🧠

Model

Beetle Bilingual L2 50 Sequential 33 67 B3 Fineweb 2b Nld Eng Arch Moe

Name: Beetle Bilingual L2 50 Sequential 33 67 B3 Fineweb 2b Nld Eng Arch Moe
Author: Beetle Nld Eng Variants

by Beetle Nld Eng Variants hf-model--beetle-nld-eng-variants--beetle-bilingual-l2-50-sequential-33-67-b3-fineweb-2b-nld-eng-arch-moe

Free2AITools Nexus Index

39.0 Top 100%

S: Semantic 50

A: Authority 0

P: Popularity 0

R: Recency 100

Q: Quality 65

Tech Context

0.89B Params

4.096K Ctx

Vital Performance

559 DL / 30D

0.0%

Source →

Audited 39 FNI Score

Tiny 0.89B Params

4k Context

559 Downloads

8G GPU ~2GB Est. VRAM

Dense BEETLEMOEHF Architecture

Model Information Summary
Entity Passport
Registry ID	hf-model--beetle-nld-eng-variants--beetle-bilingual-l2-50-sequential-33-67-b3-fineweb-2b-nld-eng-arch-moe
Provider	huggingface

💾

Compute Threshold

~2GB VRAM

Interactive

Analyze Hardware

Hardware Compatibility Test

▼

* Static estimation for 4-Bit Quantization.

📜

Cite this model

Academic & Research Attribution

BibTeX

@misc{hf_model__beetle_nld_eng_variants__beetle_bilingual_l2_50_sequential_33_67_b3_fineweb_2b_nld_eng_arch_moe,
  author = {Beetle Nld Eng Variants},
  title = {Beetle Bilingual L2 50 Sequential 33 67 B3 Fineweb 2b Nld Eng Arch Moe Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/Beetle-nld-eng-Variants/beetle-bilingual-l2-50-sequential-33-67-b3-fineweb-2b-nld-eng-arch-moe}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}

APA Style

Beetle Nld Eng Variants. (2026). Beetle Bilingual L2 50 Sequential 33 67 B3 Fineweb 2b Nld Eng Arch Moe [Model]. Free2AITools. https://huggingface.co/Beetle-nld-eng-Variants/beetle-bilingual-l2-50-sequential-33-67-b3-fineweb-2b-nld-eng-arch-moe

🔬Technical Deep Dive

Full Specifications [+]

Quick Commands

🦙 Ollama Run

ollama run beetle-bilingual-l2-50-sequential-33-67-b3-fineweb-2b-nld-eng-arch-moe

🤗 HF Download

huggingface-cli download beetle-nld-eng-variants/beetle-bilingual-l2-50-sequential-33-67-b3-fineweb-2b-nld-eng-arch-moe

📦 Install Lib

pip install -U transformers

⚖️ Free2AITools Nexus Index V2.0

Methodology Index Protocol

Semantic (S) 50

Authority (A) 0

Popularity (P) 0

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Beetle Bilingual L2 50 Sequential 33 67 B3 Fineweb 2b Nld Eng Arch Moe: 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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🚀 What's Next?

📊

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 →

📝 Limitations & Considerations

• Benchmark scores may vary based on evaluation methodology and hardware configuration.
• VRAM requirements are estimates; actual usage depends on quantization and batch size.
• 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

559Downloads

Hub Discussions

🤗 Data Source: Hugging Face ↗

🔄 Daily sync (03:00 UTC)

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

📊 FNI Methodology 📚 Knowledge Baseℹ️ Verify with original source

🛡️ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id: hf-model--beetle-nld-eng-variants--beetle-bilingual-l2-50-sequential-33-67-b3-fineweb-2b-nld-eng-arch-moe
slug: beetle-nld-eng-variants--beetle-bilingual-l2-50-sequential-33-67-b3-fineweb-2b-nld-eng-arch-moe
source: huggingface
author: Beetle Nld Eng Variants
license
tags: transformers, safetensors, pico_decoder, text-generation, custom_code, arxiv:1910.09700, region:us, beetle_moe

⚙️ Technical Specs

architecture: BeetleMoEHF
params billions: 0.89
context length: 4,096
pipeline tag: text-generation
vram gb: 2
vram is estimated: true
vram formula: VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

downloads: 559
stars: 0
forks: null

Data indexed from public sources. Updated daily.