What are the hardware requirements for Qwen2.5 32B Abliterated LORA?

Qwen2.5 32B Abliterated LORA requires approximately 1.1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Qwen2.5 32B Abliterated LORA and how large is it?

Qwen2.5 32B Abliterated LORA is developed by nbeerbower, a model with 32b 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 32B Abliterated LORA?

Qwen2.5 32B Abliterated LORA is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Qwen2.5 32B Abliterated LORA by nbeerbower — VRAM 1.1GB

Name: Qwen2.5 32B Abliterated LORA
Author: nbeerbower

Qwen2.5 32B Abliterated LORA is an open-source language model by nbeerbower. Features: 32b LLM, VRAM: 1.1GB, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.16.

Ara Base model:finetune:qwen/qwen2... Base model:qwen/qwen2.5-32b-in... Deu Endpoints compatible Eng Fra Instruct Ita Jpn Kor Lora Mergekit Peft Por Region:us Rus Safetensors Spa Tha Vie Zho

Model Card on HF 🤗: https://huggingface.co/nbeerbower/Qwen2.5-32B-abliterated-LORA

Qwen2.5 32B Abliterated LORA Parameters and Internals

LLM Name	Qwen2.5 32B Abliterated LORA
Repository 🤗	https://huggingface.co/nbeerbower/Qwen2.5-32B-abliterated-LORA
Base Model(s)	Qwen/Qwen2.5-32B-Instruct huihui-ai/Qwen2.5-32B-Instruct-abliterated Qwen/Qwen2.5-32B-Instruct huihui-ai/Qwen2.5-32B-Instruct-abliterated
Model Size	32b
Required VRAM	1.1 GB
Updated	2026-07-15
Maintainer	nbeerbower
Instruction-Based	Yes
Model Files	1.1 GB
Supported Languages	en
Model Architecture	AutoModel
License	apache-2.0
Is Biased	none
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	up_proj\|o_proj\|gate_proj\|q_proj\|embed_tokens\|k_proj\|lm_head\|down_proj\|v_proj
LoRA Alpha	64
LoRA Dropout	0
R Param	64

Rank the Qwen2.5 32B Abliterated LORA 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