What are the hardware requirements for Unsup Llama 3.1 8B Instruct Datav2 Only Mask?

Unsup Llama 3.1 8B Instruct Datav2 Only Mask 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 Unsup Llama 3.1 8B Instruct Datav2 Only Mask and how large is it?

Unsup Llama 3.1 8B Instruct Datav2 Only Mask is developed by ferrazzipietro, 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 Unsup Llama 3.1 8B Instruct Datav2 Only Mask?

Unsup Llama 3.1 8B Instruct Datav2 Only Mask is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Unsup Llama 3.1 8B Instruct Datav2 Only Mask by ferrazzipietro — VRAM 16.1GB, 128K context

Name: Unsup Llama 3.1 8B Instruct Datav2 Only Mask
Author: ferrazzipietro

Unsup Llama 3.1 8B Instruct Datav2 Only Mask is an open-source language model by ferrazzipietro. Features: 8b LLM, VRAM: 16.1GB, Context: 128K, Instruction-Based, LLM Explorer Score: 0.23.

Instruct Llama Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/ferrazzipietro/unsup-Llama-3.1-8B-Instruct-datav2-only_mask

Unsup Llama 3.1 8B Instruct Datav2 Only Mask Parameters and Internals

LLM Name	Unsup Llama 3.1 8B Instruct Datav2 Only Mask
Repository 🤗	https://huggingface.co/ferrazzipietro/unsup-Llama-3.1-8B-Instruct-datav2-only_mask
Model Size	8b
Required VRAM	16.1 GB
Updated	2026-06-24
Maintainer	ferrazzipietro
Model Type	llama
Instruction-Based	Yes
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 0.0 GB
Model Architecture	LlamaForCausalLM
Context Length	131072
Model Max Length	131072
Transformers Version	4.51.0
Tokenizer Class	PreTrainedTokenizer
Padding Token	<\|eot_id\|>
Vocabulary Size	128256
Torch Data Type	bfloat16

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Instruction Following and Task Automation
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