🧠
Vela 1.0 Encoder 307m model by Vllm Sr
⭐ 52.9
🧠
Model

Vela 1.0 Encoder 307m

by Vllm Sr vllm-sr/vela-1.0-encoder-307m
Free2AITools Nexus Index
52.9
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 41
P: Popularity 30
R: Recency 95
Q: Quality 65
Tech Context
0.31B Params
4.096K Ctx
Vital Performance
1.6K DL / 30D
Source β†’
Low FNI signal 52.9 FNI Score
Tiny 0.31B Params
4k Context
1.6K Downloads
8G GPU ~2GB Est. VRAM
Dense MODERNBERTFORMASKEDLM Architecture
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID vllm-sr/vela-1.0-encoder-307m
License MIT
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{vllm_sr_vela_1_0_encoder_307m,
  author = {Vllm Sr},
  title = {Vela 1.0 Encoder 307m Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/vllm-sr/Vela-1.0-Encoder-307M}},
  note = {Accessed via Free2AITools.}
}
APA Style
Vllm Sr. (2026). Vela 1.0 Encoder 307m [Model]. Free2AITools. https://huggingface.co/vllm-sr/Vela-1.0-Encoder-307M

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run vela-1.0-encoder-307m
πŸ€— HF Download
huggingface-cli download vllm-sr/vela-1.0-encoder-307m
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) 41
Popularity (P) 30
Recency (R) 95
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Vela 1.0 Encoder 307m: Authority (A:41), Popularity (P:30), 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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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
1.6KDownloads
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--vllm-sr--vela-1.0-encoder-307m
slug
vllm-sr--vela-1.0-encoder-307m
source
huggingface
author
Vllm Sr
license
MIT
tags
transformers, safetensors, modernbert, fill-mask, vela, multilingual, long-context, en, zh, de, fr, ja, ar, base_model:jhu-clsp/mmbert-base, base_model:finetune:jhu-clsp/mmbert-base, license:mit, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
ModernBertForMaskedLM
params billions
0.31
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
1,601

Data indexed from public sources. Updated daily.