Llama-3.1-8B-SuperNova-EtherealHermes-GGUF Open-Source Large Model - Multiple Quantization Formats Powering Conversational Communications

Llama 3.1 8B SuperNova EtherealHermes GGUF

Developed by tensorblock

An 8B-parameter large language model based on the Llama-3.1 architecture, offering multiple quantized versions in GGUF format

Large Language Model EnglishOpen Source License:Apache-2.0 #Multitask Text Generation #Instruction Fine-Tuning Optimization #Low-Resource Efficiency

Downloads 44

Release Time : 3/21/2025

Model Overview

This is an 8B-parameter large language model provided in GGUF format with multiple quantization levels, suitable for text generation tasks. Quantization support is provided by the TensorBlock team.

Model Features

Multiple Quantization Levels

Provides 11 different quantization levels from Q2_K to Q8_0 in GGUF format to meet various hardware and performance requirements

High-Performance Text Generation

Performs well on multiple benchmarks, achieving a strict accuracy rate of 73.39 on the IFEval dataset

Strong Compatibility

Compatible with llama.cpp versions up to commit b4882, facilitating deployment in various environments

Model Capabilities

Text Generation

Instruction Following

Question Answering

Content Creation

Use Cases

Education

Teaching Assistance

Can be used to generate teaching materials and answer student questions

Content Creation

Article Writing

Helps creators generate article drafts and creative content

Research

Academic Research Support

Can be used for literature reviews and generating preliminary research ideas

🚀 ZeroXClem/Llama-3.1-8B-SuperNova-EtherealHermes - GGUF

This repository offers GGUF format model files for ZeroXClem/Llama-3.1-8B-SuperNova-EtherealHermes. These files are quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4882.

Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server

✨ Features

Model Merging: Utilizes techniques like merge, mergekit, lazymergekit, and TensorBlock.
Quantization: The model files are quantized in the GGUF format, ensuring compatibility with llama.cpp.
Text Generation: Capable of high - quality text generation, as demonstrated by its performance on multiple datasets.

📦 Installation

Command line

Firstly, install Huggingface Client:

pip install -U "huggingface_hub[cli]"

Then, download the individual model file to a local directory:

huggingface-cli download tensorblock/Llama-3.1-8B-SuperNova-EtherealHermes-GGUF --include "Llama-3.1-8B-SuperNova-EtherealHermes-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you want to download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/Llama-3.1-8B-SuperNova-EtherealHermes-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

💻 Usage Examples

Prompt template

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

📚 Documentation

Model file specification

Filename	Quant type	File Size	Description
Llama-3.1-8B-SuperNova-EtherealHermes-Q2_K.gguf	Q2_K	3.179 GB	smallest, significant quality loss - not recommended for most purposes
Llama-3.1-8B-SuperNova-EtherealHermes-Q3_K_S.gguf	Q3_K_S	3.665 GB	very small, high quality loss
Llama-3.1-8B-SuperNova-EtherealHermes-Q3_K_M.gguf	Q3_K_M	4.019 GB	very small, high quality loss
Llama-3.1-8B-SuperNova-EtherealHermes-Q3_K_L.gguf	Q3_K_L	4.322 GB	small, substantial quality loss
Llama-3.1-8B-SuperNova-EtherealHermes-Q4_0.gguf	Q4_0	4.661 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Llama-3.1-8B-SuperNova-EtherealHermes-Q4_K_S.gguf	Q4_K_S	4.693 GB	small, greater quality loss
Llama-3.1-8B-SuperNova-EtherealHermes-Q4_K_M.gguf	Q4_K_M	4.921 GB	medium, balanced quality - recommended
Llama-3.1-8B-SuperNova-EtherealHermes-Q5_0.gguf	Q5_0	5.599 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Llama-3.1-8B-SuperNova-EtherealHermes-Q5_K_S.gguf	Q5_K_S	5.599 GB	large, low quality loss - recommended
Llama-3.1-8B-SuperNova-EtherealHermes-Q5_K_M.gguf	Q5_K_M	5.733 GB	large, very low quality loss - recommended
Llama-3.1-8B-SuperNova-EtherealHermes-Q6_K.gguf	Q6_K	6.596 GB	very large, extremely low quality loss
Llama-3.1-8B-SuperNova-EtherealHermes-Q8_0.gguf	Q8_0	8.541 GB	very large, extremely low quality loss - not recommended

Our projects

Awesome MCP Servers	TensorBlock Studio

A comprehensive collection of Model Context Protocol (MCP) servers.	A lightweight, open, and extensible multi-LLM interaction studio.
👉 See what we built 👈	👉 See what we built 👈

Model performance

Task	Dataset	Metrics	Value	Source
Text Generation	IFEval (0 - Shot)	strict accuracy	73.39	Open LLM Leaderboard
Text Generation	BBH (3 - Shot)	normalized accuracy	32.07	Open LLM Leaderboard
Text Generation	MATH Lvl 5 (4 - Shot)	exact match	17.45	Open LLM Leaderboard
Text Generation	GPQA (0 - shot)	acc_norm	5.7	Open LLM Leaderboard
Text Generation	MuSR (0 - shot)	acc_norm	11.32	Open LLM Leaderboard
Text Generation	MMLU - PRO (5 - shot)	accuracy	30.5	Open LLM Leaderboard

📄 License

This project is licensed under the Apache - 2.0 license.

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