README.md

metadata

license: cc-by-nc-4.0
library_name: transformers
pipeline_tag: text-classification
gated: manual
extra_gated_prompt: >-
  Access to ProKope-421M weights is provided for academic evaluation,
  benchmarking, and commercial licensing inquiries. Please provide your
  affiliation and intended use case 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:
  - text-classification
  - typed-decisions
  - modernbert
  - prokope
model_name: ProKope-421M
author: Alan Canto
creator: Alan Canto

ProKope-421M

Introduction

ProKope-421M is a 421M-parameter non-autoregressive classification and scoring model developed by ProKope AI, based on ModernBERT-large with a 1,024-token context window. It evaluates structured state inputs in a single forward pass across three typed decision primitives:

1. noul: Binary boolean classification ([0, 1] probability).
2. choice: Multi-class categorical routing.
3. score: Ordinal severity and confidence estimation ([0, 1] scalar).

For 8,192-token context workloads, see ProKope-421M-8K.

---

Evaluation

Evaluated across 400 test cases (2,000 total decisions) on the LocalLLaMA/typed-decisions benchmark:

Model	Parameters	Context	Overall Accuracy	Decisions Correct	Brier Score
ProKope-421M-8K	421M	8,192	71.85%	1,437 / 2,000	0.384 (raw)
Featherless Simple Jev	35B	8,192	71.60%	1,432 / 2,000	0.110
ProKope-421M	421M	1,024	71.40%	1,428 / 2,000	0.071 (calibrated)
prima-ratio	12B	8,192	70.20%	1,404 / 2,000	0.125
mgoeckel/oscar-1-400m	400M	1,024	70.00%	1,400 / 2,000	0.062
OpenDecider-small	4B	8,192	67.10%	1,342 / 2,000	0.117
Bekko System One v0	400M	1,024	66.80%	1,336 / 2,000	0.113

Domain Breakdown (ProKope-421M: 71.40% / 1,428 / 2,000)

• Invoice Processing: 79.00% (395 / 500)
• Agent-Trace Observability: 73.20% (366 / 500)
• Security Incidents: 69.20% (346 / 500)
• Customer Service Routing: 64.20% (321 / 500)

Decision Primitive Breakdown

• noul (Binary Decision): 79.67%
• choice (Multi-Class Routing): 69.67%
• score (Ordinal Severity): 66.50%

---

Model Details

Property	Value
Backbone	ModernBERT-large (28 bidirectional encoder layers, 1,024 hidden size) + 2-layer multi-head decision projection
Total Parameters	421,293,830 (803.57 MB in bfloat16)
Context Window	1,024 tokens (head_max_len: 256 tokens)
Throughput	23.5 decisions/sec (42 ms/decision on single GPU)
Recommended VRAM	2 GB+ for bfloat16 / float16 inference
Precision	torch.bfloat16
License	CC-BY-NC-4.0 (Commercial licensing available via ProKope AI)

---

Quickstart

1. Python Inference (RLAgent)

from rl_agent_api import RLAgent

agent = RLAgent("ProKope-AI/ProKope-421M")

state = (
    "Production API Gateway alert: 504 Gateway Timeout spiked to 14.8% over the last 3 minutes. "
    "Worker node 4 pod eviction reported 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"]
    }
}

results = agent.system_one(state, questions)
for key, ans in results["answers"].items():
    print(key, "->", ans)

2. Reproducing the Benchmark Evaluation

python eval_prokope.py

---

Limitations & Intended Use

• Intended Use: Low-latency structured classification, incident triage, workflow routing, and calibrated probability estimation over bounded decision schemas (noul, choice, score).
• Limitations: ProKope-421M is a non-autoregressive encoder-classifier and does not generate free-form text. Inputs exceeding the 1,024-token context window are truncated.

---

Contact & Citation

• Organization: ProKope AI
• Authors: Alan Canto, Wesley Foreman
• Inquiries & Commercial Licensing: prokope.model@gmail.com

@misc{canto2026prokope421m,
  author       = {Alan Canto and Wesley Foreman},
  title        = {ProKope-421M: Non-Autoregressive Structured Typed Decision Model},
  howpublished = {Hugging Face},
  year         = {2026},
  url          = {https://huggingface.co/ProKope-AI/ProKope-421M}
}