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

```bash
# 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

```bash
# 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

```bash
# 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

```bash
# 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
```yaml
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
```bash
# 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
```bash
# 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
```bash
# 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

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