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Aaie Moe Pretrain model by namquangstudy
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Model

Aaie Moe Pretrain

by namquangstudy namquangstudy/aaie-moe-pretrain
Free2AITools Nexus Index
39.0
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 0
R: Recency 98
Q: Quality 65
Tech Context
0.62B Params
4.096K Ctx
Vital Performance —
Source β†’

Task categories from upstream metadata

πŸ’¬Chat & Dialogue

Technical Constraints

Experimental / High Latency
Low FNI signal 39 FNI Score
Tiny 0.62B Params
4k Context
0 Downloads
8G GPU ~2GB Est. VRAM
Dense AAIEMOEFORCAUSALLM Architecture
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID namquangstudy/aaie-moe-pretrain
License MIT
Provider huggingface
πŸ’Ύ

Compute Threshold

~1.8GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{namquangstudy_aaie_moe_pretrain,
  author = {namquangstudy},
  title = {Aaie Moe Pretrain Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/namquangstudy/aaie-moe-pretrain}},
  note = {Accessed via Free2AITools.}
}
APA Style
namquangstudy. (2026). Aaie Moe Pretrain [Model]. Free2AITools. https://huggingface.co/namquangstudy/aaie-moe-pretrain

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run aaie-moe-pretrain
πŸ€— HF Download
huggingface-cli download namquangstudy/aaie-moe-pretrain

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) 98
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Aaie Moe Pretrain: Authority (A:0), Popularity (P:0), Recency (R:98), 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--namquangstudy--aaie-moe-pretrain
slug
namquangstudy--aaie-moe-pretrain
source
huggingface
author
namquangstudy
license
MIT
tags
safetensors, aaie_moe, text-generation, mixture-of-experts, conversational, custom_code, en, license:mit, region:us

βš™οΈ Technical Specs

architecture
AAIEMoEForCausalLM
params billions
0.62
context length
4,096
pipeline tag
text-generation
vram gb
1.8
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.