2026-07-24 00:15:27 +02:00
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id: BAAI__bge-reranker-v2-m3__candidate
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status: candidate
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name: bge-reranker-v2-m3
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org: BAAI
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source:
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kind: huggingface
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url: https://huggingface.co/BAAI/bge-reranker-v2-m3
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revision: main
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model_card_url: https://huggingface.co/BAAI/bge-reranker-v2-m3
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2026-07-28 00:25:21 +02:00
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project_url: https://github.com/FlagOpen/FlagEmbedding
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2026-07-24 00:15:27 +02:00
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license:
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spdx: Apache-2.0
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url: https://huggingface.co/BAAI/bge-reranker-v2-m3
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allows_offline_retention: true
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allows_local_ops: true
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allows_fine_tune: true
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notes: "Confirm SPDX on card."
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2026-07-28 00:25:21 +02:00
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profile:
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summary: "Cross-encoder reranker companion to BGE-M3 for higher-precision RAG."
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original_source: https://huggingface.co/BAAI/bge-reranker-v2-m3
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use_cases:
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- "Second-stage rerank of top-k chunks from BGE-M3 (or hybrid) retrieval"
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- "Improving answer grounding for NetKingdom / ops runbook Q&A"
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- "Eval A/B of retrieval quality without changing the generator"
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- "Low-cost precision boost on multilingual corpora"
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sweet_spots:
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- "Cheap RAG quality win after a solid bi-encoder"
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- "Small VRAM; easy always-on sidecar"
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- "Same BAAI/FlagEmbedding family as BGE-M3"
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not_ideal_for:
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- "First-stage retrieval over full corpora (too slow as sole retriever)"
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- "Generation / chat"
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- "When latency budget forbids a second pass"
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capability_notes: >
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Lightweight multilingual cross-encoder from the BGE family. W-tier optional
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after R spine; high value per byte when RAG is a primary workload.
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swot:
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strengths:
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- "Material precision lift for little disk/VRAM"
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- "Proven pairing with BGE-M3"
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- "Apache-friendly open stack"
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weaknesses:
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- "Cross-encoder cost scales with candidate count"
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- "Not a substitute for better chunking or generators"
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opportunities:
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- "Default two-stage pipeline for lab RAG demos"
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- "Tune candidate depth (k) per latency tier"
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threats:
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- "Newer BGE / open rerankers may supersede this checkpoint"
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- "LLM-as-judge rerank experiments may reduce need for a dedicated model"
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2026-07-24 00:15:27 +02:00
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size:
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total_bytes: 0
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total_human: "~1–2 GB (estimate)"
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hardware_class:
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min_vram_gb_q4: 1
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min_vram_gb_fp16: 2
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notes: "Companion to bge-m3"
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axes: [B]
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priority: medium
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collection:
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approved_by: ""
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approved_at: null
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downloaded_at: null
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downloaded_by: ""
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storage_path: ""
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brief_refs:
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- research/2026-07-24-baseline-field-survey.md
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2026-07-28 00:25:21 +02:00
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reason: "P1/W — cheap RAG quality win as companion to BGE-M3."
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tags: [tier-w, reranker, rag]
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2026-07-24 00:15:27 +02:00
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companions:
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- BAAI__bge-m3__candidate
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notes: ""
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history:
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- at: "2026-07-24"
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event: nominated
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by: baseline-survey
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detail: "P1 recommendation from initial deep research."
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2026-07-28 00:25:21 +02:00
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- at: "2026-07-28"
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event: profile_swot_added
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by: grok
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detail: "schema 0.2 profile + SWOT."
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2026-07-28 01:30:55 +02:00
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- at: "2026-07-28"
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event: decision_pass
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by: bernd
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detail: "W — optional after BGE-M3 verified"
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