What are the hardware requirements for LoRA Phi 4 Abliterated?

LoRA Phi 4 Abliterated 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 Phi 4 Abliterated and how large is it?

LoRA Phi 4 Abliterated is developed by ngxson. 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 Phi 4 Abliterated?

LoRA Phi 4 Abliterated is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

LoRA Phi 4 Abliterated by ngxson — VRAM 0.2GB

Name: LoRA Phi 4 Abliterated
Author: ngxson

LoRA Phi 4 Abliterated is an open-source language model by ngxson. Features: LLM, VRAM: 0.2GB, LLM Explorer Score: 0.16.

Base model:finetune:huihui-ai/... Base model:huihui-ai/phi-4-abl... Endpoints compatible Lora Mergekit Peft Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/ngxson/LoRA-phi-4-abliterated

LoRA Phi 4 Abliterated Parameters and Internals

LLM Name	LoRA Phi 4 Abliterated
Repository 🤗	https://huggingface.co/ngxson/LoRA-phi-4-abliterated
Base Model(s)	Phi 4 Phi 4 Abliterated microsoft/phi-4 huihui-ai/phi-4-abliterated
Required VRAM	0.2 GB
Updated	2026-07-23
Maintainer	ngxson
Model Files	0.2 GB
Model Architecture	AutoModel
Is Biased	none
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	gate_up_proj\|lm_head\|o_proj\|embed_tokens\|qkv_proj\|down_proj
LoRA Alpha	32
LoRA Dropout	0
R Param	32

Rank the LoRA Phi 4 Abliterated 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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