What are the hardware requirements for Llama 3.1 8B PubMedQA QLoRA?

Llama 3.1 8B PubMedQA QLoRA requires approximately 0.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama 3.1 8B PubMedQA QLoRA and how large is it?

Llama 3.1 8B PubMedQA QLoRA is developed by Legeng, 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 Llama 3.1 8B PubMedQA QLoRA?

Llama 3.1 8B PubMedQA QLoRA is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 3.1 8B PubMedQA QLoRA by Legeng — VRAM 0.2GB

Name: Llama 3.1 8B PubMedQA QLoRA
Author: Legeng

Llama 3.1 8B PubMedQA QLoRA is an open-source language model by Legeng. Features: 8b LLM, VRAM: 0.2GB, License: llama3.1, Quantized, Instruction-Based, LLM Explorer Score: 0.24.

4bit Adapter Base model:adapter:unsloth/met... Base model:unsloth/meta-llama-... Conversational Finetuned Instruct Llama-3.1 Lora Medical Peft Pubmedqa Qlora Quantized Region:us Safetensors Unsloth

Model Card on HF 🤗: https://huggingface.co/Legeng/Llama-3.1-8B-PubMedQA-QLoRA

Llama 3.1 8B PubMedQA QLoRA Parameters and Internals

LLM Name	Llama 3.1 8B PubMedQA QLoRA
Repository 🤗	https://huggingface.co/Legeng/Llama-3.1-8B-PubMedQA-QLoRA
Base Model(s)	unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
Model Size	8b
Required VRAM	0.2 GB
Updated	2026-08-08
Maintainer	Legeng
Instruction-Based	Yes
Model Files	0.2 GB
Quantization Type	4bit
Model Architecture	Adapter
License	llama3.1
Model Max Length	131072
Is Biased	none
Tokenizer Class	TokenizersBackend
Padding Token	<\|finetune_right_pad_id\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	up_proj\|k_proj\|o_proj\|q_proj\|v_proj\|gate_proj\|down_proj
LoRA Alpha	32
LoRA Dropout	0
R Param	16

Rank the Llama 3.1 8B PubMedQA QLoRA 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 Legeng/Llama-3.1-8B-PubMedQA-QLoRA.