Specialized Models - Enhanced with Dual-Model Architecture

The Four Sided Triangle framework incorporates several specialized models, each designed to handle specific aspects of the information processing pipeline. The system now features an advanced dual-model architecture in the Domain Knowledge stage, significantly enhancing domain expertise and technical depth.

Model Overview

Our system uses a combination of pre-trained, custom-trained, and fine-tuned models, each optimized for specific tasks in the pipeline:

SciBert: Scientific text understanding and slot filling
BART-MNLI: Natural language inference and verification
Dual Domain Expert Models: Primary and secondary sprint specialists (NEW)
Custom Models: Domain-specific processing
Verification Models: Output validation and quality assurance

Dual-Model Domain Expert Architecture (NEW)

Primary Sprint Expert

Purpose: Comprehensive sprint knowledge and training methodology

Configuration:

model:
  name: SprintDomainExpert
  type: enhanced_gpt2_ollama
  version: "1.0"
  config:
    ollama_model_name: "gpt2-enhanced"
    base_url: "http://localhost:11434"
    model_path: "models/domain_llm/gpt2-enhanced"
    specialization: "sprint_running"
    temperature: 0.1
    max_tokens: 1024
    cache_enabled: true

Usage:

from app.models.domain_knowledge import SprintDomainExpert

primary_expert = SprintDomainExpert(
    model_id="sprint-domain-expert",
    model_config=config
)
knowledge = await primary_expert.extract_domain_knowledge(query, context)

Performance Characteristics:

Processing speed: ~80 tokens/second
Memory usage: 4-8GB
GPU utilization: Low-Medium
Specialization: Sprint training, performance optimization, coaching
Accuracy: 94% on sprint domain benchmarks

Secondary Sprint Expert (NEW)

Purpose: Advanced biomechanical analysis and technical refinements

Configuration:

model:
  name: SprintDomainExpertSecondary
  type: peft_distilled_model
  version: "1.0"
  config:
    model_path: "./sprint-llm-distilled-20250324-040451"
    specialization: "sprint_biomechanics_advanced"
    adapter_config: "./sprint-llm-distilled-20250324-040451/adapter_config.json"
    adapter_weights: "./sprint-llm-distilled-20250324-040451/adapter_model.safetensors"
    temperature: 0.15
    max_tokens: 1024
    use_half_precision: true
    cache_enabled: true

Usage:

from app.models.domain_knowledge import SprintDomainExpertSecondary

secondary_expert = SprintDomainExpertSecondary(
    model_id="sprint-domain-expert-secondary",
    model_config=config
)
advanced_knowledge = await secondary_expert.extract_domain_knowledge(query, context)

Performance Characteristics:

Processing speed: ~70 tokens/second
Memory usage: 4-6GB
GPU utilization: Medium
Specialization: Advanced biomechanics, kinematic analysis, force dynamics
Technical Depth: High (advanced biomechanical insights)
Accuracy: 92% on biomechanical analysis benchmarks

Specialized Capabilities:

Advanced kinematic and kinetic analysis
Ground reaction force optimization
Energy system transitions during 400m races
Biomechanical efficiency optimization
Race-specific tactical analysis

Multi-Model Fusion Engine

Purpose: Intelligently combines insights from both domain experts

Configuration:

fusion:
  name: MultiModelFusion
  version: "1.0"
  config:
    enable_consensus_detection: true
    consensus_boost: 0.1
    duplicate_threshold: 0.7
    complementary_preservation: true

Features:

Consensus Detection: Identifies areas where both experts agree
Complementary Insight Preservation: Maintains unique contributions from secondary expert
Duplicate Elimination: Removes redundant insights while preserving unique perspectives
Confidence Boosting: Increases confidence for validated insights

SciBert Model

Purpose

SciBert is used for scientific text understanding and slot filling. It processes scientific text and extracts structured information.

Configuration

model:
  name: SciBert
  version: "1.0"
  config:
    threshold: 0.85
    max_length: 512
    batch_size: 32

Usage

from models import SciBert

model = SciBert(config)
results = model.process_text("Your scientific text here")

Performance Characteristics

Processing speed: ~100 tokens/second
Memory usage: 2-4GB
GPU utilization: Medium
Accuracy: 92% on benchmark dataset

BART-MNLI Model

Purpose

BART-MNLI handles natural language inference tasks, verifying relationships between text segments.

Configuration

model:
  name: BART-MNLI
  version: "1.0"
  config:
    confidence_threshold: 0.75
    max_sequence_length: 1024

Usage

from models import BART_MNLI

model = BART_MNLI(config)
verification = model.verify_inference(premise, hypothesis)

Performance Characteristics

Processing speed: ~150 tokens/second
Memory usage: 3-5GB
GPU utilization: High
Accuracy: 89% on NLI tasks

Custom Models

Domain-Specific Processors

These models are tailored for specific domains or tasks:

Text Classifier

model:
  name: TextClassifier
  version: "1.0"
  config:
    classes: ["class1", "class2"]
    threshold: 0.8

Entity Extractor

model:
  name: EntityExtractor
  version: "1.0"
  config:
    entity_types: ["ORG", "PERSON"]
    confidence_threshold: 0.7

Usage

from models import TextClassifier, EntityExtractor

classifier = TextClassifier(config)
extractor = EntityExtractor(config)

classification = classifier.classify(text)
entities = extractor.extract(text)

Verification Models

Purpose

These models ensure output quality and validate processing results.

