🧠
Esmc 6b Sae Layer60 K32 Codebook131072 model by biohub
⭐ 39
🧠
Model

Esmc 6b Sae Layer60 K32 Codebook131072

by biohub hf-model--biohub--esmc-6b-sae-layer60-k32-codebook131072
Free2AITools Nexus Index
39.0 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 0
R: Recency 100
Q: Quality 65
Tech Context
6 Params
61.44K Ctx
Vital Performance
0 DL / 30D
0.0%
Source →
Audited 39 FNI Score
6B Params
60k Context
0 Downloads
24G GPU ~9GB Est. VRAM
Dense ESMC_SAE Architecture
Commercial ["MIT","OTHER"] License
Model Information Summary
Entity Passport
Registry ID hf-model--biohub--esmc-6b-sae-layer60-k32-codebook131072
License ["mit","other"]
Provider huggingface
💾

Compute Threshold

~9GB VRAM

Interactive
Analyze Hardware
â–ŧ

* Static estimation for 4-Bit Quantization.

📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__biohub__esmc_6b_sae_layer60_k32_codebook131072,
  author = {biohub},
  title = {Esmc 6b Sae Layer60 K32 Codebook131072 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/biohub/ESMC-6B-sae-layer60-k32-codebook131072}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
biohub. (2026). Esmc 6b Sae Layer60 K32 Codebook131072 [Model]. Free2AITools. https://huggingface.co/biohub/ESMC-6B-sae-layer60-k32-codebook131072

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

đŸĻ™ Ollama Run
ollama run esmc-6b-sae-layer60-k32-codebook131072
🤗 HF Download
huggingface-cli download biohub/esmc-6b-sae-layer60-k32-codebook131072
đŸ“Ļ Install Lib
pip install -U transformers

⚖ī¸ Free2AITools Nexus Index V2.0

Methodology Index Protocol
Semantic (S) 50
Authority (A) 0
Popularity (P) 0
Recency (R) 100
Quality (Q) 65

đŸ’Ŧ Index Insight

FNI V2.0 for Esmc 6b Sae Layer60 K32 Codebook131072: Semantic (S:50), Authority (A:0), Popularity (P:0), Recency (R:100), Quality (Q:65).

Free2AITools Nexus Index

Verification Authority

HuggingFace API GitHub Metadata Arxiv Citation DB System Audit
Unbiased Data Node Refresh: VFS Live
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Technical Deep Dive

⚠ī¸ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

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📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.
🤗 Data Source: Hugging Face ↗
🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
hf-model--biohub--esmc-6b-sae-layer60-k32-codebook131072
slug
biohub--esmc-6b-sae-layer60-k32-codebook131072
source
huggingface
author
biohub
license
["mit","other"]
tags
transformers, esmc_sae, biology, esm, protein, sparse-autoencoder, interpretability, protein-embeddings, feature-extraction, protein-language-model, unsupervised-learning, en, license:mit, license:other, endpoints_compatible, region:us

⚙ī¸ Technical Specs

architecture
esmc_sae
params billions
6
context length
61,440
pipeline tag
feature-extraction
vram gb
9
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 4GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads
0
stars
0
forks
null

Data indexed from public sources. Updated daily.