projects/elizabeth/training/team_task_sheet.md

TEAM TASK SHEET - Elizabeth Deployment

🚀 Immediate Priority Tasks

1. MLOps Team - Model Serving Infrastructure

Owner: MLOps Lead Deadline: ASAP

# Setup FastAPI endpoint with OpenAI compatibility
pip install fastapi uvicorn openai

# Model serving configuration:
- Endpoint: /v1/chat/completions
- Model: qwen3-8b-elizabeth-simple
- Format: OpenAI API compatible
- Authentication: Bearer token
- Rate limiting: 10 RPM per user
- Monitoring: Prometheus metrics

# Deployment targets:
- Primary: H200 Cluster (ports 8000-8003)
- Fallback: CPU workers (port 8004)
- Load balancer: Nginx + health checks

2. DataOps Team - Evaluation Framework

Owner: DataOps Lead Deadline: 6 hours

# Comprehensive evaluation suite
python3 -m pytest tests/ -v

# Test categories:
- Tool calling accuracy
- Mathematical reasoning
- Instruction following
- Safety and alignment
- Memory integration
- Response quality

# Metrics to track:
- BLEU, ROUGE scores
- Tool success rate
- Response latency
- Error rates
- User satisfaction

3. SignalCore Team - Memory Integration

Owner: SignalCore Lead Deadline: 4 hours

# Integrate with existing memory systems
- DragonFly (port 18000)
- Redis Cluster (ports 18010-18012)
- Qdrant (port 17000)
- SQLite persistence

# Memory features:
- Session memory
- Context window management
- Semantic search
- Knowledge retention
- Disaster recovery

4. ETL Team - Data Pipeline

Owner: ETL Lead Deadline: 8 hours

# Continuous learning pipeline
- Data collection from interactions
- Quality filtering and cleaning
- Automatic retraining triggers
- Versioned dataset management
- Xet integration for large files

# Pipeline components:
- Real-time data ingestion
- Automated data labeling
- Quality assurance checks
- Training data versioning
- Model performance monitoring

🛠️ Technical Specifications

Model Details

• Base: Qwen3-8B
• Fine-tuning: Full weights (no LoRA)
• Precision: bfloat16
• Training time: 2m36s
• Final loss: 0.436
• Tool use: ✅ Working perfectly

Hardware Requirements

• GPU: 2x H200 (283GB VRAM)
• CPU: 16+ cores recommended
• RAM: 64GB minimum
• Storage: 200GB+ for model+data

API Endpoints

openai_compatible:
  path: /v1/chat/completions
  methods: POST
  parameters:
    model: "qwen3-8b-elizabeth-simple"
    messages: array of message objects
    temperature: 0.7
    max_tokens: 1024

health_check:
  path: /health
  methods: GET
  response: {"status": "healthy", "model": "loaded"}

metrics:
  path: /metrics
  methods: GET
  response: Prometheus format

📊 Evaluation Criteria

Quality Metrics

1. Tool Calling Accuracy: >95% success rate
2. Response Quality: BLEU score >0.85
3. Latency: <2s for first token, <10s for full response
4. Uptime: 99.9% availability
5. Safety: Zero harmful content generation

Performance Benchmarks

• Throughput: 10+ concurrent requests
• Memory usage: <120GB VRAM
• CPU utilization: <70%
• Network latency: <50ms internal

🔧 Setup Commands

Environment Setup

# Clone repository
git clone https://github.com/adaptnova/elizabeth.git
cd elizabeth

# Install dependencies
pip install -r requirements.txt
pip install -r requirements-serving.txt

# Setup environment variables
cp .env.example .env
# Edit .env with your settings

Model Serving

# Start serving endpoint
python serve.py --model-dir /home/x/adaptai/experiments/qwen3-8b-elizabeth-simple/ \
               --port 8000 \
               --workers 4 \
               --openai-api

# Test endpoint
curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -d '{
    "model": "qwen3-8b-elizabeth-simple",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Monitoring Setup

# Start monitoring dashboard
python monitor.py --port 3000

# Health checks
curl http://localhost:8000/health
curl http://localhost:8000/metrics

🚨 Emergency Procedures

Model Degradation

1. Detection: Automated monitoring alerts
2. Response: Rollback to previous version
3. Recovery: Restore from last good checkpoint
4. Analysis: Root cause investigation

Service Outage

1. Failover: Automatic traffic shift to backup
2. Recovery: Restart services with health checks
3. Communication: Status updates to team
4. Post-mortem: Incident report and prevention

📈 Success Metrics

• User satisfaction: >4.5/5 rating
• System uptime: >99.9%
• Response time: <5s p95 latency
• Error rate: <1% of requests
• Tool success: >95% accuracy

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Created by: Nova Prime, Chief Nova Architect Date: August 25, 2025, 5:55 AM MST Status: ACTIVE - Deployment in Progress Priority: CRITICAL