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v2.2 · 2026-06-12
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Qwen3 Reranker 0.6B

qwen3-reranker-0-6b
EMBEDDING

Qwen3 Reranker 0.6B is a 0.6B embedding model profile for local AI planning. This page records provider, license, context, quantization, hardware-fit estimates, setup hints, source links, and caveats so readers do not choose a model by name alone.

PROVIDER
Qwen / Alibaba
FAMILY
Qwen3 Embedding
MODEL TYPE
embedding
PARAMETERS
0.6B
MODALITIES
text
ARCHITECTURE
CONTEXT WINDOW
32,768 tokens
TRAINING TOKENS
RELEASE DATE
2025-06-05
LICENSE & LINKS
HARDWARE FIT
CPU only (no GPU)
no
8 GB RAM
unknown
16 GB RAM
unknown
32 GB RAM
unknown
8 GB VRAM
unknown
12 GB VRAM
unknown
24 GB VRAM
unknown
Apple Silicon (unified memory)
unknown
Hardware fit values are conservative local-inference estimates based on GGUF size plus runtime and KV-cache overhead. Actual requirements depend on context length, quantization, runtime, GPU offload, and other running apps.
BEST FOR
·Improving top results after embedding search in serious RAG apps with enough compute and patience.
AVOID IF
·You only need basic local search, or your app cannot call reranking models afterwards reliably.
CAVEATS
·This should be documented as a RAG infrastructure model, not a chat model or embedding model.
·No official Ollama command was verified.
·TEI compatibility is not recommended unless Hugging Face adds Qwen3 Reranker support to its reranking model list.
·Exact local RAM, VRAM, GGUF, and quantized package sizes were not confirmed in cited sources.
·Commercial-use status from the supplied research: Yes under Apache-2.0.
·Beginner summary from supplied research: Qwen3 Reranker 0.6B is not an embedder; it reviews candidate passages after search and scores relevance. It can improve answer quality in RAG, but adds latency, setup complexity, and another model to manage in production.
SOURCE URLS
FIELD EVIDENCE
hfGgufRepos
q4FileSizeGb
q5FileSizeGb
q8FileSizeGb
ollama
setupHints
trainingTokens
LANGUAGES
100+ languages, including programming languages
CAPABILITIES
embedding model
Qwen3 Embedding 4BQwen3-VL Embedding 2B