What are the hardware requirements for Llama 3.1 8B Instruct Simplicity RAG?

Llama 3.1 8B Instruct Simplicity RAG requires approximately 48.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama 3.1 8B Instruct Simplicity RAG and how large is it?

Llama 3.1 8B Instruct Simplicity RAG is developed by coolcat0, 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 Instruct Simplicity RAG?

Llama 3.1 8B Instruct Simplicity RAG 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 Instruct Simplicity RAG by coolcat0 — VRAM 48.2GB

Name: Llama 3.1 8B Instruct Simplicity RAG
Author: coolcat0

Llama 3.1 8B Instruct Simplicity RAG is an open-source language model by coolcat0. Features: 8b LLM, VRAM: 48.2GB, Instruction-Based, LLM Explorer Score: 0.15.

Arxiv:1910.09700 Autotrain compatible Conversational Endpoints compatible Instruct Llama Lora Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/coolcat0/Llama-3.1-8B-Instruct-Simplicity-RAG

Llama 3.1 8B Instruct Simplicity RAG Benchmarks

LLME Score: 0.15273

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Llama 3.1 8B Instruct Simplicity RAG (coolcat0/Llama-3.1-8B-Instruct-Simplicity-RAG)

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Llama 3.1 8B Instruct Simplicity RAG Parameters and Internals

LLM Name	Llama 3.1 8B Instruct Simplicity RAG
Repository 🤗	https://huggingface.co/coolcat0/Llama-3.1-8B-Instruct-Simplicity-RAG
Model Size	8b
Required VRAM	48.2 GB
Updated	2025-09-23
Maintainer	coolcat0
Instruction-Based	Yes
Model Files	4.5 GB 5.0 GB: 1-of-4 4.9 GB: 1-of-7 5.0 GB: 2-of-4 4.8 GB: 2-of-7 4.9 GB: 3-of-4 5.0 GB: 3-of-7 1.2 GB: 4-of-4 5.0 GB: 4-of-7 4.8 GB: 5-of-7 5.0 GB: 6-of-7 2.6 GB: 7-of-7
Model Architecture	AutoModelForCausalLM
Model Max Length	131072
Is Biased	none
Tokenizer Class	PreTrainedTokenizerFast
Padding Token	<\|pad\|>
PEFT Type	LORA
LoRA Model	Yes
PEFT Target Modules	mlp.down_proj\|mlp.up_proj\|self_attn.q_proj\|self_attn.k_proj\|self_attn.o_proj\|mlp.gate_proj\|self_attn.v_proj
LoRA Alpha	16
LoRA Dropout	0.05
R Param	32

Best Alternatives to Llama 3.1 8B Instruct Simplicity RAG

Best Alternatives	Context / RAM	Downloads	Likes
Llama 3.1 8B Instuct Uz	128K / 16.1 GB	1000	15
Cve Model	0K / 0.3 GB	16	0
ConfTuner Ministral	0K / 16.1 GB	13	3
Autotrain Pvqlj Odah2	0K / 0.2 GB	18	0
500tiao 100lun	0K / 0.2 GB	5	0
...8B Instruct Shimshimi 500 Ver2	0K / 16.1 GB	6	0
Codelica	0K / 16.1 GB	111	0
ModeliCo 8B	0K / 16.1 GB	5	2
Llama31 Eros	0K / 16.1 GB	8	1
Llama 3.1 8b Prop Logic Ft	0K / 0.2 GB	7	7

Note: green Score (e.g. "73.2") means that the model is better than coolcat0/Llama-3.1-8B-Instruct-Simplicity-RAG.

Rank the Llama 3.1 8B Instruct Simplicity RAG Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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Release v20260328a

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Llama 3.1 8B Instruct Simplicity RAG by coolcat0

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Llama 3.1 8B Instruct Simplicity RAG Benchmarks

Llama 3.1 8B Instruct Simplicity RAG Parameters and Internals

Best Alternatives to Llama 3.1 8B Instruct Simplicity RAG

Rank the Llama 3.1 8B Instruct Simplicity RAG Capabilities

What open-source LLMs or SLMs are you in search of? 53472 in total.