What are the hardware requirements for HyperLlama2Test?

HyperLlama2Test requires approximately 26 GB of VRAM and supports a context window of 4K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed HyperLlama2Test and how large is it?

HyperLlama2Test is developed by HyperdustProtocol, a model with 13b 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 HyperLlama2Test?

HyperLlama2Test is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

HyperLlama2Test by HyperdustProtocol — VRAM 26GB, 4K context

Name: HyperLlama2Test
Author: HyperdustProtocol

HyperLlama2Test is an open-source language model by HyperdustProtocol. Features: 13b LLM, VRAM: 26GB, Context: 4K, License: llama2, Quantized, LLM Explorer Score: 0.12.

4bit Endpoints compatible Gguf Llama Quantized Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/HyperdustProtocol/HyperLlama2Test

HyperLlama2Test Parameters and Internals

Additional Notes

Test model not ready to use yet.

LLM Name	HyperLlama2Test
Repository 🤗	https://huggingface.co/HyperdustProtocol/HyperLlama2Test
Model Size	13b
Required VRAM	26 GB
Updated	2026-07-24
Maintainer	HyperdustProtocol
Model Type	llama
Model Files	13.5 GB 4.1 GB 5.0 GB: 1-of-6 5.0 GB: 2-of-6 5.0 GB: 3-of-6 4.9 GB: 4-of-6 4.9 GB: 5-of-6 1.2 GB: 6-of-6
GGUF Quantization	Yes
Quantization Type	4bit\|gguf
Model Architecture	LlamaForCausalLM
License	llama2
Context Length	4096
Model Max Length	4096
Transformers Version	4.40.2
Tokenizer Class	LlamaTokenizer
Padding Token	<unk>
Vocabulary Size	32000
Torch Data Type	bfloat16

Rank the HyperLlama2Test 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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Note: green Score (e.g. "73.2") means that the model is better than HyperdustProtocol/HyperLlama2Test.