🧠
Rigoberta 2.0 model by IIC
⭐ 5
🧠
Model

Rigoberta 2.0

by IIC hf-model--iic--rigoberta-2.0
Nexus Index
5.0 Top 6%
P / V / C / U Breakdown Calibration Pending

Pillar scores are computed during the next indexing cycle.

Tech Context
0.56B Params
4.096K Ctx
Vital Performance
243 DL / 30D
0.0%
Source →
Audited 5 FNI Score
Tiny 0.56B Params
4k Context
243 Downloads
8G GPU ~2GB Est. VRAM
Dense XLMROBERTAFORMASKEDLM Architecture
Model Information Summary
Entity Passport
Registry ID hf-model--iic--rigoberta-2.0
Provider huggingface
💾

Compute Threshold

~1.7GB VRAM

Interactive
Analyze Hardware
â–ŧ

* Static estimation for 4-Bit Quantization.

📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__iic__rigoberta_2.0,
  author = {IIC},
  title = {Rigoberta 2.0 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/IIC/RigoBERTa-2.0}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
IIC. (2026). Rigoberta 2.0 [Model]. Free2AITools. https://huggingface.co/IIC/RigoBERTa-2.0

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

đŸĻ™ Ollama Run
ollama run rigoberta-2.0
🤗 HF Download
huggingface-cli download iic/rigoberta-2.0
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Nexus Index V16.5

Methodology Index Protocol
5.0
ESTIMATED IMPACT TIER
Popularity (P) 0
Freshness (F) 0
Completeness (C) 0
Utility (U) 0

đŸ’Ŧ Index Insight

The Free2AITools Nexus Index for Rigoberta 2.0 aggregates Popularity (P:0), Freshness (F:0), and Completeness (C:0). The Utility score (U:0) represents deployment readiness and ecosystem adoption.

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.
  • â€ĸ Source: Unknown
Top Tier

Social Proof

HuggingFace Hub
243Downloads
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

Verified data manifest for traceability and transparency.

100% Data Disclosure Active

🆔 Identity & Source

id
hf-model--iic--rigoberta-2.0
source
huggingface
author
IIC
tags
transformerspytorchsafetensorsxlm-robertafill-maskesdoi:10.57967/hf/7048license:otherendpoints_compatibleregion:us

⚙ī¸ Technical Specs

architecture
XLMRobertaForMaskedLM
params billions
0.56
context length
4,096
pipeline tag
fill-mask
vram gb
1.7
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

likes
0
downloads
243

Free2AITools Constitutional Data Pipeline: Curated disclosure mode active. (V15.x Standard)