Phocinae-Largha-150M-v1

Tiny model. Big decisions. — 小海豹，大决断。· 一斑见全豹，一点定全局。

斑海豹 Largha, the spotted seal: a 144.3M bilingual decision model (150M-class) for structured decisions — one forward pass per decision, on your own hardware. Not a chat model: it takes a state plus a list of typed questions (noul yes/no · choice pick-one · score 2–10) and returns calibrated answers with confidence, robust to option reordering.

Quick start — phocinae-server

The official runtime is phocinae-server: a local FastAPI service (127.0.0.1 only) that loads these weights with a pure PyTorch forward (no extra runtime needed). Full spec: docs/protocol.md.

pip install phocinae-server
PHOC_MODEL_DIR=/path/to/Phocinae-Largha-150M-v1 python -m phocinae.main   # http://127.0.0.1:8155

One decision:

curl -s http://127.0.0.1:8155/v1/systemone -H 'Content-Type: application/json' -d '{
  "model": "Phocinae-Largha-150M-v1",
  "state": "The agent restarted nginx after checking the logs and the health endpoint is green.",
  "questions": [
    {"id": "ok",   "type": "noul",   "threshold": 0.65},
    {"id": "act",  "type": "choice", "options": ["allow", "ask", "deny"]},
    {"id": "risk", "type": "score"}
  ]
}'

{
  "model": "Phocinae-Largha-150M-v1",
  "answers": {"ok": true, "act": 0, "risk": 2},
  "usage": {"input_tokens": 24, "output_tokens": 0},
  "answer_confidence": {"ok": 0.91, "act": 0.72, "risk": 0.18},
  "action": {"act": {"act_probability": 0.72}},
  "routing": {"model": "Phocinae-Largha-150M-v1", "device": "cpu", "perm": "none", "backend": "phocinae-pure-torch"}
}

answers values: noul = bool · choice = 0-based option index · score = integer 2–10. Errors: 422 (unknown model/type, >64 questions, choice without options, score with options, threshold outside [0,1]) · 413 (>2 MiB body) · 401 (bearer token). Extension keys answer_confidence / action / routing can be disabled with PHOC_EXTENSIONS=0.

Documentation

• BENCHMARKS.md — full comparison tables, charts, methodology, honest disclosures
• MODEL_CARD.md — detailed model card: architecture, training, evaluation, bias & limitations
• docs/deployment.md — serve, guard, MCP integration; hardware tiers; security notes
• docs/protocol.md — the /v1/systemone decision protocol
• docs/reproduce.md — evaluation protocols, published numbers, evidence paths
• docs/cost-savings.md — LLM-cost model for the escalate gate
• docs/gallery/ — real application scenarios with animated demos
• docs/faq.md — common questions (中文: faq.zh.md) · docs/technical-report.md — short technical report

Why Phocinae: slash agent costs

82% fewer LLM calls, 18.6 ms per decision. Phocinae-Largha-150M (144.3M params) routes the repetitive decisions inside agent sessions — command approvals, tool selection (12/12 on JevBench tool_selection, k≤10), step checks, output screening — to a local single-forward-pass engine (8192-token base context, 512-token default head) instead of a 500–4,000-token API call. The τ=0.6 confidence gate cuts LLM traffic 82% while combined accuracy edges up 0.789 → 0.7948; deterministic inference means auditable, replayable decisions with zero data leaving your machine — at 18.6 ms GPU p50 vs 1.5 s+ API round-trips. ≈17.2M LLM tokens ≈ $492/yr saved per 10k routed decisions/month (Claude Sonnet 5 list prices, Oct 2026). Full cost model: docs/cost-savings.md.

What it is / what it is not

• For: structured decisions — approval gates, tool routing, escalation, document triage, step checks, output screening; anywhere you want a fast, cheap, local decision layer instead of a large model.
• Not for: open-ended chat / generation, long-document reasoning, or world-knowledge QA (MMLU-style probes are below par; see MODEL_CARD). Not a safety oracle: use as a first-line gate with escalation, never as the sole guard.

Honest disclosures

• JevBench public-231: 0.5108 (118/231) vs a 58.4% acceptance gate — not passed. We publish the number as measured, and we never train on the eval rows.
• zh results are on translated cases (no native Chinese training rows).
• Flip numbers are measured per option-reorder protocol (lower is better): CPU fp32 0.0300/0.0300 (flip150/400 reversed) · random-mean 0.0233 · any 0.0433. GPU fp16 0.027/0.028 and 1k-row 0.0187/0.0205/0.0431 are note-only values from different protocols.
• Calibration: the shipped column has ECE 0.1313 (en). Calibration temperatures (0.7698/0.7879/0.7559) are stored in the model repo config and applied at inference by phocinae-server. A recommended recalibration column reaching 0.0106 is not shipped.

Weights & license

• model.safetensors sha256 db79d5ee2f16597f34e564f5a4363bddb5b5bbd9827c01819725dabcc7802697 (fp16 storage, 144.3M params); trained from jhu-clsp/mmBERT-small on LocalLLaMA/typed-decisions (train split + flip-augmented reorderings), recipe in rl_agent_config.json.
• Apache-2.0 (see LICENSE); base encoder jhu-clsp/mmBERT-small is MIT (see NOTICE). · Repo layout: model.safetensors · encoder/ · tokenizer/ · rl_agent_config.json · checkpoint_meta.json · figures/ · docs/.

Credits

• typed-decisions (Apache-2.0, LocalLLaMA HF org) — protocol & test data
• mmBERT-small (JHU CLSP) — base encoder
• JevBench — held-out protocol used for disclosure