What are the hardware requirements for F R8?

F R8 requires approximately 16.1 GB of VRAM and supports a context window of 128K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed F R8 and how large is it?

F R8 is developed by ChuGyouk, 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 F R8?

F R8 is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

F R8 by ChuGyouk — VRAM 16.1GB, 128K context

Name: F R8
Author: ChuGyouk

F R8 is an open-source language model by ChuGyouk. Features: 8b LLM, VRAM: 16.1GB, Context: 128K, LLM Explorer Score: 0.24.

Base model:chugyouk/llama-3.1-... Base model:finetune:chugyouk/l... Conversational Endpoints compatible Generated from trainer Llama Region:us Safetensors Sft Trl Unsloth

Model Card on HF 🤗: https://huggingface.co/ChuGyouk/F_R8

F R8 Parameters and Internals

LLM Name	F R8
Repository 🤗	https://huggingface.co/ChuGyouk/F_R8
Model Name	ChuGyouk/Llama-3.1-8B
Base Model(s)	ChuGyouk/Llama-3.1-8B ChuGyouk/Llama-3.1-8B
Model Size	8b
Required VRAM	16.1 GB
Updated	2026-04-26
Maintainer	ChuGyouk
Model Type	llama
Model Files	16.1 GB 0.0 GB
Model Architecture	LlamaForCausalLM
Context Length	131072
Model Max Length	131072
Transformers Version	5.2.0
Tokenizer Class	TokenizersBackend
Padding Token	<\|finetune_right_pad_id\|>
Vocabulary Size	128256

Rank the F R8 capabilities

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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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