docs: sync README with official GitHub repo https://github.com/mkeco/fast-decider-149m

README.md

@@ -16,83 +16,155 @@ library_name: transformers
 metrics:
 - accuracy
 - brier_score
-model-index:
-- name: FastDecider-149M
-  results:
-  - task:
-      type: text-classification
-      name: Agent Decision Ranking
-    dataset:
-      name: JevBench v1.2.11
-      type: jevbench
-    metrics:
-    - type: accuracy
-      value: 100.0
-      name: Easy Tier Accuracy
-    - type: accuracy
-      value: 44.14
-      name: Hard Tier Accuracy
-    - type: accuracy
-      value: 68.06
-      name: Original Tier Accuracy
 ---
 
-# FastDecider-149M (v1.3.0): Sub-15ms System-1 Neural Decision Engine
+# FastDecider-149M (v1.3.0)
 
-**FastDecider-149M** is a high-speed (14.93ms P50 latency), 149M-parameter neural Cross-Encoder designed for autonomous agents, browser DOM interaction, API routing, and high-throughput operational decisions.
+<p align="center">
+  <strong>Sub-15ms Neural Decision Engine for Autonomous Agents & Industrial Automation</strong>
+</p>
 
-## Benchmark Performance
+<p align="center">
+  <a href="https://github.com/mkeco/fast-decider-149m/releases/tag/v1.3.0"><img src="https://img.shields.io/badge/Release-v1.3.0-blue.svg" alt="Release"></a>
+  <a href="LICENSE"><img src="https://img.shields.io/badge/License-Apache_2.0-blue.svg" alt="License"></a>
+  <img src="https://img.shields.io/badge/Parameters-149M_(285MB)-success.svg" alt="Parameters">
+  <img src="https://img.shields.io/badge/P50_Latency-14.93ms-brightgreen.svg" alt="Latency">
+  <img src="https://img.shields.io/badge/JevBench_Composite-78.15-orange.svg" alt="JevBench Composite">
+  <img src="https://img.shields.io/badge/Framework-PyTorch_%7C_ModernBERT-blueviolet.svg" alt="Framework">
+</p>
 
-- **JevBench Easy Tier**: **100.0%** (48/48)
-- **JevBench Hard Tier**: **44.14%** (49/111)
-- **JevBench Original Tier**: **68.06%** (49/72)
-- **JevBench Composite Score**: **78.15 / 100** (Rank #1 Composite)
-- **P50 Latency**: **14.93 ms** on GPU
-- **Cost**: **$0.0012** per 1,000 decisions
-- **6 Core Business Pillars**:
-  - Browser DOM Control: 100.0% (50/50)
-  - API Tool Dispatch: 98.0% (49/50)
-  - E-Commerce Brands: 100.0% (50/50)
-  - E-Commerce Specs: 100.0% (50/50)
-  - E-Commerce Category: 94.0% (47/50)
-  - Legal Contracts: 96.0% (48/50)
+<p align="center">
+  <a href="README.md">English</a> | <a href="README_zh.md">简体中文</a>
+</p>
 
-## Usage with Transformers
+---
+
+## 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.
+
+<p align="center">
+  <img src="docs/images/leaderboard_verified.png" width="880" alt="JevBench Official Benchmark Leaderboard">
+</p>
+
+### 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:
+
+```text
+======================================================================
+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
+
+```bash
+git clone https://github.com/mkeco/fast-decider-149m.git
+cd fast_decider_149m
+pip install -e .
+```
+
+### Python API
 
 ```python
-import torch
-import torch.nn.functional as F
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-
-model_id = "mkzero/FastDecider-149M"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForSequenceClassification.from_pretrained(
-    model_id, dtype=torch.bfloat16
-).to("cuda")
-model.eval()
-
-context = "User wishes to cancel order #4821 within 2 hours of checkout."
-instruction = "Select the appropriate API handler."
-options = {
-    "cancel_order": "orders.cancel_immediate",
-    "request_return": "returns.create_ticket",
-    "contact_support": "support.live_agent"
-}
-
-pairs = [(f"Context:
-{context}
-
-Instruction:
-{instruction}", f"Option {k}: {v}") for k, v in options.items()]
-inputs = tokenizer(pairs, padding=True, truncation=True, max_length=2048, return_tensors="pt").to("cuda")
-
-with torch.no_grad():
-    logits = model(**inputs).logits.squeeze(-1).float()
-    probs = F.softmax(logits, dim=-1).cpu().numpy()
-
-keys = list(options.keys())
-print("Decision:", keys[probs.argmax()])
+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)
+
+```bash
+# Launch the ASGI microservice on port 8000
+python -m uvicorn fast_decider.server:app --host 0.0.0.0 --port 8000
 ```
 
-## GitHub Repository
-Source code, benchmarks, and microservice serving: [https://github.com/mkzero/FastDecider-149M](https://github.com/mkzero/FastDecider-149M)
+---
+
+## Reproducibility & Benchmark Suite
+
+All benchmark figures reported above are fully reproducible with the provided scripts:
+
+```bash
+# 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!
+
+<p align="center">
+  <img src="docs/images/buy_me_a_coffee.jpg" width="260" alt="Buy Me a Coffee">
+  <br>
+  <em>Thank you for supporting open-source software!</em>
+</p>
+
+---
+
+## License
+
+This project is licensed under the Apache 2.0 License - see the [LICENSE](LICENSE) file for details.