# Phocinae Largha 150M V1 URL: https://interfaze.ai/models/phocinaephocinae-largha-150m-v1 [All models](https://interfaze.ai/models) Phocinae Largha 150M V1 by Phocinae, a text-classification model. Understand and compare features, benchmarks, and capabilities. ## Comparison | Feature | Phocinae Largha 150M V1 | Interfaze | | --- | --- | --- | | Input Modalities | text | image, text, audio, video, document | | Native OCR | No | Yes | | Long Document Processing | No | Yes | | Language Support | unknown | 162+ | | Native Speech-to-Text | No | Yes | | Native Object Detection | No | Yes | | Guardrail Controls | Yes | Yes | | Context Input Size | unknown | 1M | | Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search | ### Scaling | Feature | Phocinae Largha 150M V1 | Interfaze | | --- | --- | --- | | Scaling | Self-hosted/Provider-hosted with quantization | Unlimited | [Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs) View model card on [Hugging Face](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1) 斑海豹 **Largha**, the spotted seal: a **144.3M decision model** (en-first; zh evaluated on machine-translated cases) (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. Downloads: [Hugging Face](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1) · [GitHub](https://github.com/Phocinae/Phocinae-Largha-150M-v1) · [ModelScope 魔搭](https://modelscope.cn/models/PerryLink/Phocinae-Largha-150M-v1) · 中文: [FAQ 中文版](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/docs/faq.zh.md) · [场景演示画廊](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/docs/gallery/README_cn.md). ## TL;DR | TL;DR | value | | --- | --- | | parameters | 144.3M (mmBERT-small base: hidden 384 × 22 layers × 6 heads, 256k-vocab tokenizer, RoPE + sliding-window + full attention; base context 8192; decision sequence ≤512 tokens with a 192-token head attention window) | | typed-decisions en (400 cases / 2000 decisions) | 0.906 (specialist: fitted on this dataset's train split) — Laya 0.766 (self-measured, native interface) · JEV 0.727 (generalist, zero-shot) · meraGPT 0.768 (generalist, zero-shot) | | typed-decisions zh (machine-translated cases; training mix includes machine-translated Chinese ≈2,400 rows and native Chinese ≈1,400 rows) | 0.848 | | option-order flip robustness (lower is better) | CPU fp32: flip150 0.0200 · flip400 0.0217 · random-mean 0.0144 · any 0.0283 (GPU fp16 0.0200/0.0217, note only) | | inference latency | GPU fp16 p50 21.0 ms (RTX 5090) · CPU single-thread p50 1.64 s per case (1 case = 1 state + 5 questions, single forward pass) · CPU 8-thread batch 8–20 decisions/s | | JevBench public-231 | 0.5455 (126/231) — below the 58.4% gate, disclosed honestly; tool\_selection 12/12 (n=12) | | escalate routing (E1 gate, τ=0.6) | +0.0876 kept-subset acc (0.906→0.9936 (kept subset)) while −55.0% LLM calls (79.6% at τ=0.5) (45.0% official-set escalate; τ swept on the eval set — re-scan per domain) | | calibration | shipped column ECE 0.2519 (en) / 0.1941 (zh); with the bundled calibration column: 0.0168 / 0.0152; calibration temperatures 0.8660205/0.8081192/0.6624661 (applied at inference) | Full numbers, methodology, and evidence: **[BENCHMARKS.md](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/BENCHMARKS.md)**. ## See it in action **📽️ 全部 27 个场景演示 → [docs/gallery/](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/docs/gallery/)**（审批安全 · 路由省费 · 实时分级 · 办公文档 · 流程工程 · 对照与可靠性；中文版 [gallery/README\_cn.md](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/docs/gallery/README_cn.md)） ## Why Phocinae: slash agent costs **55.0% fewer LLM calls, 21.0 ms per decision on an RTX 5090.** 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 55.0% with kept-subset accuracy 0.906 → 0.9936 and full routed accuracy 0.8135; deterministic inference means auditable, replayable decisions with zero data leaving your machine — at 21.0 ms GPU p50 (RTX 5090) vs 1.5 s+ API round-trips. Full cost model: [docs/cost-savings.md](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/docs/cost-savings.md). ## Quick start — phocinae-server > `phocinae-server` ships the **runtime**, not the weights — fetch the weights first (step 1). The official runtime is **[phocinae-server](https://github.com/Phocinae/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](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/docs/protocol.md). Hardware tiers: [docs/deployment.md](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/docs/deployment.md). ``` hf download Phocinae/Phocinae-Largha-150M-v1 --local-dir ./largha pip install phocinae-server PHOC_MODEL_DIR=./largha python -m phocinae.main # http://127.0.0.1:8155 ``` The directory produced by that download is exactly the layout `PHOC_MODEL_DIR` expects (`model.safetensors`, `encoder/`, `tokenizer/`, `rl_agent_config.json`). From Python (the same engine the server uses): ``` from phocinae.engine import Engine eng = Engine("./largha", device="cpu") # device="auto" selects CUDA when present answers, confidence, action, usage = eng.run( "The agent restarted nginx after checking the logs and the health endpoint is green.", [{"id": "ok", "type": "noul"}, {"id": "act", "type": "choice", "options": ["allow", "ask", "deny"]}, {"id": "risk", "type": "score"}], ) ``` One decision over HTTP: ``` 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": false, "act": 0, "risk": 3}, "usage": {"input_tokens": 146, "output_tokens": 0}, "answer_confidence": {"ok": 0.668, "act": 0.409, "risk": 0.1633}, "action": {"act": {"act_probability": 0.1581}}, "routing": {"model": "Phocinae-Largha-150M-v1", "device": "cpu", "perm": "none", "backend": "phocinae-pure-torch"} } ``` > **Known limitation in tool routing.