🧠
S2pro Egy model by ehabnegm
⭐ 39.1
🧠
Model

S2pro Egy

by ehabnegm ehabnegm/s2pro-egy
Free2AITools Nexus Index
39.1
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 4
R: Recency 90
Q: Quality 65
Tech Context
4.56 Params
4.096K Ctx
Vital Performance
39 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 39.1 FNI Score
4.56B Params
4k Context
39 Downloads
8G GPU ~5GB Est. VRAM
Dense FISH_QWEN3_OMNI Architecture
Restricted OTHER License
Model Information Summary
Entity Passport
Registry ID ehabnegm/s2pro-egy
License Other
Provider huggingface
πŸ’Ύ

Compute Threshold

~4.7GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{ehabnegm_s2pro_egy,
  author = {ehabnegm},
  title = {S2pro Egy Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/ehabnegm/s2pro-egy}},
  note = {Accessed via Free2AITools.}
}
APA Style
ehabnegm. (2026). S2pro Egy [Model]. Free2AITools. https://huggingface.co/ehabnegm/s2pro-egy

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run s2pro-egy
πŸ€— HF Download
huggingface-cli download ehabnegm/s2pro-egy

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) 4
Recency (R) 90
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for S2pro Egy: Authority (A:0), Popularity (P:4), Recency (R:90), Quality (Q:65). 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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Deployment reference

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Technical Deep Dive

⚠️ 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
39Downloads
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--ehabnegm--s2pro-egy
slug
ehabnegm--s2pro-egy
source
huggingface
author
ehabnegm
license
Other
tags
tensorboard, safetensors, fish_qwen3_omni, text-to-speech, egyptian-arabic, arz, fish-speech, lora, ar, base_model:fishaudio/s2-pro, base_model:adapter:fishaudio/s2-pro, license:other, region:us

βš™οΈ Technical Specs

architecture
fish_qwen3_omni
params billions
4.56
context length
4,096
pipeline tag
text-to-speech
vram gb
4.7
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
39

Data indexed from public sources. Updated daily.