🧠
Secroberta Cwe Flat model by Sana9
⭐ 39
🧠
Model

Secroberta Cwe Flat

by Sana9 hf-model--sana9--secroberta-cwe-flat
Nexus Index
39.0 Top 6%
S: Semantic 50
A: Authority 0
P: Popularity 0
R: Recency 100
Q: Quality 65
Tech Context
0.08B Params
512 Ctx
Vital Performance
46 DL / 30D
0.0%
Source →
Audited 39 FNI Score
Tiny 0.08B Params
1k Context
46 Downloads
8G GPU ~2GB Est. VRAM
Dense ROBERTAFORSEQUENCECLASSIFICATION Architecture
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID hf-model--sana9--secroberta-cwe-flat
License MIT
Provider huggingface
💾

Compute Threshold

~1.4GB VRAM

Interactive
Analyze Hardware
â–ŧ

* Static estimation for 4-Bit Quantization.

📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__sana9__secroberta_cwe_flat,
  author = {Sana9},
  title = {Secroberta Cwe Flat Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/Sana9/secroberta-cwe-flat}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
Sana9. (2026). Secroberta Cwe Flat [Model]. Free2AITools. https://huggingface.co/Sana9/secroberta-cwe-flat

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

đŸĻ™ Ollama Run
ollama run secroberta-cwe-flat
🤗 HF Download
huggingface-cli download sana9/secroberta-cwe-flat

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
39.0
TOP 6% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 0
Recency (R) 100
Quality (Q) 65

đŸ’Ŧ Index Insight

FNI V2.0 for Secroberta Cwe Flat: Semantic (S:50), Authority (A:0), Popularity (P:0), Recency (R:100), Quality (Q:65).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live
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🚀 What's Next?

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Deployment Guide

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Technical Deep Dive

⚠ī¸ 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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📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.
Top Tier

Social Proof

HuggingFace Hub
46Downloads
Hub Discussions
🤗 Data Source: Hugging Face ↗
🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--sana9--secroberta-cwe-flat
slug
sana9--secroberta-cwe-flat
source
huggingface
author
Sana9
license
MIT
tags
safetensors, roberta, cybersecurity, vulnerability, cwe, text-classification, fine-tuned, en, license:mit, region:us

⚙ī¸ Technical Specs

architecture
RobertaForSequenceClassification
params billions
0.08
context length
512
pipeline tag
text-classification
vram gb
1.4
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads
46
stars
0
forks
null

Data indexed from public sources. Updated daily.