What are the hardware requirements for Suavemente 8B R128 LORA?

Suavemente 8B R128 LORA requires approximately 2.8 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Suavemente 8B R128 LORA and how large is it?

Suavemente 8B R128 LORA is developed by DreadPoor, 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 Suavemente 8B R128 LORA?

Suavemente 8B R128 LORA is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Suavemente 8B R128 LORA by DreadPoor — VRAM 2.8GB

Name: Suavemente 8B R128 LORA
Author: DreadPoor

Suavemente 8B R128 LORA is an open-source language model by DreadPoor. Features: 8b LLM, VRAM: 2.8GB, LLM Explorer Score: 0.16.

Adapter Base model:adapter:dreadpoor/s... Base model:dreadpoor/suavement... Finetuned Lora Mergekit Peft Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/DreadPoor/Suavemente-8B-r128-LORA

Suavemente 8B R128 LORA Parameters and Internals

LLM Name	Suavemente 8B R128 LORA
Repository 🤗	https://huggingface.co/DreadPoor/Suavemente-8B-r128-LORA
Base Model(s)	NousResearch/Meta-Llama-3.1-8B-Instruct Suavemente 8B Model Stock NousResearch/Meta-Llama-3.1-8B-Instruct DreadPoor/Suavemente-8B-Model_Stock
Model Size	8b
Required VRAM	2.8 GB
Updated	2025-04-11
Maintainer	DreadPoor
Model Files	2.8 GB
Model Architecture	Adapter
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	v_proj\|o_proj\|q_proj\|down_proj\|gate_proj\|k_proj\|up_proj
LoRA Alpha	128
LoRA Dropout	0
R Param	128

Rank the Suavemente 8B R128 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 DreadPoor/Suavemente-8B-r128-LORA.