What are the hardware requirements for Ur Orpo Adapter?

Ur Orpo Adapter requires approximately 0.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Ur Orpo Adapter and how large is it?

Ur Orpo Adapter is developed by Dalia90, a model with 8b 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 Ur Orpo Adapter?

Ur Orpo Adapter is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Ur Orpo Adapter by Dalia90 — VRAM 0.2GB

Name: Ur Orpo Adapter
Author: Dalia90

Ur Orpo Adapter is an open-source language model by Dalia90. Features: 8b LLM, VRAM: 0.2GB, Instruction-Based, LLM Explorer Score: 0.25.

Arxiv:1910.09700 Base model:adapter:meta-llama/... Base model:meta-llama/meta-lla... Conversational Instruct Lora Orpo Peft Region:us Safetensors Trl

Model Card on HF 🤗: https://huggingface.co/Dalia90/ur-orpo-adapter

Ur Orpo Adapter Parameters and Internals

LLM Name	Ur Orpo Adapter
Repository 🤗	https://huggingface.co/Dalia90/ur-orpo-adapter
Base Model(s)	Meta Llama 3 8B Instruct meta-llama/Meta-Llama-3-8B-Instruct
Model Size	8b
Required VRAM	0.2 GB
Updated	2026-07-19
Maintainer	Dalia90
Instruction-Based	Yes
Model Files	0.2 GB 0.0 GB
Model Architecture	AutoModel
Is Biased	none
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|eot_id\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	down_proj\|q_proj\|k_proj\|gate_proj\|v_proj\|o_proj\|up_proj
LoRA Alpha	32
LoRA Dropout	0
R Param	16

Rank the Ur Orpo Adapter 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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