🧠
Kenga Embed Prophet Instruct model by GermannM
⭐ 37.8
🧠
Model

Kenga Embed Prophet Instruct

by GermannM germannm/kenga-embed-prophet-instruct
Free2AITools Nexus Index
37.8
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 5
R: Recency 95
Q: Quality 50
Tech Context
Vital Performance
47 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 37.8 FNI Score
Tiny - Params
- Context
47 Downloads
Dense KENGAEMBEDENC Architecture
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID germannm/kenga-embed-prophet-instruct
License MIT
Provider huggingface
πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{germannm_kenga_embed_prophet_instruct,
  author = {GermannM},
  title = {Kenga Embed Prophet Instruct Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/GermannM/kenga-embed-prophet-instruct}},
  note = {Accessed via Free2AITools.}
}
APA Style
GermannM. (2026). Kenga Embed Prophet Instruct [Model]. Free2AITools. https://huggingface.co/GermannM/kenga-embed-prophet-instruct

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ€— HF Download
huggingface-cli download germannm/kenga-embed-prophet-instruct

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 0
Popularity (P) 5
Recency (R) 95
Quality (Q) 50

πŸ’¬ Index Insight

FNI V2.0 for Kenga Embed Prophet Instruct: Authority (A:0), Popularity (P:5), Recency (R:95), 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.

View Original Source β†’

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
47Downloads
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--germannm--kenga-embed-prophet-instruct
slug
germannm--kenga-embed-prophet-instruct
source
huggingface
author
GermannM
license
MIT
tags
pytorch, kenga-embed, embeddings, retrieval, instruct, russian, sentence-similarity, ru, en, license:mit, region:us

βš™οΈ Technical Specs

architecture
KengaEmbedEnc
pipeline tag
sentence-similarity

πŸ“Š Engagement & Metrics

downloads
47

Data indexed from public sources. Updated daily.