🧠
Nllb 200 Distilled 1.3b 8bit model by edyrkaj
⭐ 32.7
🧠
Model

Nllb 200 Distilled 1.3b 8bit

by edyrkaj edyrkaj/nllb-200-distilled-1.3b-8bit
Free2AITools Nexus Index
32.7
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 6
R: Recency 66
Q: Quality 45
Tech Context
1.37 Params
4.096K Ctx
Vital Performance
63 DL / 30D
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 32.7 FNI Score
Tiny 1.37B Params
4k Context
63 Downloads
8G GPU ~3GB Est. VRAM
Dense M2M100FORCONDITIONALGENERATION Architecture
Model Information Summary
Entity Passport
Registry ID edyrkaj/nllb-200-distilled-1.3b-8bit
Provider huggingface
πŸ’Ύ

Compute Threshold

~2.3GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{edyrkaj_nllb_200_distilled_1_3b_8bit,
  author = {edyrkaj},
  title = {Nllb 200 Distilled 1.3b 8bit Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/edyrkaj/nllb-200-distilled-1.3b-8bit}},
  note = {Accessed via Free2AITools.}
}
APA Style
edyrkaj. (2026). Nllb 200 Distilled 1.3b 8bit [Model]. Free2AITools. https://huggingface.co/edyrkaj/nllb-200-distilled-1.3b-8bit

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run nllb-200-distilled-1.3b-8bit
πŸ€— HF Download
huggingface-cli download edyrkaj/nllb-200-distilled-1.3b-8bit
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) 6
Recency (R) 66
Quality (Q) 45

πŸ’¬ Index Insight

FNI V2.0 for Nllb 200 Distilled 1.3b 8bit: Authority (A:0), Popularity (P:6), Recency (R:66), Quality (Q:45). 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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πŸš€ What's Next?

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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
63Downloads
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--edyrkaj--nllb-200-distilled-1.3b-8bit
slug
edyrkaj--nllb-200-distilled-1.3b-8bit
source
huggingface
author
edyrkaj
tags
transformers, safetensors, m2m_100, text2text-generation, arxiv:1910.09700, endpoints_compatible, 8-bit, bitsandbytes, region:us

βš™οΈ Technical Specs

architecture
M2M100ForConditionalGeneration
params billions
1.37
context length
4,096
vram gb
2.3
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
63
stars
0
forks
0

Data indexed from public sources. Updated daily.