruct huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3 Qwen/Qwen2.5-7B-Instruct huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3
Model Size	7b
Required VRAM	0.2 GB
Updated	2026-08-01
Maintainer	ngxson
Instruction-Based	Yes
Model Files	0.2 GB
Model Architecture	AutoModel
Is Biased	none
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	gate_proj\|k_proj\|q_proj\|embed_tokens\|down_proj\|lm_head\|up_proj\|o_proj\|v_proj
LoRA Alpha	32
LoRA Dropout	0
R Param	32

Rank the LoRA Qwen2.5 7B Instruct Abliterated V3 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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Note: green Score (e.g. "73.2") means that the model is better than ngxson/LoRA-Qwen2.5-7B-Instruct-abliterated-v3.