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Mist 28m Kcwb9le5 Esol model by Mist Models
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Model

Mist 28m Kcwb9le5 Esol

by Mist Models mist-models/mist-28m-kcwb9le5-esol
Free2AITools Nexus Index
40.0
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 23
R: Recency 92
Q: Quality 65
Tech Context
0.03B Params
4.096K Ctx
Vital Performance
669 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 40 FNI Score
Tiny 0.03B Params
4k Context
669 Downloads
8G GPU ~2GB Est. VRAM
Dense MISTFINETUNED Architecture
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID mist-models/mist-28m-kcwb9le5-esol
License Apache-2.0
Provider huggingface
πŸ’Ύ

Compute Threshold

~1.3GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{mist_models_mist_28m_kcwb9le5_esol,
  author = {Mist Models},
  title = {Mist 28m Kcwb9le5 Esol Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/mist-models/mist-28M-kcwb9le5-esol}},
  note = {Accessed via Free2AITools.}
}
APA Style
Mist Models. (2026). Mist 28m Kcwb9le5 Esol [Model]. Free2AITools. https://huggingface.co/mist-models/mist-28M-kcwb9le5-esol

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run mist-28m-kcwb9le5-esol
πŸ€— HF Download
huggingface-cli download mist-models/mist-28m-kcwb9le5-esol
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) 23
Recency (R) 92
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Mist 28m Kcwb9le5 Esol: Authority (A:0), Popularity (P:23), Recency (R:92), 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.
  • ⚠ License Unknown: Verify licensing terms before commercial use.

Social Proof

HuggingFace Hub
669Downloads
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--mist-models--mist-28m-kcwb9le5-esol
slug
mist-models--mist-28m-kcwb9le5-esol
source
huggingface
author
Mist Models
license
Apache-2.0
tags
transformers, safetensors, mist_finetuned, feature-extraction, mist, chemistry, molecular-property-prediction, custom_code, en, arxiv:2510.18900, license:apache-2.0, region:us

βš™οΈ Technical Specs

architecture
MISTFinetuned
params billions
0.03
context length
4,096
pipeline tag
feature-extraction
vram gb
1.3
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
669
stars
0
forks
0

Data indexed from public sources. Updated daily.