🧠
Jun Lora V2 Safetensor model by efficiencyx
⭐ 46.4
🧠
Model

Jun Lora V2 Safetensor

by efficiencyx efficiencyx/jun-lora-v2-safetensor
Free2AITools Nexus Index
46.4
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 29
P: Popularity 1
R: Recency 86
Q: Quality 65
Tech Context
11.96 Params
8.192K Ctx
Vital Performance
8 DL / 30D
Source β†’

Task categories from upstream metadata

πŸ’¬Chat & Dialogue

Technical Constraints

Experimental / High Latency
Low FNI signal 46.4 FNI Score
11.96B Params
8k Context
8 Downloads
24G GPU ~11GB Est. VRAM
Dense GEMMA4UNIFIEDFORCONDITIONALGENERATION Architecture
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID efficiencyx/jun-lora-v2-safetensor
License Apache-2.0
Provider huggingface
πŸ’Ύ

Compute Threshold

~10.3GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{efficiencyx_jun_lora_v2_safetensor,
  author = {efficiencyx},
  title = {Jun Lora V2 Safetensor Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/efficiencyx/Jun-Lora-v2-SAFETENSOR}},
  note = {Accessed via Free2AITools.}
}
APA Style
efficiencyx. (2026). Jun Lora V2 Safetensor [Model]. Free2AITools. https://huggingface.co/efficiencyx/Jun-Lora-v2-SAFETENSOR

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run jun-lora-v2-safetensor
πŸ€— HF Download
huggingface-cli download efficiencyx/jun-lora-v2-safetensor
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) 86
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Jun Lora V2 Safetensor: Authority (A:29), Popularity (P:1), Recency (R:86), 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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⚠️ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

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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
8Downloads
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--efficiencyx--jun-lora-v2-safetensor
slug
efficiencyx--jun-lora-v2-safetensor
source
huggingface
author
efficiencyx
license
Apache-2.0
tags
transformers, safetensors, gemma4_unified, image-text-to-text, gemma4, lora, character, roleplay, chatml, conversational, text-generation, en, license:apache-2.0, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
Gemma4UnifiedForConditionalGeneration
params billions
11.96
context length
8,192
pipeline tag
text-generation
vram gb
10.3
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
8

Data indexed from public sources. Updated daily.