🧠
Ateso Model V2 Auto model by stockley
⭐ 40.7
🧠
Model

Ateso Model V2 Auto

by stockley stockley/ateso-model-v2-auto
Free2AITools Nexus Index
40.7
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 18
R: Recency 82
Q: Quality 65
Tech Context
0.28B Params
4.096K Ctx
Vital Performance
390 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 40.7 FNI Score
Tiny 0.28B Params
4k Context
390 Downloads
8G GPU ~2GB Est. VRAM
Dense XLMROBERTAFORMASKEDLM Architecture
Model Information Summary
Entity Passport
Registry ID stockley/ateso-model-v2-auto
Provider huggingface
πŸ’Ύ

Compute Threshold

~1.5GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{stockley_ateso_model_v2_auto,
  author = {stockley},
  title = {Ateso Model V2 Auto Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/stockley/ateso-model-v2-auto}},
  note = {Accessed via Free2AITools.}
}
APA Style
stockley. (2026). Ateso Model V2 Auto [Model]. Free2AITools. https://huggingface.co/stockley/ateso-model-v2-auto

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run ateso-model-v2-auto
πŸ€— HF Download
huggingface-cli download stockley/ateso-model-v2-auto
πŸ“¦ Install Lib
pip install -U transformers

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) 18
Recency (R) 82
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Ateso Model V2 Auto: Authority (A:0), Popularity (P:18), Recency (R:82), 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.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

HuggingFace Hub
390Downloads
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--stockley--ateso-model-v2-auto
slug
stockley--ateso-model-v2-auto
source
huggingface
author
stockley
license
tags
transformers, safetensors, roberta, fill-mask, generated_from_trainer, endpoints_compatible, region:us, xlm-roberta

βš™οΈ Technical Specs

architecture
XLMRobertaForMaskedLM
params billions
0.28
context length
4,096
pipeline tag
fill-mask
vram gb
1.5
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
390
stars
0
forks
0

Data indexed from public sources. Updated daily.