Text / Qwen

Qwen3 Embedding · 0.6B

Open-weight text embedding model for local semantic search. It encodes queries and documents into vectors; it does not generate chat responses.

Verified sourceSource checked 9/29/2026
LOCALRENTED GPUOPEN WEIGHTS
Freshness 15 out of 100. Repository age: 499 days old, Jun 3, 2025. Measures age, not quality.
Popularity 92 out of 100. 8,605,583 downloads in 30 days; 1,291 lifetime likes. Checked Oct 15, 2026. Measures adoption, not quality.
What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.

Check the date source
Popularity · adoption

Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source
Scores as of Oct 15, 2026. Statistics checked Oct 15, 2026. Statistics are marked for refresh after 72 hours.Full scoring method
WORKFLOWS FOR THIS MODEL 1

What do you want to do?

Choose a task. Get the software, files, settings, and steps to run it.

MAKE IT PERSONALChoose your hardware

Open a guide for its software, hardware requirements, and setup steps.

At a glance

Parameters
0.6B
Context length
32K
License
Apache 2.0
Software
Sentence Transformers
View the model source
MY HARDWARE

Will it run on your machine?

Save your machine to see a personalized rating and its reasoning.

Add my hardware

Best for

  • Build private semantic search over local documents.

Ways to run it

Sentence Transformers · Windows, macOS, Linux

Official instructions

Explore its uses

EMBEDDINGSFEATURE-EXTRACTIONSEMANTIC-SEARCHSENTENCE-SIMILARITY

Keep exploring

Qwen3 Embedding · 0.6B: hardware, VRAM & setup | YouRunAI