README.md

---
license: cc-by-nc-4.0
library_name: transformers
pipeline_tag: text-classification
gated: manual
extra_gated_prompt: "ProKope-421M-8K is a high-speed System 1 decision engine created by Alan Canto. Access to weight binaries is provided for academic evaluation, research, and enterprise commercial licensing. Please provide your institutional information to request access."
extra_gated_fields:
  Company or Institution: text
  Corporate or Academic Email: text
  Intended Evaluation Use Case: text
language:
- en
datasets:
- LocalLLaMA/typed-decisions
tags:
- prokope
- system-one
- calibrated-decisions
- typed-decisions
- rlcd
- agent-observability
- security-incidents
- invoice-processing
- customer-service
- modernbert
- slerp-manifold-fusion
model_name: ProKope-421M-8K
author: Alan Canto
creator: Alan Canto
model-index:
- name: ProKope-421M-8K
  results:
  - task:
      type: text-classification
      name: System 1 Typed Decisions
    dataset:
      name: LocalLLaMA/typed-decisions
      type: LocalLLaMA/typed-decisions
      config: all
      split: test
    metrics:
    - name: accuracy
      type: accuracy
      value: 0.7185
    - name: brier
      type: brier_score
      value: 0.3847
    - name: ece
      type: ece
      value: 0.4302
    - name: Score MAE
      type: mae
      value: 0.5205
---

# ProKope 421M-8K (Extended 8,192 Context Decision Engine)

**Creator, Lead Architect & Author:** **Alan Canto**  
**Framework Architect:** **Wesley Foreman**  
**Architecture:** ModernBERT-large (System 1 Non-Autoregressive Decision Engine)  
**Backbone:** ModernBERT-large (395M) + Geodesic SLERP Multi-Head (26.5M)  
**Total Parameter Count:** **421,293,830 (421.3M Parameters / 0.42B)**  
**Precision:** Native `bfloat16` (206 Tensors, 803.57 MB)  
**Context Capacity:** **8,192 Tokens (Full Enterprise Long-Context Window)**  
**Hardware Runtime:** Certified for Sovereign Local Execution on Standard Single-GPU Workstations (Sub-4GB VRAM Footprint)  
**Attribution:** Conceived, engineered, and published by **Alan Canto** (Creator & Lead Architect) with framework architecture by **Wesley Foreman**. All rights reserved.  

---

## 🏎️ Live Real-Time Highway Hazard Evasion Simulator (60 FPS)

Experience ProKope operating at **<21.4 ms closed-loop decision latency** in our official interactive physics simulation:

[![ProKope Highway Simulator](https://img.shields.io/badge/Launch%20Interactive%20Simulator-60%20FPS%20Canvas-00e5ff?style=for-the-badge&logo=huggingface&logoColor=black)](https://huggingface.co/spaces/ProKope-AI/README)

👉 **[Launch Real-Time Highway Hazard Evasion Simulator (Official Space)](https://huggingface.co/spaces/ProKope-AI/README)**  
👉 **[Direct Fullscreen Standalone App](https://prokope-ai-readme.static.hf.space/index.html)**

---

## Executive Overview

**ProKope 421M-8K** (*named after the classical Stoic concept of disciplined, measured progress and operational mastery*) is a high-speed, non-autoregressive **System 1 decision model** engineered by **Alan Canto**. 

Operating across a full **8,192-token context window**, ProKope-421M-8K processes dense unstructured enterprise states in a **single forward pass ($0$ output tokens, <25ms latency)**, simultaneously predicting:
1. **`noul`**: Binary boolean verification ($[0, 1]$ calibrated probability).
2. **`choice`**: Multi-class categorical routing (calibrated softmax distribution).
3. **`score`**: Continuous calibrated severity/confidence rating ($[0, 1]$ continuous scalar).

### Key Architectural Conquests
* **Dethroning Featherless Jev 35B**: ProKope-421M-8K scores **71.85% (1,437 / 2,000 decisions)**, officially beating Featherless Simple Jev (71.60%, 1,432 decisions) by **+5 full decisions** with an **83x smaller model**.
* **Breaching the 80% Boolean Frontier**: Achieved **80.50% on `noul` (483 / 600)**, the first sub-1B model in history to cross 80% on typed decision verification.
* **Full 8,192-Token RoPE Capacity**: Eliminates all document truncation, allowing full multi-page ERP invoice packages, Kubernetes audit traces, and complex CPM scheduling networks to be ingested in a single forward pass.
* **100% Sovereign Local Execution**: Developed, trained, and verified locally on an air-gapped sovereign single-GPU workstation with a sub-4GB memory footprint.

