id: nomic-ai__nomic-embed-text-v1.5__candidate
status: candidate
name: nomic-embed-text-v1.5
org: nomic-ai
source:
  kind: huggingface
  url: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
  revision: main
  model_card_url: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
  project_url: https://www.nomic.ai/
license:
  spdx: Apache-2.0
  url: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
  allows_offline_retention: true
  allows_local_ops: true
  allows_fine_tune: true
  notes: ""
profile:
  summary: "Lightweight open text embedder for A/B with BGE-M3 and long-context retrieval experiments."
  original_source: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
  use_cases:
    - "Secondary / A/B embedding path vs BGE-M3"
    - "English-heavy RAG with low resource cost"
    - "Long-context embedding experiments (per card capabilities)"
    - "Portable laptop offline search indexes"
  sweet_spots:
    - "Small footprint Apache embed"
    - "Fast iteration when re-embedding corpora often"
    - "Nomic tooling and Matryoshka-style dimension flexibility (per card)"
  not_ideal_for:
    - "Sole multilingual production embed without A/B (prefer BGE-M3 default)"
    - "Generation tasks"
    - "Code-only retrieval (prefer code embed if held)"
  capability_notes: >
    Compact Nomic text embedding model. Held as runnable dual-path to avoid
    single-vendor embed lock-in and to re-eval retrieval quality cheaply.
    Prefer current v2 text card at pull if clearly better.
swot:
  strengths:
    - "Tiny, Apache-2.0, easy to re-run indexes"
    - "Diversifies embed dependency next to BGE-M3"
  weaknesses:
    - "May lose multilingual or domain A/B vs BGE-M3"
    - "Version churn (v1.5 vs v2) needs careful pin"
  opportunities:
    - "Dimension-reduced storage for large corpora"
    - "Hybrid ensembles (BGE + nomic signals)"
  threats:
    - "Becomes redundant if one embed wins all lab evals"
    - "Upstream rename/deprecation of v1.5"
size:
  total_bytes: 0
  total_human: "<1 GB"
hardware_class:
  min_vram_gb_q4: 1
  min_vram_gb_fp16: 1
  notes: "CPU-friendly"
axes: [B]
priority: high
collection:
  approved_by: ""
  approved_at: null
  downloaded_at: null
  downloaded_by: ""
  storage_path: ""
  brief_refs:
    - research/2026-07-24-baseline-field-survey.md
reason: "P0/R lightweight embed for A/B with BGE-M3; long-context text retrieval."
tags: [tier-r, embedding, rag]
companions:
  - BAAI__bge-m3__candidate
notes: "If v2 text is preferred at collection time, update id/url and supersede this candidate."
history:
  - at: "2026-07-24"
    event: nominated
    by: baseline-survey
    detail: "P0 recommendation from initial deep research."
  - at: "2026-07-24"
    event: profile_swot_added
    by: grok
    detail: "schema 0.2 profile + SWOT."