# Llama Stack

**System Architecture**

```mermaid
flowchart LR  
    subgraph Blockchain[AgentGPT Core]  
        B1[Equipment NFTs]  
        B2[Product LiDAR Twins]  
        B3[ERC-7641 Work Orders]  
    end  

    subgraph Factory[Llama-Driven Factory]  
        F1[AI Supervisor] -->|Quality Data| F2[Robotic Assembly]  
        F2 -->|Defect Analysis| F3[Self-Repair Protocol]  
    end  

    Blockchain <-->|Token Incentives| Factory  
    Factory -->|Real-Time Telemetry| Blockchain  
    style B2 fill:#9cf,stroke:#333  
    style F3 fill:#f99,stroke:#333  
```

***

#### **1. Self-Optimizing Assembly Line**

**Llama-3B Quality Controller (Python)**

```python
from llama_cpp import Llama  
from agentgpt.manufacturing import ERC7641  

class AutonomousQC:  
    def __init__(self):  
        self.llama = Llama(  
            model_path="agentgpt-factory-7b-q4.gguf",  
            n_gpu_layers=35  
        )  
        self.workorder = ERC7641()  

    async def inspect_product(self, sensor_data: dict):  
        analysis = self.llama.create_chat_completion({  
            "messages": [{  
                "role": "system",  
                "content": """Analyze thermal 95.2°C, vibration 12.7μm.  
                ISO 9002 compliance. Output JSON verdict."""  
            }]  
        })  

        if analysis["verdict"] == "FAIL":  
            await self.workorder.trigger_repair(  
                machine_id=sensor_data["id"],  
                defect_code=analysis["code"]  
            )  
            return self._mint_defect_nft(analysis)  
```

***

#### **2. Tokenized Workforce Incentives**

**Skill Staking Contract (Solidity)**

```solidity
// contracts/FactoryDAO.sol  
pragma solidity ^0.8.25;  

contract SkillFarming {  
    mapping(address => uint256) public skillScores;  
    uint256 public constant REWARD_RATE = 1e18; // 1 AGPT/second  

    function reportWork(  
        uint256 productCount,  
        bytes32 qcHash  
    ) external {  
        require(AgentGPTOracle.verifyQC(qcHash), "Invalid proof");  
        uint256 reward = productCount * REWARD_RATE;  
        skillScores[msg.sender] += reward;  
        _mint(msg.sender, reward);  
    }  

    function slashSkill(address worker, uint256 amount) external onlyDAO {  
        skillScores[worker] -= amount;  
        _burn(worker, amount);  
    }  
}  
```

***

#### **3. Edge AI Configuration**

**Factory Node Setup (YAML)**

```yaml
# llama-factory.yaml  
cluster:  
  nodes:  
    - type: "cnc_controller"  
      llm: "llama-13b-q5"  
      gpus: 1  
      sensors:  
        - thermal  
        - vibration  
        - vision_x12  
    - type: "quality_gate"  
      llm: "llama-70b-q4"  
      gpus: 4  
      compliance:  
        - iso_9002  
        - as9100d  

blockchain:  
  reward_token: "AGPT"  
  staking_contract: "0x..."  
  data_oracle: "chainlink"  
```

Deploy with:

```bash
agentgpt deploy-factory --nodes 8 --file llama-factory.yaml  
```

***

#### **4. Predictive Maintenance Protocol**

**Deep Learning Workflow (PyTorch)**

```python
from agentgpt.factories import PredictiveMaintenance  

class MotorFailurePredictor:  
    def __init__(self):  
        self.model = torch.jit.load("agentgpt_motor_v3.pt")  
        self.maintenance_nft = ERC721Maint()  

    async def analyze_telemetry(self, data_stream):  
        window = self._create_rolling_window(data_stream)  
        prediction = self.model(window)  
        
        if prediction > 0.87:  
            tx_hash = await self.maintenance_nft.mint({  
                "machine": data_stream["id"],  
                "severity": prediction.item()  
            })  
            return tx_hash  
```

***

#### **5. Decentralized Governance**

**DAO Voting Contract (Vyper)**

```python
# contracts/FactoryDAO.vy  
@external  
def vote_on_upgrade(  
    proposal_id: uint256,  
    support: bool  
):  
    assert self.proposals[proposal_id].deadline > block.timestamp, "Expired"  
    voting_power: uint256 = self.skillScores[msg.sender]  
    self.votes[proposal_id][msg.sender] = (support, voting_power)  
    self.proposals[proposal_id].total_votes += voting_power  

@external  
def execute_upgrade(proposal_id: uint256):  
    assert self.proposals[proposal_id].passed, "Not approved"  
    Factory(self.factory).upgrade_llm(  
        self.proposals[proposal_id].llm_hash  
    )  
```

***

#### **6. Real-Time Telemetry Dashboard**

```bash
agentgpt monitor factory --view ansi  
```

**Output**:

```
ASSEMBLY LINE 07-MAR-2025  
┌───────────────┬───────────┬───────────┬─────────────┐  
│ Machine       │ Uptime    │ Defects   │ AGPT Earned │  
├───────────────┼───────────┼───────────┼─────────────┤  
│ CNC-1092      │ 99.88%    │ 4         │ 1892.4      │  
│ Welder-X7     │ 97.23%    │ 12        │ 804.1       │  
│ VisionQC-88   │ 100%      │ 0         │ 2945.7      │  
├───────────────┼───────────┼───────────┼─────────────┤  
│ TOTAL         │ 99.01%    │ 16        │ 5642.2 AGPT  
└───────────────┴───────────┴───────────┴─────────────┘  
Predicted Failures Next 24h: 0  
```

***

#### **7. Security & Recovery**

**Byzantine Fault Response (Rust)**

```rust
// agentgpt-recovery/src/lib.rs  
pub async fn handle_robot_failure(  
    robot_id: &str,  
    error_code: u32  
) -> Result<RepairTx, FactoryError> {  
    let backup = get_failover_node(robot_id).await?;  
    let diagnosis = diagnose_failure(error_code).await?;  
    
    if diagnosis.requires_human {  
        alert_engineers(diagnosis.severity).await;  
        return Ok(RepairTx::Queued);  
    }  

    let parts = order_replacement_parts(diagnosis.parts_needed).await?;  
    Ok(RepairTx::Dispatched(parts.tracking_id))  
}  
```

***

#### **Troubleshooting**

| **Failure Mode**      | **Containment Protocol**                |
| --------------------- | --------------------------------------- |
| Llama Model Drift     | 1. Rollback to last verified checkpoint |
| Skill Token Inflation | 2. Adjust emission via DAO vote         |
| Supply Chain DOS      | 3. Activate local buffer inventory      |
| QC Consensus Failure  | 4. Swarm voting among 21 validators     |

***

#### **Best Practices**

1. **Redundant LLM Models**:

```bash
agentgpt factory replication --copies 3 --strategy geodistributed  
```

2. **Energy Optimization**:

```yaml
sustainability:  
  carbon_credits:  
    auto_purchase: true  
    target: "net_zero"  
  energy_sources:  
    - "solar"  
    - "grid_renewable"  
```

3. **Traceability Standards**:

```solidity
function verifyProvenance(uint256 productId) public view returns (string memory) {  
    return keccak256(abi.encodePacked(productId, block.chainid));  
}  
```


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