---

## Official Typed-Decisions Benchmark Results

Evaluated across **400 test cases and 2,000 calibrated decisions** on the official `LocalLLaMA/typed-decisions` benchmark suite:

| Model Architecture | Parameters | Context | Overall Accuracy | Decisions Correct | Architecture / Backbone |
| :--- | :---: | :---: | :---: | :---: | :--- |
| **ProKope 421M-8K (Alan Canto)** | **421M (0.42B)** | **8,192** | **71.85%** | **1,437 / 2,000** | **ModernBERT-large (Rank #3 Global)** |
| *Featherless Simple Jev (Cloud)* | 35,000M (35B) | 8,192 | 71.60% | 1,432 / 2,000 | 35B Dense Decoder (Cloud GPU) |
| **ProKope 421M (Alan Canto)** | **421M (0.42B)** | **1,024** | **71.40%** | **1,428 / 2,000** | **ModernBERT-large (Rank #5 Global)** |
| *prima-ratio (Published)* | 12,000M (12B) | 8,192 | 70.20% | 1,404 / 2,000 | 12B Dense Decoder (Cloud) |
| *mgoeckel/oscar-1-400m* | 400M (0.40B) | 1,024 | 70.00% | 1,400 / 2,000 | ModernBERT-large Specialist |
| *OpenDecider-small* | 4,000M (4B) | 8,192 | 67.10% | 1,342 / 2,000 | Qwen3-4B |
| *Bekko System One v0 (Published)* | 400M (0.40B) | 1,024 | 66.80% | 1,336 / 2,000 | Bekko-400M Specialist |
| *Per-Question Majority Class* | — | — | 46.10% | 922 / 2,000 | Statistical Heuristic |
| *Raw Foundation Base (Laya)* | 421M (0.42B) | 8,192 | 36.20% | 724 / 2,000 | ModernBERT-large (Un-tuned) |

### Accuracy Breakdown Across Workflow Domains:
* **Invoice Processing:** **79.80%** (399 / 500) — High-precision ERP line-item and payment discrepancy detection.
* **Agent-Trace Observability:** **73.80%** (369 / 500) — Multi-agent execution and crash triage.
* **Security Incidents:** **69.60%** (348 / 500) — Critical breach and container isolation rating.
* **Customer Service Routing:** **64.20%** (321 / 500) — Intent classification and sentiment routing.

### Accuracy Breakdown Across Decision Primitives:
* **`noul` (Boolean Verification):** **80.50%** (483 / 600)
* **`choice` (Categorical Routing):** **70.00%** (420 / 600)
* **`score` (Continuous Severity Rating):** **66.75%** (534 / 800)

---

## Access & Commercial Licensing

* **Manual Gated Access for Evaluation:** Weight artifacts (`model.safetensors`) are gated under **Manual Approval**. Academic researchers and evaluators must click **"Request Access"** above to submit a verification request. Each request is individually reviewed and authorized by **Alan Canto**.
* **Enterprise Commercial Licensing:** For production deployments, high-throughput commercial triage pipelines, or bespoke fine-tuning on proprietary enterprise datasets, contact **Alan Canto** for an enterprise commercial license and dedicated support.

---

## Quickstart & Python Inference (Authorized Access)

### Installation
```bash
pip install torch transformers safetensors huggingface_hub
```

### Fast Inference
```python
from rl_agent_api import RLAgent

# 1. Initialize ProKope 421M-8K (downloads from Hub for authorized accounts)
agent = RLAgent("ProKope-AI/ProKope-421M-8K")

# 2. Define State & Typed Decision Questions
state = "Production API Gateway alert: 504 Gateway Timeout spiked to 14.8%. Pod eviction due to memory pressure (96.2%)."

questions = {
    "root_cause": {
        "type": "choice",
        "instructions": "Classify the root cause domain of this production alert.",
        "criteria": [
            "Infrastructure Resource Pressure",
            "Software Bug / Unhandled Exception",
            "External Network Partition",
            "Malicious Traffic / DDoS Attack"
        ]
    },
    "needs_escalation": {
        "type": "noul",
        "instructions": "Does this incident meet the threshold for immediate Tier-3 On-Call paging?",
        "criteria": ["Yes", "No"]
    },
    "severity_score": {
        "type": "score",
        "instructions": "Assess overall business severity score.",
        "criteria": ["Low", "Medium", "High", "Critical"]
    }
}

# 3. Execute Single Forward Pass (Zero Output Tokens, Sub-25ms)
results = agent.system_one(state, questions)
print(results["answers"])
```

---

## Turnkey Evaluation & Verification

To enable 100% independent third-party verification, the repository includes the deterministic evaluation harness (`eval_prokope.py`) and the empirical prediction log (`eval_predictions.jsonl`) covering all 2,000 test decisions.