** `Router.route_tool()` confuses semantically close tool names. Reproducible case: state _"The user wants to find the latest news about the product launch."_ with the six tools `web_search / read_file / run_command / list_files / fetch_url / ask_user` returns **`fetch_url`**, not `web_search`, at confidence ≈0.33. Treat low-confidence tool picks as escalate-worthy rather than final. `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](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/BENCHMARKS.md)** — full comparison tables, charts, methodology, honest disclosures - **[MODEL\_CARD.md](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/MODEL_CARD.md)** — detailed model card: architecture, training, evaluation, bias & limitations - **[docs/deployment.md](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/docs/deployment.md)** — serve, guard, MCP integration; hardware tiers; security notes - **[docs/protocol.md](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/docs/protocol.md)** — the `/v1/systemone` decision protocol - **[docs/reproduce.md](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/docs/reproduce.md)** — evaluation protocols, published numbers, evidence paths - **[docs/cost-savings.md](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/docs/cost-savings.md)** — LLM-cost model for the escalate gate - **[docs/gallery/](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/docs/gallery/)** — 27 application scenarios with animated demos (中文: [gallery/README\_cn.md](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/docs/gallery/README_cn.md)) - **[calib/](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/calib/)** — optional post-hoc calibration column (power transform; ECE 0.2519/0.1941 → 0.0168/0.0152) - **[docs/faq.md](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/docs/faq.md)** — common questions (中文: [faq.zh.md](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/docs/faq.zh.md)) · **[docs/technical-report.md](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/docs/technical-report.md)** — short technical report ## 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: 0.5455 (126/231) 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 machine-translated cases; the training mix includes machine-translated Chinese (≈2,400 rows) and native Chinese (≈1,400 rows) — treat zh as an in-mix (fitted) evaluation, not zero-shot cross-lingual transfer. - Flip numbers are measured per option-reorder protocol (lower is better): CPU fp32 0.0200/0.0217 (flip150/400 reversed) · random-mean 0.0144 · any 0.0283. GPU fp16 0.0200/0.0217 and 1k-row 0.0181/0.0150/0.0331 are note-only values from different protocols. - Calibration: the shipped column has ECE **0.2519** (en). Calibration temperatures (0.8660205/0.8081192/0.6624661) are stored in the model repo config and applied at inference by phocinae-server. A recommended recalibration column is bundled under [`calib/`](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/calib/). ## Weights & license - `model.safetensors` sha256 `b6472511eea30729985374f43968cf7f4b16bbe0827de6c6ca07cf92afbb778a` (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](https://huggingface.co/datasets/LocalLLaMA/typed-decisions) (Apache-2.0, LocalLLaMA HF org) — protocol & test data - [mmBERT-small](https://huggingface.co/jhu-clsp/mmBERT-small) (JHU CLSP) — base encoder - [JevBench](https://github.com/fstandhartinger/JevBench) — held-out protocol used for disclosure ## Version & machine-readable sources - **This release**: `v1.1` — pinned tag on the model repository (`v1.0.0` marks the previous release), for reproducible citation. Resolve its commit with `git ls-remote --tags https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1`. (No commit hash is written into this file on purpose: it would go stale on the next commit.) - **Machine-readable facts** (same numbers as this card, for AI systems and retrieval pipelines): [llms.txt](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/llms.txt) - **Citation metadata**: [CITATION.cff](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/CITATION.cff) - **Landing page** (mirrors this card, with structured data): [https://phocinae.github.io/Phocinae-Largha-150M-v1/](https://phocinae.github.io/Phocinae-Largha-150M-v1/) ## Citation Machine-readable: [CITATION.cff](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/CITATION.cff). BibTeX: ``` @misc{phocinae2026largha, title = {Phocinae-Largha-150M-v1: A 144M-parameter bilingual typed decision model}, author = {Phocinae Project}, year = {2026}, url = {https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1}, license = {Apache-2.0} } ``` ## Revision history - **2026-10-09 — v1.1 refresh.** Weights upgraded (each metric in this card re-measured on the new weights; previous release sha256 `db79d5ee2f16597f34e564f5a4363bddb5b5bbd9827c01819725dabcc7802697`). Figures/gallery re-rendered; optional calibration column added under [`calib/`](https://huggingface.co/Phocinae/Phocinae-Largha-150M-v1/blob/main/calib/). - **2026-10-09 — docs.** Expanded quick start (weight download, Python & HTTP examples, tool-routing caveat); added landing page & machine-readable sources section. ## Want more deterministic results? [Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)