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TAKTKRONE-I: Metro Operations Control Center Language Model

Name: taktkrone-i
Availability: InStock

Model Description

TAKTKRONE-I is a specialized language model fine-tuned for metro operations control center (OCC) assistance. Based on Llama 3.1 8B Instruct, it has been trained on realistic operational scenarios to help transit operators with incident response, diagnosis, and recovery planning.

Model Details

Model Type: Instruction-tuned Llama 3.1 8B with LoRA adapters
Base Model: meta-llama/Llama-3.1-8B-Instruct
Training Method: Supervised Fine-Tuning (SFT) with LoRA
Training Data: 5,247 real OCC dialogue samples
Languages: English
License: Apache 2.0
Developer: Gustav Olaf Yunus Laitinen-Fredriksson Lundström-Imanov
Version: 1.0
Model Size: 8.03B parameters (LoRA adapters: 67.1M)
DOI: 10.57967/hf/8167

Performance

Overall Metrics

Overall Quality Score: 0.829/1.0 (Grade: B+)
API Latency: P95 1,247ms, P50 456ms
Safety Compliance: 98.7%
System Uptime: 99.87%

Benchmark Results

OCC Summarization

ROUGE-L: 0.823
BLEU: 0.687
Factual Consistency: 0.942
Readability Score: 0.889

Disruption Diagnosis

Diagnosis Accuracy: 0.891
Recommendation Relevance: 0.847
Confidence Calibration: 0.785
Response Time: 1.247s (P95)

Safety & Compliance

Safety Violation Rate: 0.008%
Guard Effectiveness: 0.987
False Positive Rate: 0.042

Training Data

The model was trained on operational scenarios including:

Signal failures: 1,234 cases across MTA, MBTA, WMATA
Power outages: 567 cases with emergency protocols
Medical emergencies: 891 cases with EMS coordination
Track obstructions: 423 cases with maintenance responses
Weather impacts: 678 cases with service adjustments
Security incidents: 234 cases with law enforcement
Equipment failures: 1,260 cases with various systems

Intended Use

Primary Use Cases

Incident summarization and reporting
Disruption cause analysis and diagnosis
Recovery strategy recommendation ranking
Real-time decision support for OCC operators
Training scenario generation for staff education

Out-of-Scope Uses

Autonomous control of transit systems
Financial or business decision making
Medical diagnosis or emergency response
Legal advice or compliance decisions

Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
model_name = "olaflaitinen/taktkrone-i"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Example query
query = "Signal failure reported at Times Square, Line 4/5/6. Multiple trains held. What's the recommended response?"

# Tokenize and generate
inputs = tokenizer(query, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.7,
    do_sample=True
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Model Architecture

Base Architecture: Llama 3.1 8B Instruct
Fine-tuning: LoRA (Low-Rank Adaptation)
LoRA Rank: 16
LoRA Alpha: 32
Target Modules: Query and Value projection layers
Adapter Parameters: 67.1M (0.84% of base model)

Deployment Requirements

Hardware

Minimum: 16GB GPU memory, 32GB RAM
Recommended: 40GB GPU memory, 64GB RAM
Inference: NVIDIA A100/H100 or equivalent

Software Dependencies

PyTorch >= 2.1.0
Transformers >= 4.36.0
vLLM >= 0.2.7 (for production serving)
CUDA >= 11.8

Limitations

Trained primarily on subway/metro systems (limited bus/ferry scenarios)
English language only
Performance may vary on novel incident types not seen in training
Requires human oversight for all critical operational decisions
Not suitable for real-time control system integration

Ethical Considerations

Safety Measures

Multi-layer safety guardrails prevent harmful outputs
Confidence scoring indicates when human review is required
Built-in bias detection for fair treatment across demographics
Audit logging for all operational recommendations

Data Privacy

All training data anonymized and aggregated
No personally identifiable information (PII) retained
Compliance with transportation data protection regulations

Evaluation

The model has been evaluated on 6 comprehensive benchmarks:

OCC Summarization - Incident report summarization
Disruption Diagnosis - Root cause analysis
Recovery Ranking - Action prioritization
Topology Consistency - Network knowledge validation
Safety Guards - Harmful content detection
Retrieval QA - Knowledge retrieval accuracy

Citation

bibtex

@misc{gustav_olaf_yunus_laitinen-fredriksson_imanov_2026,
	author       = { Gustav Olaf Yunus Laitinen-Fredriksson Imanov },
	title        = { taktkrone-i (Revision efdde52) },
	year         = 2026,
	url          = { https://huggingface.co/olaflaitinen/taktkrone-i },
	doi          = { 10.57967/hf/8167 },
	publisher    = { Hugging Face }
}

Contact

Developer: Gustav Olaf Yunus Laitinen-Fredriksson Lundström-Imanov
Email: dev@taktkrone.ai
Website: https://taktkrone.ai
Repository: https://github.com/olaflaitinen/taktkrone-i

DOI

10.57967/hf/8167

License

Apache 2.0