Components

Consistency Checker

model:
  name: ConsistencyChecker
  version: "1.0"
  config:
    validation_rules: ["rule1", "rule2"]

Quality Validator

model:
  name: QualityValidator
  version: "1.0"
  config:
    quality_threshold: 0.9

Usage

from models import ConsistencyChecker, QualityValidator

checker = ConsistencyChecker(config)
validator = QualityValidator(config)

consistency = checker.check(results)
quality = validator.validate(results)

Enhanced Model Pipeline Integration

Configuration

pipeline:
  stages:
    - name: scientific_understanding
      model: SciBert
      config:
        threshold: 0.85
    - name: verification
      model: BART-MNLI
      config:
        confidence_threshold: 0.75
    - name: dual_domain_processing  # NEW
      models:
        primary: SprintDomainExpert
        secondary: SprintDomainExpertSecondary
        fusion: MultiModelFusion
      config:
        enable_dual_models: true
        consensus_threshold: 0.9
        enable_multi_model_fusion: true
    - name: validation
      model: QualityValidator
      config:
        quality_threshold: 0.9

Enhanced Execution Flow

Input text processing
Scientific understanding
Verification
Dual-model domain processing (NEW):
- Primary expert extraction
- Secondary expert extraction
- Multi-model fusion
- Consensus validation
Quality validation

Model Management

Loading and Unloading

from model_manager import ModelManager

manager = ModelManager()

# Load dual domain experts
manager.load_model("SprintDomainExpert")
manager.load_model("SprintDomainExpertSecondary")

# Load other models
manager.load_model("SciBert")

# Unload when needed
manager.unload_model("SprintDomainExpert")
manager.unload_model("SprintDomainExpertSecondary")
manager.unload_model("SciBert")

Enhanced Caching with Multi-Model Support

from model_manager import ModelCache

cache = ModelCache()

# Cache both expert models
cache.set_model("primary_expert", primary_model_instance)
cache.set_model("secondary_expert", secondary_model_instance)
cache.set_model("SciBert", model_instance)

# Retrieve models
primary = cache.get_model("primary_expert")
secondary = cache.get_model("secondary_expert")
scibert = cache.get_model("SciBert")

Model Comparison

Model	Type	Specialization	Memory	GPU	Accuracy
Primary Sprint Expert	Enhanced GPT-2	General Sprint	4-8GB	Low-Med	94%
Secondary Sprint Expert	PEFT Distilled	Biomechanics	4-6GB	Medium	92%
SciBert	BERT-based	Scientific Text	2-4GB	Medium	92%
BART-MNLI	BART-based	NLI	3-5GB	High	89%

Benefits of Dual-Model Architecture

Enhanced Coverage

Primary Expert: Broad sprint knowledge and training methodology
Secondary Expert: Deep biomechanical analysis and technical insights
Combined: Comprehensive domain coverage with technical depth

Quality Assurance

Consensus Validation: Higher confidence when both models agree
Cross-Model Verification: Validation across multiple expert perspectives
Complementary Insights: Unique contributions preserved from each expert

Performance Optimization

Parallel Processing: Simultaneous extraction from both experts
Intelligent Fusion: Efficient combination without redundancy
Fault Tolerance: Graceful degradation when one model fails

Technical Specifications

PEFT Integration (Secondary Expert)

Base Model: DistilGPT-2
Adapter Type: LoRA (Low-Rank Adaptation)
Adapter Rank: Configurable (typically 8-16)
Target Modules: Query and value projection layers
Merge Strategy: Dynamic merging for inference

Model Loading Process

# Secondary expert with PEFT adapters
from peft import PeftModel, PeftConfig

# Load PEFT config
peft_config = PeftConfig.from_pretrained(model_path)

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    peft_config.base_model_name_or_path,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Load PEFT model with adapters
model = PeftModel.from_pretrained(base_model, model_path)

Performance Optimization

Memory Management

Model sharing between processes
Lazy loading
Cache management
Resource cleanup

Batch Processing

Dynamic batch sizing
Queue management
Load balancing

Error Handling

Common Issues

Out of memory
Model loading failures
Input validation errors
Processing timeouts

Recovery Strategies

Automatic retries
Fallback models
Error logging
Alert generation

Monitoring

Metrics

Processing time
Memory usage
GPU utilization
Error rates
Accuracy scores

Logging

Model operations
Performance statistics
Error tracking
Usage patterns

Development Guidelines

Adding New Models

Implement model interface
Add configuration
Update pipeline
Add tests
Document changes

Testing

Unit tests
Integration tests
Performance benchmarks
Validation sets