🧠
Med Doc Classifier model by ekacare
⭐ 39.8
🧠
Model

Med Doc Classifier

by ekacare ekacare/med-doc-classifier
Free2AITools Nexus Index
39.8
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 14
R: Recency 95
Q: Quality 65
Tech Context
0.1B Params
4.096K Ctx
Vital Performance
221 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 39.8 FNI Score
Tiny 0.1B Params
4k Context
221 Downloads
8G GPU ~2GB Est. VRAM
Dense SIGLIP2HIERFORDOCCLASSIFICATION Architecture
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID ekacare/med-doc-classifier
License Apache-2.0
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{ekacare_med_doc_classifier,
  author = {ekacare},
  title = {Med Doc Classifier Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/ekacare/med-doc-classifier}},
  note = {Accessed via Free2AITools.}
}
APA Style
ekacare. (2026). Med Doc Classifier [Model]. Free2AITools. https://huggingface.co/ekacare/med-doc-classifier

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run med-doc-classifier
πŸ€— HF Download
huggingface-cli download ekacare/med-doc-classifier
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) 14
Recency (R) 95
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Med Doc Classifier: Authority (A:0), Popularity (P:14), Recency (R:95), 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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πŸš€ What's Next?

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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
221Downloads
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--ekacare--med-doc-classifier
slug
ekacare--med-doc-classifier
source
huggingface
author
ekacare
license
Apache-2.0
tags
transformers, safetensors, siglip2_hier_doc, feature-extraction, siglip2, document-classification, hierarchical, multi-task, image-classification, custom_code, license:apache-2.0, region:us

βš™οΈ Technical Specs

architecture
Siglip2HierForDocClassification
params billions
0.1
context length
4,096
pipeline tag
image-classification
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
221

Data indexed from public sources. Updated daily.