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Llavaqwen2.5 0.5b Finetune Col Mask Moe Sparse 4e 2k Sp0.9 20260418 062427 model by KKHYA
⭐ 38.3
🧠
Model

Llavaqwen2.5 0.5b Finetune Col Mask Moe Sparse 4e 2k Sp0.9 20260418 062427

by KKHYA kkhya/llavaqwen2.5-0.5b-finetune-col-mask-moe-sparse-4e-2k-sp0.9_20260418_062427
Free2AITools Nexus Index
38.3
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 0
R: Recency 71
Q: Quality 65
Tech Context
0.94B Params
2.048K Ctx
Vital Performance —
Source β†’

Task categories from upstream metadata

πŸ’¬Chat & Dialogue

Technical Constraints

Experimental / High Latency
Low FNI signal 38.3 FNI Score
Tiny 0.94B Params
2k Context
0 Downloads
8G GPU ~2GB Est. VRAM
Dense COLMASKMOELLAVAQWEN2FORCAUSALLM Architecture
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID kkhya/llavaqwen2.5-0.5b-finetune-col-mask-moe-sparse-4e-2k-sp0.9_20260418_062427
License Apache-2.0
Provider huggingface
πŸ’Ύ

Compute Threshold

~2GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{kkhya_llavaqwen2_5_0_5b_finetune_col_mask_moe_sparse_4e_2k_sp0_9_20260418_062427,
  author = {KKHYA},
  title = {Llavaqwen2.5 0.5b Finetune Col Mask Moe Sparse 4e 2k Sp0.9 20260418 062427 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/KKHYA/llavaqwen2.5-0.5b-finetune-col-mask-moe-sparse-4e-2k-sp0.9_20260418_062427}},
  note = {Accessed via Free2AITools.}
}
APA Style
KKHYA. (2026). Llavaqwen2.5 0.5b Finetune Col Mask Moe Sparse 4e 2k Sp0.9 20260418 062427 [Model]. Free2AITools. https://huggingface.co/KKHYA/llavaqwen2.5-0.5b-finetune-col-mask-moe-sparse-4e-2k-sp0.9_20260418_062427

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run llavaqwen2.5-0.5b-finetune-col-mask-moe-sparse-4e-2k-sp0.9_20260418_062427
πŸ€— HF Download
huggingface-cli download kkhya/llavaqwen2.5-0.5b-finetune-col-mask-moe-sparse-4e-2k-sp0.9_20260418_062427
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) 0
Recency (R) 71
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Llavaqwen2.5 0.5b Finetune Col Mask Moe Sparse 4e 2k Sp0.9 20260418 062427: Authority (A:0), Popularity (P:0), Recency (R:71), 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.

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.
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--kkhya--llavaqwen2.5-0.5b-finetune-col-mask-moe-sparse-4e-2k-sp0.9_20260418_062427
slug
kkhya--llavaqwen2.5-0.5b-finetune-col-mask-moe-sparse-4e-2k-sp0.9_20260418_062427
source
huggingface
author
KKHYA
license
Apache-2.0
tags
transformers, pytorch, safetensors, col_mask_moe_llava_qwen2, text-generation, generated_from_trainer, conversational, base_model:kkhya/llavaqwen2.5-0.5b-finetune, license:apache-2.0, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
ColMaskMoELLaVAQwen2ForCausalLM
params billions
0.94
context length
2,048
pipeline tag
text-generation
vram gb
2
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
0

Data indexed from public sources. Updated daily.