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ML Model Comparison Guide

🔴 CURRENT DECISIONS (Feb 2026)

These are the active, validated choices for the system. Everything else in this document is context and reference.

✅ Dev ML Authority (Smoke / Fast Feedback)

  • MAP: facebook/bart-base
  • REDUCE: allenai/led-base-16384
  • Status: Stable, fast, smoke-validated
  • Use when: local development, iteration, debugging

✅ Prod ML Authority (Benchmark-validated)

  • MAP: google/pegasus-cnn_dailymail
  • REDUCE: allenai/led-base-16384
  • Status: Benchmark-validated, clean gates, stable output
  • Use when: production ML summarization

🟡 LongT5 (8k context) — MAP option (RFC-042 / Issue #353)

  • Models: google/long-t5-tglobal-base (alias longt5-base), google/long-t5-tglobal-large (alias longt5-large)
  • Context window: 8,192 tokens (between BART/PEGASUS 1k and LED 16k)
  • Use when: MAP compression for medium-long transcripts (2k–8k tokens) where LED is overkill

🟣 Hybrid MAP-REDUCE (RFC-042 / Issue #352)

  • Provider: summary_provider: hybrid_ml
  • MAP: classic summarizer (recommended default: longt5-base, fallback to LED for very long)
  • REDUCE: instruction-tuned model (Tier 1: google/flan-t5-base via transformers; Tier 2: via Ollama)

⚠️ Any change to preprocessing, chunking, or generation semantics requires a new baseline version.

This file is intentionally minimal at the top. The remainder of the guide continues with the detailed comparison tables, rationale, and historical context unchanged from v2.