What are the hardware requirements for Llama 3 Tugas Dicoding?

Llama 3 Tugas Dicoding requires approximately 16.1 GB of VRAM and supports a context window of 8K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Llama 3 Tugas Dicoding and how large is it?

Llama 3 Tugas Dicoding is developed by RicAIEng, 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 Tugas Dicoding?

Llama 3 Tugas Dicoding is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Llama 3 Tugas Dicoding by RicAIEng — VRAM 16.1GB, 8K context

Name: Llama 3 Tugas Dicoding
Author: RicAIEng

Llama 3 Tugas Dicoding is an open-source language model by RicAIEng. Features: 8b LLM, VRAM: 16.1GB, Context: 8K, License: apache-2.0, Quantized, LLM Explorer Score: 0.32.

4bit Base model:finetune:unsloth/ll... Base model:unsloth/llama-3-8b-... Conversational En Endpoints compatible Llama Quantized Region:us Safetensors Sharded Tensorflow Unsloth

Model Card on HF 🤗: https://huggingface.co/RicAIEng/Llama-3-Tugas-Dicoding

Llama 3 Tugas Dicoding Parameters and Internals

LLM Name	Llama 3 Tugas Dicoding
Repository 🤗	https://huggingface.co/RicAIEng/Llama-3-Tugas-Dicoding
Base Model(s)	Llama 3 8B Bnb 4bit unsloth/llama-3-8b-bnb-4bit
Model Size	8b
Required VRAM	16.1 GB
Updated	2026-09-05
Maintainer	RicAIEng
Model Type	llama
Model Files	5.0 GB: 1-of-4 5.0 GB: 2-of-4 4.9 GB: 3-of-4 1.2 GB: 4-of-4
Supported Languages	en
Quantization Type	4bit
Model Architecture	LlamaForCausalLM
License	apache-2.0
Context Length	8192
Model Max Length	8192
Tokenizer Class	TokenizersBackend
Padding Token	<\|reserved_special_token_250\|>
Vocabulary Size	128256
Torch Data Type	bfloat16

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