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.
128 lines
4.5 KiB
YAML
128 lines
4.5 KiB
YAML
# Freedom Intelligence — open-weight inventory entry schema
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# Each collected (or tracked) model revision is one YAML file under catalog/
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# Filename suggestion: {org}__{name}__{short_revision}.yaml
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#
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# Schema version documents field meaning for humans and future validators.
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schema_version: "0.2.0"
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# ---------------------------------------------------------------------------
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# Example entry (illustrative only — not a real collection claim)
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# ---------------------------------------------------------------------------
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# id: meta-llama__Llama-3.2-3B-Instruct__abc1234
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# status: candidate | approved | collecting | collected | verified | superseded | evicted | rejected
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# name: Llama-3.2-3B-Instruct
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# org: meta-llama
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# source:
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# kind: huggingface # huggingface | github_release | direct_url | other
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# url: https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct
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# revision: abc1234def...
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# model_card_url: https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct
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# project_url: https://www.llama.com/ # optional: org project / paper home
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# paper_url: https://arxiv.org/... # optional
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# license: { ... }
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# profile:
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# summary: "One-line what this model is for."
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# original_source: "https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct"
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# use_cases:
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# - "Always-on local assistant on small VRAM"
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# - "Edge classification and simple tool routing"
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# sweet_spots:
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# - "Latency-sensitive chat under 3–8 GB VRAM"
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# - "Teaching / prototyping agent loops cheaply"
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# not_ideal_for:
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# - "Hard multi-file software engineering"
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# - "Long-horizon reasoning"
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# capability_notes: >
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# Compact instruct model; strong ecosystem tooling; limited depth vs mid-size.
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# swot:
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# strengths:
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# - "Tiny footprint; huge Llama tooling ecosystem"
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# weaknesses:
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# - "Shallow reasoning and coding vs 8B–70B class"
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# opportunities:
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# - "Domain LoRA for NetKingdom micro-agents"
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# threats:
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# - "Superseded quickly by next small open release"
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# size: { ... }
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# hardware_class: { ... }
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# axes: [B]
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# priority: high
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# collection: { ... }
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# reason: "..."
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# tags: [tier-r, instruct]
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# companions: []
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# notes: ""
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# history: []
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# ---------------------------------------------------------------------------
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# Required fields by status
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# ---------------------------------------------------------------------------
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# candidate: id, status, name, org, source.url, source.revision, license,
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# reason, priority, profile (summary + original_source + use_cases +
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# sweet_spots), swot (all four lists, compact)
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# approved: + collection.approved_by, collection.approved_at
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# collected: + collection.downloaded_at, collection.storage_path, size, artifacts
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# verified: + checksums re-read OK
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# rejected: + reason
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# superseded / evicted: + history event
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field_reference:
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id:
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type: string
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description: Stable catalog id; prefer {org}__{name}__{short_revision}
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status:
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type: enum
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values: [candidate, approved, collecting, collected, verified, superseded, evicted, rejected]
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name:
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type: string
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org:
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type: string
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source:
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type: object
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fields: [kind, url, revision, model_card_url, project_url, paper_url]
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license:
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type: object
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fields: [spdx, url, allows_offline_retention, allows_local_ops, allows_fine_tune, notes]
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profile:
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type: object
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description: Human-facing capability card for operators choosing models
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fields:
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summary: One-line positioning
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original_source: Canonical URL (usually HF model card or org release page)
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use_cases: List of typical lab / product uses
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sweet_spots: Where the model punches above weight or is the default pick
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not_ideal_for: Explicit anti-patterns
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capability_notes: Short free-text capability narrative
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swot:
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type: object
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description: Compact SWOT for reserve / ops decisions (2–4 bullets each)
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fields: [strengths, weaknesses, opportunities, threats]
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artifacts:
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type: list
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item_fields: [path, sha256, bytes]
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size:
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type: object
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fields: [total_bytes, total_human]
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hardware_class:
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type: object
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fields: [min_vram_gb_q4, min_vram_gb_fp16, notes]
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axes:
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type: list
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values: [A, B, C, D]
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priority:
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type: enum
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values: [low, medium, high]
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collection:
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type: object
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fields: [approved_by, approved_at, downloaded_at, downloaded_by, storage_path, brief_refs]
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reason:
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type: string
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tags:
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type: list
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companions:
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type: list
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notes:
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type: string
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history:
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type: list
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item_fields: [at, event, by, detail]
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