🧠
Esm2 T36 3b Ur50d model by nvidia
⭐ 44.2
🧠
Model

Esm2 T36 3b Ur50d

by nvidia nvidia/esm2_t36_3b_ur50d
Free2AITools Nexus Index
44.2
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 41
P: Popularity 37
R: Recency 85
Q: Quality 65
Tech Context
2.84 Params
4.096K Ctx
Vital Performance
4.1K DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 44.2 FNI Score
Tiny 2.84B Params
4k Context
4.1K Downloads
8G GPU ~4GB Est. VRAM
Dense NVESMFORMASKEDLM Architecture
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID nvidia/esm2_t36_3b_ur50d
License MIT
Provider huggingface
πŸ’Ύ

Compute Threshold

~3.4GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{nvidia_esm2_t36_3b_ur50d,
  author = {nvidia},
  title = {Esm2 T36 3b Ur50d Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/nvidia/esm2_t36_3B_UR50D}},
  note = {Accessed via Free2AITools.}
}
APA Style
nvidia. (2026). Esm2 T36 3b Ur50d [Model]. Free2AITools. https://huggingface.co/nvidia/esm2_t36_3B_UR50D

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run esm2_t36_3b_ur50d
πŸ€— HF Download
huggingface-cli download nvidia/esm2_t36_3b_ur50d
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) 41
Popularity (P) 37
Recency (R) 85
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Esm2 T36 3b Ur50d: Authority (A:41), Popularity (P:37), Recency (R:85), 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
4.1KDownloads
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--nvidia--esm2_t36_3b_ur50d
slug
nvidia--esm2_t36_3b_ur50d
source
huggingface
author
nvidia
license
MIT
tags
transformers, safetensors, nv_esm, fill-mask, custom_code, license:mit, region:us

βš™οΈ Technical Specs

architecture
NVEsmForMaskedLM
params billions
2.84
context length
4,096
pipeline tag
fill-mask
vram gb
3.4
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
4,093
stars
0
forks
0

Data indexed from public sources. Updated daily.