tegwick
a83ef0a79a
Classify freedom-intelligence for State Hub (agents domain), sync FI-WP-0001..0003 with hub IDs, enrich catalog entries with profile/SWOT, and pin the open-weight reserve to D:\vault\coulomb\freedom-intelligence\ with strategic S-tier policy.
83 lines
2.9 KiB
YAML
83 lines
2.9 KiB
YAML
id: BAAI__bge-m3__candidate
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status: candidate
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name: bge-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-m3
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revision: main
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model_card_url: https://huggingface.co/BAAI/bge-m3
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project_url: https://github.com/FlagOpen/FlagEmbedding
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license:
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spdx: MIT
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url: https://huggingface.co/BAAI/bge-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: ""
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profile:
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summary: "Default multilingual dense embedding model for local RAG."
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original_source: https://huggingface.co/BAAI/bge-m3
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use_cases:
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- "NetKingdom / Coulomb document and wiki retrieval"
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- "Multilingual (DE/EN/…) semantic search"
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- "Hybrid retrieval experiments (dense + multi-granularity features)"
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- "Offline RAG in sandboxed agents"
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- "Indexing ops runbooks and code comments"
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sweet_spots:
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- "Self-hosted production-quality multilingual embed"
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- "MIT license; easy ops story"
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- "Strong general retrieval without API spend"
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- "Pairs cleanly with a small cross-encoder reranker"
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not_ideal_for:
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- "Generation / chat (not an LLM)"
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- "Pure code retrieval if a code-specialized embed clearly wins A/B"
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- "Ultra-tiny edge when EmbeddingGemma-class is enough"
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capability_notes: >
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BAAI BGE-M3 is a staple open embedder: multi-lingual, multi-granularity,
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widely deployed for RAG. Primary R-tier retrieval backbone for the lab.
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swot:
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strengths:
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- "Proven multilingual retrieval quality for self-host"
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- "MIT; small enough for CPU/GPU flexibility"
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- "Mature FlagEmbedding ecosystem"
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weaknesses:
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- "Not optimized solely for code or for extreme long-context embed niches"
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- "Newer embed lines (Qwen/Gemma/Nomic) may win specific A/B tests"
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opportunities:
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- "Domain-adapted embed fine-tune on Coulomb corpora later"
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- "Companion bge-reranker for precision@k gains"
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threats:
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- "Leaderboard churn; risk of holding a stale 'default' without re-eval"
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- "Pipeline lock-in if vector DBs assume fixed dimension without migration plan"
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size:
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total_bytes: 0
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total_human: "~2 GB"
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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: "Runs on CPU comfortably for many workloads"
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axes: [B]
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priority: high
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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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reason: "P0/R multilingual embedding staple for local RAG (docs, ops notes, DE/EN)."
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tags: [tier-r, embedding, multilingual, rag]
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companions:
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- BAAI__bge-reranker-v2-m3__candidate
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notes: "Small download — within agent auto-collect band after license check once storage pinned."
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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: "P0 recommendation from initial deep research."
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- at: "2026-07-24"
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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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