What are the hardware requirements for Llama3 8B DWP Lora?

Llama3 8B DWP Lora requires approximately 0.7 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama3 8B DWP Lora and how large is it?

Llama3 8B DWP Lora is developed by nothingiisreal, 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 Llama3 8B DWP Lora?

Llama3 8B DWP Lora is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama3 8B DWP Lora by nothingiisreal — VRAM 0.7GB

Name: Llama3 8B DWP Lora
Author: nothingiisreal

Llama3 8B DWP Lora is an open-source language model by nothingiisreal. Features: 8b LLM, VRAM: 0.7GB, License: other, LLM Explorer Score: 0.13.

Adapter Base model:adapter:nousresearc... Base model:nousresearch/meta-l... Finetuned Generated from trainer Llama-factory Lora Peft Region:us Safetensors Unsloth

Model Card on HF 🤗: https://huggingface.co/nothingiisreal/llama3-8B-DWP-lora

Llama3 8B DWP Lora Parameters and Internals

Additional Notes

When alpha is set to different values, the model's behavior changes noticeably, generating responses that vary from default behavior to highly focused on specific themes.

LLM Name	Llama3 8B DWP Lora
Repository 🤗	https://huggingface.co/nothingiisreal/llama3-8B-DWP-lora
Base Model(s)	Meta Llama 3 8B NousResearch/Meta-Llama-3-8B
Model Size	8b
Required VRAM	0.7 GB
Updated	2026-07-16
Maintainer	nothingiisreal
Model Files	0.7 GB 0.0 GB
Model Architecture	Adapter
License	other
Is Biased	none
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|eot_id\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	q_proj\|v_proj\|up_proj\|down_proj\|gate_proj\|o_proj\|k_proj
LoRA Alpha	64
LoRA Dropout	0.1
R Param	64

Rank the Llama3 8B DWP 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

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Note: green Score (e.g. "73.2") means that the model is better than nothingiisreal/llama3-8B-DWP-lora.