🧠
Ministral 3b Vindex model by Divinci Ai
⭐ 37.5
🧠
Model

Ministral 3b Vindex

by Divinci Ai hf-model--divinci-ai--ministral-3b-vindex
Nexus Index
37.5 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 0
R: Recency 99
Q: Quality 50
Tech Context
3 Params
4.096K Ctx
Vital Performance
0 DL / 30D
0.0%
Source →
Audited 37.5 FNI Score
3B Params
4k Context
0 Downloads
8G GPU ~4GB Est. VRAM
Restricted CC License
Model Information Summary
Entity Passport
Registry ID hf-model--divinci-ai--ministral-3b-vindex
License CC-BY-NC-4.0
Provider huggingface
💾

Compute Threshold

~3.5GB VRAM

Interactive
Analyze Hardware
â–ŧ

* Static estimation for 4-Bit Quantization.

📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__divinci_ai__ministral_3b_vindex,
  author = {Divinci Ai},
  title = {Ministral 3b Vindex Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/divinci-ai/ministral-3b-vindex}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
Divinci Ai. (2026). Ministral 3b Vindex [Model]. Free2AITools. https://huggingface.co/divinci-ai/ministral-3b-vindex

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

đŸĻ™ Ollama Run
ollama run ministral-3b-vindex
🤗 HF Download
huggingface-cli download divinci-ai/ministral-3b-vindex

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
37.5
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 0
Recency (R) 99
Quality (Q) 50

đŸ’Ŧ Index Insight

FNI V2.0 for Ministral 3b Vindex: Semantic (S:50), Authority (A:0), Popularity (P:0), Recency (R:99), Quality (Q:50).

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.
🤗 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--divinci-ai--ministral-3b-vindex
slug
divinci-ai--ministral-3b-vindex
source
huggingface
author
Divinci Ai
license
CC-BY-NC-4.0
tags
larql, vindex, mechanistic-interpretability, feature-extraction, license:cc-by-nc-4.0, region:us

⚙ī¸ Technical Specs

architecture
null
params billions
3
context length
4,096
pipeline tag
feature-extraction
vram gb
3.5
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads
0
stars
0
forks
null

Data indexed from public sources. Updated daily.