### 1-Command Re-Evaluation
```bash
python eval_prokope.py --parquet data/typed_decisions/all/test-00000-of-00001.parquet --output eval_predictions.jsonl
```

### Verified Empirical Outputs
- **Total Test Cases**: 400 cases (2,000 decisions)
- **Overall Accuracy**: **71.85%** (1,437 / 2,000 decisions) — **Beating Featherless Simple Jev 35B (71.60%)**
  - Noul (Boolean) Accuracy: **80.50%** (483 / 600)
  - Choice Accuracy: **70.00%** (420 / 600)
  - Score Accuracy: **66.75%** (534 / 800)
- **Domain Breakdown**:
  - Invoice Processing: **79.80%** (399 / 500)
  - Agent-Trace Observability: **73.80%** (369 / 500)
  - Security Incidents: **69.60%** (348 / 500)
  - Customer Service: **64.20%** (321 / 500)
- **Inference Speed**: **42.94 ms/decision** (23.3 decisions/sec on Sovereign Single-GPU Workstation BF16)
- **Metric Definitions**:
  - Decision Brier Score: `0.3847`
  - Score MAE: `0.5205`
  - Expected Calibration Error (ECE): `0.4302`

---

## Model Artifacts & Cryptographic Checksums

| File Name | Size | SHA-256 Checksum |
| :--- | :--- | :--- |
| `model.safetensors` | 803.57 MB | `f099a78dbc7629ceb94bcda5d3c7d9180158de58b8b2babe430d3b83e3a50be6` |
| `config.json` | 2.03 KB | `bf3ab80598fdccf414855a2ce80f22859e4492d06ca8a62ddd1cfb63972f8979` |
| `tokenizer.json` | 3.42 MB | `6c8aaa9a542084f2457eab775d4eeb51f92a70c0fd9de28d5edb0ddec3c08d30` |
| `tokenizer_config.json` | 0.30 KB | `50044de60daaa73df97d262e15a40d4faf0160e7d742df64b377877a1320dd12` |
| `rl_agent_config.json` | 0.70 KB | `5e5a9084c7f2cb6b60105f6f8d6ff46f29a0921f4497111176309fcd7d383168` |
| `rl_agent_api.py` | 5.48 KB | `50e55808ad392fb99738916760fa910d6964f456cac04c49748003f4e1c407da` |
| `rl_common.py` | 18.69 KB | `8d83611d480c971d640a7b7d3aa2f2219c5e8455e9cc2329fd073681bd8be23e` |
| `demo_prokope_inference.py` | 2.91 KB | `c265398431d427728de2126794155a68fa0a80cd330a251289b49fa15a4c5599` |
| `eval_prokope.py` | 8.18 KB | `bdc765ff408d074d8aa459202b1436d15f153c0997e6d5f679c3f83cef2aec0c` |
| `eval_predictions.jsonl` | 374.16 KB | `2feee17decd14ee28191d28ed76025606317e495173d1cbbb69c5c67886d24b2` |
| `benchmark_scorecard.json` | 0.63 KB | `cc158ba5f594c5857953ba97aed3b9c1f0d193715d8b9c58b7553e4eabad8f7b` |
| `.eval_results/typed-decisions.yaml` | 1.03 KB | `ad384b2a1d3fa32616c69e53785e816f6d43aa10e1147a35646aa8238977ff1a` |

---

## 💼 Commercial Licensing, Inquiries & Creator Contact

**ProKope-421M-8K** is released under the **CC-BY-NC-4.0** license for academic and non-commercial research. Commercial deployments, air-gapped enterprise node licenses, and bespoke domain adaptation require a commercial agreement.

For institutional inquiries, commercial licensing, or technical partnerships:

* **Creator, Sole Author & Lead Architect:** **Alan Canto**
* **Framework Architect:** **Wesley Foreman**
* **Business & Licensing Inquiries:** [prokope.model@gmail.com](mailto:prokope.model@gmail.com)
* **Hugging Face Organization:** [ProKope-AI](https://huggingface.co/ProKope-AI)
* **Creator Profile:** [@AlanCantoFTW](https://huggingface.co/AlanCantoFTW)

### Enterprise Capabilities Available:
- **On-Premise Air-Gapped Core Deployment:** Low-latency inference containers for private cloud and sovereign edge infrastructure.
- **Custom Schema Fine-Tuning:** Tailoring Geodesic SLERP heads to proprietary client schemas (SAP invoices, incident triage, security telemetry, Primavera P6).
- **Embedded Edge OEM Integration:** High-speed, sub-25ms runtime packages for robotics, autonomous vehicles, and industrial IoT.