🧠
Multilingual E5 Base Q8a64 Gguf model by VelociQ8
⭐ 37.5
🧠
Model

Multilingual E5 Base Q8a64 Gguf

by VelociQ8 velociq8/multilingual-e5-base-q8a64-gguf
Free2AITools Nexus Index
37.5
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 5
R: Recency 100
Q: Quality 50
Tech Context
5 Params
4.096K Ctx
Vital Performance
49 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 37.5 FNI Score
5B Params
4k Context
49 Downloads
8G GPU ~5GB Est. VRAM
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID velociq8/multilingual-e5-base-q8a64-gguf
License MIT
Provider huggingface
πŸ’Ύ

Compute Threshold

~5GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{velociq8_multilingual_e5_base_q8a64_gguf,
  author = {VelociQ8},
  title = {Multilingual E5 Base Q8a64 Gguf Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/VelociQ8/multilingual-e5-base-Q8A64-GGUF}},
  note = {Accessed via Free2AITools.}
}
APA Style
VelociQ8. (2026). Multilingual E5 Base Q8a64 Gguf [Model]. Free2AITools. https://huggingface.co/VelociQ8/multilingual-e5-base-Q8A64-GGUF

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run multilingual-e5-base-q8a64-gguf
πŸ€— HF Download
huggingface-cli download velociq8/multilingual-e5-base-q8a64-gguf

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) 100
Quality (Q) 50

πŸ’¬ Index Insight

FNI V2.0 for Multilingual E5 Base Q8a64 Gguf: Authority (A:0), Popularity (P:5), Recency (R:100), 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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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
49Downloads
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--velociq8--multilingual-e5-base-q8a64-gguf
slug
velociq8--multilingual-e5-base-q8a64-gguf
source
huggingface
author
VelociQ8
license
MIT
tags
gguf, velociq8, q8a64, cpu, embeddings, sentence-similarity, arm64, aarch64, x86_64, 8-bit, quantized, retrieval, semantic-search, rag, feature-extraction, multilingual, arxiv:2402.05672, base_model:intfloat/multilingual-e5-base, license:mit, region:us

βš™οΈ Technical Specs

params billions
5
context length
4,096
pipeline tag
feature-extraction
vram gb
5
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
49

Data indexed from public sources. Updated daily.