🧠
Ramt Labse model by mytranslatenisa
⭐ 32.7
🧠
Model

Ramt Labse

by mytranslatenisa mytranslatenisa/ramt-labse
Free2AITools Nexus Index
32.7
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 3
R: Recency 59
Q: Quality 65
Tech Context
0.48B Params
4.096K Ctx
Vital Performance
32 DL / 30D
Source β†’

Task categories from upstream metadata

✨Translation

Technical Constraints

Experimental / High Latency
Low FNI signal 32.7 FNI Score
Tiny 0.48B Params
4k Context
32 Downloads
8G GPU ~2GB Est. VRAM
Dense M2M100FORCONDITIONALGENERATION Architecture
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID mytranslatenisa/ramt-labse
License Apache-2.0
Provider huggingface
πŸ’Ύ

Compute Threshold

~1.7GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{mytranslatenisa_ramt_labse,
  author = {mytranslatenisa},
  title = {Ramt Labse Model},
  year = {2025},
  howpublished = {\url{https://huggingface.co/mytranslatenisa/ramt-labse}},
  note = {Accessed via Free2AITools.}
}
APA Style
mytranslatenisa. (2025). Ramt Labse [Model]. Free2AITools. https://huggingface.co/mytranslatenisa/ramt-labse

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run ramt-labse
πŸ€— HF Download
huggingface-cli download mytranslatenisa/ramt-labse
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) 3
Recency (R) 59
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Ramt Labse: Authority (A:0), Popularity (P:3), Recency (R:59), 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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Deployment reference

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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
32Downloads
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--mytranslatenisa--ramt-labse
slug
mytranslatenisa--ramt-labse
source
huggingface
author
mytranslatenisa
license
Apache-2.0
tags
transformers, tensorboard, safetensors, m2m_100, text2text-generation, translation, machine-translation, english-to-malay, legal-domain, ramt, labse, m2m100, en, ms, license:apache-2.0, endpoints_compatible, region:us

βš™οΈ Technical Specs

architecture
M2M100ForConditionalGeneration
params billions
0.48
context length
4,096
pipeline tag
translation
vram gb
1.7
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
32
stars
0
forks
0

Data indexed from public sources. Updated daily.