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Qwen3.8 27b Oq4 model by EigenLabs
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Model

Qwen3.8 27b Oq4

by EigenLabs eigenlabs/qwen3.8-27b-oq4
Free2AITools Nexus Index
39.0
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 0
P: Popularity 0
R: Recency 93
Q: Quality 65
Tech Context
4.81 Params
32.768K Ctx
Vital Performance —
Source β†’

Technical Constraints

Experimental / High Latency
Low FNI signal 39 FNI Score
4.81B Params
32k Context
0 Downloads
8G GPU ~7GB Est. VRAM
Dense QWEN3_5FORCONDITIONALGENERATION Architecture
Commercial APACHE License
Model Information Summary
Entity Passport
Registry ID eigenlabs/qwen3.8-27b-oq4
License Apache-2.0
Provider huggingface
πŸ’Ύ

Compute Threshold

~6.1GB VRAM

Interactive
Estimate fit
β–Ό

* Static estimation for 4-Bit Quantization.

πŸ“œ

Cite this model

Academic & Research Attribution

BibTeX 
@misc{eigenlabs_qwen3_8_27b_oq4,
  author = {EigenLabs},
  title = {Qwen3.8 27b Oq4 Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/EigenLabs/Qwen3.8-27B-oQ4}},
  note = {Accessed via Free2AITools.}
}
APA Style
EigenLabs. (2026). Qwen3.8 27b Oq4 [Model]. Free2AITools. https://huggingface.co/EigenLabs/Qwen3.8-27B-oQ4

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ¦™ Ollama Run
ollama run qwen3.8-27b-oq4
πŸ€— HF Download
huggingface-cli download eigenlabs/qwen3.8-27b-oq4

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) 0
Recency (R) 93
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Qwen3.8 27b Oq4: Authority (A:0), Popularity (P:0), Recency (R:93), 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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⚠️ 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.
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--eigenlabs--qwen3.8-27b-oq4
slug
eigenlabs--qwen3.8-27b-oq4
source
huggingface
author
EigenLabs
license
Apache-2.0
tags
mlx, safetensors, qwen3_5, mlx-vlm, omlx, qwen, qwen3.8, multimodal, quantized, apple-silicon, oq4, mixed-precision, mtp, image-text-to-text, conversational, base_model:qwen/qwen3.8-27b, base_model:quantized:qwen/qwen3.8-27b, license:apache-2.0, 4-bit, region:us

βš™οΈ Technical Specs

architecture
Qwen3_5ForConditionalGeneration
params billions
4.81
context length
32,768
pipeline tag
image-text-to-text
vram gb
6.1
vram is estimated
true
vram formula
VRAM β‰ˆ (params * 0.75) + 2GB (KV) + 0.5GB (OS)

πŸ“Š Engagement & Metrics

downloads
0

Data indexed from public sources. Updated daily.