🧠
Sgpt 125m Weightedmean Nli Bitfit Linearthenpool1 Noact model by Muennighoff
⭐ 23.3
🧠
Model

Sgpt 125m Weightedmean Nli Bitfit Linearthenpool1 Noact

by Muennighoff hf-model--muennighoff--sgpt-125m-weightedmean-nli-bitfit-linearthenpool1-noact
Nexus Index
23.3 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 1
R: Recency 5
Q: Quality 50
Tech Context
Vital Performance
12 DL / 30D
0.0%
Source →
Audited 23.3 FNI Score
Tiny - Params
- Context
12 Downloads
Model Information Summary
Entity Passport
Registry ID hf-model--muennighoff--sgpt-125m-weightedmean-nli-bitfit-linearthenpool1-noact
Provider huggingface
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Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__muennighoff__sgpt_125m_weightedmean_nli_bitfit_linearthenpool1_noact,
  author = {Muennighoff},
  title = {Sgpt 125m Weightedmean Nli Bitfit Linearthenpool1 Noact Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/muennighoff/sgpt-125m-weightedmean-nli-bitfit-linearthenpool1-noact}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
Muennighoff. (2026). Sgpt 125m Weightedmean Nli Bitfit Linearthenpool1 Noact [Model]. Free2AITools. https://huggingface.co/muennighoff/sgpt-125m-weightedmean-nli-bitfit-linearthenpool1-noact

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🤗 HF Download
huggingface-cli download muennighoff/sgpt-125m-weightedmean-nli-bitfit-linearthenpool1-noact
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Nexus Index V2.0

Methodology Index Protocol
23.3
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 1
Recency (R) 5
Quality (Q) 50

đŸ’Ŧ Index Insight

FNI V2.0 for Sgpt 125m Weightedmean Nli Bitfit Linearthenpool1 Noact: Semantic (S:50), Authority (A:0), Popularity (P:1), Recency (R:5), 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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⚠ī¸ Incomplete 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.

Social Proof

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12Downloads
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--muennighoff--sgpt-125m-weightedmean-nli-bitfit-linearthenpool1-noact
slug
muennighoff--sgpt-125m-weightedmean-nli-bitfit-linearthenpool1-noact
source
huggingface
author
Muennighoff
license
tags
sentence-transformers, pytorch, gpt_neo, feature-extraction, sentence-similarity, arxiv:2202.08904, endpoints_compatible, deploy:azure, region:us

⚙ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
sentence-similarity

📊 Engagement & Metrics

downloads
12
stars
0
forks
0

Data indexed from public sources. Updated daily.