README.md

metadata

language:
  - en
  - zh
tags:
  - modernbert
  - reranker
  - cross-encoder
  - agent
  - decision-making
  - zero-shot-classification
  - fast-decider
license: apache-2.0
pipeline_tag: text-classification
library_name: transformers
metrics:
  - accuracy
  - brier_score

FastDecider-149M (v1.3.0)

Sub-15ms Neural Decision Engine for Autonomous Agents & Industrial Automation

English | 简体中文

---

Overview

FastDecider-149M is a lightweight (149M parameter, ~285MB VRAM) neural cross-encoder purpose-built as a System-1 (fast-thinking) decision engine for autonomous AI agents, web/DOM automation, API routing, and high-frequency dispatch.

While general-purpose LLMs (7B–70B) suffer from 300–2,000ms latency and autoregressive token generation overhead, FastDecider-149M leverages full bidirectional cross-attention across context, instructions, and candidate options. It delivers deterministic, calibrated decision probabilities in under 15 milliseconds at $0.0012 per 1,000 decisions—over 50x faster and 500x cheaper than commercial generative LLMs.

---

Benchmark & Performance

Official JevBench Leaderboard Ranking

FastDecider-149M achieves a composite score of 78.15 on the standard JevBench benchmark (231 tasks), leading the composite efficiency leaderboard by pairing solid operational accuracy with state-of-the-art speed and cost efficiency.

Detailed Scorecard

Evaluation Axis	Score / Metric	Industry Tier	Notes
Easy Tier Accuracy	100.00% (48/48)	Perfect	Full coverage on intent, extraction, tool pick
Hard Tier Accuracy	44.14% (49/111)	Competitive	100% on complex routing & business tradeoffs
Original Baseline	68.06% (49/72)	Solid	Natural language decision benchmarks
Intelligence Score	65.35 / 100	High Utility	149M bidirectional representation capacity
Speed Score	99.22 / 100	Top 0.1%	P50 = 14.93 ms, P99 = 17.8 ms
Cost Score	97.62 / 100	Top 0.1%	$0.0012 / 1k decisions
Calibration Score	58.92 / 100	Calibrated	Brier score = 0.6751, monotonic probabilities
★ JevBench Composite	78.15 / 100	Rank #1	Geometric mean across Intel, Calib, Speed, Cost

Industrial Domain Verification (6 Pillars)

FastDecider-149M was evaluated across six real-world industrial automation tasks:

======================================================================
INDUSTRIAL DOMAIN                        | ACCURACY     | TEST SET 
======================================================================
Browser DOM Control (XPath / Elements)   | 100.0%       | 50/50
API Dispatch / Function Routing          |  98.0%       | 49/50
E-Commerce Brand Extraction              | 100.0%       | 50/50
E-Commerce Specs & Attributes            | 100.0%       | 50/50
E-Commerce Category Classification       |  94.0%       | 47/50
Legal Contract Clause Matching           |  96.0%       | 48/50
======================================================================

---

Quickstart

Installation

git clone https://github.com/mkeco/fast-decider-149m.git
cd fast_decider_149m
pip install -e .

Python API

from fast_decider import FastDecider

# Initialize the 149M decision engine (BF16, ~285MB VRAM)
decider = FastDecider("mkzero/FastDecider-149M")

# Example: Real-time Web Browser DOM Action Selection
result = decider.decide(
    context="User wants to complete purchase. Cart contains 2 items. Page displays modal with coupon prompt.",
    instruction="Select the primary checkout button to proceed.",
    options={
        "checkout_btn": "button.btn-primary-checkout",
        "coupon_apply": "button.btn-apply-coupon",
        "continue_shop": "a.link-continue-shopping",
        "clear_cart": "button.btn-danger-clear"
    }
)

print(f"Selected Action : {result.best_option}")
print(f"Confidence      : {result.confidence:.2%}")
print(f"Inference Time  : {result.latency_ms:.2f} ms")

High-Throughput HTTP Microservice (FastAPI)

# Launch the ASGI microservice on port 8000
python -m uvicorn fast_decider.server:app --host 0.0.0.0 --port 8000

---

Reproducibility & Benchmark Suite

All benchmark figures reported above are fully reproducible with the provided scripts:

# 1. Run official JevBench CLI evaluation (231 tasks)
bash benchmarks/run_jevbench_official.sh

# 2. Run the 6 industrial domain verification
python benchmarks/verify_6_pillars.py

# 3. Benchmark latency and throughput
python benchmarks/benchmark_latency.py

# 4. Run test suite
pytest tests/

---

Support the Project / Buy Me a Coffee ☕

If FastDecider-149M is helpful to your work, agent framework, or research, feel free to sponsor or buy the author a cup of coffee!

Thank you for supporting open-source software!

---

License

This project is licensed under the Apache 2.0 License - see the LICENSE file for details.