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Swiftvln Satnav Qwen3vl 2b 1ep F32s4 Overlap0 Pf H8 Pool S2 Noembed model by Eku127
⭐ 39
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Model

Swiftvln Satnav Qwen3vl 2b 1ep F32s4 Overlap0 Pf H8 Pool S2 Noembed

by Eku127 eku127/swiftvln-satnav-qwen3vl-2b-1ep-f32s4-overlap0-pf-h8-pool-s2-noembed
Free2AITools Nexus Index
39.0
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 1
R: Recency 100
Q: Quality 65
Tech Context
2.44 Params
32.768K Ctx
Vital Performance
11 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 39 FNI Score
Tiny 2.44B Params
32k Context
11 Downloads
8G GPU ~5GB Est. VRAM
Dense SWIFTVLNQWEN3VLFORCONDITIONALGENERATION Architecture
Model Information Summary
Entity Passport
Registry ID eku127/swiftvln-satnav-qwen3vl-2b-1ep-f32s4-overlap0-pf-h8-pool-s2-noembed
Provider huggingface
πŸ’Ύ

Compute Threshold

~4.3GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{eku127_swiftvln_satnav_qwen3vl_2b_1ep_f32s4_overlap0_pf_h8_pool_s2_noembed,
  author = {Eku127},
  title = {Swiftvln Satnav Qwen3vl 2b 1ep F32s4 Overlap0 Pf H8 Pool S2 Noembed Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/Eku127/swiftvln-satnav-qwen3vl-2b-1ep-f32s4-overlap0-pf-h8-pool-s2-noembed}},
  note = {Accessed via Free2AITools.}
}
APA Style
Eku127. (2026). Swiftvln Satnav Qwen3vl 2b 1ep F32s4 Overlap0 Pf H8 Pool S2 Noembed [Model]. Free2AITools. https://huggingface.co/Eku127/swiftvln-satnav-qwen3vl-2b-1ep-f32s4-overlap0-pf-h8-pool-s2-noembed

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run swiftvln-satnav-qwen3vl-2b-1ep-f32s4-overlap0-pf-h8-pool-s2-noembed
πŸ€— HF Download
huggingface-cli download eku127/swiftvln-satnav-qwen3vl-2b-1ep-f32s4-overlap0-pf-h8-pool-s2-noembed
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) 1
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Swiftvln Satnav Qwen3vl 2b 1ep F32s4 Overlap0 Pf H8 Pool S2 Noembed: Authority (A:0), Popularity (P:1), Recency (R:100), 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.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

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--eku127--swiftvln-satnav-qwen3vl-2b-1ep-f32s4-overlap0-pf-h8-pool-s2-noembed
slug
eku127--swiftvln-satnav-qwen3vl-2b-1ep-f32s4-overlap0-pf-h8-pool-s2-noembed
source
huggingface
author
Eku127
tags
transformers, safetensors, qwen3_vl, text-generation, swiftvln, satnav, vision-language-navigation, embodied-ai, robotics, qwen3-vl, image-text-to-text, conversational, en, base_model:qwen/qwen3-vl-2b-instruct, base_model:finetune:qwen/qwen3-vl-2b-instruct, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
SwiftVLNQwen3VLForConditionalGeneration
params billions
2.44
context length
32,768
pipeline tag
image-text-to-text
vram gb
4.3
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 2GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
11

Data indexed from public sources. Updated daily.