🧠
Bart Lfqa It model by efederici
⭐ 23.8
🧠
Model

Bart Lfqa It

by efederici efederici/bart-lfqa-it
Free2AITools Nexus Index
23.8
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 29
P: Popularity 1
R: Recency 8
Q: Quality 45
Tech Context
0.14B Params
1.024K Ctx
Vital Performance
11 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 23.8 FNI Score
Tiny 0.14B Params
1k Context
11 Downloads
8G GPU ~2GB Est. VRAM
Dense BARTFORCONDITIONALGENERATION Architecture
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID efederici/bart-lfqa-it
License MIT
Provider huggingface
πŸ’Ύ

Compute Threshold

~1.4GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{efederici_bart_lfqa_it,
  author = {efederici},
  title = {Bart Lfqa It Model},
  year = {2023},
  howpublished = {\url{https://huggingface.co/efederici/bart-lfqa-it}},
  note = {Accessed via Free2AITools.}
}
APA Style
efederici. (2023). Bart Lfqa It [Model]. Free2AITools. https://huggingface.co/efederici/bart-lfqa-it

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run bart-lfqa-it
πŸ€— HF Download
huggingface-cli download efederici/bart-lfqa-it
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) 29
Popularity (P) 1
Recency (R) 8
Quality (Q) 45

πŸ’¬ Index Insight

FNI V2.0 for Bart Lfqa It: Authority (A:29), Popularity (P:1), Recency (R:8), Quality (Q:45). 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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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
11Downloads
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--efederici--bart-lfqa-it
slug
efederici--bart-lfqa-it
source
huggingface
author
efederici
license
MIT
tags
transformers, pytorch, safetensors, bart, text2text-generation, it, license:mit, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
BartForConditionalGeneration
params billions
0.14
context length
1,024
vram gb
1.4
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
11
stars
0
forks
0

Data indexed from public sources. Updated daily.