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Imperum Cybersecurityllm V1.0 Gguf model by IMPERUM
⭐ 45.9
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Model

Imperum Cybersecurityllm V1.0 Gguf

by IMPERUM imperum/imperum-cybersecurityllm-v1.0-gguf
Free2AITools Nexus Index
45.9
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 29
P: Popularity 7
R: Recency 100
Q: Quality 50
Tech Context
Vital Performance
85 DL / 30D
Source β†’

Task categories from upstream metadata

πŸ’¬Chat & Dialogue

Technical Constraints

Experimental / High Latency
Low FNI signal 45.9 FNI Score
Tiny - Params
- Context
85 Downloads
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID imperum/imperum-cybersecurityllm-v1.0-gguf
License Apache-2.0
Provider huggingface
πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{imperum_imperum_cybersecurityllm_v1_0_gguf,
  author = {IMPERUM},
  title = {Imperum Cybersecurityllm V1.0 Gguf Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/IMPERUM/Imperum-CybersecurityLLM-v1.0-GGUF}},
  note = {Accessed via Free2AITools.}
}
APA Style
IMPERUM. (2026). Imperum Cybersecurityllm V1.0 Gguf [Model]. Free2AITools. https://huggingface.co/IMPERUM/Imperum-CybersecurityLLM-v1.0-GGUF

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ€— HF Download
huggingface-cli download imperum/imperum-cybersecurityllm-v1.0-gguf

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 29
Popularity (P) 7
Recency (R) 100
Quality (Q) 50

πŸ’¬ Index Insight

FNI V2.0 for Imperum Cybersecurityllm V1.0 Gguf: Authority (A:29), Popularity (P:7), Recency (R:100), Quality (Q:50). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data
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⚠️ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

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πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ FNI scores are relative rankings and may change as new models are added.

Social Proof

HuggingFace Hub
85Downloads
Hub Discussions
πŸ€— Data Source: Hugging Face β†—
πŸ”„ Updated daily

Source summary: Based on Hugging Face metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
hf-model--imperum--imperum-cybersecurityllm-v1.0-gguf
slug
imperum--imperum-cybersecurityllm-v1.0-gguf
source
huggingface
author
IMPERUM
license
Apache-2.0
tags
llama.cpp, gguf, cybersecurity, security, soc, dfir, threat-intelligence, detection-engineering, blue-team, moe, qwen3, text-generation, en, base_model:qwen/qwen3.6-35b-a3b, base_model:quantized:qwen/qwen3.6-35b-a3b, license:apache-2.0, endpoints_compatible, region:us, conversational

βš™οΈ Technical Specs

pipeline tag
text-generation

πŸ“Š Engagement & Metrics

downloads
85

Data indexed from public sources. Updated daily.